Comparison and Continuous Estimates of Fecal 

Coliform and Escherichia Coli Bacteria in 

Selected Kansas Streams, May 1999 Through 

April 2002 

Water-Resources Investigations Report 03–4056 

Prepared in cooperation with the 

KANSAS DEPARTMENT OF HEALTH AND ENVIRONMENT 

U.S. ENVIRONMENTAL PROTECTION AGENCY 

U.S. Department of the Interior 

U.S. Geological Survey 

Kansas River at DeSoto, Kansas 

0.1 

1 

10 

100 

1,000 

10,000 

100,000 

1,000,000 

1 10 100 1,000 10,000 100,000 

Escherichia coli bacteria (ECB) density, in colonies per 100 milliliters of water 

Fecal coliform bacteria (FCB) density, in colonies per 100 milliliters of water 
U.S. Department of the Interior 

U.S. Geological Survey 

Comparison and Continuous 


Estimates of Fecal Coliform and 


Escherichia Coli Bacteria in 


Selected Kansas Streams, 


May 1999 Through April 2002


By PATRICK P. RASMUSSEN and ANDREW C. ZIEGLER 

Water-Resources Investigations Report 03–4056 

Prepared in cooperation with the


KANSAS DEPARTMENT OF HEALTH AND ENVIRONMENT and


U.S. ENVIRONMENTAL PROTECTION AGENCY


Lawrence, Kansas


2003

U.S. Department of the Interior 

Gale A. Norton, Secretary 

U.S. Geological Survey 

Charles G. Groat, Director 

The use of brand, trade, or firm names in this report is for identification purposes only and does not constitute endorsement 

by the U.S. Geological Survey. 

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


District Chief U.S. Geological Survey


U.S. Geological Survey Information Services


4821 Quail Crest Place Building 810


Lawrence, KS 66049–3839 Box 25286, Federal Center


Denver, CO 80225–0286 
CONTENTS

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


Introduction ........................................................................................................................................................................... 2


Purpose and Scope................................................................................................................................................................. 6


Methods ................................................................................................................................................................................. 6


Bacteria Sample Collection and Analysis ................................................................................................................... 6


Turbidity Measurements.............................................................................................................................................. 8


Development of Regression Models to Estimate Bacteria Densities .......................................................................... 9


Measured Bacteria Densities ................................................................................................................................................. 14


Comparison of Fecal Coliform and Escherichia Coli Densities ........................................................................................... 14


Continuously Estimated Bacteria Densities .......................................................................................................................... 31


Relation Between Turbidity and Fecal Coliform Density ........................................................................................... 31


Probability and Duration of Estimated Fecal Coliform Densities............................................................................... 41


Relation Between Turbidity and Escherichia Coli Density......................................................................................... 46


Probability and Duration of Estimated Escherichia Coli Densities ............................................................................ 61


Estimated Bacteria Loads and Yields .................................................................................................................................... 67


Summary................................................................................................................................................................................ 75


References ............................................................................................................................................................................. 79


FIGURES

1.	 Maps showing location of surface-water sites in Kansas where fecal coliform and Escherichia coli samples were


collected, May 1999 through April 2002 ............................................................................................................... 5


2.	 Photograph showing multiparameter monitor used to measure turbidity, specific conductance, pH, water


temperature, dissolved oxygen, and fluorescence in water during sample collection and for in-stream continuous


monitoring............................................................................................................................................................... 9


3–19. Graphs showing: 

3. Comparison of continuous, in-stream and cross-section turbidity values for Kansas River at Topeka, Kansas, 

July 1999 through April 2002............................................................................................................................ 10 

4. Turbidity duration curve for Kansas River at DeSoto, Kansas, July 1999 through April 2002 ........................ 10


5.	 Comparison of measured fecal coliform and Escherichia coli bacteria densities at 28 surface-water sites, 


May 1999 through April 2002 ........................................................................................................................... 16


6. Mean specific conductance for bacteria samples collected at selected surface-water sites, May 1999 through 

April 2002.......................................................................................................................................................... 18 

7.	 Comparison of measured fecal coliform and Escherichia coli bacteria densities, regression-estimated fecal


coliform bacteria density, and prediction intervals for six selected surface-water sites and six groups of 


surface-water sites, May or July 1999 through April 2002 ............................................................................... 19


8.	 Comparison of measured turbidity and fecal coliform bacteria densities at selected surface-water sites, 


May 1999 through April 2002 ........................................................................................................................... 32


9.	 Comparison of measured turbidity and fecal coliform bacteria densities, regression-estimated fecal coliform 

bacteria densities, and prediction intervals for six selected surface-water sites, May or July 1999 through 

April 2002.......................................................................................................................................................... 34 

10. Comparison of measured and regression-estimated fecal coliform bacteria densities for six selected surface-


water sites, May 1999 through April 2002, and probability of exceedance of water-quality criteria ............... 41


11.	 Comparison of measured turbidity and the probability of exceeding selected fecal coliform bacteria densities 


for six selected surface-water sites, May or July 1999 through April 2002 ...................................................... 48


12. Estimated fecal coliform bacteria densities for six selected surface-water sites, May or July 1999 through 


April 2002, and for spring, summer, and winter months................................................................................... 51


Contents III
FIGURES—Continued

13. Comparison of measured turbidity and Escherichia coli bacteria densities at selected surface-water sites, 

May 1999 through April 2002........................................................................................................................... 54 

14. Comparison of measured turbidity and regression-estimated Escherichia coli bacteria densities and 

prediction intervals for five selected surface-water sites, May or July 1999 through April 2002 .................... 56 

15. Comparison of measured and regression-estimated Escherichia coli bacteria densities in samples from 

Kansas and Little Arkansas Rivers, May or July 1999 through April 2002, and probability of exceedance 

of water-quality criteria..................................................................................................................................... 62 

16. Comparison of measured turbidity and the probability of exceeding selected Escherichia coli bacteria 

densities for five selected surface-water sites, May or July 1999 through April 2002 ..................................... 68 

17.	 Estimated Escherichia coli bacteria densities for five selected surface-water sites, May or July 1999 through 

April 2002, and for spring, summer, and winter months .................................................................................. 71 

18.	 Comparison of estimated mean daily bacteria loads for spring, summer, and winter for six selected surfacewater sites, January 2000 through December 2001 .......................................................................................... 76 

19. Comparison of regression-estimated bacteria yields from unregulated drainage areas for six selected surfacewater sites, January 2000 through December 2001 .......................................................................................... 77 

TABLES

1. Current (2003) Kansas and U.S. Environmental Protection Agency recommended indicator bacteria criteria..... 3 

2. Surface-water sites in Kansas where bacteria samples were collected during May 1999 through April 2002...... 7 

3.	 Statistical summary for fecal coliform and Escherichia coli bacteria densities measured in samples collected 

from and turbidity measurements made at surface-water sites on selected Kansas streams, May 1999 through 

April 2002............................................................................................................................................................... 15 

4.	 Regression and geometric-mean statistics for comparison of fecal coliform and Escherichia coli bacteria densities 

at selected individual surface-water sites and for selected basins in Kansas, May 1999 through April 2002 ....... 17 

5.	 Regression models and statistics for estimating fecal coliform bacteria densities using turbidity measurements 

at selected surface-water sites in Kansas, May 1999 through April 2002.............................................................. 33 

6.	 Percentage of regression-model estimates in agreement with measured fecal coliform bacteria densities in 

relation to primary and secondary contact recreational criteria for selected surface-water sites in Kansas, 

May 1999 through April 2002 ................................................................................................................................ 40 

7.	 Percentage of estimated hourly fecal coliform bacteria densities that were greater than recreational-use criteria 

for selected surface-water sites in Kansas, May or July 1999 through April 2002 ................................................ 47 

8.	 Regression models and statistics for estimating Escherichia coli bacteria densities using turbidity measurements 

at selected surface-water sites in Kansas, May 1999 through April 2002.............................................................. 55 

9.	 Percentage of regression-model estimates in agreement with measured Escherichia coli bacteria densities in 

relation to U.S. Environmental Protection Agency recommended primary contact recreational criteria for 

selected surface-water sites in Kansas, May 1999 through April 2002.................................................................. 61 

10.	 Percentage of estimated hourly Escherichia coli bacteria densities that were greater than U.S. Environmental 

Protection Agency recommended recreational criteria for five selected surface-water sites in Kansas, May or 

July 1999 through April 2002................................................................................................................................. 67 

11.	 Estimated seasonal and annual loads and yields of indicator bacteria for six surface-water sites in Kansas, 

January 2000 through December 2001................................................................................................................... 74 

12.	 Seasonal and annual mean daily streamflow for six surface-water sites in Kansas, January 

2000 through December 2001 ................................................................................................................................ 75 

IV Comparison and Continuous Estimates of Fecal Coliform and Escherichia Coli Bacteria in Selected Kansas Streams, May 1999 through April 2002 
CONVERSION FACTORS, WATER-QUALITY ABBREVIATIONS, AND DATUM 

Multiply By To obtain 

acre 4,047 square meter (m2) 

cubic foot per second (ft3

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

cubic centimeter (cm3) 0.06102 cubic inch (in3) 

liter (L) 33.82 ounce, fluid (oz) 

mile (mi) 1.609 kilometer (km) 

milliliter (mL) 0.0338 ounce, fluid (oz) 

nanometer (nm) 3.937 x 10-8 inch (in.) 

square mile (mi

2) 2.590 square kilometer (km2) 

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

F=9/5(oC) + 32. 

Water-Quality Abbreviations 

col/100 mL—colonies per 100 milliliters of water 

μS/cm—microsiemens per centimeter at 25 oC 

Datum 

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

Contents V
VI Comparison and Continuous Estimates of Fecal Coliform and Escherichia Coli Bacteria in Selected Kansas Streams, May 1999 through April 2002 
Comparison and Continuous Estimates of Fecal Coliform and 

Escherichia Coli Bacteria in Selected Kansas Streams, 

May 1999 Through April 2002 

By Patrick P. Rasmussen and Andrew C. Ziegler 

Abstract 

The sanitary quality of water and its use as a 

public-water supply and for recreational activities, 

such as swimming, wading, boating, and fishing, 

can be evaluated on the basis of fecal coliform 

and Escherichia coli (E. coli) bacteria densities. 

This report describes the overall sanitary quality 

of surface water in selected Kansas streams, the 

relation between fecal coliform and E. coli, the 

relation between turbidity and bacteria densities, 

and how continuous bacteria estimates can be 

used to evaluate the water-quality conditions in 

selected Kansas streams. 

Samples for fecal coliform and E. coli were 

collected at 28 surface-water sites in Kansas. Of 

the 318 samples collected, 18 percent exceeded 

the current Kansas Department of Health and 

Environment (KDHE) secondary contact recreational, single-sample criterion for fecal coliform 

(2,000 colonies per 100 milliliters of water). Of 

the 219 samples collected during the recreation 

months (April 1 through October 31), 21 percent 

exceeded the current (2003) KDHE single-sample 

fecal coliform criterion for secondary contact recreation (2,000 colonies per 100 milliliters of 

water) and 36 percent exceeded the U.S. Environmental Protection Agency (USEPA) recommended single-sample primary contact 

recreational criterion for E. coli (576 colonies per 

100 milliliters of water). Comparisons of fecal 

coliform and E. coli criteria indicated that more 

than one-half of the streams sampled could 

exceed USEPA recommended E. coli criteria 

more frequently than the current KDHE fecal 

coliform criteria. In addition, the ratios of E. coli 

to fecal coliform (EC/FC) were smallest for sites 

with slightly saline water (specific conductance 

greater than 1,000 microsiemens per centimeter at 

25 degrees Celsius), indicating that E. coli may 

not be a good indicator of sanitary quality for 

those streams. Enterococci bacteria may provide a 

more accurate assessment of the potential for 

swimming-related illnesses in these streams. 

Ratios of EC/FC and linear regression models 

were developed for estimating E. coli densities on 

the basis of measured fecal coliform densities for 

six individual and six groups of surface-water 

sites. Regression models developed for the six 

individual surface-water sites and six groups of 

sites explain at least 89 percent of the variability 

in E. coli densities. The EC/FC ratios and regression models are site specific and make it possible 

to convert historic fecal coliform bacteria data to 

estimated E. coli densities for the selected sites. 

The EC/FC ratios can be used to estimate E. coli 

for any range of historical fecal coliform densities, and in some cases with less error than the 

regression models. The basin- and statewide 

regression models explained at least 93 percent of 

the variance and best represent the sites where a 

majority of the data used to develop the models 

were collected (Kansas and Little Arkansas 

Basins). 

Comparison of the current (2003) KDHE geometric-mean primary contact criterion for fecal 

coliform bacteria of 200 col/100 mL to the 2002 

USEPA recommended geometric-mean criterion 

Abstract 1 
of 126 col/100 mL for E. coli results in an EC/FC 

ratio of 0.63. The geometric-mean EC/FC ratio 

for all sites except Rattlesnake Creek (site 21) is 

0.77, indicating that considerably more than 

63 percent of the fecal coliform is E. coli. This 

potentially could lead to more exceedances of the 

recommended E. coli criterion, where the water 

now meets the current (2003) 200-col/100 mL 

fecal coliform criterion. 

In this report, turbidity was found to be a reliable estimator of bacteria densities. Regression 

models are provided for estimating fecal coliform 

and E. coli bacteria densities using continuous 

turbidity measurements. Prediction intervals also 

are provided to show the uncertainty associated 

with using the regression models. Eighty percent 

of all measured sample densities and individual 

turbidity-based estimates from the regression 

models were in agreement as exceeding or being 

less than the primary and secondary contact recreational criteria. The continuous turbidity measurements and regression models were used to 

construct probability curves that can be used to 

estimate bacteria concentrations on the basis of 

measured turbidity values. Duration curves developed for six sites using the hourly estimates of 

bacteria density indicate that the current KDHE 

(fecal coliform bacteria) and USEPA recommended (E. coli bacteria) primary contact recreational criteria were exceeded for 21 to 94 and 

31 to 97 percent of the spring and summer, 

respectively. Estimated bacteria densities most 

commonly exceeded the current and recommended criteria in the spring (April through 

June). Hourly estimates provided in real time 

(available on the World Wide Web at 

http://ks.water.usgs.gov/Kansas/rtqw/) allow the 

public and water-management agencies to make 

decisions in regard to whether planned water 

activities are appropriate by considering current 

stream conditions relative to water-quality 

criteria. 

Annual and seasonal loads and yields were 

calculated using hourly estimated fecal coliform 

and E. coli bacteria densities and streamflow at 

six surface-water sites for the calendar years 2000 

and 2001. Estimated bacteria loads in 2001 were 

about 2 to 8 times larger than the bacteria loads in 

2000 for the Kansas and Little Arkansas Rivers. 

Data from major point sources upstream from the 

surface-water sites in these basins indicate that 

nonpoint sources accounted for more than 97 percent of the annual loads. Mean daily bacteria 

loads in 2000 were largest in the winter for five 

sites and in the spring for one site. In 2001, mean 

daily bacteria loads were largest in the spring for 

four sites and in the winter for two sites. Annual 

load differences are caused by varying hydrologic 

conditions and higher streamflow caused by overland runoff. Surface-water sites in the Little 

Arkansas River Basin had the largest bacteria 

yield per acre of watershed. 

INTRODUCTION 

Fecal coliform bacteria have long been used as an 

indicator organism for the sanitary quality of water for 

drinking or body-contact recreation. The presence of 

fecal coliform bacteria in water indicates the possible 

presence of pathogens, such as entero-, rota-, and 

reoviruses, found in feces of warmblooded animals. 

These bacteria and pathogens may cause human diseases ranging from mild diarrhea to respiratory disease, septicemia, meningitis, and polio (Dufour, 1977; 

Pepper and others, 1996). The fecal coliform bacteria 

group can include any combination of Escherichia coli 

(E. coli) and species of the Klebsiella, Enterobacter, 

and Citrobacter genera (Gleeson and Gray, 1997). 

Fecal coliform bacteria are found in the feces of all 

warmblooded animals, but some members of the 

group also can originate in soil and water (Holt and 

others, 1993). 

In 1986, the U.S. Environmental Protection 

Agency (USEPA) recommended that States use E. coli 

or enterococci bacteria rather than fecal coliform as 

indicators of fecal contamination for recreational 

water. E. coli is the only member of the fecal group 

that is exclusively fecal in origin and, therefore, is 

definitive evidence of fecal contamination from warmblooded animals. Measuring only E. coli or enterococci, rather than the entire fecal coliform or fecal 

streptococci group, has been shown to give a better 

indication of possible contamination by organisms 

associated with swimming illnesses (Cabelli, 1977; 

Dufour and Cabelli, 1984). USEPA also suggests that 

E. coli is not as reliable an indicator as enterococci in 

2 Comparison and Continuous Estimates of Fecal Coliform and Escherichia Coli Bacteria in Selected Kansas Streams, May 1999 Through April 2002 
marine water or freshwater streams with high salinity. 

In 2002, USEPA issued revised guidelines with recommended numeric criteria on the basis of risk exposure 

(U.S. Environmental Protection Agency, 2002). 

The Kansas Department of Health and Environment (2001) lists fecal coliform criteria for Kansas 

streams designated for either primary contact (fullbody) or secondary contact (noncontact) recreational 

use (table 1). During primary contact recreation, the 

body is immersed in surface water to the extent that 

some inadvertent ingestion of water is probable. This 

use includes boating, mussel harvesting, swimming, 

skin diving, water skiing, and wind surfing. During 

secondary contact recreation, ingestion of water is not 

probable. This use includes wading, fishing, trapping, 

and hunting (Kansas Department of Health and Environment, 2001). The State of Kansas is currently 

(2003) evaluating the use of E. coli as the primary 

indicator bacteria. 

Current (2003) surface-water-quality criteria for 

Kansas state that the geometric mean (the exponent of 

the mean of the logarithmic transformed data) for fecal 

coliform bacteria of at least five samples collected 

over separate 24-hour periods during a 30-day period 

shall not exceed 200 col/100 mL of water (Kansas 

Department of Health and Environment, 2001). This 

criterion is in effect April 1 through October 31 for 

water designated for primary contact recreational use 

(designated recreation period). Fecal coliform bacteria 

shall not exceed 2,000 col/100 mL for any single sample collected from November 1 through March 31 for 

surface water designated for primary contact recreational use. Surface water designated for secondary 

contact recreational use shall not exceed 

2,000 col/100 mL for a single sample throughout 

the year (Kansas Department of Health and 

Environment, 2001). 

USEPA recommended criteria for E. coli in water 

designated for primary contact recreational use are 

based on a geometric-mean density for five samples 

collected over 30 days and a single-sample density 

(table 1). The ranges of geometric-mean (126 to 

Table 1. Current (2003) Kansas and U.S. Environmental Protection Agency recommended indicator bacteria criteria 

[All values are in colonies per 100 milliliters of water. KDHE, Kansas Department of Health and Environment; USEPA, U.S. Environmental Protection 

Agency; E. coli, Escherichia coli bacteria; --, no criteria] 

Type of recreational water 

Secondary 

contact 

Primary contact recreation2 recreation3 

Single-sample maximum allowable density 

Lightly Single-sample 

Illness rate 

(per 1,000 Geometric Designated Moderate fullused fullbody 

Infrequently 

used full-body 

maximum 

allowable 

Indicator bacteria type swimmers) mean1 beach area body contact contact contact density 

Fecal coliform (KDHE, 2001) 8 200 -- -- -- -- 2,000 

USEPA recommended E. coli 8 126 235 298 406 576 --

criteria (USEPA, 2002) 9 160 300 381 524 736 --

10 206 383 487 669 941 --

11 263 490 622 855 1,202 --

12 336 626 795 1,092 1,536 --

13 429 799 1,016 1,396 1,962 --

14 548 1,021 1,298 1,783 2,507 --

1Geometric mean of at least five samples collected during separate 24-hour periods within a 30-day period. 2

Recreation during which the body is immersed in surface water to the extent that some inadvertent ingestion of water is probable. This use shall 

include boating, mussel harvesting, swimming, skin diving, water skiing, and wind surfing. These criteria shall be in effect from April 1 through 

October 31 of each year (KDHE, 2001). 3

Recreation during which ingestion of surface water is not probable. This use shall include wading, fishing, trapping, and hunting. These criteria 

shall be in effect from January 1 through December 31 of each year (KDHE, 2001). 

Introduction 3 
548 col/100 mL) and single-sample (235 to 

2,507 col/100 mL) criteria vary on the basis of the 

illness rate (8 to 14 illnesses per 1,000 swimmers). 

USEPA currently (2003) has no recommended criteria 

for secondary contact recreational use (U.S. Environmental Protection Agency, 2002). 

Section 303(d) of the Federal Clean Water Act of 

1972 requires States to identify all water bodies where 

State water-quality criteria are not being met. In Kansas, 64 percent of the 59,423 stream mi monitored by 

the Kansas Department of Health and Environment 

(KDHE) in 1998–99 fully supported all uses (Kansas 

Department of Health and Environment, 2000). About 

83 percent fully or partially supported all uses. In 

1998, fecal coliform bacteria was listed as an impairment for 611 of the 774 water-quality-limited stream 

segments (or 79 percent) listed on the 303 (d) list for 

Kansas (Kansas Department of Health and Environment, 2000). 

The Federal Clean Water Act also requires that 

States establish total maximum daily loads (TMDLs) 

to meet established water-quality criteria and to ensure 

protection of a water body’s designated beneficial 

uses. A TMDL is a calculation and allocation among 

sources of the maximum amount of a contaminant 

that a water body can receive and still /meet waterquality criteria (U.S. Environmental Protection 

Agency, 1999). 

In May 2000, Wichita, Kansas, water-resource 

managers were forced to cancel water activities to be 

held in the Arkansas River during an annual river festival due to unsafe bacteria densities in the stream. 

Water samples were collected daily prior to and 

throughout the planned events. Water-resource managers then would decide if the streams were safe for the 

planned events on the basis of these results. Analytical 

methods used to attain these results required 24 hours. 

Therefore, managers were making critical publichealth decisions on the basis of stream conditions for 

the previous day. Densities of bacteria can change 

substantially in just a few hours, possibly exceeding 

single-sample criteria for secondary contact 

recreational use. 

In May 1999, the U.S. Geological Survey (USGS) 

with several Federal, State and local agencies, began 

collecting samples for analysis of fecal coliform and 

E. coli bacteria at 28 surface-water sites in Kansas 

(fig. 1). A comparative bacteria data set will benefit 

the State of Kansas by helping to estimate E. coli densities on the basis of historical fecal coliform data. A 

comparative data set also can be used to determine 

how the new USEPA recommended criteria will affect 

compliance of streams within the State if adopted. A 

method is necessary to provide real-time continuous 

estimates of the sanitary quality of Kansas streams and 

to evaluate best management practices and TMDL 

goals. The USGS, in cooperation with KDHE and 

USEPA, evaluated bacteria data collected at 28 surface-water sites to address these needs. 

Indicator bacteria densities are highly variable and 

are dependent on the source of the bacteria and the 

hydrologic and environmental conditions. Possible 

sources of fecal coliform bacteria contamination 

include municipal wastewater discharges, seepage 

from domestic septic systems, combined sewer overflows, runoff or seepage from livestock-producing 

areas, and wildlife populations. Point sources such as 

wastewater treatment facilities and combined sewer 

overflows often discharge potential contaminants 

directly into streams. Fecal coliform bacteria in undisturbed feces of warmblooded animals deposited on the 

land surface can survive for a year or more (Bohn and 

Buckhouse, 1985). Rainfall on these surfaces transport 

fecal coliform bacteria into or along the surface of soil 

and eventually into surface water and sometimes 

ground water. Runoff from grazed areas can have 5 to 

10 times the amount of fecal coliform bacteria than 

runoff from ungrazed areas, but both sources of runoff 

can exceed recommended water-quality criteria 

(Doran and Linn, 1979). 

Once bacteria reach a stream, they can survive for 

days or months depending on water temperature and 

water-quality conditions (Sherer and others, 1992; 

Howell and others, 1996). The survival rate of bacteria 

can increase as temperature decreases or as ultraviolet 

penetration into water is decreased (Fujioka and 

Narikawa, 1982). Fecal coliform bacteria tend to 

adsorb to suspended sediment such as silt and clay in 

the water (Kittrel and Furfari, 1963; Hendricks, 1970), 

extending their survivability. When stream velocities 

are slow, the sediment tends to settle out of water to 

the bottom of the stream. Densities of fecal indicator 

bacteria in sediment can be 100 to 1,000 times the densities in the overlying water column (Ashbolt and others, 1993), and their survivability can increase to 

85 times the survivability in the overlying water 

column (Sherer and others, 1992; Davies and others, 

1995). These bacteria and fine sediment can be resuspended when they are disturbed, for example by 

4 Comparison and Continuous Estimates of Fecal Coliform and Escherichia Coli Bacteria in Selected Kansas Streams, May 1999 Through April 2002 
Introduction 5

Figure 1. cation of surface-water sites in Kansas where fecal coliform and Escherichia coli samples were collected, May 1999 

through April 2002.

Lo
dredging, by animals walking in the stream, and by 

higher flow when stream velocities increase. 

PURPOSE AND SCOPE 

This report was prepared in cooperation with 

KDHE and USEPA and funded in part through the 

Kansas State Water Plan Fund. This report describes 

(1) the sanitary water quality, (2) the relation between 

fecal coliform and E. coli, (3) the relations between 

turbidity and fecal coliform and E. coli, and (4) how 

continuous bacteria estimates can be used to evaluate 

water-quality conditions in selected Kansas streams. 

The relations between turbidity and fecal coliform and 

E. coli were used to estimate bacteria densities at 

selected sites for the period of the study. 

From May 1999 through April 2002, the U.S. 

Geological Survey (USGS) collected 318 samples at 

28 surface-water sites (fig. 1) for the analysis of both 

fecal coliform and E. coli bacteria. These samples 

were part of ongoing data-collection efforts partially 

funded by USGS and KDHE, USEPA, the U.S. Fish 

and Wildlife Service, Big Bend Groundwater Management District No. 5, Prairie Band Potawatomi Nation, 

and the city of Wichita. During bacteria sample collection, in-stream turbidity was measured at 11 of the 

28 sites. In-stream turbidity also was measured continuously for most of the study period at 7 of the 28 sites 

(table 2). Twenty-two of the surface-water sites were 

on stream segments that have been designated for primary contact recreation and, therefore, must adhere to 

the most stringent bacteria criteria (table 1). The 

remaining six sites were located on small streams that 

have been designated for secondary contact recreation 

and must meet less stringent criteria to be in compliance. All 28 sites represent watersheds in predominately agricultural areas. The streamflow at sites 1, 2, 

and 20 located on the Kansas River are affected by 

large reservoirs (fig. 1A). 

The USEPA recommended E. coli geometricmean criterion (126 col/100 mL) and single-sample 

criteria for designated beach area (235 col/100 mL) 

and infrequently used full-body contact 

(576 col/100 mL) for an illness rate of 8 per 

1,000 swimmers and for infrequently used full-body 

contact (2,507 col/100 mL) for an illness rate of 14 per 

1,000 swimmers will be used for comparison of measured and estimated density discussed in this report. 

The methods described in this report can be used 

to provide real-time continuous estimates of the 

sanitary quality of selected streams in Kansas. Currently (2003), a World Wide Web page 

(http://ks.water.usgs.gov/Kansas/rtqw) provides waterresource managers with the information necessary to 

make decisions about sanitary quality on the basis of 

real-time water-quality estimates, which can improve 

response times for drinking-water treatment and environmental monitoring. Long-term continuous monitoring allows users to construct bacteria duration curves 

to help assess the effectiveness of TMDLs for selected 

streams and the results of resource-management practices. The methods described in this report may be 

appropriate for monitoring water quality elsewhere in 

the Nation. 

METHODS 

Bacteria Sample Collection and Analysis 

Samples for bacteria analysis were collected at 

each surface-water site by submerging a sterile 1-L 

bottle into the stream near the center of the flow. The 

sample was chilled, then processed by a membrane filtration technique within 6 hours of collection for 

identification and enumeration of fecal coliform and 

E. coli bacteria (Myers and Wilde, 1999). 

The membrane filter technique was used, although 

this method may underestimate the number of viable 

coliform bacteria (Eaton and others, 1995). Assuming 

that bacteria were randomly distributed and followed a 

Poisson distribution, approximate 95-percent confidence limits for the true population mean were constructed as follows: 

upper limit = [c + (2 × c)] , (1) 

and 

lower limit = [c – (2 × c)] , (2) 

where c is the count of bacteria in a single petri dish. 

For ideal counts of fecal coliform, the 95-percent 

confidence interval for the lower limit of the ideal 

range is 20 to 60 colonies with corresponding confidence-interval widths of + 9 to + 15 colonies (or + 25 

to 45 percent). Ideal plate counts for E. coli range from 

20 to 80 colonies with corresponding confidenceinterval widths ranging from + 9to + 18 colonies. 

Bacteria densities calculated on the basis of counts 

outside of these ranges were considered nonideal 

counts. Of the 318 samples analyzed, 83 (26 percent) 

6 Comparison and Continuous Estimates of Fecal Coliform and Escherichia Coli Bacteria in Selected Kansas Streams, May 1999 Through April 2002 
Table 2. Surface-water sites in Kansas where bacteria samples were collected during May 1999 through April 2002 

[mi2, square miles; --, not determined] 

Total drainage Continuous area in-stream 

Site (unregulated turbidity 

number drainage area) Designated measure-

(fig. 1) Station number Station name (mi2

) recreation use ments 

1 06887500 Kansas River at Wamego, Kansas 55,280 (5,922) primary yes 

2 06889000 Kansas River at Topeka, Kansas 56,720 (7,362) primary yes 

3 06889180 Soldier Creek near St. Clere, Kansas 80 primary no 

4 391557095531100 Soldier Creek, 1 Road near Delia, Kansas -- primary no 

5 391628095452800 Little Soldier Creek, 126 Road near Hoyt, Kansas -- primary no 

6 391629095452400 Big Elm Creek, P Road near Hoyt, Kansas -- secondary no 

7 391704095441700 Little Elm Creek, Q Road near Hoyt, Kansas -- secondary no 

8 391720095445400 Big Elm Creek, 134 Road near Hoyt, Kansas -- secondary no 

9 391720095454200 Little Soldier Creek, 134 Road near Mayetta, Kansas -- primary no 

10 391721095460900 Little Soldier tributary, 134 Road near Hoyt, Kansas -- primary no 

11 391915095463100 Little Soldier Creek, 0 Road near Mayetta, Kansas -- primary no 

12 391956095544000 Soldier Creek, 158 Road near St. Clere, Kansas -- primary no 

13 392049095531300 Crow Creek, 166 Road near St. Clere, Kansas -- secondary no 

14 392143095482700 Little Soldier Creek, 174 Road near Mayetta, Kansas -- primary no 

15 392212095441800 South Cedar Creek, Highway 75 near Mayetta, Kansas -- secondary no 

16 392328095490300 Little Soldier Creek, 190 Road near Mayetta, Kansas -- primary no 

17 392425095445100 Bills Creek, Highway 75 near Holton, Kansas -- secondary no 

18 392603095563000 Soldier Creek tributary, G Road near Circleville, Kansas -- primary no 

19 392603095563000 Soldier Creek, 214 Road near Circleville, Kansas -- primary no 

20 06892350 

21 07142575 

22 07143672 

23 07144100 

24 07144601 

25 07144660 

26 07144680 

27 07144730 

28 07144780 

Kansas River at DeSoto, Kansas 59,756 (8,914) primary yes 

Rattlesnake Creek near Zenith, Kansas.

Little Arkansas River at Highway 50 near Halstead, Kansas.

Little Arkansas River near Sedgwick, Kansas.

North Fork Ninnescah River at Arlington, Kansas.

Silver Creek near Arlington, Kansas.

Goose Creek near Arlington, Kansas.

Red Rock Creek near Pretty Prairie, Kansas.

North Fork Ninnescah River above Cheney Reservoir, Kansas.

1,047 primary yes 

759 primary yes 

1,239 primary yes 

322 primary no 

194 primary no 

46.6 primary no 

53.2 primary no 

713 primary yes1 

1Although continuous in-stream turbidity measurements were made at this site during the study period, the data were insufficient for regression 

modeling. 

Methods 7 
fecal coliform and 130 (41 percent) E. coli densities 

were based on nonideal counts. 

Forty-seven samples were collected for duplicate 

analysis including both ideal and nonideal counts. 

Fecal coliform were analyzed in 44 of the 47 samples, 

and E. coli were analyzed in 35 of the 47 samples. The 

percentage difference was calculated using equation 3: 

C1 – C2 percentage difference = 100 x ---------------------- , (3) C1 + C2 ---------------------- 2

where 

C1 is the density for the first sample, in 

colonies/100 mL of water; and 

C2 is the density for the duplicate sample, in 

colonies per 100 mL of water. 

The percentage difference for the fecal coliform and 

E. coli duplicate samples ranged from 0 to 127 and 

0 to 83 percent, respectively. The high percentage differences occurred when counts were nonideal. The 

average percentage difference was 37 percent for fecal 

coliform and 14 percent for E. coli. A possible cause 

for the large uncertainty in the analysis may be the difficulty in obtaining a representative subsample, especially for highly turbid samples. 

Turbidity Measurements 

Turbidity is the reduction in the transparency of a 

solution due to the presence of suspended and dissolved substances. Primary contributors to turbidity in 

water include clay, silt, finely divided organic and 

inorganic matter, soluble colored organic compounds, 

plankton, and microscopic organisms (American 

Public Health Association and others, 1992). Turbidity 

measurement techniques record the collective optical 

properties of the solution that cause light to be scattered or attenuated rather than transmitted in straight 

lines; the higher the intensity of scatter or attenuated 

light, the higher the value of the turbidity. The smaller 

the turbidity value, the clearer the water. Turbidity typically is expressed in nephelometric turbidity units 

(NTU). Depending on the method used, turbidity as 

NTU can be defined as the intensity of light of a specified wavelength scattered or attenuated by suspended 

particles or absorbed at a method-specified angle, usually 90 degrees, from the path of incident light. 

Currently approved methods for the measurement 

of turbidity in the USGS include those that conform to 

USEPA Method 180.1 (U.S. Environmental Protection 

Agency, 1979), ASTM Method D1889–00 (American 

Society of Testing and Materials, 2000), ISO Method 

7027 (International Organization for Standardization, 

1999), GLI Method 2 (Great Lakes Instruments, Inc., 

1992), and standard methods recommended by the 

American Water Works Association and the Water 

Environment Federation (Clesceri and others, 1998). 

Turbidity measurements for this study were made with 

a YSI 6026 turbidity probe (Yellow Springs Instruments, Yellow Springs, Ohio). The YSI 6026 conforms to the ISO Method 7027 measurement standard 

with a light source of 860 + 30 nm and single detector 

oriented at 90 degrees from the incident light path. 

Turbidity values from other turbidity probes or sensors 

may not be comparable with the turbidity values and 

the relations that use turbidity in this report (Sadar, 

2002; Ziegler, 2002). 

Typically, during bacteria sample collection, turbidity was measured using a multiparameter monitor 

(fig. 2) also capable of measuring physical properties, 

including specific conductance, pH, water temperature, dissolved oxygen, and sometimes fluorescence. 

The monitor was cleaned and calibrated before each 

use to ensure accurate measurements (Wilde and 

Radtke, 1998). Prior to each measurement, a mechanical wiper on the turbidity probe rotated across the sensor, removing air bubbles and particles that may 

interfere with the turbidity reading. Turbidity measurements were recorded at a minimum of 10 locations 

throughout the cross section of the stream, termed 

onsite-monitor cross-section measurements in this 

report. The mean of the measurements was recorded as 

the turbidity for the sample collected. The turbidity 

sensor on the multiparameter monitor used during 

sample collection was capable of measuring a range 

from 0 to 6,000 NTU (very muddy water). Turbidity of 

Kansas streams can exceed 6,000 NTU during periods 

of high flow related to runoff. 

At 7 of the 28 surface-water sites, the same multiparameter monitors (fig. 2) were installed in-stream 

and used to continuously measure the turbidity and 

other physical properties of the water. The in-stream 

monitors were cleaned and calibrated every 2 to 

6 weeks, and recorded measurements were adjusted on 

the basis of measurements made just before and after 

monitor cleaning and calibration (Wagner and others, 

2000). The turbidity sensors on the continuous, instream monitors were capable of measuring a range 

from 0 (clear water) to 1,000–1,500 NTU (muddy 

water). Measurements from the water-quality monitor 

8 Comparison and Continuous Estimates of Fecal Coliform and Escherichia Coli Bacteria in Selected Kansas Streams, May 1999 Through April 2002 
stream values (fig. 3). The closer 

the regression slope was to 1.0, 

the more representative the data 

from the continuous in-stream 

monitor were of the turbidity of 

the stream cross section without 

correction. At least 20 to 30 measurements over a 2-year period 

throughout the range of turbidity 

values were necessary to develop 

a robust relation. The number 

of measurements at site 28 were 

not sufficient to develop such 

a relation. 

Turbidity duration curves 

were used for determining at what 

turbidity level a cross-section 

measurement or a bacteria sample was necessary to adequately 

represent the range of conditions 

(fig. 4). Cross-section turbidity 

measurements plotted on the 

duration curve represent ranges of 

turbidity values for which crosssectional measurements need to 

be made and when bacteria samples need to be collected. The 

more evenly distributed the meaFigure 2. Multiparameter monitor used to measure turbidity, specific conductance, pH, water surements and samples are along 

temperature, dissolved oxygen, and fluorescence in water during sample collection and for in- the duration curve, the more rep- stream continuous monitoring. 

were recorded hourly and transmitted via satellite 

every 4 hours and were made available on the World 

Wide Web (http://ks.water.usgs.gov/Kansas/rtqw). 

Every 2 to 6 weeks the hourly data were downloaded 

from the data-collection platform and then uploaded to 

the USGS database. 

The continuous, in-stream monitors also were calibrated to the stream cross section (Rasmussen and 

others, 2002). Onsite-monitor cross-section measurements were compared with the point measurement of 

the in-stream monitor. If the comparison differed by 

more than 10 percent, the in-stream monitor was relocated to a more representative location within the cross 

section. The in-stream monitor was not relocated on 

the basis of temporary stream conditions (as the result 

of storm runoff), but only as a result of long-term variations. A check of the continuous in-stream turbiditysensor measurements was made by comparing the 

average of the cross-section measurements with the inresentative those turbidity values 

and bacteria samples are for the site for the period of 

record. The duration curve also provides a complete 

summary of the turbidity conditions at a particular site 

for a particular time period. Instantaneous measurements every hour were used to construct these duration 

curves (rather than daily values), so the maximum 

and the minimum value of the curve are the maximum 

and minimum measured values for this period. The 

50-percent exceedance value is the median of the 

instantaneous values for the time period. 

Development of Regression Models to Estimate 

Bacteria Densities 

One purpose of this report is to relate the density 

of fecal coliform bacteria to E. coli bacteria in surface 

water. Also, the density of bacteria was related to 

turbidity. 

Methods 9 
10,000 

Average cross-section turbidity, in nephelometric 

y = 1.0026x - 2.9118 

R2

 = 0.9871 

turbidity units (YSI 6026 turbidity sensor) 1,000 

100 

10 

10 100 1,000 10,000 

Turbidity reading from continuous in-stream monitor, in nephelometric 

turbidity units (YSI 6026 turbidity sensor) 

Figure 3. Comparison of continuous, in-stream and cross-section turbidity values for Kansas River at Topeka, Kansas, 

July 1999 through April 2002. 

10,000 

1,000 

100 

10 

1 

0 10 20 30 40 50 60 70 80 90 100 

Percentage of exceedance 

Turbidity duration curve, 

July 1999 through April 2002 

Turbidity values for cross-section 

measurements, July 1999 

through April 2002 

Figure 4. Turbidity duration curve for Kansas River at DeSoto, Kansas, July 1999 through April 2002. 

It is possible to express one constituent concentra- 1992). The regression analysis used in this report has 

tion in terms of another constituent or constituents been modified from work originally done by 

using simple regression models (Helsel and Hirsch, Christensen and others (2000). Although constituent Turbidity, in nephelometric turbidity units (YSI 6026 turbidity sensor) 

10 Comparison and Continuous Estimates of Fecal Coliform and Escherichia Coli Bacteria in Selected Kansas Streams, May 1999 Through April 2002 
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measurements may be related statistically, it does not 

necessarily mean that the independent variable causes 

the concentrations of the dependent variable to occur. 

Linear regression was used for this study because the 

estimators of the parameters are from an explicit mathematical expression. The simplest regression model 

can be expressed as: 

yi = mxi + b + ei i = 1,2,....n, (4) 

where 

yi is the ith observation of the dependent 

variable; 

m is the slope; 

xi is the ith observation of the independent 

(explanatory) variable; 

b is the intercept; 

ei is the random error for the ith observation; 

and 

n is the sample size. 

The terms m and b represent the parameters that need 

to be estimated from the data set. The most common 

estimation technique is least squares (Helsel and Hirsch, 1992). In least-squares estimation, the error term, 

ei

, usually is assumed to be normally distributed with a 

mean equal to zero and constant variance, σ2

. 

Regression models were first developed for estimating E. coli from fecal coliform bacteria densities. 

As a member of the fecal coliform group, E. coli 

should correlate well with fecal coliform. The data 

were log transformed to improve the linearity of the 

relation. 

Regression models then were developed to estimate bacteria densities (fecal coliform and E. coli) on 

the basis of varying water-quantity and -quality characteristics. The first step in developing an effective 

regression model for a specific surface-water site was 

to plot each possible explanatory variable against the 

response variable and examine patterns in the data. All 

explanatory and response variables (except time) were 

log transformed to convert all models presented herein 

to linear models. Log transformations of variables can 

eliminate curvature and simplify analysis of the data 

(Ott, 1993, p. 454). 

Next, to determine which explanatory variable or 

variables to include in the regression model for each 

constituent of concern, an overall model-building 

method (Helsel and Hirsch, 1992, p. 312–314) was 

used. The possible explanatory variables included each 

of the cross-section-averaged sensor measurements 

(specific conductance, pH, water temperature, turbidity, and dissolved oxygen) from the multiparameter 

monitor, streamflow, stage, and time. All possible 

regression models were evaluated. Explanatory 

variables were considered significant if the p-value 

(probability value) was less than 0.05. If there were 

several acceptable models (p-value less than 0.05), the 

one with the lowest PRESS statistic was chosen. Minimizing PRESS (acronym for "PRediction Error Sum 

of Squares”) means that the equation produces the 

least error when making new predictions (Helsel and 

Hirsch, 1992, p. 248). Additionally, explanatory variables were included in a model only if there was a 

physical basis or explanation for their inclusion. 

In addition to the PRESS, three common diagnostic statistics were used to evaluate the regression models described in this report. These statistics are the 

mean square error (MSE), the coefficient of determination (R2), and the relative mean absolute error (RMAE). 

MSE is calculated as follows: 

n

∑ [yi – ( )]

2 

i

MSE i = 1 = ---------------------------------------- , (5) n k

where 

yi represents the value of y, in log units, at 

the ith data point; 

E(yi

) is the estimated value of y, in log units, 

at the ith data point (where E(yi

) = mxi 

+ b); 

n is the number of samples; and 

k is the number of explanatory variables 

in the model. 

The MSE is determined for each regression model 

to assess the variance between estimated and measured 

values. MSE in this report is expressed in log units. 

Using the MSE, the model standard prediction error as 

a percentage was calculated using equation 6: 

model standard prediction error, as a percentage = 

100 × e (2.3026 2 [ × MSE – 1 ) ] , (6) 

where e is the base of natural logarithms. 

MSE is a dimensional measure. Dimensional measures often are required in practice for the purpose of 

comparing constituents or properties with different 

dimensions (units of measure). A dimensionless 

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

variance explained by the regression: 

Methods 11 
E y

--

-

E y

E y

R2=1.0 - (SSE/SSy). (7) 

1 1

n

-- xi xa ( – 2

SSx

++ ---------------------- 

)   ( )–t s ' , SSE (error sum of squares) and SSy (sums of squares y) 10 i × 

are calculated as follows: 

n



1 1

n

-- xi xa ( – 2

SSx

++ ---------------------- 

) 

i i SSE = × ∑ [yi – ( )]

2 , and (8) 10

 ( ) + t s 



, (11) 

i = 1 where n

SSy = ∑ (yi – y)

2 , (9) E(yi

) is the regression-estimated value, in 

log units, at xi

; 

i = 1 t is the value of the student’s t distribuwhere y is the mean of y, in log units./ tion having n-2 degrees of freedom 

with the exceedance probability of The R2 ranges from 0 to 1 and often is called the multi- α/2 (value obtained from t tables in 

ple coefficient of determination in multiple linear the appendix of most statistics textregression. books); 

The RMAE, expressed as a percentage, is calcu- s is the standard error of regression callated as follows: culated using equation 12; 

n n is the number of samples; 

1

- – n∑ A B xi is a specified value of x, in log units; 

xa is the mean (average) of x, in log i = 1 RMAE = ----------------------------- × 100 , (10) units; MB SSx is the sum of squares x, in log units; 

where and 

A 

100 milliliters of water; 

B is the measured density, in colonies per where 

100 milliliters of water; and SSy is the sum of squares y, in log units; 

MB is the mean (average) of all the measured b1 is the estimate of β1 ; 

densities, in colonies per 100 milliliters SSxy is the sums of xy cross products, in log 

of water. units, using equation 13; and 

is the estimated density, in colonies per s = Sy b1Sxy (S – ) ⁄ (n – 2) , (12) 

Graphical plots were constructed to examine the n is the number of samples. 

linearity of the relation between explanatory and n

response variables. Outliers were identified graphi- Sxy = ∑ (xi – x)(yi – y) , (13) 

cally and investigated to determine their validity. No 

outliers were eliminated from the data used to develop i = 1 

the models contained in this report. where 

Prediction intervals were determined to evaluate xi represents the value of x at the ith data 

the uncertainty of the estimates using the regression point, in log units; 

model (Helsel and Hirsch, 1992). Prediction intervals x is the mean of x, in log units; 

defined a range of values for the dependent variable yi represents the value of y at the ith data 

for a given level of uncertainty. For this report, both point, in log units; and 

50- and 90-percent prediction intervals were deter- y is the mean of y, in log units. 

mined for each model. For a given independent vari- SSx is calculated using equation 14: 

able(s), the 90-percent prediction interval represented n

the range of values expected for the dependent variable SSx = ∑ (xi – xa)

2 (14) 90 percent of the time. The larger the range of values, 

the more uncertainty there was associated with the i = 1 

regression model. The prediction interval for a single A regression-estimated 30-day geometric mean 

response, yˆ , is: was calculated every hour for comparison to 

12 Comparison and Continuous Estimates of Fecal Coliform and Escherichia Coli Bacteria in Selected Kansas Streams, May 1999 Through April 2002 
E y

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geometric-mean criteria. The following equation was 

used: 

GM = 720 y1 y2 y3 ...y720 × × × , (15) 

where 

GM	 is the 30-day geometric mean for 

720 regression-estimated hourly 

values of y, in colonies per 100 milliliters of water; and 

yi	 is the regression-estimated value, in 

colonies per 100 milliliters of water, 

for the ith hour. 

Although prediction intervals are good indicators 

of uncertainty, a range of values is not very useful for 

determining recreational quality of a stream. Probability of exceedance provides water managers with a single value for decisionmaking. For this study, probabilities of exceeding primary and secondary contact 

recreational use criteria were determined for each 

regression model as follows: 

Prob (E(yi) > Std) = 1 – the area below the standard normal 

curve for a value greater than Z, (16) 

where 

Z is ( ( ) – Log10(Std)) ⁄ MSE ; i

E(yi

)/ is either the regression-estimated 

value at xi when comparing hourly 

estimates to the single-sample criteria 

or the 30-day geometric mean of 

hourly measurements when comparing to the geometric-mean criteria; 

Std	 is 200 col/100 mL for fecal coliform 

bacteria, geometric mean of five samples collected over a 30-day period for 

primary contact recreational use at an 

illness rate of 8 per 1,000 swimmers; 

2,000 col/100 mL for fecal coliform, 

single sample for primary and secondary recreational use at an illness rate 

of 8 illnesses per 1,000 swimmers; 

126 col/100 mL for E. coli bacteria, 

geometric mean of five samples collected over a 30-day period for primary contact recreational use at an 

illness rate of 8 per 1,000 swimmers; 

235 col/100 mL for E. coli bacteria, 

USEPA recommended single-sample 

criterion for designated beach areas at 

8 illnesses per 1,000 swimmers; and 

2,507 col/100 mL for E. coli bacteria, 

USEPA recommended single-sample 

criterion for infrequently used fullbody contact at 14 illnesses per 

1,000 swimmers. 

The area under the standard normal curve can be 

obtained from any statistics textbook that has a table 

for upper-tailed areas for the standard normal curve. 

To assess the utility of the regression models, the 

percentage of samples that were in agreement with 

measured samples as to whether the criterion was 

exceeded or not exceeded was calculated. Comparisons were made between the turbidity-estimated and 

measured values that were used to develop the regression model. The estimate was in agreement if it and 

the measured value both exceeded the criterion or if 

both values were less than the criterion. A false negative occurred if the estimated value was less than the 

criterion and the measured value exceeded the criterion. A false positive occurred when the estimated 

value exceeded the criterion and the measured value 

was less than the criterion. 

Because all of the response and explanatory variables were log transformed, retransformation of 

regression-estimated concentrations was necessary. 

However, retransformation can cause an underestimation of chemical loads when adding individual load 

estimates over a long period of time. Applying Duan's 

bias correction factor (Duan, 1983) to the annual load 

calculation allows correction for this underestimation. 

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

Hirsch and others (1993) provide additional information on interpreting the results of regression-based 

load estimates: 

n

∑ 10ei

+ i = 1 LD = 10[b mlog (NTU)]

× ------------------ × Q , (17) n

where 

LD is the load of bacteria, in colonies; 

b is y-intercept from the regression 

model; 

m is the slope from the regression model; 

NTU is the measured turbidity, in nephelometric turbidity units; 

ei is the residual or the difference between 

each measured and estimated bacteria 

density, in log units; 

n is the number of samples; and 

Q is the streamflow, in cubic feet per 

second. 

Methods 13 
MEASURED BACTERIA DENSITIES 

Three hundred and eighteen samples were collected from the 28 surface-water sites and analyzed 

for fecal coliform and E. coli from May 1999 through 

April 2002 (table 3). Measured densities of fecal 

coliform and E. coli bacteria ranged from 1 to 

71,000 and 1 to 75,000 col/100 mL of water, respectively. Eighteen percent of all 318 samples collected 

exceeded the current (2003) KDHE secondary contact 

recreational criterion for fecal coliform bacteria 

(2,000 col/100 mL of water). Samples collected in the 

summer and fall (July 1–October 31) when higher than 

normal flow (runoff from rainfall) and large turbidity 

values occur (data on file with U.S. Geological Survey, 

Lawrence, Kansas, http://water.usgs.gov/ks/nwis/qw/) 

had the largest densities of bacteria. During the recreational period (April 1 through October 31), 219 samples were collected. Of these samples, fecal coliform 

densities in 47 exceeded 2,000 col/100 mL (21 percent), and E. coli densities in 78 samples exceeded 

576 col/100 mL (36 percent). 

The smallest bacteria densities occurred primarily 

during low flow and small turbidity values. In this 

report, low flow is defined as streamflow that was 

unaffected by storm runoff. Of the 99 samples collected during the winter months (November 1 through 

March 31), fecal coliform densities in 10 (10 percent) 

exceeded the 2,000-col/100 mL criterion for secondary contact recreation. 

COMPARISON OF FECAL COLIFORM AND 

ESCHERICHIA COLI DENSITIES 

Escherichia coli (E. coli) is the dominant bacteria 

of the fecal coliform group and the relation between 

the two bacteria in water is apparent in figure 5. Siteby-site, basin- or subbasin-wide, and statewide comparisons were made using E. coli/fecal coliform 

(EC/FC) ratios and regression models. Data sets with 

15 or more samples were used for comparison. Both 

EC/FC ratios and regression models were developed 

so that E. coli densities could be estimated on the basis 

of historical fecal coliform data at these sites and a 

statewide comparison between the two indicator bacteria could be made. 

Ratios of EC/FC were calculated using geometric 

means for samples (table 4). The EC/FC ratios ranged 

from 0.48 for Rattlesnake Creek near Zenith (site 21) 

to 0.96 for Kansas River at Wamego (site 1). The 

geometric mean of the EC/FC ratio for all 318 samples 

was 0.77. The variation between sites probably is due 

to site-specific sources of bacteria and water-quality 

conditions. The EC/FC ratios were smallest in streams 

with elevated salinity (or specific conductance greater 

than 1,000 μS/cm). For example, the mean specific 

conductance for Rattlesnake Creek near Zenith 

(site 21) was 3,790 μS/cm compared to 855 μS/cm for 

Kansas River at Topeka (site 2, fig. 6). Elevated salinity decreases the survivability of E. coli bacteria (U.S. 

Environmental Protection Agency, 1986) and, therefore, decreases the EC/FC ratio. However, the survivability of enterococci is not affected by saline water, 

and therefore, it may be a more reliable indicator of 

swimming-related illnesses in these streams (U.S. 

Environmental Protection Agency, 1986). 

Simple linear regression was used to further define 

the relation between fecal coliform and E. coli bacteria 

at six individual surface-water sites and six groups 

of surface-water sites (table 4). The R2 for the 

E. coli/fecal coliform regression models for individual 

sites ranged from 0.32 for Rattlesnake Creek near 

Zenith (site 21) to 0.98 for Little Arkansas River at 

Highway 50 near Halstead (site 22). In models for 

individual sites on the Kansas (sites 1, 2, and 20) and 

Little Arkansas Rivers (sites 22 and 23), the slopes (m) 

ranged from 0.901 to 1.00, and the R2

s were 0.89 or 

greater. The high R2

s for the models indicate a strong 

correlation between fecal coliform and E. coli. At 

these sites, fecal coliform is a reliable indicator, 

explaining at least 89 percent of the variability of 

E. coli. For these sites, E. coli could be estimated from 

historical fecal coliform data with a good degree of 

reliability. The low R2 for the Rattlesnake Creek near 

Zenith (site 21) regression model is a further indication that water-quality conditions at this site are 

decreasing the survivability of the E. coli and, therefore, decreasing the correlation between E. coli and 

fecal coliform. For this site, E. coli cannot be reliably 

estimated with this model. 

The two models for 17 sites in and around the Soldier Creek Basin (sites 3–19) and 5 sites in the North 

Fork Ninnescah River Basin (sites 24–28) have R2s of 

0.88 and 0.70, respectively. The slope (0.936) and the 

R2

 for the Soldier Creek sites are comparable to the 

slope and R2 for the Kansas River sites. The lower R2 

for the North Fork Ninnescah River Basin model compared to the Kansas River and Soldier Creek sites 

probably is an indication of water-quality conditions 

unfavorable for the survivability of E. coli (specific 

14 Comparison and Continuous Estimates of Fecal Coliform and Escherichia Coli Bacteria in Selected Kansas Streams, May 1999 Through April 2002 
Table 3. Statistical summary for fecal coliform and Escherichia coli (E. coli) bacteria densities measured in samples collected from an

at surface-water sites on selected Kansas streams, May 1999 through April 2002 

[KLR, Kansas-lower Republican River Basin; RSC, Rattlesnake Creek Basin; LARK, Little Arkansas River Basin; NFNR, North Fork Ninnescah River Basin; --, not 

Fecal coliform bacteria E. coli bacteria 

Densities Percentage of samples exceeding Densities Percentage of samples exceedin

(colonies per indicated water-quality criteria (colonies per indicated recommended water-qua

100 milliliters) (colonies per 100 milliliters) 1 100 milliliters) criteria (colonies per 100 milliliter

Site April 1– July 1– November 1– April 1– July 1– Novembe

number Basin Number of June 30 October 31 March 31 June 30 October 31 March 

(fig. 1) name samples Minimum Maximum (2,000) (2,000) (2,000) Minimum Maximum (576) (576) (576) 

1 KLR 46 2 11,000 2 4 2 1 5,200 4 7 7 2 KLR 47 2 71,000 4 13 6 2 75,000 6 15 9 

3–19 KLR 76 1 2,500 0 1 0 1 1,500 4 14 1 

20 KLR 52 2 32,000 8 25 8 1 23,000 10 27 10 21 RSC 18 14 3,100 6 0 0 13 1,800 11 11 0 22 LARK 23 17 36,000 9 17 4 20 41,000 9 22 4 23 LARK 28 7 25,000 7 18 4 4 23,000 14 21 7 

24–28 NFNR 28 44 39,000 0 14 0 6 10,000 0 32 0 

1Water-quality criteria from Kansas Department of Health and Environment (2001). 

2Recommended water-quality criteria from U.S. Environmental Protection Agency (2002). 

Comparison of Fecal Coliform and Escherichia Coli Densities 15 
16 Comparison and Continuous Estimates of Fecal Coliform and Escherichia Coli Bacteria in Selected Kansas Streams, May 1999 Through April 2002

Figure 5. red fecal coliform and Escherichia coli (E. coli) bacteria densities at 28 surface-water sites, May 1999 

through April 2002. Current (2003) water-quality criteria from Kansas Department of Health and Environment (2001), and recommended 

criteria from U.S. Environmental Protection Agency (2002).

Comparison of measu
Table 4. Regression and geometric-mean statistics for comparison of fecal coliform and Escherichia coli (E. coli) bacteria densities a

water sites and for selected basins in Kansas, May 1999 through April 2002 

[R2, coefficient of determination; MSE, mean square error; n, number of samples; RMAE, relative mean absolute error; SSx, sum of squares x; ECB, Escherichia coli (E

bacteria] 

Regression statistics 

Range in bacteria densities 

(colonies per 100 milliliters of water) 

R

(pe

Model 

standard 

error of 

estimate 

(percent) n 

Site number 

(fig. 1) Model R2 

MSE 

(log units) 

Fecal coliform E. coli 

Individual sites 

1 Log10ECB = 0.901log10FCB + 0.173 0.89 0.0840 75 46 2–1,000 1–5,200 

2 Log10ECB = 0.977log10FCB – 0.00966 .94 .0696 65 47 2–71,000 2–75,000 

20 Log10ECB = 1.00log10FCB – 0.0916 .97 .0453 62 52 2–32,000 40–23,000 21 Log10ECB = 0.595log10FCB + 0.708 .32 .240 160 18 14–3,100 6–1,800 22 Log10ECB = 0.983log10FCB + 0.00391 .98 .0191 33 23 17–36,000 30–41,000 23 Log10ECB = 0.998log10FCB – 0.115 .95 .0504 55 28 7–25,000 29–23,000 

Kansas-lower Republican River Basin 

1–20 Log10ECB = 0.960log10FCB + 0.00780 .93 .0655 64 221 1–71,000 1–75,000 

1, 2, 20 Log10ECB = 0.966log10FCB + 0.0209 .94 .0657 64 145 2–71,000 1–75,000 

Soldier Creek Basin 

3–19 Log10ECB = 0.936log10FCB + 0.0119 .88 .0623 52 76 1–2,500 1–1,500 

Little Arkansas River Basin 

22, 23 Log10ECB = 0.993log10FCB – 0.0645 .96 .0366 46 51 7–36,000 4–41,000 

North Fork Ninnescah River Basin 

24–28 Log10ECB = 0.932log10FCB – 0.0493 .70 .158 114 28 44–39,000 14–10,000 

Kansas-lower Republican and Little Arkansas River Basins (statewide) 

1–20, 

Comparison of Fecal Coliform and Escherichia Coli Densities 

Log10ECB = 0.966log10FCB – 0.00428 .94 .0599 61 272 1–71,000 1–75,000 

22, 23 

17 
Figure 6. Mean specific conductance for bacteria samples collected at selected surface-water sites, 

May 1999 through April 2002. 

conductance = 1,210 μS/cm, fig. 6). Even so, both 

models could be used to estimate E. coli densities with 

some degree of reliability. The regression models in 

table 4 are specific to the sites or groups of sites that 

they were developed for and only relevant for the density ranges listed. 

Simple linear regression was used to further 

determine if a single model could be used to estimate 

E. coli bacteria at more than one site within a river 

basin. Data were combined for the three sites on the 

Kansas River (sites 1, 2, 20), all 20 sites in the Kansaslower Republican River Basin (sites 1–20), and for 

both sites in the Little Arkansas River Basin (sites 22, 

23) to develop a single model for each group to estimate E. coli from fecal coliform (table 4). The slopes 

and R2

s for the two Kansas-lower Republican River 

Basin models are nearly identical indicating that a single model probably is sufficient for all the sites in the 

basin. Although the Kansas-lower Republican River 

Basin model appears to be nearly as reliable as the 

individual site and group models, the Kansas-lower 

Republican River Basin model best represents the 

three Kansas River sites where a majority of the data 

were collected (145 of the 221 samples were from 

three Kansas River sites). The Kansas-lower Republican River Basin model reasonably represents the Soldier Creek sites (sites 3–19, fig. 1) for the limited 

amount of data collected in that basin (76 samples representing 17 sites). To determine if the model represents all the Soldier Creek sites, more samples would 

need to be collected at each site for a variety of 

hydrologic conditions. The Little Arkansas River 

Basin model indicates that 96 percent of the variability 

is explained. The data for the Little Arkansas River 

Basin model are more evenly distributed between 

sites 22 and 23 and, therefore, reliably estimate E. coli 

concentrations at each site. 

The statewide model only included sites with 

mean specific conductance less than 1,000 μS/cm 

(sites 1–20, 22, 23). The model describes 94 percent of 

the variability and appears to sufficiently explain the 

EC/FC relation at the 22 sites. For the reasons 

previously discussed, sites that have fewer samples are 

underrepresented by the statewide model and, therefore, it is not appropriate for use at these sites. 

Comparisons of fecal coliform and regressionestimated E. coli bacteria densities for all 12 models 

are shown in figure 7. The uncertainty for each of the 

models is graphically displayed by the prediction 

intervals. The closer the intervals are to one another, 

the less uncertainty for that particular regression 

model at a specified probability. The 50- and 90-

percent prediction intervals were plotted to show the 

difference in ranges. Given any measured fecal 

18 Comparison and Continuous Estimates of Fecal Coliform and Escherichia Coli Bacteria in Selected Kansas Streams, May 1999 Through April 2002 
Comparison of Fecal Coliform and Escherichia Coli Densities 19

Figure 7A. Comparison of measured fecal coliform and Escherichia coli bacteria densities, regression-estimated fecal 

coliform bacteria density, and prediction intervals for Kansas River at Wamego (site 1, fig. 1), July 1999 through April 2002. 

Current (2003) water-quality criterion from Kansas Department of Health and Environment (2001), and recommended 

criteria from U.S. Environmental Protection Agency (2002).
20 Comparison and Continuous Estimates of Fecal Coliform and Escherichia Coli Bacteria in Selected Kansas Streams, May 1999 Through April 2002

Figure 7B. Comparison of measured fecal coliform and Escherichia coli bacteria densities, regression-estimated fecal 

coliform bacteria density, and prediction intervals for Kansas River at Topeka (site 2, fig. 1), July 1999 through April 2002. 

Current (2003) water-quality criterion from Kansas Department of Health and Environment (2001), and recommended 

criteria from U.S. Environmental Protection Agency (2002).
Comparison of Fecal Coliform and Escherichia Coli Densities 21

Figure 7C. Comparison of measured fecal coliform and Escherichia coli bacteria densities, regression-estimated fecal 

coliform bacteria density, and prediction intervals for Kansas River at DeSoto (site 20, fig. 1), July 1999 through April 2002.

Current (2003) water-quality criterion from Kansas Department of Health and Environment (2001), and recommended 

criteria from U.S. Environmental Protection Agency (2002).
22 Comparison and Continuous Estimates of Fecal Coliform and Escherichia Coli Bacteria in Selected Kansas Streams, May 1999 Through April 2002

Figure 7D. Comparison of measured fecal coliform and Escherichia coli bacteria densities, regression-estimated fecal 

coliform bacteria density, and prediction intervals for Rattlesnake Creek near Zenith (site 21, fig. 1), May 1999 through 

April 2002. Current (2003) water-quality criterion from Kansas Department of Health and Environment (2001), and 

recommended criteria from U.S. Environmental Protection Agency (2002).
Comparison of Fecal Coliform and Escherichia Coli Densities 23

Figure 7E. Comparison of measured fecal coliform and Escherichia coli bacteria densities, regression-estimated fecal 

coliform bacteria density, and prediction intervals for Little Arkansas River at Highway 50 near Halstead (site 22, fig. 1), 

May 1999 through April 2002. Current (2003) water-quality criterion from Kansas Department of Health and Environment 

(2001), and recommended criteria from U.S. Environmental Protection Agency (2002).
24 Comparison and Continuous Estimates of Fecal Coliform and Escherichia Coli Bacteria in Selected Kansas Streams, May 1999 Through April 2002

Figure 7F. Comparison of measured fecal coliform and Escherichia coli bacteria densities, regression-estimated fecal 

coliform bacteria density, and prediction intervals for Little Arkansas River near Sedgwick (site 23, fig. 1), May 1999 

through April 2002. Current (2003) water-quality criterion from Kansas Department of Health and Environment (2001), and 

recommended criteria from U.S. Environmental Protection Agency (2002).
Comparison of Fecal Coliform and Escherichia Coli Densities 25

Figure 7G. Comparison of measured fecal coliform and Escherichia coli bacteria densities, regression-estimated fecal 

coliform bacteria density, and prediction intervals for sites in the Kansas-lower Republican River Basin (sites 1–20; fig. 1), 

May 1999 through April 2002. Current (2003) water-quality criterion from Kansas Department of Health and Environment 

(2001), and recommended criteria from U.S. Environmental Protection Agency (2002).
26 Comparison and Continuous Estimates of Fecal Coliform and Escherichia Coli Bacteria in Selected Kansas Streams, May 1999 Through April 2002

Figure 7H. Comparison of measured fecal coliform and Escherichia coli bacteria densities, regression-estimated fecal 

coliform bacteria density, and prediction intervals for sites in Kansas-lower Republican River (sites 1, 2, 20; fig. 1), 

May 1999 through April 2002. Current (2003) water-quality criterion from Kansas Department of Health and Environment 

(2001), and recommended criteria from U.S. Environmental Protection Agency (2002).
Comparison of Fecal Coliform and Escherichia Coli Densities 27

Figure 7I. Comparison of measured fecal coliform and Escherichia coli bacteria densities, regression-estimated fecal 

coliform bacteria density, and prediction intervals for sites in Soldier Creek Basin (sites 3–19, fig. 1), May 1999 through 

April 2002. Current (2003) water-quality criterion from Kansas Department of Health and Environment (2001), and 

recommended criteria from U.S. Environmental Protection Agency (2002).
28 Comparison and Continuous Estimates of Fecal Coliform and Escherichia Coli Bacteria in Selected Kansas Streams, May 1999 Through April 2002

Figure 7J. Comparison of measured fecal coliform and Escherichia coli bacteria densities, regression-estimated fecal 

coliform bacteria density, and prediction intervals for sites in the Little Arkansas River Basin (sites 22, 23; fig. 1), May 1999

through April 2002. Current (2003) water-quality criterion from Kansas Department of Health and Environment (2001), and 

recommended criteria from U.S. Environmental Protection Agency (2002).
Comparison of Fecal Coliform and Escherichia Coli Densities 29

Figure 7K. Comparison of measured fecal coliform and Escherichia coli bacteria densities, regression-estimated fecal 

coliform bacteria density, and prediction intervals for sites in the North Fork Ninnescah Basin (site 24–28; fig. 1), May 1999 

through April 2002. Current (2003) water-quality criterion from Kansas Department of Health and Environment (2001), and 

recommended criteria from U.S. Environmental Protection Agency (2002).
30 Comparison and Continuous Estimates of Fecal Coliform and Escherichia Coli Bacteria in Selected Kansas Streams, May 1999 Through April 2002

Figure 7L. Comparison of measured fecal coliform and Escherichia coli bacteria densities, regression-estimated fecal 

coliform bacteria density, and prediction intervals for sites in the Kansas-lower Republican River and Little Arkansas River 

Basins (sites 1–20, 22, 23; fig. 1), May 1999 through April 2002. Current (2003) water-quality criterion from Kansas 

Department of Health and Environment (2001), and recommended criteria from U.S. Environmental Protection Agency 

(2002).
coliform within the range of values plotted, there is 

a 90-percent chance that the resulting estimated 

E. coli density will be within the 90-percent 

prediction interval. 

The geometric-mean EC/FC ratio for each site or 

group of sites is the preferred method for estimating 

E. coli from fecal coliform densities. Fecal coliform 

density was multiplied by the appropriate ratio to 

obtain an estimate of the E. coli density in the sample. 

Unlike regression models that are to be used only for 

the range of fecal coliform densities that were used to 

develop the model, the EC/FC ratios can be used for 

any fecal coliform density. A comparison of the RMAE 

for the two methods indicated that there was some siteto-site variation, and the EC/FC ratio was the better or 

equally good estimator for 8 of the 12 sites. 

Comparison of the current (2003) geometric-mean 

criterion for fecal coliform bacteria of 200 col/100 mL 

to the 2002 USEPA recommended geometric-mean 

criterion of 126 col/100 mL for E. coli results in an 

EC/FC ratio of 0.63. The geometric-mean EC/FC ratio 

for all sites except Rattlesnake Creek (site 21) is 0.77, 

indicating that considerably more than 63 percent of 

the fecal coliform is E. coli. This potentially could 

lead to more exceedances of the recommended E. coli 

criterion, where the water now meets the current 

KDHE (2003) 200-col/100 mL fecal coliform 

criterion. 

CONTINUOUSLY ESTIMATED BACTERIA 

DENSITIES 

Statistical relations between in-stream turbidity 

measurements and bacteria densities were developed 

for 6 of the 28 surface-water sites. These relations 

allow for continuous estimates of both fecal coliform 

and E. coli bacteria. Continuous estimates of indicator 

bacteria densities can be used to define duration curves 

to examine seasonal variability and the frequency at 

which densities potentially exceed water-quality criteria. Continuous estimates of loads and yields of indicator bacteria can be used to examine annual and 

seasonal variation in loads. Additionally, real-time 

estimates of bacteria densities and the probability that 

current (2003) water-quality criteria at surface-water 

sites may be exceeded can be made available through 

the World Wide Web. 

Relation Between Turbidity and Fecal Coliform 

Density 

A defined statistical relation between turbidity and 

fecal coliform bacteria densities was developed for six 

of the seven surface-water sites where real-time, continuous multiparameter monitors with turbidity sensors were deployed (table 2). A comparison of 

measured turbidity and fecal coliform bacteria densities generally shows a strong correlation (fig. 8). Simple linear regression analysis was performed on data 

from these six sites to define a relation between turbidity and fecal coliform. The regression models are site 

specific and applicable only for the range of turbidity 

values listed in table 5. 

Turbidity was the best estimator of all the averaged cross-section water-quality measurements and 

streamflow [Note: Only turbidity values from a YSI 

6026 turbidity sensor are appropriate for the relations 

described in this report (see “Methods” section of this 

report).] The explanatory and response variables were 

log transformed prior to fitting the regression models. 

The regression models used to estimate fecal coliform 

bacteria densities for the Kansas River (sites 1, 2, and 

20, fig. 1) and the Little Arkansas River (sites 22 and 

23) had slopes (m) that ranged from 1.13 (site 22) to 

1.40 (site 20). The difference in slope probably is due 

to differences in bacteria sources, flow regimes, and 

water chemistry. The R2

s for these sites were equal to 

or greater than 0.62, indicating more than 62 percent 

of the variability in the bacteria concentrations was 

explained by turbidity. The range of turbidity and fecal 

coliform data spanned three orders of magnitude, 

describing a majority of the streamflow and turbidity 

conditions at these sites. The slope and R2 for site 21 

(Rattlesnake Creek near Zenith) were 0.542 and 0.16, 

respectively. The lower R2

 indicates that turbidity and 

fecal coliform were not well correlated for this site. 

The poor relation probably is related to the decreased 

survivability of E. coli (the dominant member of the 

fecal coliform group) because of elevated salinity concentrations at this site. 

For the six fecal coliform regression models, 

uncertainties, expressed as model standard error of 

estimate in percent (SEE), ranged from +145 to 

+310 percent. The smallest SEEs were for sites with 

the smallest range of turbidities for collected samples 

indicating that sites with highly variable turbidity 

increase the uncertainty. When considering the uncertainty for estimating bacteria concentrations on the 

basis of turbidity measurements, it is important to 

Continuously Estimated Bacteria Densities 31 
Figure 8. Comparison of measured turbidity and fecal coliform bacteria densities at selected surface-water sites, May 1999 through 

April 2002. 

remember the uncertainty associated with the membrane filtration technique, which was discussed in the 

“Methods” section of this report. 

Comparisons of measured turbidity and regression-estimated fecal coliform bacteria densities for the 

six surface-water sites are shown in figure 9. The 

uncertainty for each of the models is graphically displayed with the prediction intervals. The closer the 

intervals are to one another, the less uncertainty for 

that particular regression model at a specified probability. The 50- and 90-percent prediction intervals 

were plotted to show the difference in ranges. Given 

any measured turbidity within the range of values plotted, there is a 90-percent chance that the resulting 

estimated fecal coliform value will be within the 

90-percent prediction interval. 

Estimates from the regression models were compared to measured sample densities, and the 

percentage of estimated values that were in agreement 

as exceeding or being less than the water-quality criteria are reported in table 6. At least 70 percent of the 

regression-estimated values were in agreement with 

measured sample densities as being less than or 

greater than 200 col/100 mL for all sites except Rattlesnake Creek near Zenith (site 21). All of the model 

estimates were in agreement with the measured densities as being less than 2,000 col/100 mL at least 

84 percent of the time. The regression-model estimates 

were in agreement with measured densities greater 

than 2,000 col/100 mL at least 81 percent of the time 

for sites 20, 22, and 23. The models estimates for sites 

1, 2, and 21 were in agreement with measured densities greater than 2,000 col/100 mL less than 55 percent 

of the time. The regression-model estimates for the 

Kansas River at Wamego (site 1) were in agreement 

for only one of the four samples that had measured 

densities greater than 2,000 col/100 mL. The regression model for Rattlesnake Creek (site 21) did not 

accurately estimated densities less than 200 or greater 

than 2,000 col/100 mL due to the low slope of the 

model and the limitation of the continuous turbidity 

sensor. The maximum turbidity the continuous sensor 

could measure was 1,000 to 1,500 NTU, and the corresponding estimated fecal coliform bacteria density 

was 1,700 col/100 mL. 

The regression models and continuous (hourly) 

turbidity data collected at the six surface-water sites 

were used to continuously estimate fecal coliform bacteria densities for May 1999 through April 2002 

(fig. 10). Fecal coliform bacteria densities from 

32 Comparison and Continuous Estimates of Fecal Coliform and Escherichia Coli Bacteria in Selected Kansas Streams, May 1999 Through April 2002 
Table 5. Regression models and statistics for estimating fecal coliform bacteria densities using turbidity measurements at selected s

May 1999 through April 2002 

[R2, coefficient of determination; MSE, mean square error; n, number of samples; RMAE, relative mean absolute error; SSx, sum of squares x; RPD, relative percentage 

NTU, turbidity] 

Model Range in fecal 

standard coliform bacteria 

Site MSE error of densities 

number (log estimate (colonies per 

(fig. 1) Regression model R2 units) (percent) n 100 milliliters) 

Range in turbidity 

(nephelometric 

turbidity units) 

RMAE 

(percent

1 Log10FCB = 1.30log10NTU – 0.663 0.66 0.273 180 46 2–11,000 11–1,211 127 

2 Log10FCB = 1.36log10NTU – 0.740 .62 .449 310 47 2–71,000 12–6,240 100 

20 Log10FCB = 1.40log10NTU – 0.793 .78 .304 200 52 2–32,000 9–4,210 64 21 Log10FCB = 0.542log10NTU + 1.51 .16 .274 180 18 14–3,100 5–350 63 22 Log10FCB = 1.13log10NTU + 0.378 .69 .249 165 23 17–36,000 4–860 73 23 Log10FCB = 1.19log10NTU + 0.198 .79 .210 145 28 7–25,000 5–1,300 66 

Continuously Estimated Bacteria Densities 33 
Figure 9A. Comparison of measured turbidity and fecal coliform bacteria densities, regression-estimated fecal coliform 

bacteria densities, and prediction intervals for Kansas River at Wamego (site 1, fig. 1), July 1999 through April 2002. 

Recreational water-quality criteria established by Kansas Department of Health and Environment (2001). 

34 Comparison and Continuous Estimates of Fecal Coliform and Escherichia Coli Bacteria in Selected Kansas Streams, May 1999 Through April 2002 
Figure 9B. Comparison of measured turbidity and fecal coliform bacteria densities, regression-estimated fecal coliform 

bacteria densities, and prediction intervals for Kansas River at Topeka (site 2, fig. 1), July 1999 through April 2002. 

Recreational water-quality criteria established by Kansas Department of Health and Environment (2001). 

Continuously Estimated Bacteria Densities 35
Figure 9C. Comparison of measured turbidity and fecal coliform bacteria densities, regression-estimated fecal coliform 

bacteria densities, and prediction intervals for Kansas River at DeSoto (site 20, fig. 1), July 1999 through April 2002. 

Recreational water-quality criteria established by Kansas Department of Health and Environment (2001). 

36 Comparison and Continuous Estimates of Fecal Coliform and Escherichia Coli Bacteria in Selected Kansas Streams, May 1999 Through April 2002 
Figure 9D. Comparison of measured turbidity and fecal coliform bacteria densities, regression-estimated fecal coliform 

bacteria densities, and prediction intervals for Rattlesnake Creek near Zenith (site 21, fig. 1), May 1999 through April 

2002. Recreational water-quality criteria established by Kansas Department of Health and Environment (2001). 

Continuously Estimated Bacteria Densities 37
Figure 9E. Comparison of measured turbidity and fecal coliform bacteria densities, regression-estimated fecal coliform 

bacteria densities, and prediction intervals for Little Arkansas River at Highway 50 near Halstead (site 22, fig. 1), May 

1999 through April 2002. Recreational water-quality criteria established by Kansas Department of Health and 

Environment (2001). 

38 Comparison and Continuous Estimates of Fecal Coliform and Escherichia Coli Bacteria in Selected Kansas Streams, May 1999 Through April 2002 
Figure 9F. Comparison of measured turbidity and fecal coliform bacteria densities, regression-estimated fecal coliform 

bacteria densities, and prediction intervals for Little Arkansas River near Sedgwick (site 23, fig. 1), May 1999 through 

April 2002. Recreational water-quality criteria established by Kansas Department of Health and Environment (2001). 

Continuously Estimated Bacteria Densities 39
Table 6. Percentage of regression-model estimates in agreement with measured fecal coliform bacteria densities in relation 

to primary and secondary contact recreational criteria for selected surface-water sites in Kansas, May 1999 through 

April 2002 

[Recreational water-quality criteria from Kansas Department of Health and Environment (2001). <, less than; >, greater than; col/100 mL, colonies per 

100 milliliters of water] 

Percentage of estimated Percentage of estimated Percentage of estimated 

values <200 col/100 mL values >200 col/100 mL values <2,000 col/100 mL 

that were in agreement that were in agreement that were in agreement 

with measured sample with measured sample with measured sample 

densities densities densities 

<200 col/100 mL >200 col/100 mL <2,000 col/100 mL 

Site (total number of (total number of (total number of samples 

number samples with densities samples with densities with densities 

Percentage of estimated

values >2,000 col/100 mL

that were in agreement 

with measured sample

densities 

>2,000 col/100 mL 

(total number of samples 

with densities 

>2,000 col/mL)

25 (4) 

55 (11) 

81 (21) 

0 (1) 

86 (7) 

88 (8) 

(fig. 1) Site name <200 col/100 mL) 

1 Kansas River at Wamego 83(30) 

2 Kansas River at Topeka 81 (26) 

20 Kansas River at DeSoto 82 (22) 

21 Rattlesnake Creek near Zenith 0 (5) 

22 Little Arkansas River at 70 (10) 

Highway 50 near Halstead 

23 Little Arkansas River near 85 (13) 

Sedgwick 

collected water samples were plotted with the regression-estimated densities to give some indication of 

how well the regression models represented in-stream 

conditions. The percentage of time that the stream 

exceeded a water-quality criteria was calculated and is 

shown on these graphs, which gives an indication of 

the probability that a stream will meet water-quality 

criteria and TMDL goals. A moving 30-day geometric 

mean was also plotted for comparison with the primary contact recreational geometric-mean criteria. 

The percentage of estimated hourly fecal coliform 

bacteria densities that were greater than recreationaluse criteria were divided into three seasons—spring 

(April through June), summer (July through October), 

and winter (November through March). The seasons 

are consistent with the seasons KDHE uses to 

determine TMDL listings. Spring had the highest percentage of hourly estimated fecal coliform densities 

greater than the geometric-mean and single-sample 

criteria. For instance, estimated fecal coliform 

densities in the spring at the three Kansas River sites 

(1, 2, 20) indicate that the single-sample criterion 

(2,000 col/100 mL) was exceeded between 1 and 

10 percent of the time for the period July 1999 through 

April 2002 (table 7). The regression-estimated hourly 

fecal coliform density exceedances at the two sites 

(22, 23) on the Little Arkansas River were between 

20 and 22 percent in the spring. A comparison of fecal 

coliform bacteria densities at sites on the two rivers for 

>200 col/mL) <2,000 col/100 mL) 

75 (16) 98(42) 

85 (20) 97 (36) 

87 (30) 94 (31) 

92 (13) 100 (17) 

92 (13) 100 (16) 

100 (15) 84 (19) 

the spring indicated that the geometric-mean criterion 

(200 col/100 mL) was exceeded 54 to 83 percent of 

the time. The percentages of exceedance of 200 and 

2,000 col/100 mL for the three sites on the Kansas 

River generally increased from upstream to downstream. The number of estimated hourly densities 

greater than 200 col/100 mL for the Little Arkansas 

River sites decreased from upstream to downstream in 

the spring and summer. 

The continuous turbidity data presented in this 

report had days of no data or incomplete data mostly 

due to removal of the multiparameter monitor during 

periods of ice conditions or equipment malfunctions. 

For these periods, turbidity values were interpolated 

between the values prior to and after the period of no 

data. During these periods, streamflow was stable indicating that turbidity also was probably stable. These 

data are highlighted in red on the graphs (fig. 10). 

None of the interpolated turbidity values were greater 

than 240 NTU, corresponding to an estimated fecal 

coliform density of 314 col/100 mL for that particular 

site. A limitation of applying regression models to 

continuous data is that the in-stream turbidity sensor 

maximum varies between 1,000 to 1,500 NTU, truncating the actual turbidity peak. This truncation is evident in the graphs (fig. 10) where the estimated fecal 

coliform density was at its maximum for several hours 

(or days). For these instances, actual fecal coliform 

40 Comparison and Continuous Estimates of Fecal Coliform and Escherichia Coli Bacteria in Selected Kansas Streams, May 1999 Through April 2002 
Continuously Estimated Bacteria Densities 41

bacteria densities were unknown but were likely 

greater than the regression-estimated density.

Probability and Duration of Estimated Fecal 

Coliform Densities

The continuous estimated fecal coliform bacteria 

densities can be displayed as probabilities and durations that allow for easier identification of water quality for a particular stream segment relative to waterquality criteria. The public and water-management 

agencies can use probability values and duration 

curves to assess short- and long-term water-quality 

conditions relative to water-quality criteria. These 

assessments can assist in evaluating best management 

practices and in determining or evaluating TMDLs. 

The estimated fecal coliform bacteria densities 

provided in the previous section need to be considered 

with the uncertainty of the estimate in mind. To

simplify this consideration process, the probability of 

Figure 10A. Comparison of measured and regression-estimated fecal coliform bacteria densities for Kansas River at Wamego (site 1, 

fig. 1), May 1999 through April 2002, and probability of exceedance of water-quality criteria. Current (2003) recreational water-quality 

criteria established by Kansas Department of Health and Environment (2001).
42 Comparison and Continuous Estimates of Fecal Coliform and Escherichia Coli Bacteria in Selected Kansas Streams, May 1999 Through April 2002

exceeding (at the 95-percent confidence level) current 

(2003) geometric-mean criterion (200 col/100 mL) or 

single-sample criterion (2,000 col/100 mL) can be displayed for each 30-day geometric mean of the hourly

estimates or of the hourly estimates, respectively. Figure 10 illustrates the probability (expressed as a percentage) that the maximum 30-day geometric-mean 

estimate and the maximum hourly estimate for each 

day of the study period exceeds the respective criteria. 

Real-time hourly probability values available on the 

World Wide Web (http://ks.water.usgs.gov/

Kansas/rtqw/) provide the public and water managers 

information when considering public health and safety 

for recreation water bodies. 

The relation between turbidity and fecal coliform 

bacteria also can be displayed as probability curves 

(fig. 11). Each curve represents a fecal coliform density and is plotted using turbidity (x axis) and the probability that the actual fecal coliform density is equal to 

or greater than the estimated density (y axis). The 

Figure B. Comparison of measured and regression-estimated fecal coliform bacteria densities for Kansas River at Topeka (site 2, 

fig. 1), May 1999 through April 2002, and probability of exceedance of water-quality criteria. Current (2003) recreational water-quality 

criteria established by Kansas Department of Health and Environment (2001).

10
Continuously Estimated Bacteria Densities 43

figures can be used to estimate fecal coliform concentrations on the basis of measured turbidity values. 

[Note: Only turbidity values from a YSI 6026 turbidity 

sensor are appropriate for relations described in report. 

See “Methods” section of this report.] For instance, the 

actual fecal coliform bacteria density in the Kansas 

River at Wamego (site 1) for a turbidity value of 

100 NTU has a 99-percent chance of being less than 

2,000 col/100 mL and a 25-percent chance of being 

greater than 200 col/100 mL (fig. 11A). For a turbidity 

value of 1,000 NTU in the Kansas River at Wamego

(site 1), there is a 45-percent chance that the actual 

fecal coliform bacteria density is greater than 

2,000 col/100 mL and a 96-percent chance that it is 

greater than 200 col/100 mL.

Duration curves were plotted using the hourly estimates of fecal coliform bacteria density for the six 

selected surface-water sites from May or July 1999 

through April 2002 (fig. 12). The data are plotted 

against the frequency of each hourly value occurring 

Figure C. Comparison of measured and regression-estimated fecal coliform bacteria densities for Kansas River at DeSoto (site 20, 

fig. 1), May 1999 through April 2002, and probability of exceedance of water-quality criteria. Current (2003) recreational water-quality 

criteria established by Kansas Department of Health and Environment (2001).

10
44 Comparison and Continuous Estimates of Fecal Coliform and Escherichia Coli Bacteria in Selected Kansas Streams, May 1999 Through April 2002

within the period. The duration curves are an excellent 

summary of the estimated bacteria densities for the 

given period and can be used for many purposes. The 

minimum (100-percent exceedance), median 

(50-percent exceedance), and maximum (0-percent 

exceedance) estimated bacteria densities can be easily 

obtained from the curve. The curves also give an indication of how frequently the estimated bacteria 

densities exceeded a specified water-quality criteria 

for a given period. 

Duration curves for the entire study period indicate the single-sample secondary contact criterion for 

fecal coliform bacteria density (2,000 col/100 mL) 

was exceeded between 0 and 14 percent of the time for 

the six surface-water sites. During the designated recreation period (April through October), exceedances 

of the geometric-mean primary contact criterion 

(200 col/100 mL) occurred between 54 and 94 percent 

of the spring (April through June) and 21 and 59 percent of the summer (July through October). Duration 

Figure D. Comparison of measured and regression-estimated fecal coliform bacteria densities for Rattlesnake Creek near Zenith 

(site 21, fig. 1), May 1999 through April 2002, and probability of exceedance of water-quality criteria. Current (2003) recreational 

water-quality criteria established by Kansas Department of Health and Environment (2001).

10
Continuously Estimated Bacteria Densities 45

curves for estimated fecal coliform densities for spring

(April through June), summer (July through October), 

and winter (November through March) illustrate the 

differences between the three seasons. For all sites 

except Rattlesnake Creek, the median estimated bacteria density in the spring was at least 2 times the 

median summer density and about 10 times the 

median winter density. The seasonal differences are

largest for the three Kansas River sites (1, 2, 20) indicating that reservoir releases (very low turbidity and 

bacteria densities) predominate the streamflow in the 

winter. The streamflow duration curve for each site 

(fig. 12) is plotted with the bacteria duration curves to 

provide a comparison of streamflow and bacteria

density.

Figure E. Comparison of measured and regression-estimated fecal coliform bacteria densities for Little Arkansas River at 

Highway 50 near Halstead (site 22, fig. 1), May 1999 through April 2002, and probability of exceedance of water-quality criteria. 

Current (2003) recreational water-quality criteria established by Kansas Department of Health and Environment (2001).

10
46 Comparison and Continuous Estimates of Fecal Coliform and Escherichia Coli Bacteria in Selected Kansas Streams, May 1999 Through April 2002

Relation Between Turbidity and Escherichia Coli 

Density

A statistically defined relation between turbidity 

and E. coli bacteria densities also was developed for 

six of the seven surface-water sites where real-time, 

continuous multiparameter monitors with turbidity 

sensors are deployed (table 2). A comparison of 

turbidity measurements and E. coli densities (fig. 13) 

is somewhat less correlated than that between turbidity 

and fecal coliform densities (fig. 8). Simple linear 

regression analysis was performed on data from these 

six sites to define a relation between turbidity and 

E. coli (table 8). The regression models are site specific and applicable only for the range of turbidity 

values listed in table 8.

Turbidity was the best estimator of all the averaged cross-section water-quality measurements and 

streamflow [Note: Only turbidity values from a 

YSI 6026 turbidity sensor are appropriate for relations 

described in report. See “Methods” section of this 

Figure F. Comparison of measured and regression-estimated fecal coliform bacteria densities for Little Arkansas River near 

Sedgwick (site 23, fig. 1), May 1999 through April 2002, and probability of exceedance of water-quality criteria. Current (2003) 

recreational water-quality criteria established by Kansas Department of Health and Environment (2001).

10
Table 7. Percentage of estimated hourly fecal coliform bacteria densities that were greater than recreational-use criteria for 

selected surface-water sites in Kansas, May or July 1999 through April 2002 

[Numbers are percentage of estimated hourly fecal coliform densities. Recreational-use criteria from Kansas Department of Health and Environment 

(2001)] 

Percentage greater than geometric-mean criterion 

Site (200 col/mL) Percentage greater than single-sample criterion (2,000 col/mL) 

number Spring Summer Winter Spring Summer Winter 

(fig. 1) (April–June) (July–October) (November–March) (April–June) (July–October) (November–March) 

1

1 54 21 11 1 2 2 

22 62 29 12 7 4 3 

3

20 58 41 13 10 6 3 

21 94 53 68 0 0 0 

22 83 59 22 20 12 9 

23 70 40 24 22 14 8 

1

July 15, 1999, through April 2002. 2July 16, 1999, through April 2002. 3July 1, 1999, through April 2002. 

report]. The explanatory and response variables were 

log transformed prior to fitting the regression models. 

The regression models used to estimate E. coli bacteria 

densities for the Kansas River (sites 1, 2, and 20, 

fig. 1) and the Little Arkansas River (sites 22 and 23) 

had slopes (m) that ranged from 1.01 (site 22) to 1.40 

(site 20). The site-to-site variation in slope probably 

results from differences in bacteria sources, flow 

regimes, and water chemistry. The R2s were equal to 

or greater than 0.59, indicating more than 59 percent 

of the variability in the bacteria concentrations was 

explained by turbidity. The range of turbidity and 

E. coli data spanned three orders of magnitude, 

describing a majority of the streamflow conditions at 

these sites. The regression model for site 21 was not 

significant, and therefore, no model was reported. The 

poor relation between turbidity and E. coli at this site 

probably is related to the decreased survivability of 

E. coli (the dominant member of the fecal coliform 

group) because of elevated salinity concentrations at 

this site. 

For the five E. coli regression models, uncertainties, expressed as model standard error of estimate 

(SEE) in percent, ranged from +185 to +350 percent. 

The smallest SEEs were for sites with the smallest 

range of turbidities for collected samples. When considering the uncertainty for estimating bacteria concentrations on the basis of turbidity measurements, it 

is important to remember the uncertainty associated 

with the membrane filtration technique, which was 

discussed in the “Methods” section of this report. 

Comparisons of measured turbidity versus regression-estimated E. coli bacteria densities for all five 

surface-water sites are shown in figure 14. The uncertainty for each of the models is graphically displayed 

with the prediction intervals. The closer the intervals 

are to one another, the less uncertainty for that particular regression model at a specified probability. The 50-

and 90-percent prediction intervals were plotted to 

show the difference in ranges. Given any measured 

turbidity within the range of values plotted, there is a 

90-percent chance that the resulting estimated fecal 

coliform value will be within the 90-percent prediction 

interval. 

Estimates from the regression models were compared to measured sample densities and the percentage 

of estimated values that were in agreement as exceeding or being less than the recommended criteria are 

reported in table 9. All of the model estimates were in 

agreement for measured densities greater than 126 and 

less than 576 col/100 mL at least 84 percent of the 

time. At least 79 percent of the estimates were in 

agreement with measured densities less than 

126 col/100 mL for all sites except site 22 where 

50 percent were in agreement. The regression-model 

estimates were in agreement with measured densities 

greater than 576 col/100 mL at least 83 percent of the 

time for sites 20, 22, and 23. The regression-model 

estimates for sites 1 and 2 were in agreement with 

measured densities greater than 576 col/100 mL at 

least 38 percent of the time. The regression-model 

estimates for Kansas River at Wamego (site 1) were in 

agreement with only three of the eight samples that 

had measured densities greater than 576 col/100 mL 

(table 9), whereas estimates for the Kansas River at 

DeSoto (site 20) were in agreement with 83 percent of 

Continuously Estimated Bacteria Densities 47 
Figure 11A. Comparison of measured turbidity and the probability of exceeding selected fecal coliform 

bacteria densities for Kansas River at Wamego (site 1, fig. 1), July 1999 through April 2002. 

Figure 11B. Comparison of measured turbidity and the probability of exceeding selected fecal coliform 

bacteria densities for Kansas River at Topeka (site 2, fig.1), July 1999 through April 2002. 

48 Comparison and Continuous Estimates of Fecal Coliform and Escherichia Coli Bacteria in Selected Kansas Streams, May 1999 Through April 2002 
Figure 11C. Comparison of measured turbidity and the probability of exceeding selected fecal coliform 

bacteria densities for Kansas River at DeSoto (site 20, fig. 1), July 1999 through April 2002. 

Figure 11D. Comparison of measured turbidity and the probability of exceeding selected fecal coliform 

bacteria densities for Rattlesnake Creek near Zenith (site 21, fig. 1), May 1999 through April 2002. 

Continuously Estimated Bacteria Densities 49 
Figure 11E. Comparison of measured turbidity and the probability of exceeding selected fecal coliform 

bacteria densities for Little Arkansas River at Highway 50 near Halstead (site 22, fig. 1), May 1999 through 

April 2002. 

Figure 11F. Comparison of measured turbidity and the probability of exceeding selected fecal coliform 

bacteria densities for Little Arkansas River near Sedgwick (site 23, fig. 1), May 1999 through April 2002. 

50 Comparison and Continuous Estimates of Fecal Coliform and Escherichia Coli Bacteria in Selected Kansas Streams, May 1999 Through April 2002 
Continuously Estimated Bacteria Densities 51

Figure 12A. Estimated fecal coliform bacteria densities for Kansas River at Wamego (site 1, fig. 1), July 1999 through 

April 2002, and for spring, summer, and winter months. Recreational water-quality criteria established by Kansas

Department of Health and Environment (2001).

Figure 12B. Estimated fecal coliform bacteria densities for Kansas River at Topeka (site 2, fig. 1), July 1999 through

April 2002, and for spring, summer, and winter months. Recreational water-quality criteria established by Kansas

Department of Health and Environment (2001).
52 Comparison and Continuous Estimates of Fecal Coliform and Escherichia Coli Bacteria in Selected Kansas Streams, May 1999 Through April 2002

Figure 12C. Estimated fecal coliform bacteria densities for Kansas River at DeSoto (site 20, fig. 1), July 1999 through 

April 2002, and for spring, summer, and winter months. Recreational water-quality criteria established by Kansas

Department of Health and Environment (2001).

Figure 12D. Estimated fecal coliform bacteria densities for Rattlesnake Creek near Zenith (site 21, fig. 1), May 1999 

through April 2002, and for spring, summer, and winter months. Recreational water-quality criteria established by

Kansas Department of Health and Environment (2001).
Continuously Estimated Bacteria Densities 53

Figure 12E. Estimated fecal coliform bacteria densities for Little Arkansas River at Highway 50 near Halstead (site

22, fig. 1), May 1999 through April 2002, and for spring, summer, and winter months. Recreational water-quality 

criteria established by Kansas Department of Health and Environment (2001).

Figure 12F. Estimated fecal coliform bacteria densities for Little Arkansas River near Sedgwick (site 23, fig. 1), May 

1999 through April 2002, and for spring, summer, and winter months. Recreational water-quality criteria established 

by Kansas Department of Health and Environment (2001).
Figure 13. Comparison of measured turbidity and Escherichia coli bacteria densities at selected surface-water sites, May 1999 

through April 2002. 

the 24 samples that had measured densities greater 

than 576 col/100 mL. 

The regression models and continuous (hourly) 

turbidity data collected at the five surface-water sites 

were used to continuously estimate E. coli bacteria 

densities for May or July 1999 through April 2002 

(fig. 15). Measured E. coli bacteria densities from collected water samples were plotted with the regressionestimated densities to give some indication of how 

well the regression models represented in-stream conditions. The percentage of time that the stream 

exceeded a water-quality criteria was calculated and is 

shown on these graphs, which gives an indication of 

the probability that a stream will meet water-quality 

criteria and TMDL goals. 

The percentage of estimated hourly E. coli 

densities that were greater than USEPA recommended 

recreational-use criteria were divided into three seasons (table 10). Spring had the highest percentage of 

samples that exceeded the geometric-mean and singlesample criteria followed by summer and then winter. 

Estimated E. coli densities in the spring (April through 

June) at the three Kansas River sites (1, 2, 20) indicate 

that the single-sample criterion (576 col/100 mL) was 

exceeded between 9 and 29 percent of the time 

(table 10). The E. coli density single-sample criterion 

was exceeded at the two sites on the Little Arkansas 

River (sites 22, 23) 41 and 39 percent, respectively, of 

the time in the spring. A comparison of estimated 

E. coli densities in the two rivers for the spring 

indicate that the geometric-mean criterion of 

126 col/100 mL was exceeded 62 to 97 percent 

of the time. The percentage of exceedance of 

576 col/100 mL for sites on the Kansas River 

increase from upstream to downstream in the spring, 

summer, and winter. The percentage of hourly concentrations greater than 576 col/100 mL in the Little 

Arkansas River decreased slightly from upstream to 

downstream in the spring, summer, and winter. 

The continuous turbidity data for this report had 

days of no data or incomplete data due to removal of 

the multiparameter monitor during periods of ice conditions or equipment malfunctions. For these periods, 

turbidity values were interpolated between the values 

prior to and after the period of no data. These data are 

highlighted in red on the graphs (fig. 15). None of 

the interpolated turbidity values were greater than 

240 NTU, corresponding to an estimated E. coli density of 269 col/100 mL for the specific site. A 

limitation to applying regression models to continuous 

data is that the in-stream turbidity sensor maximum 

varies between 1,000 to 1,500 NTU, truncating the 

actual turbidity peak. This truncation is evident in the 

graphs (fig. 15) where the estimated E. coli density is 

54 Comparison and Continuous Estimates of Fecal Coliform and Escherichia Coli Bacteria in Selected Kansas Streams, May 1999 Through April 2002 
-- -- -- -- -- -- 

Table 8. Regression models and statistics for estimating Escherichia coli (E. coli) bacteria densities using turbidity measurements (us

surface-water sites in Kansas, May 1999 through April 2002 

[R2 , coefficient of determination; MSE, mean square error; n, number of samples; RMAE, relative mean absolute error; SSx, sum of squares x; RPD, relative percentag

Escherichia coli bacteria; NTU, turbidity measured using a YSI model 6026 turbidity probe; --, not determined] 

Site 

number 

(fig. 1) Regression model R2 

MSE 

(log units) 

Range in 

Standard E. coli bacteria 

error of densities 

estimate (colonies per 

(percent) n 100 milliliters) 

Range in turbidity 

(nephelometric 

turbidity units) 

RMAE 

(percent

1 Log10ECB = 1.18log10NTU - 0.457 0.60 0.294 195 46 1–5,200 11–1,200 127 

2 Log10ECB = 1.33log10NTU – 0.746 .59 .492 350 47 2–75,000 12–6,200 84 

20 Log10ECB = 1.40log10NTU – 0.883 .76 .350 230 52 1–23,000 9–4,210 71 21 No significant relation 18 22 Log10ECB = 1.01log10NTU + 0.439 .63 .290 190 23 20–41,000 4–860 77 23 Log10ECB = 1.17log10NTU + 0.111 .73 .278 185 28 4–23,000 5–1,300 76 

Continuously Estimated Bacteria Densities 55 
Figure 14A. Comparison of measured turbidity and regression-estimated Escherichia coli bacteria densities and 

prediction intervals for Kansas River at Wamego (site 1, fig. 1), July 1999 through April 2002. Recreational water-quality 

criteria recommended by U.S. Environmental Protection Agency (2002). 

56 Comparison and Continuous Estimates of Fecal Coliform and Escherichia Coli Bacteria in Selected Kansas Streams, May 1999 Through April 2002 
Figure 14B. Comparison of measured turbidity and regression-estimated Escherichia coli bacteria densities and 

prediction intervals for Kansas River at Topeka (site 2, fig. 1), July 1999 through April 2002. Recreational water-quality 

criteria recommended by U.S. Environmental Protection Agency (2002). 

Continuously Estimated Bacteria Densities 57
58 Comparison and Continuous Estimates of Fecal Coliform and Escherichia Coli Bacteria in Selected Kansas Streams, May 1999 Through April 2002

Figure 14C. Comparison of measured turbidity and regression-estimated Escherichia coli bacteria densities and 

prediction intervals for Kansas River at DeSoto (site 20, fig. 1), July 1999 through April 2002. Recreational water-quality 

criteria recommended by U.S. Environmental Protection Agency (2002).
Figure 14D. Comparison of measured turbidity and regression-estimated Escherichia coli bacteria densities and 

prediction intervals for Little Arkansas River at Highway 50 near Halstead (site 22, fig. 1), May 1999 through April 2002. 

Recreational water-quality criteria recommended by U.S. Environmental Protection Agency (2002). 

Continuously Estimated Bacteria Densities 59
Figure 14E. Comparison of measured turbidity and regression-estimated Escherichia coli bacteria densities and 

prediction intervals for Little Arkansas River near Sedgwick (site 23, fig. 1), May 1999 through April 2002. Recreational 

water-quality criteria recommended by U.S. Environmental Protection Agency (2002). 

60 Comparison and Continuous Estimates of Fecal Coliform and Escherichia Coli Bacteria in Selected Kansas Streams, May 1999 Through April 2002 
Table 9. Percentage of regression-model estimates in agreement with measured Escherichia coli bacteria densities in 

relation to U.S. Environmental Protection Agency recommended primary contact recreational criteria for selected surfacewater sites in Kansas, May 1999 through April 2002 

[Recommended recreational water-quality criteria from U.S. Environmental Protection Agency (2002). <, less than; >, greater than; col/100 mL, colonies per 

100 milliliters of water; --, not determined] 

Site 

no. 

(fig. 1) Site name 

1 Kansas River at Wamego 

2 Kansas River at Topeka 

20 Kansas River at DeSoto 

Percentage of estimated Percentage of estimated Percentage of estimated 

values <126 col/100 mL values >126 col/100 mL values <576 col/100 mL 

that were in agreement that were in agreement that were in agreement 

with measured sample with measured sample with measured sample 

densities <126 col/100 mL densities >126 col/100 mL densities <576 col/100 mL 

(total number of samples (total number of samples (total number of samples 

with densities with densities with densities 

<126 col/100 mL) >126 col/100 mL) <576 col/100 mL) 

85 (27) 84 (19) 100 (38) 

79 (24) 91 (23) 94 (33) 

81 (21) 87 (31) 96 (28) 

21 Rattlesnake Creek near Zenith -- (7) -- (10) -- (14) 

22 Little Arkansas River at 50 (8) 100 (15) 93 (15) 

Percentage of estimated 

values >576 col/100 mL that 

were in agreement 

with measured sample 

densities >576 col/100 mL 

(total number of samples 

with densities 

>576 col/100 mL) 

38 (8) 

64 (14) 

83 (24) 

-- (4) 

88 (8) 

83 (12) 

Highway 50 near Halstead 

23 Little Arkansas River near 82 (11) 

Sedgwick 

at its maximum for several hours (or days). For these 

instances, actual E. coli bacteria densities were likely 

greater than the plotted regression-estimated density. 

Probability and Duration of Estimated 

Escherichia Coli Densities 

The continuous estimated E. coli bacteria densities 

can be displayed as probabilities and durations that 

allow for easier identification of water quality for a 

particular stream segment. The public and watermanagement agencies can use probability values and 

duration curves to assess short- and long-term waterquality conditions relative to water-quality criteria. 

These assessments can assist in evaluating best management practices and in determining or evaluating 

TMDLs. 

The estimated E. coli bacteria densities provided 

in the previous section need to be considered with the 

uncertainty of the estimate in mind. To simplify this 

consideration process, the probability (at the 95-

percent confidence level) of exceeding the USEPA 

recommended criteria can be displayed for each of the 

hourly estimates. Figure 15 illustrates the probability 

(expressed as a percentage) that the maximum hourly 

estimate for each day of the study period exceeds the 

given criteria. Real-time hourly probability values 

available on the World Wide Web 

(http://ks.water.usgs.gov/Kansas/rtqw) provide the 

88 (17) 88 (16) 

public and water managers information when 

considering public health and safety for recreation 

water bodies. 

The relation between turbidity and E. coli bacteria 

density also can be displayed as probability curves 

(fig. 16). Each curve represents an E. coli density and 

is plotted using turbidity (x axis) and the probability 

that the actual E. coli density is equal to the estimated 

density (y axis). The figures can be used to estimate 

E. coli concentrations based on turbidity values. 

[Note: Only turbidity values from a YSI 6026 turbidity 

sensor are appropriate for relations described in report. 

See “Methods” section of this report.] For instance, the 

actual E. coli bacteria density for the Kansas River at 

Wamego (site 1) for a turbidity value of 100 NTU has 

a 94-percent chance of being less than 576 col/100 mL 

and a 36-percent chance of being greater than 

126 col/100 mL (fig. 16A). For a turbidity value of 

1,000 NTU for the Kansas River at Wamego (site 1), 

there is a 28-percent chance that the actual E. coli 

bacteria density is greater than 576 col/100 mL 

and a 97-percent chance that it is greater than 

126 col/100 mL. 

Duration curves were plotted using the hourly estimates of E. coli bacteria density for each of five 

surface-water sites (fig. 17). The duration curves represent the hourly estimated E. coli densities from May 

or July 1999 through April 2002. The data are plotted 

against the frequency of each hourly value occurring 

Continuously Estimated Bacteria Densities 61 
62 Comparison and Continuous Estimates of Fecal Coliform and Escherichia Coli Bacteria in Selected Kansas Streams, May 1999 Through April 2002

Figure 15A. Comparison of measured and regression-estimated Escherichia coli bacteria densities in samples from Kansas River at 

Wamego (site 1, fig. 1), July 1999 through April 2002, and probability of exceedance of water-quality criteria. Recreational waterquality criteria recommended by U.S. Environmental Protection Agency (2002).
Continuously Estimated Bacteria Densities 63

Figure 15B. Comparison of measured and regression-estimated Escherichia coli bacteria densities in samples from Kansas River at 

Topeka (site 2, fig. 1), July 1999 through April 2002, and probability of exceedance of water-quality criteria. Recreational waterquality criteria recommended by U.S. Environmental Protection Agency (2002).
64 Comparison and Continuous Estimates of Fecal Coliform and Escherichia Coli Bacteria in Selected Kansas Streams, May 1999 Through April 2002

Figure 15C. Comparison of measured and regression-estimated Escherichia coli bacteria densities in samples from Kansas River at 

DeSoto (site 20, fig. 1), July 1999 through April 2002, and probability of exceedance of water-quality criteria. Recreational waterquality criteria recommended by U.S. Environmental Protection Agency (2002).
Continuously Estimated Bacteria Densities 65

Figure 15D. Comparison of measured and regression-estimated Escherichia coli bacteria densities in samples from Little Arkansas 

River at Highway 50 near Halstead (site 22, fig. 1), May 1999 through April 2002, and probability of exceedance of water-quality 

criteria. Recreational water-quality criteria recommended by U.S. Environmental Protection Agency (2002).
66 Comparison and Continuous Estimates of Fecal Coliform and Escherichia Coli Bacteria in Selected Kansas Streams, May 1999 Through April 2002

Figure 15E. Comparison of measured and regression-estimated Escherichia coli bacteria densities in samples from Little Arkansas 

River near Sedgwick (site 23, fig. 1), May 1999 through April 2002, and probability of exceedance of water-quality criteria. 

Recreational water-quality criteria recommended by U.S. Environmental Protection Agency (2002).
Table 10. Percentage of estimated hourly Escherichia coli bacteria densities that were greater than U.S. Environmental 

Protection Agency recommended recreational criteria for five selected surface-water sites in Kansas, May or July 1999 

through April 2002 

[Numbers are percentage of estimated hourly Escherichia coli densities. Recreational criteria from U.S. Environmental Protection Agency (2002). col/100 

mL, colonies per 100 milliliters of water] 

Site Percentage greater than geometric-mean criterion (126 col/mL) Percentage greater than single-sample criterion (576 col/mL) 

number Spring Summer Winter Spring Summer Winter 

(fig. 1) (April–June) (July–October) (November–March) (April–June) (July–October) (November–March) 

1

1 68 31 13 9 7 4 

22 68 37 13 26 10 5 

3

20 62 47 15 29 14 6 

22 97 83 22 41 23 15 

23 78 47 25 39 24 14 

1July 16, 1999, through April 2002. 2July 15, 1999, through April 2002. 3July 1, 1999, through April 2002. 

within the period. The duration curves are an excellent 

summary of the estimated bacteria densities for the 

given period and can be used for many purposes. 

The minimum (100-percent exceedance), median 

(50-percent exceedance), and maximum (0-percent 

exceedance) estimated bacteria densities can be easily 

obtained from the curve. The curves also give an indication of how frequently the estimated bacteria densities exceeded a specific water-quality criteria over a 

given period. 

Durations curves for the entire study period indicate that the USEPA recommended single-sample criterion for E. coli bacteria density for infrequently used 

full-body contact (576 col/100 mL) was exceeded 

between 8 and 24 percent of the time for the five surface-water sites. During the designated recreation 

period (April through October), exceedances of the 

USEPA recommended geometric-mean criterion 

(126 col/100 mL) occurred between 62 and 97 percent 

of the spring (April through June) and 31 and 83 percent of the summer (July through October). Duration 

curves for estimated E. coli densities for spring (April 

through June), summer (July through October), and 

winter (November through March) illustrate the differences among the three seasons. For all five sites, the 

median estimated E. coli density in the spring was at 

least 2 times greater than the median winter density. 

The seasonal differences were largest for the three 

Kansas River sites (1, 2, 20), indicating that reservoir 

releases (low turbidity and low E. coli densities) 

predominate the streamflow in the winter. The streamflow duration curve for each site is plotted with the 

bacteria duration curves to provide a comparison of 

streamflow and bacteria density. 

ESTIMATED BACTERIA LOADS AND YIELDS 

Bacteria loads were calculated to determine total 

number of colonies being transported in each stream 

annually and during spring, summer, and winter for 

2000 and 2001. Hourly regression-estimated fecal 

coliform and E. coli bacteria densities were multiplied 

by streamflow and the bias-correction factor (tables 5 

and 8) to estimate seasonal and annual loads and 

yields at six surface-water sites with continuous turbidity measurements. Continuous loads of bacteria can 

be used to evaluated point- and nonpoint-source contributions and seasonal differences. Bacteria yields 

were calculated by dividing loads by corresponding 

drainage areas to determine the number of colonies per 

acre for a given time period. Land use is similar for the 

three stream basins represented by the six surfacewater sites, but the drainage area for the Kansas River 

Basin is more than 44 times as large as the drainage 

areas for the Rattlesnake Creek and Little Arkansas 

River Basins. Considering the entire drainage area 

when calculating bacteria yields at Wamego, Topeka, 

and DeSoto on the Kansas River is inappropriate due 

to the reservoirs within the basin. Sediment and bacteria flowing into a reservoir are trapped by the reservoir. Two previous studies in Kansas (Pope, 1995; 

Mau and Pope, 1999) determined that bacteria 

densities in the outflow of three reservoirs were usually less than 15 col/100 mL. Therefore, the drainage 

areas for the sites in the Kansas River Basin were 

modified so that only the unregulated portions of the 

basin were used to define yield. 

There are limitations for calculating loads and 

yields using the continuous data. The maximum for 

the turbidity sensors used at the continuous sites was 

Estimated Bacteria Loads and Yields 67 
Figure 16A. Comparison of measured turbidity and the probability of exceeding selected Escherichia coli bacteria 

densities for Kansas River at Wamego (site 1, fig. 1), July 1999 through April 2002. 

Figure 16B. Comparison of measured turbidity and the probability of exceeding selected Escherichia coli bacteria 

densities for Kansas River at Topeka (site 2, fig. 1), July 1999 through April 2002. 

68 Comparison and Continuous Estimates of Fecal Coliform and Escherichia Coli Bacteria in Selected Kansas Streams, May 1999 Through April 2002 
Figure 16C. Comparison of measured turbidity and the probability of exceeding selected Escherichia coli bacteria 

densities for Kansas River at DeSoto (site 20, fig. 1), July 1999 through April 2002. 

Figure 16D. Comparison of measured turbidity and the probability of exceeding selected Escherichia coli bacteria 

densities for Little Arkansas River at Highway 50 near Halstead (site 22, fig. 1), May 1999 through April 2002. 

Estimated Bacteria Loads and Yields 69 
Figure 16E. Comparison of measured turbidity and the probability of exceeding selected Escherichia coli bacteria 

densities for Little Arkansas River near Sedgwick (site 23, fig. 1), May 1999 through April 2002. 

between 1,000 and 1,500 NTU depending on the sensor. When the actual turbidity was greater than the 

maximum a sensor can measure, the sensor reported 

only the maximum value. During these truncated periods, loads were calculated on the basis of the sensor’s 

maximum reading. For these reasons the regressionestimated loads are conservative by an unknown 

amount. Comparisons of measured load from samples 

and the corresponding regression-estimated load indicate that the truncated estimates of bacteria load 

underrepresent the actual load by as much as 20 times. 

If the in-stream turbidity sensor could measure turbidity values greater than 1,000 NTU (minimum sensor 

maxima), the regression-estimated bacteria loads 

would be higher. Values greater than 1,000 NTU were 

reported for five of the six surface-water sites in 0.8 to 

8.9 percent of the hourly turbidity measurements 

recorded during 2000 and 2001 (table 11). However, 

the loads for these periods when turbidity was greater 

than 1,000 NTU accounted for as much as 77 percent 

of the annual bacteria load. Regression-estimated 

loads can be greatly underestimated in some cases and 

only slightly underestimated in others. For these 

reasons, caution is advised when considering and 

comparing estimated seasonal and annual bacteria 

loads and yields among years and sites. 

Another limitation when calculating estimated 

loads and yields is bacteria loss rate. Bacteria loss rate 

represents bacteria mortality rate, loss due to solar 

radiation, and loss due to settling. The loss rate varies 

on the basis of environmental, water-quality, and 

streamflow conditions. The bacteria loss rate is 

unknown for the six sites during the study period. For 

this study, bacteria loss is ignored, and therefore, the 

point-source loads are overestimated and yields are 

underestimated. 

Regression-estimated total annual bacteria loads 

in 2001 were about 2 to 8 times larger than the total 

annual bacteria loads in 2000 for all six surface-water 

sites except site 21, where bacteria loads in 2000 were 

1.2 times higher than bacteria loads in 2001 (table 11). 

The annual difference probably can be attributed 

mostly to varying hydrologic conditions. Wet periods 

tend to contribute more overland runoff and, therefore, 

more nonpoint-source bacteria to the stream. Mean 

daily streamflows for the Kansas and Little Arkansas 

70 Comparison and Continuous Estimates of Fecal Coliform and Escherichia Coli Bacteria in Selected Kansas Streams, May 1999 Through April 2002 
Estimated Yield and Load 71

Figure 17. Estimated Escherichia coli bacteria densities for (A) Kansas River at Wamego (site 1, fig. 1) and (B) Kansas River at 

Topeka (site 2, fig. 1), July 1999 through April 2002, and for spring, summer, and winter months. Recreational water-quality 

criteria recommended by U.S. Environmental Protection Agency (2002).
72 Comparison and Continuous Estimates of Fecal Coliform and Escherichia Coli Bacteria in Selected Kansas Streams, May 1999 Through April 2002

Figure 17. Estimated Escherichia coli bacteria densities for (C) Kansas River at DeSoto (site 20, fig. 1), July 1999 through April 

2002, and (D) Little Arkansas River at Highway 50 near Halstead (site 22, fig. 1), May 1999 through April 2002, and for spring, 

summer, and winter months. Recreational water-quality criteria recommended by U.S. Environmental Protection Agency (2002).
Estimated Yield and Load 73

River sites were higher in 2001 (table 12) than in

2000. Bacteria loads in 2000 and 2001 were largest for 

the Kansas River at Topeka (site 2) and DeSoto 

(site 20). Loads for Topeka were slightly more than the 

loads for DeSoto in 2000, indicating bacterial decay 

and that reservoir releases from Perry and Clinton 

Lakes may have diluted the bacteria densities. The 

loads in the Little Arkansas River for 2000 and 2001 

generally were larger for the Sedgwick site (site 23) 

than for the Halstead site (site 22).

Fecal coliform bacteria and streamflow data for 

major point sources (municipal sewage-treatment 

facilities) upstream from the six surface-water sites 

were obtained from KDHE (written commun., 2002). 

For the Kansas River (sites 1, 2, 20), data from nine 

point sources from Wamego to DeSoto were used to 

estimate that 2.9 percent or less of the regressionestimated total fecal coliform bacteria loads in the 

Kansas River for 2000 and 2001 were from point 

sources. There were no point-source discharges into

Rattlesnake Creek upstream from site 21 in 2000 and 

2001. Six point-source discharges into the Little 

Arkansas River contributed less than about 0.4 percent 

of the regression-estimated total fecal coliform bacteria load for 2000 and 2001. The small percentages of 

point-source fecal coliform bacteria load contributions 

indicate that nonpoint sources account for at least 

97 percent of the regression-estimated annual fecal 

coliform bacteria load at these six surface-water sites. 

Agriculture is the predominant land use in the three 

basins; therefore, nonpoint sources of fecal coliform 

bacteria in the three basins are largely from 

agricultural runoff from cropland, pastures, and rangeland, with a small percentage resulting from urban 

runoff. 

Figure 17. Estimated Escherichia coli bacteria densities for (E) Little Arkansas River near Sedgwick (site 23, fig. 1), May 1999 

through April 2002, and for spring, summer, and winter months. Recreational water-quality criteria recommended by U.S. 

Environmental Protection Agency (2002).
74 Comparison and Continuous Estimates of Fecal Coliform and Escherichia Coli Bacteria in Selected Kansas Streams, May 1999 Through April 2002 

Table 11. Estimated seasonal and annual loads and yields of indicator bacteria for six surface-water sites in Kansas, January 2000 th[All values are rounded to three significant figures. --, not deter

mined; >, greater than; NTU, nephelometric turbidity units; values in parentheses are percentage of es

than 1,000 N

TU; KDHE, Kansas Department of Health and Environment] 

Loads 

(billion colonies) 

Percentage 

of hourly (KDHE, Spring 

Site turbidity Winter written (April– 

number values Spring Summer (November–

March, 152 days commun., June, 

(fig. 1) >1,000 NTU (April–June, 91 days) (July–October, 123 days) in 2000 and 151 days in 2001) Total annual 2002) 91 days) 

2000 

Fecal coliform bacteria 

1 0.9 2,790,000 (1.6) 1,030,000 (0) 7,180,000 (53) 11,000,000 (35) 32,600 736 2 1.7 9,250,000 (36) 5,870,000 (49) 14,000,000 (55) 29,100,000 (42) 33,100 1,960 

20 1.5 9,680,000 (36) 6,980,000 (27) 9,180,000 (49) 25,800,000 (40) 739,000 1,700 21 0 75,800 (0) 65,200 (0) 181,000 (0) 322,000 (0) 0 113 22 1.7 2,390,000 (36) 2,440,000 (12) 8,910,000 (15) 13,700,000 (18) 60,100 4,930 23 .8 1,680,000 (31) 3,830,000 (3.9) 8,230,000 (5.6) 13,700,000 (8.2) 60,200 2,120 

2001 

Fecal coliform bacteria 

1 4.5 22,500,000 (7.1) 12,300,000 (41) 13,600,000 (50) 48,400,000(39) 9,750 5,930 2 7.9 90,900,000 (41) 43,700,000 (63) 46,300,000 (62) 181,000,000 (39) 13,500 19,300 

20 8.9 110,000,000 (48) 40,100,000 (44) 62,400,000 (67) 213,000,000 (53) 447,000 19,300 21 0 166,000 (0) 1,270 (0) 91,700 (0) 270,000 (0) 0 248 22 4.7 5,360,000 (24) 3,390,000 (72) 16,300,000 (96) 25,000,000 (77) 25,600 11,000 23 5.0 7,540,000 (20) 4,580,000 (77) 21,300,000 (97) 33,400,000 (77) 26,600 9,510 

2000 

Escherichia coli bacteria 

1 .9 2,510,000 (1.6) 1,030,000 (0) 5,880,000 (53) 9,410,000 (35) -- 661 2 1.7 9,510,000 (36) 6,070,000 (49) 14,200,000 (55) 29,800,000 (42) -- 2,020 

20 1.5 8,720,000 (36) 6,290,000 (27) 8,270,000 (49) 23,300,000 (40) -- 1,530 21 -- -- -- -- -- -- -- 22 1.7 2,130,000 (36) 2,180,000 (12) 8,000,000 (15) 12,300,000 (18) -- 4,380 23 .8 1,480,000 (31) 3,370,000 (3.9) 7,670,000 (5.6) 12,100,000 (8.2) -- 1,870 

2001 

Escherichia coli bacteria 

1 4.5 19,200,000 (7.1) 10,200,000 (41) 11,300,000 (50) 40,700,000 (39) -- 5,080 2 7.9 92,900,000 (41) 44,500,000 (63) 47,600,000 (62) 185,000,000 (39) -- 19,700 

20 8.9 99,400,000 (48) 36,100,000 (44) 56,200,000 (67) 192,000,000 (53) -- 17,400 21 -- -- -- -- -- -- -- 22 4.7 4,800,000 (24) 2,960,000 (72) 14,000,000 (96) 21,800,000 (77) -- 9,890 23 5.0 6,640,000 (20) 4,010,000 (77) 18,500,000 (97) 29,100,000 (77) -- 8,370 

Total pointsource loads 
Table 12. Seasonal and annual mean daily streamflow for six surface-water sites in Kansas, January 2000 through 

December 2001 

[Streamflows are in cubic feet per second. All values are rounded to three significant figures] 

Site 2000 mean daily streamflow 2001 mean daily streamflow 

number Spring Summer Winter Spring Summer Winter 

(fig. 1) (April–June) (July–October) (November–March) Annual mean (April–June) (July–October) (November–March) Annual mean 

1 2,170 2,300 2,500 2,320 

2 2,460 2,340 2,520 2,440 

20 3,220 2,820 3,320 3,120 

21 59 24 61 48 

22 182 118 385 228 

23 239 263 583 362 

Regression-estimated winter bacteria loads were 

greater than spring or summer bacteria loads for all 

sites except site 21 in 2000 (table 11). The large winter 

loads primarily were due to increased streamflow that 

occurred in late February and March and the unequal 

number of days in each season. Bacteria loads in 2001 

at sites 1, 2, 20, and 21 were largest in the spring when 

mean daily streamflows were highest for the year 

(table 12). 

To compare each season on an equal basis, mean 

daily loads were calculated by dividing the regressionestimated seasonal load by the number of days within 

the season. Seasonal mean daily bacteria loads indicated that in 2000 mean daily bacteria loads were largest in the winter for sites 1, 21, 22, and 23 and in the 

spring for sites 2 and 20 (fig. 18). In 2001, mean daily 

bacteria load and streamflows were greatest in the 

spring at surface-water sites on the Kansas River and 

Rattlesnake Creek (sites 1, 2, 20, and 21) and largest 

in the winter at sites on the Little Arkansas River 

(sites 22 and 23). 

Estimated fecal coliform and E. coli yields generally were largest for the Little Arkansas River Basin 

(sites 22, 23) for 2000 and 2001 compared to other 

sites (fig. 19, table 11). The larger yields for the Little 

Arkansas River Basin indicate that the land use in the 

basin is contributing more bacteria per acre than 

similar agricultural land use in the Kansas River 

Basin. Such differences might include a greater number of livestock, more areas dedicated to livestock, or 

fewer structures controlling runoff from pastures. 

SUMMARY 

Current State criteria for sanitary quality of 

streams in Kansas are based on fecal coliform bacteria 

9,050 3,840 3,960 5,620 

10,500 4,350 5,180 6,680 

14,200 7,000 6,300 9,170 

81 9 40 43 

385 102 275 254 

678 191 491 453 

densities. In 1986, the U.S. Environmental Protection 

Agency (USEPA) recommended that fecal coliform 

bacteria be replaced by either Escherichia coli (E. coli) 

or enterococci densities in recreational water-quality 

criteria as an indicator of fecal contamination. E. coli 

bacteria are a definitive indicator of fecal contamination and give a better indication of possible exposure 

to swimming-associated illnesses. In 2002, the USEPA 

issued revised guidelines with recommended numeric 

criteria on the basis of risk exposure. The State of 

Kansas is currently (2003) evaluating the use of E. coli 

as the primary indicator bacteria. 

In May 1999, the U.S. Geological Survey (USGS), 

in cooperation with several Federal, State, and local 

agencies, began collecting samples for analysis of 

fecal coliform and E. coli bacteria at 28 surface-water 

sites in Kansas. This report, prepared by the USGS in 

cooperation with KDHE and USEPA and funded in 

part through the Kansas State Water Plan Fund, 

describes the overall sanitary quality of surface water 

in selected Kansas streams, compares the samples to 

current (2003) State of Kansas water-quality criteria 

for fecal coliform and U.S. Environmental Protection 

Agency (USEPA) recommended criteria for E. coli, 

and describes the relation of bacteria densities to turbidity and how this relation can be used to estimate the 

occurrence of bacteria. 

Results indicate that, of the 219 samples collected 

during the designated recreation period (April 1 

through October 31), 21 percent exceeded the current 

(2003) Kansas Department of Health and Environment 

(KDHE) single-sample fecal coliform criterion for 

secondary contact recreation (2,000 col/100 mL of 

water) and that 36 percent exceeded the USEPA recommended single-sample primary contact recreational 

criterion for E. coli (576 colonies/100 mL of water). 

Summary 75 
Figure 18. Comparison of estimated mean daily bacteria loads for spring, summer, and winter for six selected surface-water sites, 

January 2000 through December 2001. 

The exceedances occurred mostly during high flow 

and increased turbidity conditions when surfacewater runoff was greatest. Eighteen percent of all 

318 samples collected exceeded the current (2003) 

KDHE secondary contact recreational criterion for 

fecal coliform bacteria (2,000 colonies/100 mL 

of water). 

The ratio of the USEPA recommended E. coli criterion (126 col/100 mL) and the current (2003) KDHE 

fecal coliform criterion (200 col/100 mL) is 0.63. 

Comparison of this ratio to the single-site ratios 

indicates that five of the six ratios calculated for six 

selected surface-water sites exceeded 0.63. Therefore, 

at those five sites, the USEPA recommended E. coli 

criteria could be exceeded more frequently than the 

current (2003) KDHE fecal coliform criteria. The 

surface-water sites with ratios that were less than 

0.63 probably would have E. coli bacteria densities 

that exceed the recommended E. coli criteria less 

frequently. The geometric mean of the E. coli/fecal 

coliform (EC/FC) ratios for all 28 surface-water sites 

was 0.77. The smaller ratios for Rattlesnake Creek 

near Zenith (0.48) and the sites on the North Fork 

Ninnescah River (0.58) probably were caused by the 

large salinity (or specific conductance greater than 

1,000 μS/cm) values in the streams that may decrease 

the survivability of the E. coli and may not affect the 

other members of the fecal coliform group to the same 

extent. Enterococci survivability is not greatly affected 

by saline water and may be a better indicator bacteria 

for sites with saline water. 

Ratios of EC/FC and linear regression models 

were developed for estimating E. coli densities on the 

basis of measured fecal coliform densities for six individual and six groups of surface-water sites. The relative mean absolute errors (RMAEs) for EC/FC ratios 

were less than or equal to the RMAEs for 8 of the 

12 regression models, indicating that the ratios might 

be a better method for estimating E. coli. For regression models developed for individual surface-water 

sites on the Kansas River (sites 1, 2, and 20) and the 

Little Arkansas River (sites 22 and 23), the coefficients of determination (R2

) were greater than 0.89. 

For these sites, fecal coliform densities were a reliable 

76 Comparison and Continuous Estimates of Fecal Coliform and Escherichia Coli Bacteria in Selected Kansas Streams, May 1999 Through April 2002 
Figure 19. Comparison of regression-estimated bacteria yields from unregulated drainage areas for six selected surface-water 

sites, January 2000 through December 2001. 

indicator bacteria, explaining at least 89 percent of the 

variability in E. coli densities. The regression models 

are site specific and only relevant for the ranges of 

measured densities. Regression models can be used to 

convert historic fecal coliform bacteria densities to 

estimated E. coli densities for the selected sites only 

for the ranges indicated. The EC/FC ratios can be used 

to estimate E. coli densities for any historical fecal 

coliform density, and in some cases with less error. 

Simple linear regression was used to further determine if one model could be used to estimate E. coli 

bacteria on a basin- or statewide basis. The explained 

variance for the two Kansas-lower Republican River 

Basin regression models exceeded 93 percent, indicating that a single model probably is sufficient for all the 

sites sampled in the basin. The regression model best 

represents the three Kansas River sites where a 

majority of the data used to develop the model were 

collected. The Little Arkansas River Basin regression 

model indicates that 96 percent of the variability for 

the two sites is explained. The data for the Little 

Arkansas River Basin model are more evenly distributed between the two sites (22 and 23) than are data 

used to develop models for the other basins and, therefore, reliably estimate E. coli concentrations at each 

site. The statewide model only included sites with 

mean specific conductance less than 1,000 μS/cm 

(site 1–20, 22, 23). The model describes 94 percent of 

the variability and appears to sufficiently explain the 

EC/FC relation at the 22 sites. However, sites that have 

fewer samples are underrepresented by the statewide 

model and, therefore, the statewide model may not be 

appropriate for use at these sites. 

Linear regression models were developed for 

selected surface-water sites to estimate fecal coliform 

and E. coli bacteria densities on the basis of continuous turbidity measurements. These regression models 

are site specific and only relevant for the range of turbidity values measured. The fecal coliform and E. coli 

regression models for surface-water sites on the Kansas and Little Arkansas Rivers had R2

s ranging from 

0.59 to 0.79. The ability to estimate fecal coliform and 

E. coli bacteria densities on the basis of continuous 

turbidity measurements allows water users to assess 

Summary 77 
whether streams are safe for recreational activities 

such as swimming, boating, and fishing. Only 16 percent of the variance for fecal coliform is explained 

(R2

=0.16) by the regression model for Rattlesnake 

Creek, indicating that turbidity and fecal coliform density are not well correlated for this site. 

With a defined relation for turbidity and bacteria 

and continuous monitoring of turbidity, instantaneous, 

daily, and annual estimates of bacteria densities are 

possible. These estimates are displayed almost instantaneously via the World Wide Web (URL 

http://ks.water.usgs.gov/Kansas/rtqw), providing realtime data indicating the sanitary quality of the water 

relative to water-quality criteria. Annual continuous 

data indicate the possibility of the stream meeting 

water-quality criteria and total maximum daily load 

(TMDL) goals. For instance, the continuous data used 

in this report show that, proportionally, spring generally has the greatest number of estimated bacteria densities that exceed KDHE and recommended USEPA 

criteria. Sites along the Kansas River from upstream to 

downstream showed an increase in the number of 

estimates that exceeded the KDHE and recommended 

USEPA criteria. 

The log-normal probability at the 95-percent confidence level was calculated for each of the hourly estimates of the current (KDHE) and recommended 

(USEPA) criteria. These values illustrate the probability (expressed as a percentage) that each hourly estimate exceeds the criteria. These estimates, displayed 

in real time (URL http://ks.water.usgs.gov/Kansas/ 

rtqw/), can be used by the public and watermanagement agencies to make decisions in regard to 

whether planned water activities are appropriate by 

considering current stream conditions relative to the 

criteria. Water suppliers can use the timely 

information to determine when and how much to 

adjust water-treatment strategies. 

Accuracy of the regression-model estimates was 

compared to measured sample densities and also was 

assessed by calculating the percentage of estimated 

values that were in agreement as exceeding or being 

less than the specified water-quality criteria. A majority of the regression-model estimates were in agreement with measured densities as either exceeding or 

not exceeding the specified water-quality criteria for at 

least 80 percent of the measured densities. 

The relations between turbidity and fecal coliform 

bacteria and turbidity and E. coli bacteria were displayed as probability curves. The curves can be used 

to estimate fecal coliform or E. coli concentrations on 

the basis of measured turbidity values. Hourly estimated bacteria densities also were used to develop 

bacteria duration curves. Durations curves for the 

entire study period indicate that the current singlesample criterion for fecal coliform (2,000 col/100 mL) 

and the USEPA recommended single-sample criterion 

for E. coli (576 col/100 mL) were exceeded between 

0 and 14 percent and 8 to 24 percent of the time, 

respectively. Exceedances of the primary-contact geometric-mean fecal coliform criterion (200 col/100 mL) 

and the USEPA recommended E. coli criterion 

(126 col/100 mL) occurred between 21 to 94 percent 

and 31 to 97 percent, respectively, of the time during 

the designated recreation period (April through 

October). 

Duration curves for estimated fecal coliform for 

spring (April through June), summer (July through 

October), and winter (November through March) illustrate the differences among the three seasons. For all 

sites except Rattlesnake Creek, the median estimated 

bacteria density in the spring was at least 2 times the 

median summer density and about 10 times the 

median winter density. The seasonal differences were 

largest at the three Kansas River sites, indicating that 

reservoir releases (very low turbidity and bacteria densities) predominate the streamflow during the winter. 

Hourly estimated fecal coliform and E. coli bacteria densities and streamflow were used to compute 

seasonal and annual loads and yields at six surfacewater sites with continuous turbidity measurements for 

the calendar years 2000 and 2001. Overall, estimated 

total annual bacteria loads for the Kansas and Little 

Arkansas Rivers in 2001 were about 2 to 8 times larger 

than the estimated bacteria loads in 2000. The difference probably is due to wet conditions in 2001 

contributing greater overland runoff and, therefore, 

greater nonpoint-source contributions of bacteria to 

the stream. Bacteria loads in 2000 and 2001 were largest for the Kansas River at Topeka (site 2) and DeSoto 

(site 20). Data for major point sources upstream from 

the surface-water sites were obtained from KDHE. 

Point sources accounted for 2.9 percent or less of the 

regression-estimated annual bacteria load for 2000 and 

2001 for the six surface-water sites. Nonpoint sources 

were the predominant source for bacteria loads in 

these streams. 

Winter bacteria loads were larger than spring or 

summer loads for all sites except site 21 in 2000. 

These large winter loads primarily were due to high 

78 Comparison and Continuous Estimates of Fecal Coliform and Escherichia Coli Bacteria in Selected Kansas Streams, May 1999 Through April 2002 
streamflow that occurred in late February and March 

and dissimilar time periods for each season. Bacteria 

loads at sites 1, 2, 20, and 21 were largest for the 

spring in 2001. Mean daily bacteria loads in 2000 were 

largest in the winter for sites 1, 21, 22, and 23 and in 

the spring for sites 2 and 20. In 2001, mean daily bacteria loads and streamflows were largest in the spring 

at sites on the Kansas River and Rattlesnake Creek 

(sites 1, 2, 20, and 21) and largest in the winter at sites 

on the Little Arkansas River (sites 22 and 23). 

Bacteria yields were calculated by dividing the 

regression-estimated loads by the unregulated drainage area for each surface-water site. Yields calculated 

in this fashion indicate that the Little Arkansas Basin 

had the greatest number of colonies per acre of the 

three streams (including the Kansas River and 

Rattlesnake Creek surface-water sites). 

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80 Comparison and Continuous Estimates of Fecal Coliform and Escherichia Coli Bacteria in Selected Kansas Streams, May 1999 Through April 2002