Prepared in cooperation with the Illinois Environmental Protection Agency

Continuous Monitoring of Sediment and Nutrients in the 

Illinois River at Florence, Illinois, 2012–13

Scientific Investigations Report 2015–5040

U.S. Department of the Interior

U.S. Geological Survey
Cover. A tranquil morning on the Illinois River. Photograph by Tim Straub, 

U.S. Geological Society, Illinois Water Science Center.
Continuous Monitoring of Sediment and Nutrients 

in the Illinois River at Florence, Illinois, 2012–13

By Paul J. Terrio, Timothy D. Straub, Marian M. Domanski, and Nicolas A. Siudyla

Prepared in cooperation with the Illinois Environmental Protection Agency

Scientific Investigations Report 2015–5040

U.S. Department of the Interior

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

SALLY JEWELL, Secretary

U.S. Geological Survey

Suzette M. Kimball, Acting Director

U.S. Geological Survey, Reston, Virginia: 2015

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Suggested citation:

Terrio, P.J., Straub, T.D., Domanski, M.M., and Siudyla, N.A., 2015, Continuous monitoring of sediment and nutrients 

in the Illinois River at Florence, Illinois, 2012–13: U.S. Geological Survey Scientific Investigations Report 2015–5040, 

61 p., http://dx.doi.org/10.3133/sir20155040.

ISSN 2328-0328 (online)
iii

Contents

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

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

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

Study Area.......................................................................................................................................................2

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

Suspended-Sediment Methods .........................................................................................................4

Phosphate ..............................................................................................................................................5

Dissolved Nitrite Plus Nitrate .............................................................................................................7

Instrument Performance and Operational Considerations.....................................................................8

Sediment.................................................................................................................................................8

Nitrate.....................................................................................................................................................8

Phosphate ............................................................................................................................................10

Concentrations.............................................................................................................................................10

Suspended Sediment .........................................................................................................................10

Turbidity and Suspended-Sediment Concentration Linear Regression Model...............10

Acoustic Backscatter and Suspended-Sediment Concentration Regression Model....16

Nitrate...................................................................................................................................................16

Data Correspondence for Nitrate Sensors ...........................................................................18

Seasonality..................................................................................................................................19

Phosphate ............................................................................................................................................20

Comparison of Continuously Monitored Phosphate to other Forms of Phosphate ........22

Seasonality and Flow-Related Differences...........................................................................24

Loadings ........................................................................................................................................................27

Suspended Sediment .........................................................................................................................27

Nitrate...................................................................................................................................................28

Phosphate ............................................................................................................................................31

Discussion.....................................................................................................................................................33

Suspended Sediment .........................................................................................................................33

Nitrate...................................................................................................................................................33

Phosphate ............................................................................................................................................34

Conclusion.....................................................................................................................................................34

References Cited..........................................................................................................................................34

Appendix 1. SOLITAX Turbidity Formazin Backscatter Ratio Units (FBRU) 

Model Information and Data ........................................................................................................37

Appendix 2. YSI Turbidity Formazin Nephelometric Units (FNU) 

Model Information and Data ........................................................................................................43

Appendix 3. Streamflow Model Information and Data.......................................................................49

Appendix 4. Total Phosphorus Model Information and Data ............................................................54
iv

Figures

1. Map showing the Illinois River Basin and the location of the

continuous-monitoring stations at Florence, Illinois (05586300), 

and Valley City, Ill. (05586100) .....................................................................................................3

2. Images of A. WET Labs Cycle-PO4 phosphate analyzer, B. Hach NITRATAX

nitrate sensor, C. Satlantic SUNA nitrate sensor, D. Hach SOLITAX turbidity

sensor, E-F. Installation structure at Illinois River at Florence, Illinois (05586100)............6

3. Graphs showing results of the U.S. Geological Survey Illinois Water Science

Center experiments to determine the effects of suspended-sediment

concentrations on NITRATAX nitrate sensor readings ..........................................................9

4. Graph showing suspended-sediment concentration and turbidity linear

regression models using turbidity data from the Illinois River at Florence,

Illinois (05586300), and samples collected at the Illinois River at Florence, Ill.,

(05586300) and the Illinois River at Valley City, Ill. (05586100)..............................................11

5. Graphs showing the relations between the regression residuals, estimated

suspended-sediment concentrations, and dates for the linear regression

models shown in figure 4...........................................................................................................12

6. Graph showing predicted and observed suspended-sediment concentration

using turbidity and streamflow re-transformed linear regression models

presented in table 4 using turbidity data from the Illinois River at Florence,

Illinois (05586300), and samples collected at the Illinois River at Florence, Ill.

(05586300), and the Illinois River at Valley City, Ill. (05586100), and streamflow data 

reported at the Illinois River at Valley City, Ill. (05586100)....................................................13

7. Graph showing time series streamflow and suspended-sediment concentration

(SSC) for samples, modeled SSC (estimated) for SOLITAX, and Graphical

Constituent Loading Analysis System (GCLAS) method.......................................................15

8. Graph showing observed and predicted suspended-sediment concentration

using a linear regression model with an acoustic parameter, the sediment

attenuation coefficient, using acoustic data from an ADVM in the Illinois River

at Florence, Illinois (05586300), and samples collected at the Illinois River at

Florence, Ill. (05586300), and the Illinois River at Valley City, Ill. (05586100)......................16

9. Graph showing concentrations of nitrite plus nitrate nitrogen as determined by

the NITRATAX sensor and samples analyzed at the U.S. Geological Survey

National Water Quality Laboratory, relative percent differences among

concentrations, and streamflow for the period June 1, 2012–October 30, 2013,

at the Illinois River at Florence, Illinois (05586300)................................................................17

10. Graph showing continuous nitrate data from the NITRATAX and 

SUNA nitrate sensors at the Illinois River at Florence, Illinois (05586300), 

October 2012–October 2013 ......................................................................................................18

11. Graph showing comparison of continuous nitrate data collected by the

NITRATAX and SUNA nitrate sensors at the Illinois River at Florence, Illinois

(06686300), October 2012–October 2013..................................................................................19

12. Graph showing concentrations of dissolved phosphate as determined by the

Cycle-PO4 analyzer and streamflow for April 25, 2013–October 30, 2013, at the

Illinois River at Florence, Illinois (05586300)..........................................................................21

13. Graph showing concentrations of phosphate measured by the Cycle-PO4

analyzer and dissolved orthophosphate analyzed by the U.S. Geological Survey

National Water Quality Laboratory for April 25–October 30, 2013......................................22
v

14. Graph showing concentrations of various forms of phosphorus in the Illinois

River at Valley City, Illinois (05586100), and concurrent continuous phosphate

concentrations at the Illinois River at Florence, Ill. (05586300), 

April 25–October 30, 2013 ..........................................................................................................23

15. Graph showing observed and predicted total phosphorus concentrations using

a linear regression model with orthophosphate and suspended sediment

prediction variables....................................................................................................................25

16. Graph showing continuous ophosphate concentrations versus streamflow for

April–October 2013 at the Illinois River at Florence, Illinois (05586300) ...........................26

17. Graph showing monthly suspended-sediment load for each instrument and

method used in the study at the Illinois River at Florence, Illinois (0558630)....................27

18. Graph showing annual suspended-sediment loads for the study period and

historical data..............................................................................................................................28

19. Graph showing instantaneous nitrate loads computed using data and the

LOADEST program for the Illinois River at Florence, Illinois (05586300)............................29

20. Graphs showing instantaneous nitrate loads and the differences in loads

computed using continuous data and the LOADEST program for 

June 2012–October 2013 at the Illinois River at Florence, Illinois (05586300)...................30

21. Graph showing monthly nitrate loads in the Illinois River at Florence, Illinois

(05586300), June 2012–October 2013 .......................................................................................31

22. Graph showing phosphate loads at the Illinois River at Florence, Illinois

(05586300), April–October 2013.................................................................................................32

23. Graph showing relative percent differences and quantitative differences in

computed phosphate loads for the Illinois River at Florence, Illinois (05586300),

April–October 2013 .....................................................................................................................32

 Tables

1. Physical and operational characteristics of the Hach NITRATAX and 

Satlantic SUNA in-situ ultraviolet absorbance nitrate sensors............................................7

2. Predictor and response variable statistics for suspended-sediment

concentration, turbidity, and streamflow linear regression models..................................14

3. Suspended-sediment concentration, turbidity, and streamflow linear

regression models and coefficient of determinations using turbidity data from

the Illinois River at Florence (05586300), and samples collected at the Illinois

River at Florence (05586300) and the Illinois River at Valley City (05586100) ....................14

4. Suspended-sediment concentration, turbidity, and streamflow re-transformed

linear regression models and non-parametric smearing bias correction factor

using turbidity data from the Illinois River at Florence (05586300), and samples

collected at the Illinois River at Florence (05586300) and the Illinois River at 

Valley City (05586100) .................................................................................................................15

5. Summary statistics for nitrite plus nitrate nitrogen, as N, measured by the

NITRATAX in-situ sensor and point samples analyzed by the U.S. Geological

Survey National Water-Quality Laboratory from the Illinois River at

Florence, Illinois (05586300), 2012–13 ......................................................................................17

Figures—continued
vi

6. Summary statistics for phosphate data measured by the Cycle-PO4 analyzer

and discrete samples analyzed by the U.S. Geological Survey National

Water-Quality Laboratory for Illinois River at Florence, Illinois (05586300) ......................21

7. Concentrations of nitrate plus nitrite and phosphate in samples collected using

the equal-width-increment method and in point samples at the location of the

in situ instruments at Illinois River at Florence, Illinois (05586300) ....................................23

8. Summary statistics for dissolved orthophosphate and total phosphorus

concentrations from samples collected at Illinois River at Valley City, Illinois

(05586100), 1991–2013.................................................................................................................24

9. Final model equation and predictor and response variable statistics for total

phosphorus (TP) concentration multivariate linear regression models

developed using data from the Illinois River at Valley City, Illinois (05586100),

1991–2013 .....................................................................................................................................25

10. Calculated suspended-sediment load for each instrumentand method used in

the study.......................................................................................................................................27

Tables—continued

Conversion Factors

Inch/Pound to International System of Units

Multiply By To obtain

Length

inch (in.) 2.54×107 nanometer (nm)

inch (in.) 2.54 centimeter (cm)

inch (in.) 25.4 millimeter (mm)

foot (ft) 0.3048 meter (m)

mile (mi) 1.609 kilometer (km)

Area

acre 0.4047 hectare (ha)

square mile (mi2

) 2.590 square kilometer (km2) 

Volume

cubic foot (ft3

) 28.317 liter 

Flow rate

cubic foot per second (ft3

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

Mass

pound, avoirdupois (lb) 0.4536 kilogram (kg) 

pounds per day (lb/d) 0.4536 kilogram per day (kg/d)

ton 0.9072 metric ton 

ton per day (ton/d) 0.9072 metric ton per day

ton per month (ton/m) 0.9072 metric ton per month

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

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

°F = (1.8 × °C) + 32.

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

or micrograms per liter (μg/L).
vii

Abbreviations

ADVM acoustic Doppler velocity meter

ASTM American Society for Testing and Materials

BCF bias correction factor

CDOM colored dissolved organic matter

DCP data-collection platform

DIN Deutsches Institut für Normung

DOP dissolved orthophosphate

EWI equal width increment

FBRU formazin backscatter ratio units

FNU formazin nephelometric units

GB gigabyte

GCLAS Graphical Constituent Loading Analysis System

Illinois EPA Illinois Environmental Protection Agency

IL WSC Illinois Water Science Center

ISO International Organization for Standardization

kHz kilohertz

LOADEST load estimator software

MC method code

NAWQA National Water-Quality Assessment

NWQL National Water-Quality Laboratory

OBS optical backscatter

OLS ordinary least squares

PO4 phosphate

PVC polyvinyl chloride

R2 coefficient of determination

SSC suspended-sediment concentration

SSL suspended-sediment load

TP total phosphorus

USGS U.S. Geological Survey

μm micrometer

Continuous Monitoring of Sediment and Nutrients

in the Illinois River at Florence, Illinois, 2012–13

By Paul J. Terrio, Timothy D. Straub, Marian M. Domanski, and Nicolas A. Siudyla

Abstract

The Illinois River is the largest river in Illinois and is 

the primary contributing watershed for nitrogen, phosphorus, 

and suspended-sediment loading to the upper Mississippi 

River from Illinois. In addition to streamflow, the following water-quality constituents were monitored at the Illinois 

River at Florence, Illinois (U.S.Geological Survey station 

number 05586300), during May 2012–October 2013: phosphate, nitrate, turbidity, temperature, specific conductance, 

pH, and dissolved oxygen. The objectives of this monitoring 

were to (1) determine performance capabilities of the in-situ 

instruments; (2) collect continuous data that would provide an 

improved understanding of constituent characteristics during 

normal, low-, and high-flow periods and during different 

climatic and land-use seasons; (3) evaluate the ability to use 

continuous turbidity as a surrogate constituent to determine 

suspended-sediment concentrations; and (4) evaluate the 

ability to develop a regression model for total phosphorus 

using phosphate, turbidity, and other measured parameters. 

Reliable data collection was achieved, following some initial 

periods of instrument and data-communication difficulties. 

The resulting regression models for suspended sediment had 

coefficient of determination (R2) values of about 0.9. Nitrate 

plus nitrite loads computed using continuous data were found 

to be approximately 8 percent larger than loads computed 

using traditional discrete-sampling based models. A regression 

model for total phosphorus was developed by using historic 

orthophosphate data (important during periods of low flow 

and low concentrations) and historic suspended-sediment data 

(important during periods of high flow and higher concentrations). The R2

 of the total phosphorus regression model using 

orthophosphorus and suspended sediment was 0.8. Data collection and refinement of the regression models is ongoing.

Introduction

There have been significant advancements in the 

development of in-situ water-quality monitors during the 

past decade, including instruments for monitoring turbidity, nitrate, and phosphate. These three constituents are 

particularly important in the biological and ecological 

production and health of stream systems, are often present 

in substantial quantity, and are of concern from an ecological and water-resources perspective in Illinois. Nitrate and 

phosphate are vital nutrients required to support aquatic floral 

and faunal growth, but can be problematic when present in 

large amounts. Excessive nutrients can cause eutrophication 

and subsequent water-column oxygen depletion during the 

decomposition of plant material and algae. In Illinois, most 

in-stream turbidity is composed of inorganic soil particulate 

matter, which can be present in amounts harmful to stream 

ecosystems by limiting light penetration and euphotic zone 

depth, transportation of hydrophobic organic compounds, and 

streambed deposition and sedimentation. The U.S. Geological Survey (USGS) Illinois Water Science Center (IL WSC), 

in cooperation with the Illinois Environmental Protection 

Agency (Illinois EPA), operated a suite of in-situ, continuously recording, water-quality instruments at the Illinois 

River at Florence, Illinois (USGS station number 05586300), 

during May 2012–October 2013. This continuous nutrient 

and sediment data record facilitates an improved understanding of annual, seasonal, and storm-event concentrations and 

loads. The use of continuous in-situ sensors and the associated development of surrogate models to estimate concentrations and loads of other parameters also has the potential to 

reduce sample collection and laboratory expenses.
2

Continuous Monitoring of Sediment and Nutrients in the Illinois River at Florence, Illinois, 2012–

Purpose and Scope

The purpose of this report is to provide analyses of the 

following:

• Instrument performance and deployment issues including comparisons of similar data collected by multiple 

instruments (nitrate, sediment, and phosphate)

• Baseline, seasonal, and storm-event concentrations 

and loads of nitrogen, phosphorus, and suspended 

sediment, as determined from periodic samples and 

continuous data

• Evaluations of applicability of using turbidity data to 

determine suspended-sediment concentration (SSC) 

and loads

• Evaluations of applicability of using backscatter data 

from acoustic Doppler velocity meter (ADVM) instruments to determine SSC and loads

Study Area

The Illinois River Basin in Illinois, Wisconsin, and 

Indiana (fig. 1) is often cited as one of the primary contributing watersheds of nitrogen, phosphorus, and sediment to the 

Upper Mississippi River Basin and, subsequently, to the Gulf 

of Mexico (Alexander and others, 2008; Sprague and others, 

2011; Heimann and others, 2011). The Illinois River Basin 

encompasses large urban areas, including the Chicago metropolitan area, and extensive agricultural areas including some 

of the most intensively farmed and productive row-crop corn 

and soybean acreage in the Nation. Modeling studies related to 

hypoxia in the Gulf of Mexico have identified subwatersheds 

in the Illinois River Basin as having some of the highest yields 

per square mile of nitrogen and phosphorus.

There is a rich historical record of streamflow (since 

1938), suspended-sediment (since 1980), and water-quality 

(since 1974) data for the USGS monitoring station at Illinois 

River at Valley City, Ill. This location is a critical component 

of the Illinois EPA, USGS, U.S. Army Corps of Engineers, and 

other Federal and State agency monitoring programs because 

it represents the largest single-river outflow from Illinois and 

includes virtually the entire Chicago metropolitan area, plus 

approximately 18,500 square miles (mi2) of intensively farmed 

agricultural land. A continuous data record provides information that will allow for a much better understanding and 

determination of baseline, seasonal, and storm-event loads of 

nutrients and sediment. Such information is required to assess 

the degree of success and the downstream effects of best-management practices, regulatory changes, and source reductions 

of nutrients and sediment in the Illinois River Basin, upstream 

of the Illinois River contribution to the Mississippi River.

Owing to the site’s location in the watershed and historical significance, the USGS planned to install a suite of 

instruments at the Illinois River at Valley City station to 

monitor sediment and water-quality parameters on a continuous basis and to improve the density and quality of the data 

record at this location. The USGS streamgaging station at 

Valley City, Ill. (05586100), has been maintained in cooperation with multiple Federal and State partners during the many 

years of operation. The pending installation of continuous 

water-quality monitoring equipment and the scheduled reconstruction and slight relocation of the streamgaging station at 

the historical Valley City location prompted consideration of 

installation of the continuous water-quality monitoring equipment at the State Route 100 bridge near Florence, Ill.; approximately 5.4 miles (mi) downstream of the Valley City station. 

It was determined that the installation of the continuousmonitoring equipment at the Florence bridge was preferable 

because of superior protection from debris and ice, a larger 

working platform, better physical access, and the elimination 

of access and safety concerns from railroad traffic. A monitoring station—Illinois River at Florence, Ill. (05586300)—was 

established in May 2012. 

There is minimal input to the Illinois River from tributary 

streams between the two monitoring stations. Blue Creek is 

the largest tributary in this reach to the Illinois River. Blue 

Creek drains a watershed of approximately 40 mi2

 to the west 

and enters the Illinois River approximately 2.2 mi upstream 

from the Florence station. The Blue Creek watershed comprises 0.15 percent of the Illinois River at Florence drainage 

area. During the first year of data collection following the 

establishment of the Florence monitoring station, comparison 

samples were collected at both the Valley City and Florence 

stations and periodically from the mouth of Blue Creek, particularly during high-flow periods. These samples were analyzed 

for suspended sediment, nitrogen, and phosphorus concentration. The streamflow in Blue Creek also was estimated when 

samples were collected. It was determined through simple 

statistical comparisons (means, quartiles, ranges) of waterquality samples and discharge measurements at the Valley 

City, Ill., and Florence, Ill. locations, as well as samples and 

measurements from Blue Creek, that the stream-water quality 

at the two Illinois River station locations was not different and 

that the input (both water quality and water quantity) from 

Blue Creek did not significantly affect the water quality of the 

Illinois River as measured at the Florence monitoring station. 
Study Area

0

0

20

20

40

40

60 MILES

60 KILOMETERS

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

Albers Equal-Area Conic projection

Standard parallels 33°00’N and 45°00’N

Central meridian 89°00’W Illinois River drainage basin

U.S. Geological Survey 

 streamflow-gaging station

City

WI

MI

MI

IL

IN

Upper

Illinois

River Basin

(UIRB)

Lower

Illinois

River

Basin

(LIRB)

Illinois River at Florence, Il Illinois River at Florence, Ill.

Illinois River at

Valley City, Ill.

ILLINOIS

INDIANA

MICHIGAN

WISCONSIN

MISSOURI

IOWA

Lake

Michigan

Peoria

Kankakee

Ottawa

Rockford

Springfield

Champaign

Decatur

Quincy

Grafton

Moline

Chicago

Illinois

River

BloomingtonNormal

Peoria

Kankakee

Ottawa

Springfield

Decatur

BloomingtonNormal

WISCONSIN

MISSOURI

IOWA

Mississippi

River

91o

43o

42o

41o

40o

39o

90o 89o 88o 87o 86o

Figure 1. The Illinois River Basin and the location of the continuous-monitoring stations at Florence, Illinois (05586300), and 

Valley City, Ill. (05586100).
4

Continuous Monitoring of Sediment and Nutrients in the Illinois River at Florence, Illinois, 2012–

Methods

Suspended-Sediment Methods 

Three instruments were deployed to be used as a surrogate for SSC. Two of the instruments were turbidity sensors 

and the third was an acoustic device. One of the turbidity 

sensors used nephelometric principles, YSI 6136 turbidity 

probe (YSI Incorporated, 2014), and the other used opticalbackscatter (OBS) principles, SOLITAX turbidity probe (Hach 

Company, 2014). Both turbidity methods are commonly used 

to measure turbidity as a surrogate for computing SSC (Anderson, 2005; Gray and Gartner, 2006), but OBS principles are 

recommended where the majority of the suspended-sediment 

load (SSL) is transported above 2,000 milligrams per liter 

(mg/L) (Rasmussen and others, 2009). The nephelometric sensor upper range varies from 1,000 to 2,000 formazin nephelometric units (FNUs) (Rasmussen and others, 2009), and the 

maximum limit depends upon the particle types in suspension. 

The maximum recorded value was 1,260 FNU during the 

study period.

OBS sensors measure light scattering like the nephelometric sensors, except the light detector(s) is at a different 

angle (Rasmussen and others, 2009; U.S. Environmental Protection Agency, 1999). The units assigned by the USGS to the 

SOLITAX measurements are formazin backscatter ratio units 

(FBRU), and the manufacturer denotes an upper range of totalsuspended solids of 50,000 mg/L (Hach Company, 2014). As 

noted in the Hach information, it is an infrared duo scattered 

light technique for color-independent turbidity measurement 

in accordance with Deutsches Institut für Normung (DIN) EN 

27027 / TS equivalent DIN 38414 (Hach Company, 2014). 

The YSI 6136 sensor conforms to the International Organization for Standardization (ISO) Method 7027 and American 

Society for Testing and Materials (ASTM) D7315 measurement standards (Rasmussen and others, 2009). 

Acoustic technology is increasingly being used for 

velocity measurements, and similar to turbidity sensors, the 

potential for it to be used also as a surrogate for SSC would be 

an extraordinary benefit. A fixed-mount side-looking ADVM 

was installed for this purpose near the turbidity sensors. The 

ADVM was an Argonaut-SL with a 1,500 kilohertz (kHz) 

frequency (Sontek, 2014). Unlike turbidity that uses a single 

value near the instrument, the ADVM method uses values 

from multiple cells along the acoustic axis of the beam in the 

computation of a single value for use as a surrogate. For this 

method, the SSC is assumed to be constant along the acoustic 

axis of the beam. This method is relatively newer in testing 

than turbidity, and some of the earliest USGS applications and 

research were done by Topping and others (2004, 2006) and 

Rubin and others (2007).

Discrete SSC samples are collected across the entire 

range of stream conditions to relate to the surrogate values. 

Sampling and lab methods used for discrete SSC samples 

are outlined in the following reports: Field Methods for 

Measurement of Fluvial Sediment (Edwards and Glysson, 

1999) and Analysis of Fluvial Sediment by the Northeastern 

Region, Kentucky Science Center Sediment Lab (Sholar and 

Shreve, 1998). Suspended sediment daily loads using discrete 

samples were computed by the subdivided-day method (timedischarge weighted average) (Guy, 1970; Porterfield, 1972) 

using the Graphical Constituent Loading Analysis System 

(GCLAS) (Koltun and others, 2006).

For both turbidity and acoustic parameters, a relation 

between the predictor variable (for example, turbidity) and 

SSC (response variable) was done using an ordinary least 

squares (OLS) linear regression. With a linear regression, 

observed data are fit to a linear model and terms in the model 

are estimated (Helsel and Hirsch, 2002). The continuous surrogate and discrete sample data collection used for SSC was 

July 2012–June 2013 (appendixes 1, 2, and 3). One-hundred 

forty discrete SSC sample results were retrieved and analyzed 

during July 2012–June 2013 at the Illinois River at Florence 

(05586300) (125 samples) and the Illinois River at Valley 

City (05586100) (15 samples). Sample times from results at 

Valley City were adjusted for time of travel between the gage 

at Valley City and the gage at Florence (4.2 mi) using stream 

velocity data from Florence. All of the discrete samples at 

Valley City were depth integrated Equal Width Increment 

(EWI) samples (Method Code (MC) 10 or 15 in appendixes 1, 

2, and 3). The discrete samples at Florence were EWI, Equal 

Discharge Increment (MC-20), Fixed Single Vertical (from 

box on bridge) (MC-920), Single Vertical (from boat near box 

on bridge) (MC-30), Grab (near instrument) (MC-70), or Point 

(near instrument) (MC-50). All discrete samples were included 

in the linear regression model building. 

Given a linear regression model for estimating SSC and 

a continuous time series of acoustic data, a time series of SSC 

can be calculated. Then the SSL can be calculated by multiplying the SSC, flow (Q), time step ∆t, and conversion factor (c).

SSL Q SSC = × × × ∆t c

The SSL for each time step can then be added for the time 

period of interest. Below is an example calculation of a 

conversion factor to calculate load in tons per day (short tons 

where 1 ton = 2,000 pounds) when SSC is in milligrams per 

liter and Q is in cubic feet per second.

c

s

day

L

ft

lb

mg

ton

lb = × × × = 86400 28 32453 592 2 000 3

.

, ,

s L ton

da

0 0027.

y ft mg 3

The SSLs were then compared among the different 

methods used during this study. Also, the relative loads from 

previous years were compared with the loads during this study.
Methods

Phosphate

Continuous (4- to 6-hour interval) phosphate data were 

collected using a WET Labs Cycle-PO4 (Cycle) in-situ phosphate analyzer. The Cycle analyzer is housed in a cylindrical 

plastic case measuring approximately 56 × 18 centimeters 

(cm) and weighing approximately 6.8 kilograms (kg) (fig. 2). 

An external non-rechargeable battery pack is typically used 

with the analyzer; however, following an initial data-collection 

period using the external battery pack, the Cycle analyzer used 

for this deployment was powered from the USGS streamgage 

power system. The standard battery packs are non-rechargeable and would need to be replaced several times a year at the 

sampling frequency used for this study. The Cycle analyzer 

used in this study was equipped from the manufacturer to 

provide RS-232 serial output. The instrument was modified by 

WET Labs, at USGS expense, to provide SDI-12 signal output 

for input into the gaging station data-communications system. 

The Cycle has 1 gigabyte (GB) of internal data storage. The 

analyzer has a nominal phosphate concentration detection 

range of 0–0.31 mg/L, as P, with a larger range up to 1.2 mg/L, 

as P, with potential variances beyond the instrument specifications of ± 0.002 mg/L, as P (WET Labs, Inc., 2011).

Phosphorus is generally present in natural waters in dissolved, particulate, organic, and inorganic forms. Particulate 

matter that contains phosphorus can include living and dead 

fauna, phosphorus adsorbed to soil, and other precipitates. 

Dissolved phosphorus can be associated with either inorganic 

or organic compounds and is usually present in the oxidized 

phosphate form (PO4). Inorganic phosphates include orthophosphate and polyphosphates (Hem, 1989, p. 127). Dissolved 

orthophosphate, also called soluble reactive phosphorus or 

bioavailable phosphorus, is the form most readily available for 

use by biological organisms. Most analytical determinations 

of dissolved phosphorus aim to reduce all phosphorus forms to 

orthophosphate species and express the resultant concentration 

in terms of elemental phosphorus equivalent. 

The Cycle analyzer determines the concentration of 

phosphate in-situ using ascorbic-acid digestion and colorimetric analysis. To do so, the Cycle pumps a sample of 

ambient stream water through a 10 micrometer (μm) particle 

filter and into a mixing chamber in the analyzer. A rinse of 

the analyzer tubing and components with ambient stream 

water is performed during this initial step prior to collection 

of the sample. An initial measurement of light transmittance 

through the sample is made prior to the addition of reagents 

and a period of mixing and chemical reaction. Phosphate ions 

(H2

PO4

-

, HPO4

2-, PO43-, Hem, 1989, p. 127) in the sample 

react with a molybdate reagent to form a phosphomolybdate 

compound. This compound is then reduced to a molybdenum 

blue complex, and its colorimetric attributes are measured 

and compared to those of the initial ambient water sample. 

The instrument monitors the color of the solution during the 

reaction and records the final result once the reaction has 

completed. Another sample of stream water is then pumped 

through the analyzer to flush the instrument components prior 

to the next sample. Concentration of phosphate () in the sample is then calculated from a comparison of the transmittance 

of the native sample water and processed sample. A filter pore 

size of 0.45 μm is commonly used in water-quality analyses to 

operationally differentiate between a total and dissolved water 

sample. The Cycle analyzer filters stream water through a particle filter larger than 0.45 μm. accordingly, concentrations of 

phosphate determined by the Cycle analyzer will be referred to 

as phosphate in this report. 

The Cycle also can be programmed to initiate a spike of 

calibration solution into a sample to better define the algorithm 

used to calculate phosphate concentrations and to accommodate waters of differing matrixes and characteristics. The user 

is able to select the frequency of samples at which the spiking 

is performed. It was found that spiking every 3–6 samples was 

beneficial for data quality and consistency during deployment 

in the Illinois River.

Servicing and maintenance of the Cycle analyzer was 

typically performed on a monthly, or greater, frequency in 

coordination with the USGS National Water-Quality Assessment (NAWQA) sample collection at the site. During these 

field visits, discrete samples were collected from the Illinois 

River at the analyzer deployment location and depth using 

a stainless steel Kemmerer sampler and sent to the National 

Water-Quality Laboratory (NWQL) for analysis of dissolved 

orthophosphate. Laboratory analysis for dissolved orthophosphate also was part of the NAWQA program analysis suite 

from a sample collected as an EWI sample across the entire 

river cross section. NWQL samples were analyzed using 

NWQL Method I-2602-90 (Fishman, 1993).
6

Continuous Monitoring of Sediment and Nutrients in the Illinois River at Florence, Illinois, 2012–

A

E

F

B

D

C

Figure 2. Images of A, WET Labs Cycle-PO4 phosphate analyzer; B, Hach NITRATAX nitrate sensor; C, Satlantic SUNA 

nitrate sensor; D, Hach SOLITAX turbidity sensor; and E-F, Installation structure at Illinois River at Florence, Illinois (05586100).
Methods

Dissolved Nitrite Plus Nitrate

Continuous (15-minute interval) dissolved nitrite plus 

nitrate nitrogen (hereafter referred to as nitrate) data were 

collected from May 24, 2012 to November 20, 2013, using 

two sensors. The sensors used were a Hach NITRATAX and a 

Satlantic SUNA (fig. 2). Both of these nitrate sensors determine nitrite plus nitrate, as N, concentration via ultraviolet 

light-absorbance measurement. The NITRATAX sensor was 

deployed in a 4-inch (in.) diameter schedule 80 polyvinyl 

chloride (PVC) conduit mounted vertically to the pier-protection piling and alongside the Cycle PO4 analyzer. The SUNA 

sensor was deployed in an 8-in. diameter schedule 80 PVC 

conduit mounted alongside the other sensors. The anti-fouling 

wiper for the SUNA sensor is mounted externally to the sensor 

housing and requires a larger diameter conduit and customfabricated mounting hardware. The NITRATAX sensor 

accommodates a user 1-, 2-, or 3-point calibration, whereas 

the SUNA sensor cannot be calibrated by the user with nitrate 

standard solutions. However, a clean deionized water solution 

can be used to re-zero the SUNA sensor readings.

While the two nitrate sensors differ in some specifications 

and operational details, they use similar physical and chemical principles for the determination of nitrate concentration. A 

brief summary of the measurement method is provided below 

and a detailed description of the measurement method and 

sensor designs and components can be found in Pellerin and 

others, 2013. 

The nitrate ion absorbs light in the 200–250 nanometer 

(nm) wavelength range, and this absorbance can be measured 

by passing a beam of light within this wavelength through 

a small sample of ambient water. The transmittance of light 

passing through the sample is attenuated by nitrate in the 

sample and can be measured by a photometer. The light 

intensity measured by the receiving photometer is converted 

to output as a voltage signal. An algorithm is used to compute the associated concentration of nitrate from the voltage 

and remove effects from interferences and other constituents. 

The NITRATAX and SUNA instruments differ somewhat 

in the specific components and wavelengths used. Table 1 

provides physical and operational characteristics of the Hach 

NITRATAX and Satlantic SUNA in-situ nitrate sensors.

Table 1. Physical and operational characteristics of the Hach NITRATAX and Satlantic SUNA in-situ ultraviolet 

absorbance nitrate sensors (Hach Company, 2012; Pellerin and others, 2013; Satlantic Incorporated, 2011).

[mg/L, milligrams per liter; N, nitrate; ±, plus or minus; nm, nanometer; mm, millimeter; in., inch; °C, degrees Celsius; m, meter; VDC, volts 

direct current]

Characteristic Hach NITRATAX Satlantic SUNA v1

Lower detection limit 0.1 mg/L as N 0.007 mg/L as N

Upper detection limit 50 mg/L as N 28 mg/L as N

Accuracy 3 percent of reading

± 0.05 mg/L as N 

10 percent of reading or 0.06 mg/L as N, 

whichever is greater

Precision 0.1 mg/L as N 0.028 mg/L as N

Light source wavelength 220 nm (measurement)

350 nm (reference)

190–370 nm

Lamp type Xenon Deuterium

Reference beam Yes No

Measurement path length 2 mm (0.08 in.) 5 mm (0.20 in.)

Wiper Incorporated, silicon blade External, nylon brush

Communication Modbus, RS485, RS232, analog USB, RS232, SDI-12, analog

Dimensions (length x diameter) 13.1 in. by 3.0 in. 21.0 in. by 2.3 in.

Power 24 VDC 8–18 VDC

Operating temperature 2–40 °C 0–40 °C

Operating depth 5 m maximum 100 m maximum

External materials Stainless steel Acetal
8

Continuous Monitoring of Sediment and Nutrients in the Illinois River at Florence, Illinois, 2012–

Instrument Performance and 

Operational Considerations

Sediment

Both turbidity sensors (Hach SOLITAX, and YSI 6136 

as part of the multi-parameter YSI Sonde) had a complete 

record for the study period except for occasional and unexplained spikes in concentration and some periods of fouling. 

The stream conditions did reach the upper range (1,000 to 

2,000 FNUs) of the YSI 6136 (Rasmussen and others, 2009), 

but the data never appeared to truncate; this should be a 

consideration at other sites where higher turbidity values are 

expected. The SOLITAX requires 24 volt power and needs a 

separate controller on site to then attach to an external datalogger, as opposed to the YSI 6136 that only requires 12 volt 

power and can be hooked directly to an external datalogger. 

The ADVM had a complete record for the study period; 

in general, is not as susceptible to fouling; and does not 

require a wiper. However, at this site there appears to be some 

condition (environmental or anthropogenic) that has an effect 

on the backscatter data, but possibly does not affect the final 

velocity values. These effects are greatest for SSC values less 

than 155 mg/L. The condition appears to be somewhat masked 

at SSC values above 155 mg/L, and a linear regression model 

was made with these values as will be shown in the Concentrations section. An additional ADVM was added to this site in 

a less turbulent area, and provisional results indicate that this 

may have fixed the issue. Additional testing is ongoing to help 

ensure the problem is identified and fixed.

Nitrate

The NITRATAX sensor was deployed on May 24, 2012. 

The NITRATAX sensor provided reliable data for the majority 

of time it was deployed. The NITRATAX sensor was typically 

calibrated using deionized water as a zero nitrate standard and 

a 10.0 or 11.3 mg/L manufacturer-produced nitrate standard. 

The NITRATAX sensor generally read accurately at the 

zero standard and within 5 percent of the 10.0 or 11.3 mg/L 

standards. The NITRATAX sensor was recalibrated whenever 

the readings differed from the nitrate-standard values by more 

than 0.2 mg/L as N.

The SUNA sensor was not deployed until November 1, 

2012. Prior to deployment, the sensor was sent to the manufacturer for incorporation of a power cable in order to use power 

provided by the streamgaging station system in preference 

to the battery pack. Following several months of data collection, the SUNA was pulled from the deployment because of 

inconsistent and unreliable data values. It was determined that 

the flash tube (emission light source) had broken (possibly 

owing to cold temperatures), and the sensor was sent to the 

manufacturer for repair. The sensor was redeployed following repair, and the SUNA sensor provided data for most of the 

remaining deployment period. However, measurements by the 

SUNA were subject to signal interference from sediment and 

particulates in the water during high-flow and high turbidity 

events, resulting in some loss of data. The SUNA V1 has a 

10 millimeters (mm) path length, which makes it more susceptible to interference from turbidity and particulate matter.

The SUNA provided a consistent data record after the 

deployment in November 2012. Nitrate concentrations from 

the SUNA sensor were within the manufacturer specifications 

when compared to laboratory analyses, but were typically less 

than nitrate concentrations reported by the NITRATAX sensor. 

This report will focus primarily on the continuous data record 

provided from the NITRATAX sensor because of the longer 

and more consistent period of data record provided by the 

NITRATAX sensor. 

There were two short periods when the NITRATAX 

sensor failed to provide data: February 9–13, 2013 and 

May 27–29, 2013. During the February period, ambient 

water temperatures dropped below 2 degrees Celsius (°C), 

which is the lower operating temperature specification for the 

NITRATAX. At the time of failure, a 5 mm measurement path 

length sensor was deployed. The 5 mm sensor was pulled from 

the site and a 2 mm path length version sensor was installed 

in its place. Although both sensor versions have a minimum 

operating temperature specification of 2 °C, the physical 

configuration of the sensor and the outer housing are different 

between the two versions. When the 5 mm sensor was pulled 

from the installation, it was noted by the field technician that 

part of the outer housing was loose. Subsequent manufacturer 

service indicated damage had occurred to the flash bulb in the 

sensor, possibly caused by minor water leakage through seals 

that had contracted and screws that had become loose owing 

to contraction in the cold temperatures. The housing and 

configuration of the 2 mm version of the sensor has proven to 

be a more robust design, which maintains integrity during cold 

temperatures. 

The failure of the NITRATAX during the May 27–29, 2013, 

period is believed to have been owing to high in-stream turbidity. High levels of turbidity limit or prohibit the ultra-violet 

light beam from penetrating through the ambient water sample 

and reaching the receiving photometer. The NITRATAX sensors include a built-in reference beam to measure the potential interference from an ambient water sample and make 

adjustments to the final nitrate concentration value reported. 

However, high levels of turbidity, sediment, or colored dissolved organic matter (CDOM) can overwhelm the sensor’s 

ability to measure light transmitted through the ambient water 

sample. The IL WSC conducted informal laboratory experiments using local streambed and terrestrial sediments to 

determine the range of SSC at which the NITRATAX sensors 

have problems providing reasonable readings of nitrate. From 

these experiments, the limiting concentrations were found to 

be around 6,000 mg/L suspended sediment for the 5 mm path 

length version of the NITRATAX and around 35,000 mg/L 

suspended sediment for the 2 mm path length version of the 

NITRATAX (fig. 3).
Instrument Performance and Operational Considerations

0

2

4

6

8

10

12

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

0 10 20 30 40 50 60 70 80

Milligrams of sediment added to 5.5 liters deionized water

0

2

4

6

8

10

12

14

16

18

20

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

0 10 20 30 40 50 60 70

Sediment concentration, in milligrams per liter

Nitrate concentration, in milligrams per liter 

Milligrams of sediment added to 2.86 liters deionized water

A

B 

Suspended sediment

Nitrate

EXPLANATION

Suspended sediment

Nitrate

EXPLANATION

Figure 3. Results of the U.S. Geological Survey Illinois Water Science Center experiments to determine the 

effects of suspended-sediment concentrations on NITRATAX nitrate sensor readings. A, NITRATAX sc clear with 

5 millimeter path length. B, NITRATAX sc plus with 2 millimeter path length.
10

Continuous Monitoring of Sediment and Nutrients in the Illinois River at Florence, Illinois, 2012–

Phosphate

The Cycle analyzer was initially deployed on May 24, 

2012, with an external battery pack and internal data logging. 

Reliable and consistent data collection from the analyzer was 

not initially obtained even though the analyzer was routinely 

cleansed of algal growth and sediment deposition during maintenance and service visits, and the internal filters were changed 

several times. The internal clock did not keep accurate time, 

and inaccurate data values were being determined. The analyzer was not providing useable phosphate concentration data. 

The instrument was pulled from deployment on August 3, 

2012, for more extensive cleaning and evaluation back at the 

IL WSC. The analyzer was redeployed on September 6, 2012, 

with new filters, set to sample once every 4 hours, and to 

initiate a spiked sample with every environmental sample. By 

September 27, 2012, the analyzer’s internal clock had drifted 

by more than 4 hours. However, the external data-collection 

platform (DCP) controls the Cycle analyzer sampling time 

and time-stamps the data stored in the DCP, so that accurate 

sample times were recorded in the data record. The analyzer 

was again removed from deployment on November 1, 2012, 

owing to continued inaccurate data collection. The Cycle 

analyzer was sent to the manufacturer during the winter 

months of 2012–13 for functional evaluation and for installation of a communications and power cable that would allow 

the analyzer to be integrated into the USGS streamgaging 

station power, data logging, and data-transmission systems. 

It was determined that the reagent and water pump was not 

functioning properly. The manufacturer also stated that there 

was possible air bubble interference in the optical chamber of 

the analyzer, which could have been caused by the weak pump 

or lack of water pressure at the analyzer intakes. WET Labs 

replaced the intake and reagent pump, and the Cycle analyzer 

was redeployed with at least 4 feet (ft) of head above the pump 

intakes to help purge air bubbles from the optical chamber. 

The Cycle was again deployed at the Florence station on 

April 25, 2013. The analyzer provided satisfactory phosphate 

concentration data from April 25 to November 20, 2013. At 

a 4-hour sampling frequency, the reagent cartridges lasted 

about 3.5 months. Occasional periods of missing data resulted 

when the reagent supply was exhausted more quickly than 

anticipated during the initial periods of deployment. Extensive 

cleaning and flushing of the instrument and flow-path components was performed every field visit. Sample measurements 

were made every 4 hours with a spike executed every six 

samples.

Concentrations

Suspended Sediment

One-hundred forty discrete SSC sample results were collected and analyzed during July 2012–June 2013 at the Illinois 

River at Florence (05586300) (125 samples) and the Illinois 

River at Valley City (05586100) (15 samples); they were 

available for linear regression model building with turbidity, 

streamflow, and acoustic parameters, as described in the Methods section. Times associated with samples collected at Valley 

City were adjusted to account for the time of travel between 

Valley City, Ill., and Florence, Ill., as discussed in the Methods 

section. The SSC values (response variable) were matched 

with the nearest-in-time predictor variable value (turbidity, 

streamflow, or acoustic parameter). The statistics for the data 

and models are discussed in the following sections.

Turbidity and Suspended-Sediment 

Concentration Linear Regression Model

Linear regression models for SSC as the response variable were built for the SOLITAX instrument (referenced as 

Turbidity FBRU or TurbFBRU) and the YSI 6136 instrument 

(referenced as Turbidity FNU or TurbFNU) (figs. 4, 5, and 6; 

tables 2 and 3; and appendixes 1 and 2). For comparative 

purposes, a linear regression model for SSC and streamflow was built (fig. 6, tables 2 and 3, and appendix 3). Both 

turbidity models have coefficients of determination (R2) of 

approximately 0.9, which means that the turbidity measurements explain 90% of the variation of the logs of the SSC 

data. The Root Mean Square Error of both models are both 

between 0.12 and 0.15. The slopes of the turbidity models 

are also similar–1.080 and 1.122. Full model statistics are 

included in appendix 1 and 2. The SSC values used in the 

linear models ranged from 15 to 1,970 mg/L, which encompasses the ranges of conditions that occur on an annual 

basis at the site. The stream conditions did reach the upper 

range (1,000 to 2,000 FNUs) of the YSI 6136 (Rasmussen 

and others, 2009), but the data never appeared to truncate; this 

should be a consideration at other sites where higher turbidity 

values are expected.
Concentrations 11

log10(SSC)=1.080log10(TurbFBRU)–0.232

R2 = 0.92

log10(SSC)=1.122log10(TurbFNU)–0.055

R2 = 0.88

Florence turbidity, in FNU

Florence turbidity, in FBRU 

EXPLANATION

10

100

1,000

10 100 1,000

Suspended-sediment concentration (SSC), in milligrams per liter

Turbidity, in formazin nephelometric (FNU) or formazin backscatter ratio units (FBRU)

Figure 4. Suspended-sediment concentration and turbidity linear regression models using turbidity data from 

the Illinois River at Florence, Illinois (05586300), and samples collected at the Illinois River at Florence, Ill. (05586300) 

(125 samples) and the Illinois River at Valley City, Ill. (05586100) (15 samples). [R2

, coefficient of determination]
12

Continuous Monitoring of Sediment and Nutrients in the Illinois River at Florence, Illinois, 2012–

EXPLANATION

Residuals for turbidity model using formazin nephelometric units (FNU)

Residuals for turbidity model using formazin backscatter ratio units (FBRU)

EXPLANATION

Residuals for turbidity model using formazin nephelometric units (FNU)

Residuals for turbidity model using formazin backscatter ratio units (FBRU)

–0.600

–0.400

–0.200

0.000

0.200

0.400

0.600

10 100 1,000

Residuals

Estimated suspended-sediment concentration, in milligrams per liter

–0.600

–0.400

–0.200

0.000

0.200

0.400

0.600

Residuals

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

2012 2013

A

B

Figure 5. Relations between the regression residuals, estimated suspended-sediment concentrations, 

and dates for the linear regression models shown in figure 4. A, Residuals and estimated suspendedsediment concentration. B, Residuals and date.
IConcentrations 13

Streamflow model R2 = 0.43

Turbidity formazin backscatter ratio units (FBRU) model R2

 = 0.92 

Turbidity formazin nephelometric units (FNU) model R2

 = 0.88

Streamflow

Turbidity (FNU)

Turbidity (FBRU)

1:1 Line

EXPLANATION

10

100

1,000

10 100 1,000

Predicted suspended-sediment concentration, in milligrams per liter

Observed suspended-sediment concentration, in milligrams per liter

Figure 6. Predicted and observed suspended-sediment concentration using turbidity and streamflow 

re-transformed linear regression models presented in table 4 using turbidity data from the Illinois River at Florence, 

Illinois (05586300); samples collected at the Illinois River at Florence, Ill. (05586300) (125 samples), and the Illinois 

River at Valley City, Ill. (05586100) (15 samples); and streamflow data reported at the Illinois River at Valley City, Ill. 

(05586100). [R2

, coefficient of determination]
14

Continuous Monitoring of Sediment and Nutrients in the Illinois River at Florence, Illinois, 2012–

Table 2. Predictor and response variable statistics for suspended-sediment concentration, turbidity, 

and streamflow linear regression models.

[FBRU, formazin backscatter ratio units; log10, base-10 logarithmic transform; TurbFBRU, Solitax turbidity in formazin 

backscatter ratio units; SSC, suspended-sediment concentration; mg/L, milligrams per liter; FNU, formazin nephelometric 

units; TurbFNU, YSI 6136 turbidity in formazin nephelometric units; ft3/s, cubic feet per second]

Predictor variable Response variable

Turbidity (FBRU) log10(TurbFBRU) SSC (mg/L) log10(SSC)

Minimum 15 1.176 15 1.176

1st Quartile 47 1.667 34 1.532

Median 77 1.884 59 1.767

Mean 148 1.953 140 1.876

3rd Quartile 132 2.119 130 2.114

Maximum 1,660 3.220 1,970 3.295

Turbidity (FNU) log10(TurbFNU) SSC (mg/L) log10(SSC)

Minimum 14 1.146 15 1.176

1st Quartile 29 1.462 34 1.532

Median 41 1.613 59 1.767

Mean 85 1.720 140 1.876

3rd Quartile 75 1.874 130 2.114

Maximum 1,130 3.053 1,970 3.295

Streamflow (ft3

/s) log10(Streamflow) SSC (mg/L) log10(SSC)

Minimum 3,370 3.528 15 1.176

1st Quartile 5,345 3.728 33 1.519

Median 8,190 3.913 54 1.732

Mean 28,890 4.154 139 1.852

3rd Quartile 40,950 4.612 129 2.111

Maximum 112,000 5.049 1,970 3.295

Table 3. Suspended-sediment concentration, turbidity, and streamflow linear regression models and 

coefficient of determinations using turbidity data from the Illinois River at Florence (05586300), and 

samples collected at the Illinois River at Florence (05586300) (125 samples) and the Illinois River at 

Valley City (05586100) (15 samples)

[log10, base-10 logarithmic transform; SSC, suspended-sediment concentration; TurbFBRU, Solitax turbidity in formazin 

backscatter ratio units; TurbFNU, YSI 6136 turbidity in formazin nephelometric units]

Linear regression model Coefficient of 

determination (R2

)

Root Mean Square Error 

(RMSE)

log10(SSC) = 1.080log10(TurbFBRU) - 0.232 0.92 0.122

log10(SSC) = 1.122log10(TurbFNU) - 0.055 0.88 0.151

log10(SSC) = 0.578log10(Streamflow) - 0.551 0.43 0.341
Concentrations

The residuals of each turbidity model are shown in 

figure 5. A residual is the difference between the observed and 

estimated response variable. To meet the linear model assumptions, the residuals should have a constant variance (known as 

homoscedasticity), and this is the case for both models shown 

in figure 5A. Plotting the residuals versus time (fig. 5B) also 

can help reveal a trend in error, violating the linear model 

assumption of independence owing to one of the following or 

some combination of them (Helsel and Hirsch, 2002): seasonality, a long-term trend, dependence on some other serially 

correlated variable not used in the model, or serial dependence 

of residuals. Neither of the models shows a trend in time; 

therefore, the assumption independence is met.

Re-transformed linear regression models for SSC, turbidity, and streamflow are presented in table 4. The predicted 

mean of the variable may be biased and needs to be multiplied 

by a non-parametric smearing bias correction factor (BCF) 

(Duan, 1983; Helsel and Hirsch, 2002) (table 4) because SSC 

was transformed for the regression model building. These 

equations can then be used to predict SSC and plot with 

observed SSC around a line of perfect agreement (fig. 6). 

A time series of predicted SSC using the FBRU turbidity model is presented in figure 7, along with the 90-percent 

prediction intervals, which represent the range in which the 

predicted response will lie (Helsel and Hirsch, 2002). The 

streamflow time series also is plotted, which gives evidence of 

why simultaneous streamflow is a poor surrogate of SSC. The 

peak of the sediment is always occurring before the peak of 

the streamflow, which is not unusual in many natural streams. 

Lastly, the SSC samples and GCLAS estimated time series 

SSC are plotted. The GCLAS method starts with the SSC 

samples and then estimated values can be added manually by 

the use of a transport curve or by the use of time-series data, 

like turbidity. The resulting suspended-sediment loadings for 

each method will be compared in the Loadings section.

Table 4. Suspended-sediment concentration, turbidity, and 

streamflow re-transformed linear regression models and 

non-parametric smearing bias correction factor using turbidity 

data from the Illinois River at Florence (05586300), and samples 

collected at the Illinois River at Florence (05586300) (125 samples) 

and the Illinois River at Valley City (05586100) (15 samples).

[BCF, non-parametric smearing bias correction factor; SSC, suspendedsediment concentration; TurbFBRU, Solitax turbidity in formazin backscatter 

ratio units; TurbFNU, YSI 6136 turbidity in formazin nephelometric units]

Linear regression model BCF

SSC = 0.586 TurbFBRU1.080 × BCF 1.04

SSC = 0.881 TurbFNU1.122 × BCF 1.06

SSC = 0.281 Streamflow0.578 × BCF 1.48

1,000

10,000

100,000

1

10

100

1,000

July August September October November December January February March April May June

Streamflow, in cubic feet per second

Suspended-sediment concentration, in milligrams per liter

2012 2013

SSC sample used in regression

SSC sample excluded from regression

Solitax (model estimated SSC)

Solitax (model estimated SSC prediction intervals)

GCLAS method (estimated SSC)

Streamflow

Figure 7. Time series streamflow and suspended-sediment concentration (SSC) for samples, modeled SSC (estimated) 

for SOLITAX, and Graphical Constituent Loading Analysis System (GCLAS) method. The results use turbidity data from the 

Illinois River at Florence, Illinois (05586300), and samples collected at the Illinois River at Florence, Ill. (05586300) 

(125 samples), and the Illinois River at Valley City, Ill. (05586100) (15 samples), and streamflow data reported 

at the Illinois River at Valley City, Ill. (05586100).
16

Continuous Monitoring of Sediment and Nutrients in the Illinois River at Florence, Illinois, 2012–

Acoustic Backscatter and Suspended-Sediment 

Concentration Regression Model

As discussed in the Instrument Performance and Operational Considerations section, there appears to be some condition (environmental or anthropogenic) that affects the computed acoustic parameters used as a surrogate for SSC, and 

this problem is being further evaluated. The condition appears 

to be somewhat masked at SSC values above 155 mg/L; a 

linear regression model was made with these values, and the 

predicted and observed values are presented in figure 8. Only 

the R2 for this model is given, and the model is not used to calculate a time-series dataset, because these issues are still being 

worked out. An additional ADVM was added to this site in a 

less turbulent area, and provisional results indicate that this 

may have fixed the issue. As additional concurrent acoustic 

and SSC data are collected at this site, it will be determined 

whether the data presented in this report can be used for SSC 

values greater than 155 mg/L. 

Nitrate

Nitrogen occurs in natural waters as the anions nitrite 

(NO2−) and nitrate (NO3−), as the cation ammonium (NH4+), 

and in various transitional forms as well as incorporated into 

organic solutes. Ammonium ions are often adsorbed to mineral 

surfaces, while nitrate is soluble and stable in most natural 

waters. The nitrite and organic forms of nitrogen are generally 

considered to be intermediate forms of nitrogen, indicative of 

organic or wastewater contribution, and not stable in aerated 

water (Hem, 1985). 

The in-situ NITRATAX and SUNA nitrate sensors 

were designed to provide concentration of nitrite plus nitrate 

nitrogen, as N. From June 1, 2012 to October 30, 2013, nitrate 

concentration was measured approximately 49,600 times by 

the in-situ nitrate sensors. These measurements consisted 

of readings every 15 minutes throughout this period. Water 

samples were collected at the sensor locations during maintenance and calibration visits and sent to the USGS NWQL to 

be analyzed for nitrite plus nitrate, as N. During the monitoring period, 20 discrete samples were analyzed by the NWQL. 

Table 5 presents the summary statistics of the NITRATAX 

Sediment attenuation coefficient

1:1 Line

EXPLANATION

100

1,000

100 1,000

Predicted suspended sediment concentration in, milligrams per liter

Observed suspended-sediment concentration, in milligrams per liter

Sediment attenuation coefficient model R2

=0.90

Figure 8. Observed and predicted suspended-sediment concentration using a linear regression model with an 

acoustic parameter, the sediment attenuation coefficient, using acoustic data from an ADVM in the Illinois River 

at Florence, Illinois (05586300), and samples collected at the Illinois River at Florence, Ill. (05586300) (23 samples), 

and the Illinois River at Valley City, Ill. (05586100) (3 samples).
Concentrations

in-situ sensor and laboratory nitrate concentrations. The data 

record from the NITRATAX sensor is used as the primary 

data for analysis and discussion in this report because it is a 

more comprehensive and complete dataset than the dataset 

from the SUNA sensor. The continuous nitrate dataset includes 

flow periods and river conditions that were not represented by 

discrete samples sent to the laboratory, specifically periods of 

high and low streamflow.

Figure 9 presents a graph of continuous in-situ nitrate 

concentrations from the NITRATAX sensor, laboratory concentrations, and the relative percent difference between concurrent in-situ and laboratory samples. The continuous nitrate 

concentrations generally agreed well with laboratory concentrations, and relative percent differences between the two data 

values were always less than 5 percent. The manufacturer 

specifications for the NITRATAX sensor accuracy is 3 percent 

of the measured concentration plus or minus 0.5 mg/L. The 

relative percent difference in concentrations was greater 

than the manufacturer’s accuracy specifications in only two 

instances. One of these instances was during a high-flow event 

in February 2013, and the other instance was in May 2013 

when the field technician noted that several of the optically 

based sensors were affected by high turbidity. 

Table 5. Summary statistics for nitrite plus nitrate nitrogen, 

as N, measured by the NITRATAX in-situ sensor and point 

samples analyzed by the U.S. Geological Survey National 

Water-Quality Laboratory (NWQL) from the Illinois River at 

Florence, Illinois (05586300), 2012–13.

[Concentrations in milligrams per liter; see figure 9 for graphical 

comparison]

NITRATAX1 NWQL

Mean 3.58 4.09

Median 2.93 4.68

Standard deviation 2.13 2.03

Sample variance 4.54 4.11

Range 9.87 5.98

Minimum 0.88 1.23

Maximum 10.75 7.21

Count 12,253 22

1

Statistics based on 60-minute data values.

0

20,000

40,000

60,000

80,000

100,000

120,000

0

2

4

6

8

10

12

Nitrite plus nitrate, as nitrogen, in milligrams per liter or 

relative percent difference 

Streamflow, in cubic feet per second

May 11, 2012 August 19, 2012 November 27, 2012 March 7, 2013 June 15, 2013 September 23, 2013

Date

EXPLANATION

Continuous nitrate

Laboratory nitrate

Relative percent difference

Streamflow

Figure 9. Concentrations of nitrite plus nitrate nitrogen as determined by the NITRATAX sensor and samples 

analyzed at the U.S. Geological Survey National Water Quality Laboratory, relative percent differences among 

concentrations, and streamflow for the period June 1, 2012–October 30, 2013, at the Illinois River at Florence, 

Illinois (05586300).
18

Continuous Monitoring of Sediment and Nutrients in the Illinois River at Florence, Illinois, 2012–

The largest relative percent difference values occurred 

when nitrate concentrations were at or below 2 mg/L. The 

highest relative percent difference (4.94) was associated with 

concurrent in-situ and laboratory nitrate concentrations of 

1.2 and 1.5 mg/L. At low concentrations, small discrepancies 

between values can result in larger relative percent difference values, although the actual difference in concentration is 

small.

Data Correspondence for Nitrate Sensors 

This continuous-monitoring effort presented the opportunity to deploy a Hach NITRATAX and Satlantic SUNA in-situ 

nitrate sensors side-by-side for a practical evaluation of suitability, reliability, and comparability in a large-river, multiple 

land-use watershed. Some issues and considerations regarding 

the physical deployment of each of the sensors were discussed 

previously. 

Concentrations reported by the two sensors were typically within 10 percent of each other (fig.10); however, there 

were some periods when the two instruments measured 

substantially different concentrations: early January 2013, 

the second half of June 2013, and during October 2013. 

Concentrations reported by the NITRATAX were typically 

higher than concentrations reported by the SUNA (fig. 11), 

but there were exceptions to this generalization. At the lower 

concentrations (1.0 to around 2.7 mg/L), the SUNA generally 

reported concentrations from 0.1–0.3 mg/L higher than the 

NITRATAX. For concentrations higher than about 3.3 mg/L, 

the NITRATAX typically reported higher concentrations 

than the SUNA. For concentrations above 4.0 mg/L, the 

NITRATAX concentrations were typically around 0.8 mg/L 

higher than concentrations reported by the SUNA. The January 2013 and June 2013 periods mentioned earlier are evident 

in figures 10 and 11 and show that the NITRATAX readings 

were substantially higher than the SUNA readings during these 

periods when concentrations ranged from 5.0 to 6.0 mg/L. 

Other than the period of lower concentrations in fall 2013, the 

concentrations reported by the SUNA were higher than those 

reported by the NITRATAX only during a high-flow event 

in early February 2013, when in-stream concentrations were 

elevated and measured around 7.0 to 8.0 mg/L. 

0

1

2

3

4

5

6

7

8

9

10

Nitrite plus nitrate, as nitrogen, in milligrams per liter

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

2012 2013

NITRATAX

SUNA

EXPLANATION

Figure 10. Continuous nitrate data from the NITRATAX and SUNA nitrate sensors at the Illinois River at 

Florence, Illinois (05586300), October 2012–October 2013.
Concentrations

0

1

2

3

4

5

6

7

8

9

0 1 2 3 4 5 6 7 8 9

Nitrite plus nitrate, as nitrogen, in milligrams per liter (SUNA sensor) 

Nitrite plus nitrate, as nitrogen, in milligrams per liter (NITRATAX sensor) 

1:1 Line

Figure 11. Comparison of continuous nitrate data collected by the NITRATAX and SUNA nitrate sensors at 

the Illinois River at Florence, Illinois (06686300), October 2012–October 2013.

The NITRATAX sensor deployed for most of the datacollection period had a measurement path length of 2 mm. 

The SUNA sensor had a measurement path length of 10 mm. 

The shorter path length of the NITRATAX sensor allows for 

measurement of nitrate at higher concentrations and in more 

turbid water. The longer path length of the SUNA might provide marginally more accurate measurement at lower concentrations, which often are concurrent with lower flow conditions and less turbid water. Figure 11 illustrates the differences 

in recorded values at various concentrations during the period 

of deployment.

For the remainder of this report, the continuous nitrate 

data record from the NITRATAX sensor will be used for discussion and analysis purposes because it provides a longer and 

more complete data record.

Seasonality

A comprehensive analysis of the seasonality of nitrate 

concentrations in the Illinois River at Florence is not possible 

with the limited period of data collected through this effort; 

however, some initial observations and inferences can be 

presented. It is important to be reminded that precipitation and 

streamflow were lower than normal in 2012, which has been 

identified as a year of drought in Illinois (http://www.isws.

illinois.edu/hilites/drought/). 

The following definitions of climatic season were used 

for this analysis:

Winter–January, February, and March

Spring–April, May, and June

Summer–July, August, and September

Fall–October, November, and December

Substantially different nitrate concentrations were present 

during the spring seasons of 2012 (represented only by June 

2012) and 2013 as a result of different climatic and streamflow conditions. Continuous nitrate-data collection began in 

June 2012 during a period of declining streamflow and nitrate 

concentrations (fig. 9). Nitrate concentrations in June 2012 

ranged from about 1.7 to 3.5 mg/L. In 2013, discharge of 

the Illinois River was drastically higher and corresponding 

nitrate concentrations ranged from about 4.2 to greater than 

10.0 mg/L. Even though both streamflow and nitrate concentrations were substantially higher in 2013 than in 2012, nitrate 

concentrations were responsive to streamflow conditions in 

both years, increasing and decreasing in response to similar 

changes in streamflow. Nitrate concentrations typically lagged 

behind streamflow changes, but did so for both increases and 

decreases. This behavior likely reflects the general transport of 

nitrate from agricultural surfaces and soils through overland 

runoff or tile-drainage, as well as the transport of nitrate from 

residential lots and urban land surfaces. 
20

Continuous Monitoring of Sediment and Nutrients in the Illinois River at Florence, Illinois, 2012–

Both nitrate concentrations and streamflow conditions 

were generally similar during the summer months of 2012 and 

2013. In these summer months, nitrate concentrations were 

typically less than 3 mg/L and often less than 2 mg/L. An 

exception to this generalization was the first half of July 2013 

when nitrate concentrations were decreasing rapidly, along 

with streamflow, from high levels present in June.

The data for fall 2013 is limited to data collected through 

October 2013, because the period of data for this effort continued only through October 2013. Nitrate concentrations began 

to increase throughout the fall months in both 2012 and 2013 

even though streamflow remained stable. These increases in 

nitrate concentrations could have resulted in response to agricultural fertilizer applications or nitrogen from the release of 

decaying aquatic and other plant materials. Regardless of the 

cause, the data indicate increases in nitrate source loading and 

(or) increases of in-stream transport, since nitrate concentrations increased without corresponding increases in streamflow.

Data representing nitrate concentrations during the winter 

months (January–March) were available only for 2013. During these months, nitrate concentrations generally increased 

from around 4 to greater than 6 mg/L. There was a correlation between nitrate concentrations and streamflow, but not 

as strong or consistent as during the spring months. These 

seasonal increases in nitrate concentrations might indicate 

reduced biological consumption of nitrate, continuing contribution of nitrate from organic and agricultural sources, and 

(or) reduced denitrification during colder periods.

Phosphate

The period of continuous phosphate data collected by the 

Cycle analyzer and used for this report was April 25, 2013–

October 21, 2013. As mentioned earlier, there were several 

issues with the Cycle analyzer deployment and performance 

that prevented reliable continuous phosphate-data collection 

and transmission prior to this period.

The data from the phosphate analyzer documented a 

substantial increase in phosphate concentrations during the 

deployment period (fig. 12). Phosphate concentrations from 

the initial deployment (April 25 through the end of June) were 

consistent and ranged from about 0.15 to 0.20 mg/L, as P. 

Phosphate concentrations began to increase steadily around 

July 1 and continued to increase through October, when they 

reached a high concentration of 0.53 mg/L, as P. 

Nine discrete samples were collected from the river 

adjacent to the analyzer and were subsequently analyzed for 

dissolved orthophosphate at the USGS NWQL. These samples 

were collected during routine NAWQA sampling trips and 

equipment maintenance visits. The dates of the discrete samples collected in 2013 were April 25, April 29, May 8, May 29, 

June 18, July 22, August 14, August 30, and September 25.

Table 6 shows summary statistics for the continuous 

phosphate data collected by the Cycle analyzer and for samples analyzed at the NWQL. While the analytical results from 

these analyses can be compared and contrasted, the concentration values should not necessarily be identical because of a 

difference in filtration between the two methods. The Cycle 

analyzer incorporates a 10 micron filter, while the NWQL 

analysis is determined on a sample passed through a filter with 

0.45 micron pore size, which is the standard operational pore 

size typically used to differentiate between total and dissolved 

constituent analyses. Regardless, some useful observations 

are readily made from comparison of these data, including the 

obvious difference in the number of concentration determinations acquired and the stream conditions represented by the 

data. The continuous dataset has higher mean, median, and 

maximum concentrations; includes a larger range of concentrations; and has a lower minimum concentration. These data 

characteristics indicate a more comprehensive description 

of stream concentrations throughout the monitored period. 

However, the standard deviations of the two datasets are not 

substantially different, indicating that both datasets capture 

the general tendencies and characteristics of the in-stream 

concentrations throughout the period sampled. The continuous 

dataset was able to capture periods of phosphate concentrations both lower and higher than those represented by the 

discrete samples data.

The Cycle analyzer cannot be calibrated by the user in 

the field, and standard solutions were not used in this study to 

check field readings of the analyzer. Future field maintenance 

of the analyzer will include a check with a standard phosphate 

solution in the range of expected environmental concentrations. Use of field checks with standard phosphate solutions 

will provide additional information regarding the analyzer values and will assist with adjustment of continuous data values 

for user-specific purposes. 

Figure 13 shows a time-series plot of the continuous 

phosphate dataset, the orthophosphate concentrations determined from the discrete laboratory samples, and the relative 

percent differences in concentrations between the discrete 

sample concentrations and the Cycle analyzer readings at the 

time of discrete sample collection. Relative percent differences in concentrations were generally less than 25 percent. 

The Cycle analyzer concentrations were consistently higher 

than the laboratory concentrations except for the April 25 and 

May 29 samples. Results from the Cycle analyzer were not 

adjusted based upon laboratory analyses primarily because of 

the differences in filtration pore sizes and because the analyzer 

concentrations were consistently higher than the laboratory 

concentrations; a relation that arguably would be expected 

from the difference in filter pore size. The relation between 

analyzer phosphate concentrations and laboratory orthophosphate concentrations should be examined for each individual 

deployment location, and a determination should be made 

regarding the consistency or variability and the implications of 

the relation.
Concentrations

Cycle-PO4 analyzer

Streamflow

EXPLANATION

0

20,000

40,000

60,000

80,000

100,000

120,000

0

0.1

0.2

0.3

0.4

0.5

0.6

Dissolved phosphate, in miligrams per liter, as phosphorus

Streamflow, in cubic feet per second

May June July August September October

2013

Figure 12. Concentrations of dissolved phosphate as determined by the Cycle-PO4 analyzer and 

streamflow for April 25, 2013–October 30, 2013, at the Illinois River at Florence, Illinois (05586300).

Table 6. Summary statistics for phosphate data measured by the 

Cycle-PO4 analyzer and discrete samples analyzed by the U.S. Geological 

Survey National Water-Quality Laboratory (NWQL) for Illinois River at 

Florence, Illinois (05586300).

1

Cycle PO4

phosphate

NWQL

orthophosphate

Mean 0.286 0.213

Median 0.255 0.172

Standard deviation 0.110 0.086

Range 0.473 0.276

Minimum 0.057 0.142

Maximum 0.53 0.418

Count 3,950 9

1

Based on 60-minute interpolation of 240- to 360-minute data values.
22

Continuous Monitoring of Sediment and Nutrients in the Illinois River at Florence, Illinois, 2012–

0

5

10

15

20

25

30

35

0

0.1

0.2

0.3

0.4

0.5

0.6

Phosphate, in milligrams per liter, as phosphorus

Relative percent difference

May June July August September October

2013

Cycle-PO4 analyzer phosphate

U.S. Geological Survey National

 Water Quality Laboratory

 dissolved orthophosphate

Relative percent difference

EXPLANATION

Figure 13. Concentrations 

of phosphate measured 

by the Cycle-PO4 

analyzer and dissolved 

orthophosphate analyzed 

by the U.S. Geological 

Survey National Water 

Quality Laboratory for 

April 25–October 30, 2013.

When a consistent relation among continuous data values 

and laboratory values is determined, the continuous data 

record could be adjusted through the use of algorithms to more 

closely agree with the laboratory values and adjust for consistent bias in the continuous dataset. Other constituent data, such 

as discharge and turbidity, and the determination of interferences from other constituents in the water matrix might prove 

valuable in deriving an algorithm for adjusting continuous 

phosphate concentrations after a more comprehensive dataset 

is acquired. 

Comparison of Continuously Monitored 

Phosphate to other Forms of Phosphate

Total phosphorus (TP) includes all phosphorus present 

in a water sample, including that which is incorporated in 

biological organisms and absorbed to particulate matter. A 

0.45-micron membrane filter is generally used as the standard 

operational pore size for filtration of water samples (American 

Public Health Association, 1998). The Cycle analyzer utilizes 

two coarse-material screens and a 10-μm pore size filter. The 

phosphate results reported by the Cycle analyzer are therefore 

considered, through this operational definition, to be unfiltered 

phosphate concentrations. 

Most water-quality standardsare developed for TP in 

order to account for phosphorus that is bound to particulate 

matter or organic material, but has the potential of being 

released and converted to a biologically available form. The 

State of Illinois water-quality standard for TP in general-use 

waters is 0.05 mg/L in any reservoir or lake with a surface area 

of 8.1 hectares (20 acres) or more, or in any stream at the point 

where it enters any such reservoir or lake (Illinois Pollution 

Control Board, 2013). 

As previously discussed, discrete water samples were 

collected during April 25–October 30, 2013, from the Illinois 

River at Florence near the in-situ Cycle analyzer and were 

analyzed at the NWQL for dissolved orthophosphate (DOP) 

concentration (table 6). In addition, seven cross-sectional 

composite samples were collected for the NAWQA program 

during this time period at the Illinois River at Valley City, Ill. 

(05586100), and analyzed for TP, dissolved phosphorus, and 

DOP. Figure 14 depicts the concentrations of the different 

phosphorus forms from these cross-sectional composite samples as well as the concurrent Cycle analyzer phosphate concentrations at the Illinois River at Florence, Ill. (05586300).

In addition, four composite cross-sectional samples were 

collected using the EWI method during the initial months of 

the Cycle analyzer deployment and compared to point samples 

collected at the Cycle analyzer location to determine whether 

or not the location of the Cycle analyzer was representative of 

the entire stream. The results of these comparison samples are 

shown in table 7.

There was good correlation (R2 = 0.8687) between DOP 

and TP concentrations in the samples analyzed at the NWQL. 

However, the limited number of samples in this comparative dataset was insufficient to determine relations between 

the two forms of phosphorus, especially when these samples 
Concentrations

May June July August

2013

0

20,000

40,000

60,000

80,000

100,000

120,000

0

0.1

0.2

0.3

0.4

Concentration, in milligrams per liter, as phosphorus

Streamflow, in cubic feet per second

Cycle-PO4 analyzer phosphate

Laboratory orthophosphate

Laboratory total phosphorus

Laboratory dissolved phosphorus

Streamflow

EXPLANATION

Figure 14. Concentrations of various forms of phosphorus in the Illinois River at Valley City, Illinois (05586100), and 

concurrent continuous phosphate concentrations at the Illinois River at Florence, Ill. (05586300), April 25–October 30, 2013.

Table 7. Concentrations of nitrate plus nitrite and phosphate in samples collected using the 

equal-width-increment method and in point samples at the location of the in situ instruments at 

Illinois River at Florence, Illinois (05586300). 

[N, nitrogen; P, phosphorus]

Date

Nitrate plus nitrate, as N, 

in milligrams per liter

Orthophosphate, as P, 

in milligrams per liter

Equal width 

increment sample Point sample Equal width increment sample Point sample

August 3, 2012 1.49 1.46 0.577 0.574

August 21, 2012 1.23 1.23 0.570 0.559

March 8, 2013 5.70 5.68 0.169 0.170

April 24–25, 2013 4.88 4.94 0.164 0.170
24

Continuous Monitoring of Sediment and Nutrients in the Illinois River at Florence, Illinois, 2012–

represent a limited range of environmental and streamflow 

conditions. Therefore, an analysis was performed using results 

from water samples collected for the NAWQA program from 

1991 through April 2013 at the Illinois River at Valley City 

(05586100) to determine relations between DOP and TP in this 

reach of the Illinois River and to determine whether continuous phosphate concentrations from the Cycle analyzer can be 

used to infer concentrations or characteristics of TP. 

Samples were collected at the Illinois River at Valley 

City (05586100) for the NAWQA program at various times 

and frequencies since 1991. From 1991 to 2013, 246 samples 

were analyzed for TP and DOP. Table 8 provides the summary statistics from these samples. As mentioned in the Study 

Area section of this report, the nutrient concentrations in the 

Illinois River were determined to be similar at the Valley City 

and Florence locations; therefore, use of the long-term dataset 

at the Valley City station is appropriate for determination of 

relations among various forms of phosphorus and chemical 

constituents at the Florence station.

A linear regression model was developed relating TP and 

DOP concentrations using the 246 data pairs from the samples 

collected during 1991–2013. The R2

 of the regression model 

was 0.24. There were obvious outliers in the dataset that exert 

strong influence on this model, excluding them from the model 

could not be justified. The primary three outliers had concentrations of TP greater than 1.30 mg/L with corresponding DOP 

concentrations less than 0.15 mg/L. There also were six TP 

concentrations above 0.6 mg/L that corresponded to DOP concentrations at or below about 0.2 mg/L. This regression model 

did not provide a good relation between the variables when TP 

concentration was higher than about 0.6 mg/L and DOP was 

less than about 0.3 mg/L. 

A linear regression model also was developed relating TP 

and SSC using the 246 data pairs for 1991–2013. This model 

had a slightly better R2 of 0.43 than the previous model and 

more effectively accommodated the high TP concentrations, 

although it was not as accurate for the low TP concentrations. 

It was presumed that this model reflects the association of 

phosphorus with particulate material and the occasional high 

and co-occurring concentrations of both constituents.

The initial linear regression models using DOP or SSC 

to predict TP indicated that each of these independent variables was better for predicting TP at different turbidity and 

TP concentration levels. That is, DOP was a better predictor 

variable at low TP concentrations, which typically coincide with 

lower turbidity levels, while SSC was a better prediction variable at higher concentrations of TP, which typically coincide with 

elevated turbidity levels. To take advantage of these characteristics, a multivariate linear regression model using both of these 

predictor variables was developed with a resulting R2 of 0.802. 

The multivariate regression model equation follows.

TP = 0.0816 + 1.10(DOP) + 0.00063(SSC)

Table 9 presents the three TP-prediction model summaries, 

and figure 15 depicts the observed-to-predicted correspondence 

from the final multivariate linear regression model using both 

DOP and SSC (units for TP, DOP, and SSC are all mg/L). The 

multiple-linear-regression model had an R2 of 0.8 (RMSE = 

0.079), which was much higher than the single variable models 

and indicates a very good prediction model of TP in this reach of 

the Illinois River.

These initial analyses of the relations between DOP and TP 

concentrations indicate there is good potential for developing 

reasonable algorithms to calculate TP concentrations from continuous phosphate and turbidity data. However, these predictive 

algorithms will likely have greater uncertainty at higher phosphorus concentrations and during periods of increased streamflow 

and will likely need a seasonal component built in to account for 

point-source inputs in watersheds with point-source contributions. Algorithms will need to be developed for each water body 

independently owing to differences in watershed hydrologic factors, phosphorus inputs, and landscape and stream-sediment and 

other particulate-matter composition.

Seasonality and Flow-Related Differences

Streamflow in the Illinois River generally decreased 

during April–October 2013, the period during which the 

phosphate analyzer was deployed (fig. 12). During this 

period, streamflow decreased from nearly 110,000 to about 

7,000 cubic feet per second (ft3/s). A significant flow event 

occurred near the end of May and lasted through June during 

which time streamflow increased from around 50,000 to about 

90,000 ft3/s and subsequently returned to 50,000 ft3/s by the end 

of June. Streamflow decreased to about 10,000 ft3/s by August 1 

and generally ranged from 4,000 to 10,000 ft3/s through October. 

During this time period, phosphate concentrations ranged 

from about 0.22 to 0.53 mg/L, as P, and generally increased 

consistently during the relatively stable low-flow period from 

mid-July through October 2013 when streamflow was less 

than 20,000 ft3/s. The reasons for the increasing concentrations of phosphate during this period are not known, but could 

include increased or accumulating phosphate contributions from 

upstream wastewater-treatment facilities, release and oxidation 

of particulate-bound phosphorus in stream and bank sediments, 

and releases of phosphorus from dying and decomposing aquatic 

plants and algae. The high-flow period of June 2013 was an 

exception to this relation. As discussed previously, there was a 

Table 8. Summary statistics for dissolved orthophosphate 

and total phosphorus concentrations from samples collected at 

Illinois River at Valley City, Illinois (05586100), 1991–2013.

Dissolved 

orthophosphorus Total phosphorus

Minimum 0.051 0.11

Mean 0.201 0.415

Median 0.176 0.390

Maximum 0.600 1.380

Range 0.549 1.27

Count 246 246
Concentrations

Table 9. Final model equation and predictor and response variable statistics for total phosphorus (TP) concentration 

multivariate linear regression models developed using data from the Illinois River at Valley City, Illinois (05586100), 1991–2013.

[R2, coefficient of determination; TP, total phosphorus; mg/L, milligrams per liter; P, phosphorus]

Predictor variable(s) R2 Root mean 

square error

Dissolved orthophosphorus (DOP)

Suspended-sediment concentration (SSC)

DOP and SSC

TP and DOP model: TP = 0.237 + 0.873(DOP)

TP and SSC model: TP = 0.318 + 0.000541(SSC)

Final Model (TP, DOP, and SSC): TP = 0.0816 + 1.10(DOP) + 0.00063(SSC)

0.238

0.432

0.802

0.154

0.134

0.079

Predictor variable

Orthophosphorus Suspended sediment 

(mg/L, as P) (mg/L)

Response variable

Total phosphorus 

(mg/L, as P

Minimum

1st Quartile

Median

Mean

3rd Quartile

Maximum

0.051

0.139

0.176

0.201

0.241

0.600

29

78

116

178

200

1,970

0.11

0.30

0.39

0.41

0.48

1.38

1:1 Line

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6

Predicted total phosphorus, as phosphorus, in milligrams per liter

Observed total phosphorus, as phosphorus, in milligrams per liter

Figure 15. Observed and predicted total phosphorus concentrations using a linear 

regression model with orthophosphate and suspended sediment prediction variables.
26

Continuous Monitoring of Sediment and Nutrients in the Illinois River at Florence, Illinois, 2012–

substantial increase and subsequent decrease in streamflow 

during June. phosphate concentrations, however, remained 

consistent for most of this period and did not demonstrate a 

dilution effect, except during the early period of the hydrograph (May 26 to June 3). There is some uncertainty in the 

accuracy of the Cycle analyzer data during this initial period 

of increased discharge and decreased phosphate concentration because turbidity concentrations were high and turbidity is known to interfere with the performance of the Cycle 

analyzer. The laboratory-analyzed sample from May 29 had 

a concentration of 0.142 mg/L, which would indicate that 

phosphate concentrations might not have been diluted as much 

as indicated by the analyzer data and that turbidity might have 

been interfering with the accuracy of readings by the analyzer. 

These data, however, represent the only substantial flow event 

of the analyzer deployment period and could be an appropriate representation of phosphate concentration changes during 

high-flow events. 

With the exception of the June period, only minor 

increases in streamflow occurred during the analyzer deployment, and it is not possible to determine a consistent response 

in phosphate concentrations to changes in streamflow from 

the available dataset. The data indicate both increases and 

decreases of phosphate concentrations in response to relatively small increases in streamflow. These different responses 

likely result from various contributing factors including 

environmental conditions, biological activity, and runoff contributions from different parts of the watershed (particularly 

urban and agricultural areas). 

The continuous data collected during April–October 

2013 indicated a general inverse relation between phosphate 

concentrations and discharge. This relation is not unexpected 

based upon the simple process of phosphate point-source dilution during periods of increased streamflow; however, this is a 

generalized relation from a limited dataset with demonstrated 

periods of exception. 

Figure 16 depicts continuous phosphate concentration 

data versus streamflow, plotted as individual points for April–

October 2013. It is almost possible to trace the progression of 

phosphate data through the period of deployment because of 

the limited period of data and the nature of the continuous data 

record. A basic power-function regression equation provides 

the best single-line fit to the data with an R2 value of 0.8094. 

The data for May 26–June 3 (indicated in red) deviate somewhat from the general trend of the data, but if this period of 

data is removed from the dataset, the best-fit equation and R2

value improve only slightly. This relation indicates that there 

is a significant inverse correspondence between phosphate 

concentration and streamflow at medium to high streamflows. 

y = 6.488x-0.315

R2 = 0.8094

0

0.1

0.2

0.3

0.4

0.5

0.6

0 20,000 40,000 60,000 80,000 100,000 120,000

Phosphate, as phosphorus, in milligrams per liter

Streamflow, in cubic feet per second

Data for

May 26–June3, 2013

are shown in red

Figure 16. Continuous phosphate concentrations versus streamflow for April–October 2013 at the Illinois River at 

Florence, Illinois (05586300). [R2

, coefficient of determination]
Loadings

Loadings

Suspended Sediment

Utilizing the time series SSC and streamflow data, 

the SSL was calculated for each instrument and method as 

outlined in the Methods section (table 9, fig. 17). The variation in the load is apparent from the first 6 months of drought 

followed by flooding and gives further evidence of the broad 

range of conditions in which the instruments were tested. The 

annual SSL was calculated for July 1, 2012–June 30, 2013. 

The annual SSLs calculated from the two turbidity sensors 

are within 5 percent of each other. The annual SSL calculated from the GCLAS method is within 7 percent of both 

turbidity loads. The historical annual SSLs (July 1–June 30) 

are plotted in figure 18 with the data from this study (July 1, 

2012–June 30, 2013). The average annual SSL for 1981–2012 

was approximately 5.2 million tons. The SSL for July 1, 2012–

June 30, 2013, was within 10 percent of the average historical 

annual SSL.

Table 10. Calculated suspended-sediment load for each 

instrumentand method used in the study.

[GCLAS, Graphical Constituent Loading Analysis System]

Time period Suspended-sediment load (tons)

Solitax YSI 6136 GCLAS

July 2012 24,830 25,107 23,818

August 2012 20,862 20,303 22,719

September 2012 26,409 25,588 20,397

October 2012 19,743 25,530 19,902

November 2012 9,176 12,012 10,387

December 2012 23,123 21,698 24,047

January 2013 74,697 77,187 73,508

February 2013 346,352 341,861 423,800

March 2013 611,024 612,353 716,800

April 2013 1,843,570 1,987,953 1,671,010

May 2013 987,609 1,083,143 909,350

June 2013 720,164 701,822 699,790

Annual total 4,707,559 4,934,557 4,615,528

Graphical Constituent Loading Analysis System

Solitax turbidity

YSI turbidity

EXPLANATION

0

500,000

1,000,000

1,500,000

2,000,000

July Aug.

Suspended-sediment load, in tons per month

2012 2013

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

Figure 17. Monthly suspended-sediment load for each instrument and method used in the 

study at the Illinois River at Florence, Illinois (0558630).
28

Continuous Monitoring of Sediment and Nutrients in the Illinois River at Florence, Illinois, 2012–

Graphical Constituent Loading Analysis System

Solitax turbidity

YSI turbidity

EXPLANATION

Suspended-sediment load, in tons per month

Year

0

2,000,000

4,000,000

6,000,000

8,000,000

10,000,000

12,000,000 1980

1981198219831984198519861987198819891990199119921993199419951996199719981999200020012002200320042005200620072008200920102011201220132014

Figure 18. Annual suspendedsediment loads for the study 

period and historical data. 

Annual loads were calculated for 

July 1–June 30 and are labeled 

as the ending year at the Illinois 

River at Valley City, Illinois 

(05586100), prior to 2013 and 

the Illinois River at Florence, Ill. 

(05586300), in 2013.

Nitrate

Nitrate loads were computed for June 1, 2012–

October 31, 2013, the period of continuous nitrate data collection. Streamflow values used for the load computations were 

from the USGS streamgaging station at Illinois River at Valley 

City, Ill. (05586100). The time required for water to travel 

from the streamgaging station at Valley City to the continuous 

monitoring station at Illinois River at Florence, Ill. (05586300), 

varies with the stage of the river and corresponding flow velocity. A travel time of 5 hours between the two locations was 

used in this analysis to pair a nitrate sensor concentration to a 

corresponding streamflow value in the calculation of continuous nitrate loads.

Nitrate loads were computed in two ways for comparative 

purposes. The calculation of instantaneous loads from continuous data was done by simply multiplying the reported nitrate 

concentration by the corresponding streamflow value and 

applying a unit-conversion factor. For this analysis, the following equation was used to compute an instantaneous nitrate load 

value for each continuous nitrate concentration value:

Load × N03(lb/d)=Concentration(mg/L) Streamflow( ft3 × /s) 5.393

Nitrate loads also were computed by traditional regressionmodel technique using 22 discrete nitrate samples collected at 

the Florence station during maintenance and service visits of the 

continuous-monitoring equipment. The USGS Load Estimator 

(LOADEST) program (Runkel and others, 2004) was used to 

compute a regression equation relating nitrate concentrations 

to corresponding streamflow values and incorporating a seasonal time component. The regression equation selected by the 

LOADEST program was 

LoadN03(lb/d)=12.5820+1.5963(LnQ)−0.2612(LnQ2

)−

0.2904Sin(2pi dtime)−0.0294Cos(2pi dtime)−0.4237(dtime)

Figure 19 depicts the time-series loads calculated by the 

two different methods, as well as the instantaneous load values 

associated with the discrete samples. The plot shows that loads 

calculated by the two methods are in general agreement, and 

the calculated loads change similarly in magnitude and direction in response to streamflow. There are, however, substantial 

differences in load quantities determined by the two methods 

at various times during both low- and high-flow periods. At 

times, the load computed from continuous data is larger, while 

at other times the load computed by the LOADEST program is 

larger. The load computed from the continuous data is in good 

agreement with the instantaneous loads associated with the 

discrete samples, whereas there can be substantial differences 

between the loads computed by the LOADEST program and 

the instantaneous loads associated with the discrete samples. 

It is particularly important to assess differences between 

the load values computed by the two methods during periods of increased streamflow, when the majority of in-stream 

nitrate transport occurs. The target period in this case was 

February–June 2013. Separation of the two time-series plots 

for the load-computation methods is evident during this time 

even though the y-axis is presented with a logarithmic scale. 
Loadings

Continuous nitrate load

LOADEST nitrate load

Instantaneous load from U.S. Geological 

 Survey National Water Quality 

 Laboratory sample

Load difference

EXPLANATION

–1,000,000

0

1,000,000

2,000,000

10,000

100,000

1,000,000

10,000,000

Nitrate load, as nitrogen, in pounds per day and streamflow,

in cubic feet per second

NITRTAX load minus LOADEST load, in pounds per day

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

2012 2013

Figure 19. Instantaneous nitrate loads computed using data and the LOADEST program for the Illinois River at 

Florence, Illinois (05586300).

The continuous data load values are in reasonable agreement 

with the instantaneous discrete load values during a couple of 

important peaks on the plot, whereas the loads computed using 

the LOADEST program appear to underestimate and overestimate loads during some peaks and valleys on the plot. Also 

shown in figure 19 is a line showing the difference in instantaneous load quantities between the two computational methods 

(blue line). The difference in computed load was calculated as 

the NITRATAX load minus the LOADEST load. These differences are greatest during periods of increased loading and 

streamflow with differences of up to 1,500,000 pounds per day 

(lb/d). The differences in loads during periods of stable and 

lower streamflow are minor in comparison. 

Figure 20 shows time-series plots of (A) streamflow, 

(B) nitrate load computed from the continuous data, (C) the 

relative percent difference between the loads computed by 

the two methods, and (D) the absolute values of the differences in actual nitrate loads between the two load-computation 

methods. These plots show several important observations 

and illustrate the advantages of a continuous data record. 

First, the majority of the nitrate loading occurs during periods 

of increased streamflow. Second, the relative percent differences between the two methods of load computations typically 

ranges from 10 to 20 percent and is somewhat lower during 

periods of high streamflow owing to the order of magnitude 

of the load values. Third, the quantitative differences in load 

values are substantial and range greatly, and it is during the 

critical high-flow periods that the continuous-data based 

load calculations show distinct advantage and an increase in 

accuracy. 

Figure 21 depicts total monthly nitrate loads for 

June 2012–October 2013, as computed from the continuous 

data and by the LOADEST program. This figure illustrates 

how the LOADEST program might overestimate loads during 

stable and low-flow periods (fall months), but underestimate 

loads during high-flow and storm-event periods (spring 

months). The uncertainty and modeling capability of the 

LOADEST program is of course dependent upon the number 

of sample values available to input into the program, and the 

uncertainty can be reduced with more frequent sampling and 

a longer period of data record. Throughout the entire period of 

June 2012–October 2013, the continuous data indicated that 

approximately 25.2 million pounds of additional nitrate were 

transported past the Florence station above what the traditional 

LOADEST program estimated using laboratory results from 

22 samples collected during the 17-month period. Many routine ambient-monitoring programs collect samples no greater 

than monthly and without additional high-flow samples. 

Consequently, they might not provide sample data that include 

many of the high-load events or periods. There also was a 

low-to-normal flow period, November 2012 and January 2013, 

during which the continuous data showed nitrate concentrations, and associated loads, to be substantially higher than 

accounted for in the LOADEST program.
30

Continuous Monitoring of Sediment and Nutrients in the Illinois River at Florence, Illinois, 2012–

0

30,000

60,000

90,000

120,000

0

1,000,000

2,000,000

3,000,000

0

6

12

18

24

30

0

300,000

600,000

900,000

1,200,000

1,500,000

A. Streamflow

B. Nitrate load

C. Relative percent difference, in nitrate loads

D. Nitrate load difference

Streamflow, in cubic

feet per second

Nitrate load, as nitrogen,

in pounds per day

Load difference, in pounds

per day (absolute value) Relative percent difference

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

2012 2013

Figure 20. Instantaneous nitrate loads and the differences in loads computed using continuous data and the LOADEST 

program for June 2012–October 2013 at the Illinois River at Florence, Illinois (05586300). A, Streamflow, in cubic feet 

per second. B, Nitrate load. C, Relative percent difference in nitrate loads. D, Nitrate load difference, in pounds per day 

(absolute value).
Loadings

Monthly nitrate loads for

June 2012−October 2013

Continuous data = 316,384,482 pounds

LOADEST program = 291,165,917 pounds

Continuous Data

LOADEST program

Streamflow

EXPLANATION

100,000

1,000,000

10,000,000

100,000,000

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

Load, in pounds, as nitrogen

2012 2013

Figure 21. Monthly nitrate loads in the Illinois River at Florence, Illinois (05586300), June 2012–October 2013.

Phosphate

Calculation of phosphate loads at the Illinois River at 

Florence, Ill. (05586300), was done in the same manner as for 

nitrate loads using both continuous- and discrete-sample based 

calculations. Data used for the LOADEST program included 

DOP and streamflow data for August 2012–November 2013. 

This period of record included data from before the Cycle 

analyzer was installed, but provided a more comprehensive 

set of data from which to calculate an appropriate regression 

equation for the station. The equation selected by LOADEST 

for DOP load calculation was

LoadP04(lb/d)=9.4695+1.0475(LnQ)+0.0209(LnQ2

)−

0.3629Sin(2pi dtime)−0.3054Cos(2pi dtime)−

0.6554(dtime)+1.0466(dtime2)

Figure 22 shows time-series plots of the loads calculated 

by the two methods, as well as instantaneous load values 

associated with each of the discrete samples for April–

October 2013. The Cycle analyzer was deployed only during 

April–October 2013, and figure 23 depicts only this period. 

The plot indicates substantial differences, at times, between 

the load values computed by the two methods. The load values 

computed using the continuous data are virtually always 

greater than the load values computed by the LOADEST program. As with nitrate, the loads calculated from the continuous 

data also show a greater response to changes in streamflow 

than the LOADEST computed values. Instantaneous loads 

computed from discrete samples generally are closer to the 

LOADEST loads than to the continuous-data based loads, 

although they commonly are somewhere in between the 

LOADEST and continuous load values. Quantitative differences between the two load estimates are greatest during 

periods of higher streamflow. 

In the discussion regarding the continuous phosphate 

data, it was noted that the continuous Cycle analyzer data 

had a positive bias when compared to discrete laboratory data 

(fig. 13). This bias would carry through in the calculation 

of continuous-data based load values and warrants further 

evaluation.

The quantitative differences and the relative percent differences between the two methods of calculating phosphate 

loads are shown in figure 23. Relative percent differences in 

the load values were generally less than 10 percent, with the 

primary exception of the high-flow period in early June 2013 

when loads computed from the continuous data were substantially larger than the loads computed by the LOADEST 

program.
32

Continuous Monitoring of Sediment and Nutrients in the Illinois River at Florence, Illinois, 2012–

CYCLE-PO4 analyzer phosphate load

LOADEST phosphate load

Instantaneous load from U.S. Geological 

 Survey National Water Quality 

 Laboratory sample

Streamflow

EXPLANATION

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

Phosphate load, in pounds per day, as phosphorus 

Streamflow, in cubic feet per second

1,000

10,000

100,000

May June July August September October

2013

Figure 22. Phosphate loads at the Illinois River at Florence, Illinois (05586300), April–October 2013.

Streamflow

Load difference (absolute value)

Relative percent difference in load

EXPLANATION

May June July August September October

2013

0

2

4

6

8

10

12

14

16

18

20

0

20,000

40,000

60,000

80,000

100,000

Relative percent difference in load

Difference in load, in pounds per day or

streamflow, in cubic feet per second

Figure 23. Relative percent differences and quantitative differences in computed phosphate loads for the 

Illinois River at Florence, Illinois (05586300), April–October 2013.
Discussion

Discussion

Suspended Sediment

The instruments and methods used in this study were 

tested for a wide range of conditions that occurred during the 

study period. The SSC values used in the linear models ranged 

from 15 to 1,970 mg/L, which encompasses the ranges of 

conditions that occur at the site on an annual basis. 

The techniques and methods for using turbidity as a 

surrogate for SSC are well documented in Rasmussen and 

others (2009). These methods were followed in this study, and 

two different turbidity linear regression models had great R2, 

near 0.9. The SOLITAX R2 (0.92) was slightly better than the 

YSI 6136 R2 (0.88). SSC samples are still needed on an annual 

basis to verify whether or not the linear regression models still 

apply, but sampling could be cut to 6 to 12 samples per year 

for a variety of turbidity and SSC conditions. The following 

factors may cause a deviation from the existing model, and 

shifts or new models may need to be created depending upon 

the severity of the problems.

• Changes in sediment source and type

• Changes in longitudinal or vertical location of the 

instrument

• Changes in channel configuration (meandering, aggradation, degradation)

• Changes to sensor setup, model, or type

The stream conditions did reach the upper range (1,000 

to 2,000 FNUs) of the YSI 6136 (Rasmussen and others, 

2009), but the data never appeared to truncate. This should 

be a consideration at other sites where higher turbidity values 

are expected. For the SOLITAX, the manufacturer denotes an 

upper range of 50,000 mg/L for total-suspended solids (Hach 

Company, 2014). The SSLs calculated from the two turbidity 

sensors were within 5 percent of each other and within 7 percent of loads calculated by traditional methods using discrete 

storm and routine samples.

For sediment acoustics at this site, there appears to be 

some condition (environmental or anthropogenic) that has an 

effect on the backscatter data. Even with the issues, the linear 

regression model for SSC values greater than 155 mg/L had 

great R2

, near 0.9. Utilizing acoustics as a surrogate for SSC 

is advantageous because of the dual use of one instrument, 

velocity and SSC. Testing that uses acoustics as a surrogate 

for SSC is ongoing at the Spoon River near Seville, Ill., and 

the Kickapoo Creek near Bloomington, Ill.; provisional results 

are pending. These results, along with provisional results of a 

second ADVM installed at Florence (in a less turbulent area), 

are promising for the future of using acoustic parameters as a 

surrogate for SSC, and a USGS techniques and methods report 

is currently being drafted. 

Nitrate

In-situ nitrite plus nitrate concentrations were measured 

on a continuous basis using two ultraviolet absorption sensors with slightly different characteristics;a Hach NITRATAX 

and a Satlantic SUNA. There are some physical differences 

between these two instruments that require consideration 

when deploying the instruments in the field. Each instrument 

provided a reliable data record once some initial deployment 

issues were addressed. The measurement path lengths of the 

instruments differ, and it has been found that a shorter path 

length is advantageous because it is less affected by sediment 

and turbidity and typically provides a broader range of concentration measurement. 

Reliable data can be provided by in-situ nitrate sensors, 

and the temporal completeness of the data record is greatly 

increased with continuous monitoring of nitrate. The in-situ 

sensors, when adequately maintained and calibrated, provided 

concentration values that were reasonably similar to concentrations determined by the laboratory. Some of the advantages 

of a continuous and near-real-time record of nitrate concentration data include

• ability to adjust sampling or operational procedures in 

response to changing conditions

• more complete coverage of nitrate concentration over 

extreme (low- or high-flow) hydrologic events

• ability to calculate more comprehensive and accurate 

nitrate loading quantities and subsequent determination 

of riverine transport

• increased ability to assess seasonal and flow-related 

changes

• ability to identify and respond to developing or occurring high-nitrate events

Perhaps the most advantageous and compelling use of 

continuous nitrate data is the determination of nitrate loading. 

This study determined that while the general nitrate loading 

in the Illinois River can be determined by interpretive modeling using periodic sample collection and a continuous record 

of streamflow, there are some periods and flow conditions 

that are mischaracterized with respect to potential or actual 

nitrate loading. Typically, these are low- and high-flow periods 

that are critically important with respect to the total amount 

of nitrate transported or the potential effects on the stream 

ecosystem. The greatest differences in computed nitrate loadings based upon periodic or continuous nitrate-concentration 

data records were during periods of increased streamflow and 

greatest nitrate transport. However, while the quantitative 

differences were small in comparison to high-flow periods, 

there also were several periods in the data record of this 

study that identified substantial differences in loadings during 

low-flow periods using the two different approaches. For the 
34

Continuous Monitoring of Sediment and Nutrients in the Illinois River at Florence, Illinois, 2012–

June 2012–October 2013 period, the continuous data record 

yielded a cumulative nitrate load approximately 8.3 percent 

larger than the LOADEST-derived load.

Phosphate

Continuous in-situ phosphate data were measured using 

a WET Labs Cycle-PO4 phosphate analyzer. The analyzer 

installation and operation presented some difficulties and complications with respect to physical configuration, data communications, and performance. However, these issues were 

overcome and consistent and reliable phosphate data were 

provided by the analyzer from April through October 2013. 

In comparison to laboratory-analyzed samples, the phosphate 

concentrations provided by the analyzer were generally around 

10 to 15 percent higher than laboratory orthophosphate concentrations. This difference appears to be fairly uniform and 

can therefore be determined for each deployment location or 

instrument, and a correction can be applied to the data.

The in-situ phosphate analyzer determines only phosphate concentration. Relations between phosphate and total 

phosphorus appear to be determinable and practical with correlation coefficients greater than 0.8 when suspended sediment 

or turbidity variables are included in linear regression models 

to account for phosphorus associated with particulate matter.

Conclusion

The results from this study indicate that in-situ instruments can provide reliable, real-time continuous nutrient data 

that can greatly increase the understanding of nitrogen and 

phosphorus in Illinois water bodies, improve comprehensive 

data collection during extreme hydrologic events, and reduce 

uncertainties in nutrient-loading computations. The increased 

data record achievable with continuous monitoring allows 

for improved and more comprehensive regression algorithms 

for modeling nutrient loading. For nitrate, the continuous 

data record yielded a cumulative nitrate load approximately 

8.3 percent larger than the LOADEST-derived load. Results 

indicate that suspended sediment and orthophosphate can be 

used to predict total phosphorus. Regression equations incorporating phosphate and suspended sediment improved the 

prediction of total phosphorus (correlation coefficients greater 

than 0.8) because phosphate is associated with particulate 

matter. In-situ instruments like continuous turbidity provide a 

surrogate to determine suspended-sediment concentrations and 

loads. 

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Appendix 1. Estimating Suspended Sediment Concentration 

from In-situ Turbidity Measurements in Formazin Backscatter 

Ratio Units (FBRU)—Model Information and Data (Hach Solitax 

Instrument to Measure Turbidity)

Model form

log10SSC = –0.232 +1.080log10TurbFBRU

Predictor variable summary statistics Response variable summary statistics

Turbidity (FBRU) log10(TurbFBRU) SSC (mg/L) log10(SSC)

Minimum 15 1.176 15 1.176

1st Quartile 47 1.667 34 1.532

Median 77 1.884 59 1.767

Mean 148 1.953 140 1.876

3rd Quartile 132 2.119 130 2.114

Maximum 1,660 3.220 1,970 3.295

Model calibration

Basic data

Number of observations 140

Error degrees of freedom 138

Root mean squared error (Standard error of 

regression)

0.12169

R-squared 0.924

Adjusted R-squared 0.924

F-statistic versus constant model 1,680

p-value 3.35E-79

Non-parametric smearing bias correction factor 1.0413

Probability plot correlation coefficient 0.98985

Estimated coefficients

Estimate SE tStat pValue Lower 90 percent Upper 90 percent

(Intercept) –0.23211 0.052391 –4.4303 1.9E-05 –0.318867 –0.145352

log10TurbFBRU 1.0796 0.02631 41.034 3.35E-79 1.03605 1.12319
Appendix 1. Estimating Suspended Sediment Concentration from In-situ Turbidity Measurements in Formazin Backscatter Ratio Units (FBRU)—ModData(Hach Solitax Instrument to Measure Turbidity).—Continued [CST, central standard time; SSC, suspended-sediment concentration; mg/L, milligrams per liter; log10, base-10 logarithmic transform; mm, millimeter; FBRU, formazin backsc

Suspended sediment Solitax Date and time (CST) Station number Method code1 Concentration (SSC) Percent finer than 0.0625 mm Date and time (CST) Turbidity Regression estim

SSC (mg/L) 

(mg/L) log 

10 (mg/L) (FBRU) log10 (FBRU) 07/09/2012 17:20 05586300 920 50 1.699 07/09/2012 17:15 47 1.672 39 

07/11/2012 17:30 05586300 920 49 1.690 07/11/2012 17:30 58 1.763 49 

07/17/2012 19:00 05586300 920 47 1.672 07/17/2012 19:00 54 1.732 45 

07/18/2012 18:00 05586300 920 45 1.653 07/18/2012 18:00 48 1.681 40 

07/22/2012 16:00 05586300 920 75 1.875 07/22/2012 16:00 83 1.919 72 

07/25/2012 19:00 05586300 920 42 1.623 07/25/2012 19:00 51 1.708 43 

07/29/2012 16:00 05586300 920 53 1.724 07/29/2012 16:00 79 1.898 68 

08/01/2012 16:00 05586300 920 48 1.681 08/01/2012 16:00 63 1.799 53 

08/03/2012 08:44 05586300 30 130 2.114 08/03/2012 08:45 100 2.000 88 

08/03/2012 08:53 05586300 20 69 1.839 08/03/2012 09:00 93 1.968 81 

08/03/2012 09:20 05586300 30 72 1.857 99 08/03/2012 09:15 96 1.982 84 

08/03/2012 09:26 05586300 30 58 1.763 08/03/2012 09:30 80 1.903 69 

08/03/2012 09:55 05586300 10 97 1.987 62 08/03/2012 10:00 82 1.914 71 

08/04/2012 00:44 05586100 10 54 1.732 87 08/04/2012 00:45 51 1.708 43 

08/05/2012 18:00 05586300 920 48 1.681 08/05/2012 18:00 61 1.785 52 

08/08/2012 15:30 05586300 920 50 1.699 08/08/2012 15:30 62 1.792 53 

08/12/2012 18:00 05586300 920 52 1.716 08/12/2012 18:00 66 1.820 56 

08/15/2012 06:30 05586300 920 39 1.591 08/15/2012 06:30 51 1.708 43 

08/21/2012 14:27 05586300 30 89 1.949 97 08/21/2012 14:30 89 1.947 77 

08/21/2012 14:45 05586300 10 113 2.053 97 08/21/2012 14:45 92 1.964 80 

08/22/2012 09:28 05586100 10 63 1.799 93 08/22/2012 09:30 57 1.756 48 

08/22/2012 16:00 05586300 920 40 1.602 08/22/2012 16:00 59 1.771 50 

08/26/2012 15:00 05586300 920 26 1.415 08/26/2012 15:00 39 1.591 32 

08/29/2012 06:30 05586300 920 33 1.519 08/29/2012 06:30 45 1.653 37 

09/04/2012 15:00 05586300 920 49 1.690 09/04/2012 15:00 75 1.875 65 

09/05/2012 17:00 05586300 920 52 1.716 09/05/2012 17:00 50 1.696 41 

09/09/2012 16:00 05586300 920 47 1.672 09/09/2012 16:00 65 1.813 55 

09/11/2012 19:00 05586300 920 35 1.544 09/11/2012 19:00 53 1.724 44 

09/17/2012 15:00 05586300 920 36 1.556 09/17/2012 15:00 47 1.672 39 
Appendix 1. Estimating Suspended Sediment Concentration from In-situ Turbidity Measurements in Formazin Backscatter Ratio Units (FBRU)—ModData(Hach Solitax Instrument to Measure Turbidity).—Continued[CST, central standard time; SSC, suspended-sediment concentration; mg/L, milligrams per liter; log10, base-10 logarithmic transform; mm, millimeter; FBRU, formazin backsc

Suspended sediment Solitax

Date and time

(CST)

Station 

number

Method 

code1

Concentration (SSC) Percent finer 

than 0.0625 mm

Date and time

(CST)

Turbidity Regression estim

SSC (mg/L)

(mg/L) log 

10 (mg/L) (FBRU) log10 (FBRU)

09/19/2012 17:00 05586300 920 32 1.505 09/19/2012 17:00 50 1.699 42 09/23/2012 16:00 05586300 920 39 1.591 09/23/2012 16:00 59 1.771 50 09/26/2012 19:00 05586300 920 54 1.732 09/26/2012 19:00 77 1.886 66 09/27/2012 09:30 05586300 30 56 1.748 09/27/2012 09:30 80 1.903 69 09/30/2012 18:30 05586300 920 30 1.477 09/30/2012 18:30 66 1.820 56 10/03/2012 17:30 05586300 920 33 1.519 10/03/2012 17:30 36 1.556 29 10/07/2012 18:00 05586300 920 33 1.519 10/07/2012 18:00 43 1.633 35 10/10/2012 19:00 05586300 920 34 1.531 10/10/2012 19:00 33 1.519 27 10/14/2012 15:00 05586300 920 46 1.663 10/14/2012 15:00 68 1.833 58 10/16/2012 16:30 05586300 920 56 1.748 10/16/2012 16:30 74 1.869 64 10/17/2012 18:30 05586300 920 32 1.505 10/17/2012 18:30 43 1.633 35 

10/18/2012 15:55 05586300 30 31 1.491 10/18/2012 16:00 40 1.602 33 

10/18/2012 15:58 05586300 30 33 1.519 10/18/2012 16:00 40 1.602 33 

10/18/2012 16:01 05586300 30 30 1.477 10/18/2012 16:00 40 1.602 33 

10/18/2012 16:06 05586300 20 34 1.531 98 10/18/2012 16:00 40 1.602 33 

10/18/2012 16:18 05586300 30 37 1.568 10/18/2012 16:15 41 1.613 34 

10/21/2012 18:00 05586300 920 24 1.380 10/21/2012 18:00 28 1.447 22 

10/24/2012 18:00 05586300 920 34 1.531 10/24/2012 18:00 61 1.785 52 

10/28/2012 17:00 05586300 920 40 1.602 10/28/2012 17:00 32 1.505 26 

10/31/2012 10:00 05586300 30 23 1.362 10/31/2012 10:00 31 1.491 25 

10/31/2012 15:53 05586100 15 25 1.398 98 10/31/2012 16:00 28 1.447 22 

10/31/2012 17:00 05586300 920 26 1.415 10/31/2012 17:00 25 1.398 20 

11/05/2012 15:00 05586300 920 17 1.230 11/05/2012 15:00 21 1.322 16 

11/07/2012 15:00 05586300 920 15 1.176 11/07/2012 15:00 15 1.176 11 

11/13/2012 15:00 05586300 920 21 1.322 11/13/2012 15:00 34 1.531 27 

11/14/2012 17:00 05586300 920 32 1.505 11/14/2012 17:00 36 1.556 29 

11/27/2012 09:40 05586300 30 35 1.544 11/27/2012 09:45 41 1.613 34 

11/27/2012 15:00 05586300 920 29 1.462 11/27/2012 15:00 41 1.613 34 

12/02/2012 15:00 05586300 920 18 1.255 12/02/2012 15:00 24 1.380 19 
Appendix 1. Estimating Suspended Sediment Concentration from In-situ Turbidity Measurements in Formazin Backscatter Ratio Units (FBRU)—ModData(Hach Solitax Instrument to Measure Turbidity).—Continued [CST, central standard time; SSC, suspended-sediment concentration; mg/L, milligrams per liter; log10, base-10 logarithmic transform; mm, millimeter; FBRU, formazin backsc

Suspended sediment Solitax Date and time (CST) Station number Method code1 Concentration (SSC) Percent finer than 0.0625 mm Date and time (CST) Turbidity Regression estim

SSC (mg/L) 

(mg/L) log 

10 (mg/L) (FBRU) log10 (FBRU) 12/04/2012 15:00 05586300 920 20 1.301 12/04/2012 15:00 30 1.477 24 

12/08/2012 07:00 05586300 920 18 1.255 12/08/2012 07:00 26 1.415 21 

12/10/2012 15:00 05586300 920 26 1.415 12/10/2012 15:00 49 1.690 41 

12/12/2012 12:30 05586300 70 47 1.672 12/12/2012 12:30 65 1.813 55 

12/13/2012 09:30 05586300 920 31 1.491 12/13/2012 09:30 46 1.663 38 

12/13/2012 09:37 05586300 20 37 1.568 12/13/2012 09:30 46 1.663 38 

12/13/2012 09:45 05586300 920 29 1.462 12/13/2012 09:45 54 1.732 45 

12/13/2012 09:50 05586300 920 38 1.580 12/13/2012 09:45 54 1.732 45 

12/14/2012 16:47 05586100 10 26 1.415 99 12/14/2012 16:45 31 1.491 25 

12/15/2012 07:30 05586300 920 21 1.322 12/15/2012 07:30 29 1.462 23 

12/18/2012 15:10 05586300 920 18 1.255 12/18/2012 15:15 29 1.462 23 

12/23/2012 16:00 05586300 920 31 1.491 12/23/2012 16:00 41 1.613 34 

12/26/2012 15:00 05586300 920 62 1.792 12/26/2012 15:00 97 1.987 85 

12/30/2012 15:00 05586300 920 27 1.431 12/30/2012 15:00 45 1.653 37 

01/02/2013 15:00 05586300 920 25 1.398 01/02/2013 15:00 40 1.602 33 

01/08/2013 09:45 05586300 70 23 1.362 01/08/2013 09:45 39 1.591 32 

01/09/2013 16:50 05586300 920 22 1.342 01/09/2013 16:45 41 1.613 34 

01/13/2013 15:00 05586300 920 63 1.799 01/13/2013 15:00 104 2.017 92 

01/15/2013 15:00 05586300 920 59 1.771 01/15/2013 15:00 86 1.935 75 

01/28/2013 15:00 05586300 920 28 1.447 01/28/2013 15:00 49 1.690 41 

01/30/2013 17:45 05586300 920 843 2.926 01/30/2013 17:45 718 2.856 740 

02/03/2013 16:30 05586300 920 405 2.607 02/03/2013 16:30 372 2.571 364 

02/04/2013 12:40 05586300 70 355 2.550 89 02/04/2013 12:45 295 2.470 283 

02/05/2013 10:55 05586300 920 440 2.643 02/05/2013 11:00 266 2.425 253 

02/05/2013 11:09 05586300 20 375 2.574 02/05/2013 11:15 267 2.427 254 

02/05/2013 11:22 05586300 920 382 2.582 02/05/2013 11:15 267 2.427 254 

02/05/2013 15:00 05586300 920 361 2.558 02/05/2013 15:00 253 2.403 240 

02/06/2013 15:00 05586300 920 328 2.516 02/06/2013 15:00 191 2.281 177 

02/10/2013 16:30 05586300 920 122 2.086 02/10/2013 16:30 147 2.167 133 
Appendix 1. Estimating Suspended Sediment Concentration from In-situ Turbidity Measurements in Formazin Backscatter Ratio Units (FBRU)—ModData(Hach Solitax Instrument to Measure Turbidity).—Continued[CST, central standard time; SSC, suspended-sediment concentration; mg/L, milligrams per liter; log10, base-10 logarithmic transform; mm, millimeter; FBRU, formazin backsc

Suspended sediment Solitax

Date and time

(CST)

Station 

number

Method 

code1

Concentration (SSC) Percent finer 

than 0.0625 mm

Date and time

(CST)

Turbidity Regression estim

SSC (mg/L)

(mg/L) log 

10 (mg/L) (FBRU) log10 (FBRU)

02/13/2013 16:31 05586100 10 372 2.571 98 02/13/2013 16:30 407 2.610 401 

02/17/2013 15:00 05586300 920 373 2.572 02/17/2013 15:00 182 2.260 168 

02/19/2013 11:00 05586300 70 137 2.137 98 02/19/2013 11:00 155 2.190 141 

02/19/2013 15:00 05586300 920 294 2.468 02/19/2013 15:00 157 2.196 143 

02/25/2013 17:00 05586300 920 131 2.117 02/25/2013 17:00 124 2.093 111 

03/06/2013 16:00 05586300 920 309 2.490 outlier not used in regression model

03/08/2013 12:30 05586300 15 26 1.415 outlier not used in regression model

03/08/2013 13:00 05586300 70 75 1.875 03/08/2013 13:00 77 1.886 66 

03/08/2013 13:20 05586300 10 80 1.903 03/08/2013 13:15 72 1.857 62 

03/08/2013 14:52 05586100 10 79 1.898 95 03/08/2013 14:45 72 1.857 62 

03/11/2013 17:30 05586300 920 826 2.917 03/11/2013 17:30 700 2.845 720 

03/13/2013 15:00 05586300 920 728 2.862 03/13/2013 15:00 590 2.771 598 

03/20/2013 16:00 05586300 920 154 2.188 03/20/2013 16:00 123 2.090 110 

03/21/2013 11:30 05586300 50 113 2.053 03/21/2013 11:30 117 2.068 104 

03/21/2013 14:47 05586100 10 121 2.083 75 03/21/2013 14:45 108 2.033 96 

03/25/2013 18:30 05586300 920 88 1.944 03/25/2013 18:30 76 1.881 65 

03/27/2013 15:00 05586300 920 76 1.881 03/27/2013 15:00 64 1.806 54 

04/01/2013 16:00 05586300 920 95 1.978 04/01/2013 16:00 82 1.914 71 

04/03/2013 17:00 05586300 920 109 2.037 04/03/2013 17:00 99 1.996 87 

04/11/2013 12:56 05586100 10 1,970 3.294 100 04/11/2013 13:00 1,660 3.220 1,828 

04/15/2013 18:00 05586300 920 bottle broken at lab

04/17/2013 17:30 05586300 920 bottle broken at lab 

04/21/2013 15:30 05586300 920 bottle broken at lab

04/22/2013 19:30 05586300 920 409 2.612 04/22/2013 19:30 506 2.704 507 

04/23/2013 10:53 05586300 920 326 2.513 91 04/23/2013 11:00 477 2.679 476 

04/23/2013 11:19 05586300 10 320 2.505 89 04/23/2013 11:15 470 2.672 468 

04/23/2013 11:23 05586300 10 280 2.447 87 04/23/2013 11:30 470 2.672 468 

04/23/2013 11:33 05586300 920 309 2.490 04/23/2013 11:30 470 2.672 468 

04/24/2013 14:50 05586300 10 274 2.438 04/24/2013 14:45 385 2.585 377 
Appendix 1. Estimating Suspended Sediment Concentration from In-situ Turbidity Measurements in Formazin Backscatter Ratio Units (FBRU)—ModData(Hach Solitax Instrument to Measure Turbidity).—Continued [CST, central standard time; SSC, suspended-sediment concentration; mg/L, milligrams per liter; log10, base-10 logarithmic transform; mm, millimeter; FBRU, formazin backsc

Suspended sediment Solitax Date and time (CST) Station number Method code1 Concentration (SSC) Percent finer than 0.0625 mm Date and time (CST) Turbidity Regression estim

SSC (mg/L) 

(mg/L) log 

10 (mg/L) (FBRU) log10 (FBRU) 04/24/2013 17:33 05586100 10 249 2.396 89 04/24/2013 17:30 373 2.572 365 

04/24/2013 18:00 05586300 920 306 2.486 04/24/2013 18:00 371 2.569 363 

04/25/2013 15:30 05586300 50 228 2.358 04/25/2013 15:30 281 2.449 269 

04/26/2013 15:57 05586100 20 128 2.107 88 04/26/2013 16:00 203 2.308 189 

04/26/2013 16:06 05586100 20 146 2.164 04/26/2013 16:00 203 2.308 189 

04/28/2013 19:00 05586300 920 165 2.217 04/28/2013 19:00 135 2.130 122 

04/29/2013 11:20 05586300 50 75 1.875 04/29/2013 11:15 128 2.107 115 

04/30/2013 19:00 05586300 920 71 1.851 04/30/2013 19:00 111 2.045 99 

05/01/2013 20:00 05586300 920 64 1.806 05/01/2013 20:00 108 2.033 96 

05/05/2013 20:00 05586300 920 68 1.833 05/05/2013 20:00 117 2.068 104 

05/07/2013 20:00 05586300 920 178 2.250 05/07/2013 20:00 109 2.037 97 

05/08/2013 11:26 05586100 10 82 1.914 95 05/08/2013 11:30 101 2.004 89 

05/08/2013 14:00 05586300 50 60 1.778 05/08/2013 14:00 104 2.017 92 

05/22/2013 11:43 05586100 10 119 2.076 97 05/22/2013 11:45 82 1.912 71 

05/28/2013 17:00 05586300 920 946 2.976 97 05/28/2013 17:00 1,057 3.024 1,122 

05/29/2013 13:40 05586300 50 493 2.693 05/29/2013 13:45 577 2.761 584 

06/01/2013 09:30 05586300 920 273 2.436 98 06/01/2013 09:30 415 2.618 409 

06/03/2013 17:30 05586300 920 81 1.908 06/03/2013 17:30 154 2.188 140 

06/05/2013 13:01 05586100 10 122 2.086 96 06/05/2013 13:00 130 2.114 117 

06/05/2013 19:30 05586300 920 75 1.875 06/05/2013 19:30 133 2.124 120 

06/09/2013 19:00 05586300 920 57 1.756 06/09/2013 19:00 91 1.959 80 

06/11/2013 20:00 05586300 920 40 1.602 06/11/2013 20:00 59 1.771 50 

06/16/2013 15:00 05586300 920 425 2.628 06/16/2013 15:00 398 2.600 391 

06/18/2013 17:20 05586300 50 74 1.869 99 06/18/2013 17:15 101 2.004 89 

06/19/2013 09:44 05586100 10 64 1.806 94 06/19/2013 09:45 80 1.903 69 

06/19/2013 17:30 05586300 920 56 1.748 06/19/2013 17:30 68 1.833 58 

06/24/2013 18:30 05586300 920 80 1.903 06/24/2013 18:30 97 1.987 85 

06/26/2013 20:00 05586300 920 130 2.114 06/26/2013 20:00 129 2.111 116 

06/30/2013 18:30 05586300 920 193 2.286 06/30/2013 18:30 193 2.286 179 

1Method code for sampling methods: Equal Width Increment (EWI)-10, EWI non-isokinetic-15, Equal Discharge Increment (EDI)-20, Single-Vertical-30, Point-50, Fixed Si
Appendix 2. Estimating Suspended Sediment Concentration 

from In-situ Turbidity Measurements in Formazin Nephelometric 

Units (FNU)—Model Information and Data (YSI 6136 Instrument 

to Measure Turbidity)

Model form

log10SSC =1.122log10TurbFNU–0.055

Predictor variable summary statistics Response variable summary statistics

Turbidity (FNU) log10(TurbFNU) SSC (mg/L) log10(SSC)

Minimum 14 1.146 15 1.176

1st Quartile 29 1.462 34 1.532

Median 41 1.613 59 1.767

Mean 85 1.720 140 1.876

3rd Quartile 75 1.874 130 2.114

Maximum 1,130 3.053 1,970 3.295

Model calibration

Basic data

Number of observations 140

Error degrees of freedom 138

Root mean squared error (Standard error of 

regression)

0.1512

R-squared 0.883

Adjusted R-squared 0.882

F-statistic versus constant model 1,040

p-value 3.54E-66

Non-parametric smearing bias correction factor 1.0627

Probability plot correlation coefficient 0.99657

Estimated coefficients

Estimate SE tStat pValue Lower 90 percent Upper 90 percent

(Intercept) –0.054624 0.061154 –0.89323 0.37329 –0.155893 0.0466442

log10TurbFNU 1.1223 0.034768 32.28 3.54E-66 1.06475 1.1799
Appendix 2. Estimating Suspended Sediment Concentration from In-situ Turbidity Measurements in Formazin Nephelometric Units (FNU)—Model In(YSI 6136 Instrument to Measure Turbidity). [CST, central standard time; SSC, suspended-sediment concentration; mg/L, milligrams per liter; log10, base-10 logarithmic transform; mm, millimeter; FNU, formazin nephelom

Suspended sediment YSI Date and time (CST) Station number Method code1 Concentration (SSC) Percent finer than 0.0625 mm Date and time (CST) Turbidity Regression estim

SSC (mg/L) 

(mg/L) log 

10 (mg/L) (FNU) log10 (FNU) 07/09/2012 17:20 05586300 920 50 1.699 07/09/2012 17:26 28 1.447 39 

07/11/2012 17:30 05586300 920 49 1.690 07/11/2012 17:26 34 1.531 49 

07/17/2012 19:00 05586300 920 47 1.672 07/17/2012 18:56 33 1.519 47 

07/18/2012 18:00 05586300 920 45 1.653 07/18/2012 17:56 30 1.477 43 

07/22/2012 16:00 05586300 920 75 1.875 07/22/2012 15:56 58 1.763 89 

07/25/2012 19:00 05586300 920 42 1.623 07/25/2012 18:56 33 1.516 47 

07/29/2012 16:00 05586300 920 53 1.724 07/29/2012 15:56 40 1.602 59 

08/01/2012 16:00 05586300 920 48 1.681 08/01/2012 15:56 41 1.613 61 

08/03/2012 08:44 05586300 30 130 2.114 08/03/2012 08:41 53 1.724 81 

08/03/2012 08:53 05586300 20 69 1.839 08/03/2012 08:56 47 1.672 71 

08/03/2012 09:20 05586300 30 72 1.857 99 08/03/2012 09:26 45 1.653 67 

08/03/2012 09:26 05586300 30 58 1.763 08/03/2012 09:26 45 1.653 67 

08/03/2012 09:55 05586300 10 97 1.987 62 08/03/2012 09:56 39 1.591 57 

08/04/2012 00:44 05586100 10 54 1.732 87 08/04/2012 00:41 26 1.415 36 

08/05/2012 18:00 05586300 920 48 1.681 08/05/2012 17:56 34 1.531 49 

08/08/2012 15:30 05586300 920 50 1.699 08/08/2012 15:26 32 1.505 46 

08/12/2012 18:00 05586300 920 52 1.716 08/12/2012 17:56 38 1.580 56 

08/15/2012 06:30 05586300 920 39 1.591 08/15/2012 06:26 32 1.505 46 

08/21/2012 14:27 05586300 30 89 1.949 97 08/21/2012 14:26 53 1.724 81 

08/21/2012 14:45 05586300 10 113 2.053 97 08/21/2012 14:41 44 1.643 66 

08/22/2012 09:28 05586100 10 63 1.799 93 08/22/2012 09:26 28 1.447 39 

08/22/2012 16:00 05586300 920 40 1.602 08/22/2012 15:56 38 1.580 56 

08/26/2012 15:00 05586300 920 26 1.415 08/26/2012 14:56 32 1.505 46 

08/29/2012 06:30 05586300 920 33 1.519 08/29/2012 06:26 40 1.602 59 

09/04/2012 15:00 05586300 920 49 1.690 09/04/2012 14:56 41 1.613 61 

09/05/2012 17:00 05586300 920 52 1.716 09/05/2012 16:56 33 1.519 47 

09/09/2012 16:00 05586300 920 47 1.672 09/09/2012 15:56 35 1.544 51 

09/11/2012 19:00 05586300 920 35 1.544 09/11/2012 18:56 28 1.447 39 

09/17/2012 15:00 05586300 920 36 1.556 09/17/2012 14:56 30 1.477 43 
Appendix 2. Estimating Suspended Sediment Concentration from In-situ Turbidity Measurements in Formazin Nephelometric Units (FNU)—Model In(YSI 6136 Instrument to Measure Turbidity).—Continued[CST, central standard time; SSC, suspended-sediment concentration; mg/L, milligrams per liter; log10, base-10 logarithmic transform; mm, millimeter; FNU, formazin nephelom

Suspended sediment YSI

Date and time

(CST)

Station 

number

Method 

code1

Concentration (SSC) Percent finer 

than 0.0625 mm

Date and time

(CST)

Turbidity Regression estim

SSC (mg/L)

(mg/L) log 

10 (mg/L) (FNU) log10 (FNU)

09/19/2012 17:00 05586300 920 32 1.505 09/19/2012 16:56 30 1.477 43 

09/23/2012 16:00 05586300 920 39 1.591 09/23/2012 15:56 30 1.477 43 

09/26/2012 19:00 05586300 920 54 1.732 09/26/2012 18:56 42 1.623 62 

09/27/2012 09:30 05586300 30 56 1.748 09/27/2012 09:26 48 1.681 72 

09/30/2012 18:30 05586300 920 30 1.477 09/30/2012 18:26 31 1.491 44 

10/03/2012 17:30 05586300 920 33 1.519 10/03/2012 17:26 19 1.279 26 

10/07/2012 18:00 05586300 920 33 1.519 10/07/2012 17:56 24 1.380 33 

10/10/2012 19:00 05586300 920 34 1.531 10/10/2012 18:56 23 1.362 32 

10/14/2012 15:00 05586300 920 46 1.663 10/14/2012 14:56 38 1.574 55 

10/16/2012 16:30 05586300 920 56 1.748 10/16/2012 16:26 51 1.707 77 

10/17/2012 18:30 05586300 920 32 1.505 10/17/2012 18:26 28 1.447 39 

10/18/2012 15:55 05586300 30 31 1.491 10/18/2012 15:56 29 1.462 41 

10/18/2012 15:58 05586300 30 33 1.519 10/18/2012 15:56 29 1.462 41 

10/18/2012 16:01 05586300 30 30 1.477 10/18/2012 15:56 29 1.462 41 

10/18/2012 16:06 05586300 20 34 1.531 98 10/18/2012 16:11 25 1.398 35 

10/18/2012 16:18 05586300 30 37 1.568 10/18/2012 16:11 25 1.398 35 

10/21/2012 18:00 05586300 920 24 1.380 10/21/2012 17:56 21 1.322 29 

10/24/2012 18:00 05586300 920 34 1.531 10/24/2012 17:56 40 1.602 59 

10/28/2012 17:00 05586300 920 40 1.602 10/28/2012 16:56 48 1.686 73 

10/31/2012 10:00 05586300 30 23 1.362 10/31/2012 09:56 35 1.544 51 

10/31/2012 15:53 05586100 15 25 1.398 98 10/31/2012 15:56 34 1.528 49 

10/31/2012 17:00 05586300 920 26 1.415 10/31/2012 16:56 34 1.525 48 

11/05/2012 15:00 05586300 920 17 1.230 11/05/2012 14:56 24 1.380 33 

11/07/2012 15:00 05586300 920 15 1.176 11/07/2012 14:56 25 1.398 35 

11/13/2012 15:00 05586300 920 21 1.322 11/13/2012 14:56 18 1.255 24 

11/14/2012 17:00 05586300 920 32 1.505 11/14/2012 16:56 25 1.398 35 

11/27/2012 09:40 05586300 30 35 1.544 11/27/2012 09:41 26 1.415 36 

11/27/2012 15:00 05586300 920 29 1.462 11/27/2012 14:56 27 1.433 38 

12/02/2012 15:00 05586300 920 18 1.255 12/02/2012 14:56 23 1.362 32 
Appendix 2. Estimating Suspended Sediment Concentration from In-situ Turbidity Measurements in Formazin Nephelometric Units (FNU)—Model In(YSI 6136 Instrument to Measure Turbidity).—Continued [CST, central standard time; SSC, suspended-sediment concentration; mg/L, milligrams per liter; log10, base-10 logarithmic transform; mm, millimeter; FNU, formazin nephelom

Suspended sediment YSI Date and time (CST) Station number Method code1 Concentration (SSC) Percent finer than 0.0625 mm Date and time (CST) Turbidity Regression estim

SSC (mg/L) 

(mg/L) log 

10 (mg/L) (FNU) log10 (FNU) 12/04/2012 15:00 05586300 920 20 1.301 12/04/2012 14:56 26 1.415 36 

12/08/2012 07:00 05586300 920 18 1.255 12/08/2012 06:56 29 1.462 41 

12/10/2012 15:00 05586300 920 26 1.415 12/10/2012 14:56 38 1.580 56 

12/12/2012 12:30 05586300 70 47 1.672 12/12/2012 12:26 22 1.345 30 

12/13/2012 09:30 05586300 920 31 1.491 12/13/2012 09:26 24 1.380 33 

12/13/2012 09:37 05586300 20 37 1.568 12/13/2012 09:41 22 1.342 30 

12/13/2012 09:45 05586300 920 29 1.462 12/13/2012 09:41 22 1.342 30 

12/13/2012 09:50 05586300 920 38 1.580 12/13/2012 09:56 24 1.380 33 

12/14/2012 16:47 05586100 10 26 1.415 99 12/14/2012 16:41 16 1.204 21 

12/15/2012 07:30 05586300 920 21 1.322 12/15/2012 07:26 15 1.176 20 

12/18/2012 15:10 05586300 920 18 1.255 12/18/2012 15:11 14 1.146 18 

12/23/2012 16:00 05586300 920 31 1.491 12/23/2012 15:56 20 1.301 27 

12/26/2012 15:00 05586300 920 62 1.792 12/26/2012 14:56 48 1.681 72 

12/30/2012 15:00 05586300 920 27 1.431 12/30/2012 14:56 22 1.342 30 

01/02/2013 15:00 05586300 920 25 1.398 01/02/2013 14:56 16 1.204 21 

01/08/2013 09:45 05586300 70 23 1.362 01/08/2013 09:41 17 1.230 23 

01/09/2013 16:50 05586300 920 22 1.342 01/09/2013 16:56 18 1.255 24 

01/13/2013 15:00 05586300 920 63 1.799 01/13/2013 14:56 55 1.740 84 

01/15/2013 15:00 05586300 920 59 1.771 01/15/2013 14:56 44 1.643 66 

01/28/2013 15:00 05586300 920 28 1.447 01/28/2013 14:56 22 1.342 30 

01/30/2013 17:45 05586300 920 843 2.926 01/30/2013 17:41 450 2.653 890 

02/03/2013 16:30 05586300 920 405 2.607 02/03/2013 16:26 220 2.342 399 

02/04/2013 12:40 05586300 70 355 2.550 89 02/04/2013 12:41 150 2.176 259 

02/05/2013 10:55 05586300 920 440 2.643 02/05/2013 10:56 140 2.146 240 

02/05/2013 11:09 05586300 20 375 2.574 02/05/2013 11:11 130 2.114 221 

02/05/2013 11:22 05586300 920 382 2.582 02/05/2013 11:26 140 2.146 240 

02/05/2013 15:00 05586300 920 361 2.558 02/05/2013 14:56 130 2.114 221 

02/06/2013 15:00 05586300 920 328 2.516 02/06/2013 14:56 100 2.000 165 

02/10/2013 16:30 05586300 920 122 2.086 02/10/2013 16:26 75 1.875 119 
Appendix 2. Estimating Suspended Sediment Concentration from In-situ Turbidity Measurements in Formazin Nephelometric Units (FNU)—Model In(YSI 6136 Instrument to Measure Turbidity).—Continued[CST, central standard time; SSC, suspended-sediment concentration; mg/L, milligrams per liter; log10, base-10 logarithmic transform; mm, millimeter; FNU, formazin nephelom

Suspended sediment YSI

Date and time

(CST)

Station 

number

Method 

code1

Concentration (SSC) Percent finer 

than 0.0625 mm

Date and time

(CST)

Turbidity Regression estim

SSC (mg/L)

(mg/L) log 

10 (mg/L) (FNU) log10 (FNU)

02/13/2013 16:31 05586100 10 372 2.571 98 02/13/2013 16:26 210 2.322 379 

02/17/2013 15:00 05586300 920 373 2.572 02/17/2013 14:56 96 1.982 157 

02/19/2013 11:00 05586300 70 137 2.137 98 02/19/2013 10:56 84 1.924 135 

02/19/2013 15:00 05586300 920 294 2.468 02/19/2013 14:56 80 1.903 128 

02/25/2013 17:00 05586300 920 131 2.117 02/25/2013 16:56 63 1.799 98 

03/06/2013 16:00 05586300 920 309 2.490 outlier not used in regression model

03/08/2013 12:30 05586300 15 26 1.415 outlier not used in regression model

03/08/2013 13:00 05586300 70 75 1.875 03/08/2013 12:56 41 1.613 61 

03/08/2013 13:20 05586300 10 80 1.903 03/08/2013 13:26 39 1.591 57 

03/08/2013 14:52 05586100 10 79 1.898 95 03/08/2013 14:56 40 1.602 59 

03/11/2013 17:30 05586300 920 826 2.917 03/11/2013 17:26 400 2.602 780 

03/13/2013 15:00 05586300 920 728 2.862 03/13/2013 14:56 320 2.505 607 

03/20/2013 16:00 05586300 920 154 2.188 03/20/2013 15:56 67 1.826 105 

03/21/2013 11:30 05586300 50 113 2.053 03/21/2013 11:26 58 1.763 89 

03/21/2013 14:47 05586100 10 121 2.083 75 03/21/2013 14:41 58 1.763 89 

03/25/2013 18:30 05586300 920 88 1.944 03/25/2013 18:26 41 1.613 61 

03/27/2013 15:00 05586300 920 76 1.881 03/27/2013 14:56 35 1.544 51 

04/01/2013 16:00 05586300 920 95 1.978 04/01/2013 15:56 43 1.633 64 

04/03/2013 17:00 05586300 920 109 2.037 04/03/2013 16:56 56 1.748 86 

04/11/2013 12:56 05586100 10 1,970 3.294 100 04/11/2013 12:56 1,130 3.053 2,502 

04/15/2013 18:00 05586300 920 bottle broken at lab

04/17/2013 17:30 05586300 920 bottle broken at lab

04/21/2013 15:30 05586300 920 bottle broken at lab

04/22/2013 19:30 05586300 920 409 2.612 04/22/2013 19:26 260 2.415 481 

04/23/2013 10:53 05586300 920 326 2.513 91 04/23/2013 10:56 230 2.362 419 

04/23/2013 11:19 05586300 10 320 2.505 89 04/23/2013 11:26 240 2.380 440 

04/23/2013 11:23 05586300 10 280 2.447 87 04/23/2013 11:26 240 2.380 440 

04/23/2013 11:33 05586300 920 309 2.490 04/23/2013 11:26 240 2.380 440 

04/24/2013 14:50 05586300 10 274 2.438 04/24/2013 14:56 210 2.322 379 
Appendix 2. Estimating Suspended Sediment Concentration from In-situ Turbidity Measurements in Formazin Nephelometric Units (FNU)—Model In(YSI 6136 Instrument to Measure Turbidity).—Continued [CST, central standard time; SSC, suspended-sediment concentration; mg/L, milligrams per liter; log10, base-10 logarithmic transform; mm, millimeter; FNU, formazin nephelom

Suspended sediment YSI Date and time (CST) Station number Method code1 Concentration (SSC) Percent finer than 0.0625 mm Date and time (CST) Turbidity Regression estim

SSC (mg/L) 

(mg/L) log 

10 (mg/L) (FNU) log10 (FNU) 04/24/2013 17:33 05586100 10 249 2.396 89 04/24/2013 17:26 210 2.322 379 

04/24/2013 18:00 05586300 920 306 2.486 04/24/2013 17:56 220 2.342 399 04/25/2013 15:30 05586300 50 228 2.358 04/25/2013 15:26 160 2.204 279 04/26/2013 15:57 05586100 20 128 2.107 88 04/26/2013 15:56 120 2.079 202 04/26/2013 16:06 05586100 20 146 2.164 04/26/2013 16:11 120 2.079 202 04/28/2013 19:00 05586300 920 165 2.217 04/28/2013 18:56 79 1.898 126 04/29/2013 11:20 05586300 50 75 1.875 04/29/2013 11:26 75 1.874 119 04/30/2013 19:00 05586300 920 71 1.851 04/30/2013 18:56 67 1.826 105 05/01/2013 20:00 05586300 920 64 1.806 05/01/2013 19:56 64 1.806 100 05/05/2013 20:00 05586300 920 68 1.833 05/05/2013 19:56 69 1.839 109 

05/07/2013 20:00 05586300 920 178 2.250 05/07/2013 19:56 65 1.813 102 

05/08/2013 11:26 05586100 10 82 1.914 95 05/08/2013 11:26 62 1.792 96 

05/08/2013 14:00 05586300 50 60 1.778 05/08/2013 13:56 64 1.806 100 

05/22/2013 11:43 05586100 10 119 2.076 97 05/22/2013 11:41 45 1.653 67 

05/28/2013 17:00 05586300 920 946 2.976 97 05/28/2013 16:56 676 2.830 1406 

05/29/2013 13:40 05586300 50 493 2.693 05/29/2013 13:41 320 2.505 607 

06/01/2013 09:30 05586300 920 273 2.436 98 06/01/2013 09:26 190 2.279 338 

06/03/2013 17:30 05586300 920 81 1.908 06/03/2013 17:26 88 1.944 143 

06/05/2013 13:01 05586100 10 122 2.086 96 06/05/2013 12:56 77 1.886 123 

06/05/2013 19:30 05586300 920 75 1.875 06/05/2013 19:26 74 1.869 117 

06/09/2013 19:00 05586300 920 57 1.756 06/09/2013 18:56 51 1.708 77 

06/11/2013 20:00 05586300 920 40 1.602 06/11/2013 19:56 34 1.531 49 

06/16/2013 15:00 05586300 920 425 2.628 06/16/2013 14:56 250 2.398 460 

06/18/2013 17:20 05586300 50 74 1.869 99 06/18/2013 17:26 53 1.724 81 

06/19/2013 09:44 05586100 10 64 1.806 94 06/19/2013 09:41 42 1.623 62 

06/19/2013 17:30 05586300 920 56 1.748 06/19/2013 17:26 38 1.580 56 

06/24/2013 18:30 05586300 920 80 1.903 06/24/2013 18:26 53 1.724 81 

06/26/2013 20:00 05586300 920 130 2.114 06/26/2013 19:56 71 1.851 112 

06/30/2013 18:30 05586300 920 193 2.286 06/30/2013 18:26 96 1.982 157 

1Method code for sampling methods: Equal Width Increment (EWI)-10, EWI non-isokinetic-15, Equal Discharge Increment (EDI)-20, Single-Vertical-30, Point-50, Fixed Si
Appendix 3. Estimating Suspended Sediment Concentration 

from Streamflow—Model Information and Data

Model form

log10SSC = –0.551 +0.578log10Discharge

Predictor variable summary statistics Response variable summary statistics

Streamflow (ft3

/s) log10(Streamflow) SSC (mg/L) log10(SSC)

Minimum 3,370 3.528 15 1.176

1st Quartile 5,345 3.728 33 1.519

Median 8,190 3.913 54 1.732

Mean 28,890 4.154 139 1.852

3rd Quartile 40,950 4.612 129 2.111

Maximum 112,000 5.049 1,970 3.295

Model calibration

Basic data

Number of observations 108

Error degrees of freedom 106

Root mean squared error (Standard error of 

regression)

0.34108

R-squared 0.428

Adjusted R-squared 0.423

F-statistic versus constant model 79.3

p-value 1.62E-14

Non-parametric smearing bias correction factor 1.4778

Probability plot correlation coefficient 0.97057

Estimated coefficients

Estimate SE tStat pValue Lower 90 percent Upper 90 percent

(Intercept) –0.55057 0.27176 –2.0259 0.045284 –1.00152 –0.09962

log10Discharge 0.57837 0.064941 8.9061 1.6172E-14 0.470609 0.686128
Appendix 3. Estimating Suspended Sediment Concentration from Streamflow—Model Information and Data. [CST, central standard time; SSC, suspended-sediment concentration; mg/L, milligrams per liter; log10, base-10 logarithmic transform; mm, millimeter; ft3/s, cubic feet per secon

Suspended sediment Date and time (CST) Station number Method code1 Concentration (SSC) Percent finer than 0.0625 mm Date and time (CST) Streamflow Regression esti

SSC (mg/L) 

(mg/L) log 

10 (mg/L) (ft3/s) log10 (ft3/s) 07/09/2012 17:20 05586300 920 50 1.699 07/09/2012 17:00 5,690 3.755 62 

07/17/2012 19:00 05586300 920 47 1.672 07/17/2012 19:00 4,460 3.649 54 

07/18/2012 18:00 05586300 920 45 1.653 07/18/2012 18:00 4,510 3.654 54 

07/22/2012 16:00 05586300 920 75 1.875 07/22/2012 16:00 7,580 3.880 73 

07/25/2012 19:00 05586300 920 42 1.623 07/25/2012 19:00 4,660 3.668 55 

07/29/2012 16:00 05586300 920 53 1.724 07/29/2012 16:00 6,460 3.810 66 

08/01/2012 16:00 05586300 920 48 1.681 08/01/2012 16:00 4,710 3.673 55 

08/03/2012 08:44 05586300 30 130 2.114 08/03/2012 09:00 4,830 3.684 56 

08/03/2012 08:53 05586300 20 69 1.839 08/03/2012 09:00 4,830 3.684 56 

08/03/2012 09:20 05586300 30 72 1.857 99 08/03/2012 09:00 4,830 3.684 56 

08/03/2012 09:55 05586300 10 97 1.987 62 08/03/2012 10:00 4,860 3.687 56 

08/04/2012 00:44 05586100 10 54 1.732 87 08/04/2012 01:00 4,540 3.657 54 

08/05/2012 18:00 05586300 920 48 1.681 08/05/2012 18:00 3,370 3.528 46 

08/12/2012 18:00 05586300 920 52 1.716 08/12/2012 18:00 5,910 3.772 63 

08/21/2012 14:45 05586300 10 113 2.053 97 08/21/2012 15:00 4,460 3.649 54 

08/22/2012 16:00 05586300 920 40 1.602 08/22/2012 16:00 4,540 3.657 54 

08/26/2012 15:00 05586300 920 26 1.415 08/26/2012 15:00 5,430 3.735 60 

09/04/2012 15:00 05586300 920 49 1.690 09/04/2012 15:00 5,940 3.774 63 

09/05/2012 17:00 05586300 920 52 1.716 09/05/2012 17:00 5,510 3.741 61 

09/09/2012 16:00 05586300 920 47 1.672 09/09/2012 16:00 6,700 3.826 68 

09/11/2012 19:00 05586300 920 35 1.544 09/11/2012 19:00 5,400 3.732 60 

09/17/2012 15:00 05586300 920 36 1.556 09/17/2012 15:00 6,070 3.783 64 

09/19/2012 17:00 05586300 920 32 1.505 09/19/2012 17:00 6,520 3.814 67 

09/23/2012 16:00 05586300 920 39 1.591 09/23/2012 16:00 5,290 3.723 59 

09/26/2012 19:00 05586300 920 54 1.732 09/26/2012 19:00 5,940 3.774 63 

10/07/2012 18:00 05586300 920 33 1.519 10/07/2012 18:00 5,110 3.708 58 

10/10/2012 19:00 05586300 920 34 1.531 10/10/2012 19:00 3,430 3.535 46 

10/14/2012 15:00 05586300 920 46 1.663 10/14/2012 15:00 7,440 3.872 72 

10/18/2012 15:55 05586300 30 31 1.491 10/18/2012 16:00 5,970 3.776 64 

10/18/2012 15:58 05586300 30 33 1.519 10/18/2012 16:00 5,970 3.776 64 
Appendix 3. Estimating Suspended Sediment Concentration from Streamflow—Model Information and Data.—Continued[CST, central standard time; SSC, suspended-sediment concentration; mg/L, milligrams per liter; log10, base-10 logarithmic transform; mm, millimeter; ft3/s, cubic feet per secon

Suspended sediment

Date and time

(CST)

Station 

number

Method 

code1

Concentration (SSC) Percent finer than 

0.0625 mm

Date and time

(CST)

Streamflow Regression esti

SSC (mg/L)

(mg/L) log 

10 (mg/L) (ft3/s) log10 (ft3/s)

10/18/2012 16:01 05586300 30 30 1.477 10/18/2012 16:00 5,970 3.776 64 

10/18/2012 16:06 05586300 20 34 1.531 98 10/18/2012 16:00 5,970 3.776 64 

10/18/2012 16:18 05586300 30 37 1.568 10/18/2012 16:00 5,970 3.776 64 

10/21/2012 18:00 05586300 920 24 1.380 10/21/2012 18:00 8,280 3.918 77 

10/24/2012 18:00 05586300 920 34 1.531 10/24/2012 18:00 8,100 3.908 76 

10/28/2012 17:00 05586300 920 40 1.602 10/28/2012 17:00 8,760 3.943 79 

10/31/2012 10:00 05586300 30 23 1.362 10/31/2012 10:00 6,110 3.786 64 

10/31/2012 15:53 05586100 15 25 1.398 98 10/31/2012 16:00 5,710 3.757 62 

10/31/2012 17:00 05586300 920 26 1.415 10/31/2012 17:00 5,690 3.755 62 

11/05/2012 15:00 05586300 920 17 1.230 11/05/2012 15:00 4,260 3.629 52 

11/07/2012 15:00 05586300 920 15 1.176 11/07/2012 15:00 4,570 3.660 54 

11/13/2012 15:00 05586300 920 21 1.322 11/13/2012 15:00 4,710 3.673 55 

11/14/2012 17:00 05586300 920 32 1.505 11/14/2012 17:00 8,340 3.921 77 

11/27/2012 09:40 05586300 30 35 1.544 11/27/2012 10:00 4,910 3.691 57 

11/27/2012 15:00 05586300 920 29 1.462 11/27/2012 15:00 5,400 3.732 60 

12/02/2012 15:00 05586300 920 18 1.255 12/02/2012 15:00 4,230 3.626 52 

12/04/2012 15:00 05586300 920 20 1.301 12/04/2012 15:00 4,340 3.637 53 

12/08/2012 07:00 05586300 920 18 1.255 12/08/2012 07:00 4,540 3.657 54 

12/10/2012 15:00 05586300 920 26 1.415 12/10/2012 15:00 4,660 3.668 55 

12/13/2012 09:37 05586300 20 37 1.568 12/13/2012 10:00 4,910 3.691 57 

12/13/2012 09:45 05586300 920 29 1.462 12/13/2012 10:00 4,910 3.691 57 

12/13/2012 09:50 05586300 920 38 1.580 12/13/2012 10:00 4,910 3.691 57 

12/14/2012 16:47 05586100 10 26 1.415 99 12/14/2012 17:00 4,170 3.620 52 

12/18/2012 15:10 05586300 920 18 1.255 12/18/2012 15:00 6,800 3.833 68 

12/23/2012 16:00 05586300 920 31 1.491 12/23/2012 16:00 8,480 3.928 78 

12/26/2012 15:00 05586300 920 62 1.792 12/26/2012 15:00 11,200 4.049 91 

12/30/2012 15:00 05586300 920 27 1.431 12/30/2012 15:00 7,720 3.888 74 

01/02/2013 15:00 05586300 920 25 1.398 01/02/2013 15:00 8,380 3.923 77 

01/08/2013 09:45 05586300 70 23 1.362 01/08/2013 10:00 7,720 3.888 74 
Appendix 3. Estimating Suspended Sediment Concentration from Streamflow—Model Information and Data.—Continued [CST, central standard time; SSC, suspended-sediment concentration; mg/L, milligrams per liter; log10, base-10 logarithmic transform; mm, millimeter; ft3/s, cubic feet per secon

Suspended sediment Date and time (CST) Station number Method code1 Concentration (SSC) Percent finer than 0.0625 mm Date and time (CST) Streamflow Regression esti

SSC (mg/L) 

(mg/L) log 

10 (mg/L) (ft3/s) log10 (ft3/s) 01/09/2013 16:50 05586300 920 22 1.342 01/09/2013 17:00 7,260 3.861 71 

01/13/2013 15:00 05586300 920 63 1.799 01/13/2013 15:00 9,080 3.958 81 

01/15/2013 15:00 05586300 920 59 1.771 01/15/2013 15:00 12,400 4.093 97 

01/28/2013 15:00 05586300 920 28 1.447 01/28/2013 15:00 8,450 3.927 78 

01/30/2013 17:45 05586300 920 843 2.926 01/30/2013 18:00 18,200 4.260 121 

02/04/2013 12:40 05586300 70 355 2.550 89 02/04/2013 13:00 25,400 4.405 147 

02/05/2013 10:55 05586300 920 440 2.643 02/05/2013 11:00 26,000 4.415 149 

02/05/2013 11:09 05586300 20 375 2.574 02/05/2013 11:00 26,000 4.415 149 

02/05/2013 11:22 05586300 920 382 2.582 02/05/2013 11:00 26,000 4.415 149 

02/05/2013 15:00 05586300 920 361 2.558 02/05/2013 15:00 26,100 4.417 149 

02/06/2013 15:00 05586300 920 328 2.516 02/06/2013 15:00 26,000 4.415 149 

02/17/2013 15:00 05586300 920 373 2.572 02/17/2013 15:00 26,100 4.417 149 

02/19/2013 11:00 05586300 70 137 2.137 98 02/19/2013 11:00 22,100 4.344 135 

02/19/2013 15:00 05586300 920 294 2.468 02/19/2013 15:00 19,900 4.299 127 

02/25/2013 17:00 05586300 920 131 2.117 02/25/2013 17:00 15,800 4.199 112 

03/06/2013 16:00 05586300 920 309 2.490 outlier not used in regression model 03/08/2013 12:30 05586300 15 26 1.415 outlier not used in regression model 03/08/2013 13:00 05586300 70 75 1.875 03/08/2013 13:00 21,400 4.330 133 

03/08/2013 13:20 05586300 10 80 1.903 03/08/2013 13:00 21,400 4.330 133 

03/08/2013 14:52 05586100 10 79 1.898 95 03/08/2013 15:00 21,400 4.330 133 

03/13/2013 15:00 05586300 920 728 2.862 03/13/2013 15:00 42,300 4.626 197 

03/20/2013 16:00 05586300 920 154 2.188 03/20/2013 16:00 48,000 4.681 212 

03/21/2013 14:47 05586100 10 121 2.083 75 03/21/2013 15:00 47,400 4.676 211 

03/27/2013 15:00 05586300 920 76 1.881 03/27/2013 15:00 39,600 4.598 190 

04/01/2013 16:00 05586300 920 95 1.978 04/01/2013 16:00 34,400 4.537 175 

04/03/2013 17:00 05586300 920 109 2.037 04/03/2013 17:00 32,700 4.515 170 

04/11/2013 12:56 05586100 10 1,970 3.294 100 04/11/2013 13:00 34,000 4.531 174 

04/15/2013 18:00 05586300 920 bottle broken at lab

04/17/2013 17:30 05586300 920 bottle broken at lab 
Appendix 3. Estimating Suspended Sediment Concentration from Streamflow—Model Information and Data.—Continued[CST, central standard time; SSC, suspended-sediment concentration; mg/L, milligrams per liter; log10, base-10 logarithmic transform; mm, millimeter; ft3/s, cubic feet per secon

Suspended sediment

Date and time

(CST)

Station 

number

Method 

code1

Concentration (SSC) Percent finer than 

0.0625 mm

Date and time

(CST)

Streamflow Regression esti

SSC (mg/L)

(mg/L) log 

10 (mg/L) (ft3/s) log10 (ft3/s)

04/21/2013 15:30 05586300 920 bottle broken at lab

04/23/2013 10:53 05586300 920 326 2.513 91 04/23/2013 11:00 103,000 5.013 330 

04/23/2013 11:19 05586300 10 320 2.505 89 04/23/2013 11:00 103,000 5.013 330 

04/23/2013 11:23 05586300 10 280 2.447 87 04/23/2013 11:00 103,000 5.013 330 

04/24/2013 14:50 05586300 10 274 2.438 04/24/2013 15:00 110,000 5.041 343 

04/24/2013 18:00 05586300 920 306 2.486 04/24/2013 18:00 111,000 5.045 344 

04/26/2013 15:57 05586100 20 128 2.107 88 04/26/2013 16:00 111,000 5.045 344 

04/26/2013 16:06 05586100 20 146 2.164 04/26/2013 16:00 111,000 5.045 344 

04/28/2013 19:00 05586300 920 165 2.217 04/28/2013 19:00 112,000 5.049 346 

04/29/2013 11:20 05586300 50 75 1.875 04/29/2013 11:00 110,000 5.041 343 

04/30/2013 19:00 05586300 920 71 1.851 04/30/2013 19:00 106,000 5.025 335 

05/01/2013 20:00 05586300 920 64 1.806 05/01/2013 20:00 104,000 5.017 332 

05/05/2013 20:00 05586300 920 68 1.833 05/05/2013 20:00 95,300 4.979 315 

05/07/2013 20:00 05586300 920 178 2.250 05/07/2013 20:00 94,500 4.975 314 

05/08/2013 14:00 05586300 50 60 1.778 05/08/2013 14:00 93,700 4.972 312 

05/22/2013 11:43 05586100 10 119 2.076 97 05/22/2013 12:00 52,100 4.717 222 

05/28/2013 17:00 05586300 920 946 2.976 97 05/28/2013 17:00 58,000 4.763 237 

05/29/2013 13:40 05586300 50 493 2.693 05/29/2013 14:00 61,900 4.792 246 

06/05/2013 13:01 05586100 10 122 2.086 96 06/05/2013 13:00 85,800 4.933 297 

06/09/2013 19:00 05586300 920 57 1.756 06/09/2013 19:00 85,000 4.929 295 

06/11/2013 20:00 05586300 920 40 1.602 06/11/2013 20:00 79,600 4.901 284 

06/16/2013 15:00 05586300 920 425 2.628 06/16/2013 15:00 69,200 4.840 262 

06/18/2013 17:20 05586300 50 74 1.869 99 06/18/2013 17:00 66,900 4.825 257 

06/19/2013 09:44 05586100 10 64 1.806 94 06/19/2013 10:00 64,700 4.811 252 

06/26/2013 20:00 05586300 920 130 2.114 06/26/2013 20:00 51,400 4.711 221 

1Method code for sampling methods: Equal Width-Increment (EWI)-10, EWI non-isokinetic-15, Equal Discharge Increment(EDI)-20, Single-Vertical-30, Point-50, Fixed Si
54 

Appendix 4. Estimating Total Phosphorus Concentration from 

Orthophosphate and Suspended-Sediment Concentrations at 

USGS station number 05586100—Model Information and Data

Model form

Total phosphorus = 0.0816+1.10(Orthophosphate)+0.00063(SSC)

Predictor variable summary statistics Response variable summary statistics

Orthphosphate (SSC) Total phosphorus

Minimum 0.051 29 0.11

1st Quartile 0.139 78 0.30

Median 0.176 116 0.39

Mean 0.201 178 0.41

3rd Quartile 0.241 200 0.48

Maximum 0.600 1,970 1.38

Model calibration

Basic data

Number of observations 246

Error degrees of freedom 243

Root mean squared error (Standard error of 

regression)

0.079

R-squared 0.803

Adjusted R-squared 0.802

F-statistic versus constant model 497

p-value 1.45E-86

Probability plot correlation coefficient 0.88

Estimated coefficients

Estimate SE tStat pValue Lower 90 percent Upper 90 percent

(Intercept) 0.081552 0.012871 6.3362 1.133E-09 0.0603005 0.102804

Orthophosphate 1.0987 0.05144 21.36 1.072E-57 1.0138 1.18367

SSC 0.000629 2.3715e–05 26.546 9.122E-74 0.000590 0.000669
Appendix 4 55

Appendix 4. Estimating Total Phosphorus Concentration from Orthophosphate and 

Suspended-Sediment Concentrations at USGS station number 05586100—Model Information 

and Data.—Continued

Date and time

(Central Standard Time) Method code1 Total phosphorus Orthophosphate

Suspendedsediment 

concentration

06/27/1991 11:00 --- 0.280 0.110 124

07/01/1991 09:30 --- 0.277 0.130 83

07/03/1991 11:00 --- 0.257 0.110 86

07/09/1991 10:00 --- 0.240 0.110 59

07/11/1991 10:45 --- 0.288 0.150 66

07/18/1991 11:15 --- 0.248 0.080 124

07/25/1991 13:40 --- 0.237 0.120 38

08/06/1991 10:15 --- 0.278 0.140 68

08/13/1991 12:00 --- 0.416 0.270 60

08/21/1991 12:30 --- 0.262 0.130 59

08/28/1991 10:15 --- 0.253 0.130 45

09/03/1991 10:50 --- 0.283 0.160 41

09/10/1991 12:00 --- 0.313 0.160 88

09/17/1991 11:00 --- 0.322 0.160 102

09/24/1991 11:00 --- 0.291 0.160 53

10/01/1991 11:30 --- 0.294 0.150 76

10/08/1991 13:30 --- 0.535 0.140 476

10/15/1991 12:15 --- 0.410 0.250 86

10/22/1991 11:30 --- 0.369 0.240 38

11/05/1991 12:00 --- 0.790 0.240 707

11/15/1991 11:30 --- 0.336 0.180 90

12/03/1991 10:30 --- 0.452 0.140 344

12/18/1991 12:00 --- 0.328 0.140 147

01/02/1992 13:30 --- 0.288 0.130 101

01/30/1992 13:45 --- 0.359 0.200 92

02/12/1992 12:00 --- 0.331 0.170 100

02/28/1992 12:45 --- 0.314 0.150 108

03/03/1992 11:00 --- 0.261 0.100 111

03/17/1992 10:50 --- 0.315 0.160 92

03/25/1992 10:45 --- 0.286 0.120 115

05/16/1996 14:50 10 0.283 0.100 146

06/04/1996 15:20 10 0.231 0.090 81

07/09/1996 17:00 10 0.440 0.150 308

08/05/1996 14:00 10 0.297 0.130 116

09/10/1996 12:30 10 0.336 0.200 55

10/31/1996 15:10 10 0.372 0.220 78

11/22/1996 12:40 10 0.540 0.380 65

12/13/1996 12:30 10 0.535 0.360 92

01/21/1997 16:00 30 0.427 0.270 77

02/11/1997 16:40 10 0.472 0.210 253
56 Continuous Monitoring of Sediment and Nutrients in the Illinois River at Florence, Illinois, 2012–13

Appendix 4. Estimating Total Phosphorus Concentration from Orthophosphate and 

Suspended-Sediment Concentrations at USGS station number 05586100—Model Information 

and Data.—Continued

Date and time

(Central Standard Time) Method code1 Total phosphorus Orthophosphate

Suspendedsediment 

concentration

03/18/1997 15:40 10 0.290 0.140 87

04/01/1997 12:30 10 0.371 0.140 215

04/15/1997 14:20 10 0.415 0.140 286

04/29/1997 12:30 10 0.276 0.120 100

05/13/1997 12:50 10 0.436 0.180 249

05/28/1997 13:40 10 0.481 0.160 356

06/10/1997 12:50 10 0.668 0.140 687

06/17/1997 13:00 10 0.615 0.140 603

06/26/1997 13:00 10 0.370 0.140 214

07/08/1997 16:10 10 0.372 0.180 147

07/22/1997 14:40 10 0.345 0.160 139

08/05/1997 13:50 10 0.396 0.190 168

08/19/1997 12:50 10 0.490 0.210 282

09/02/1997 17:21 10 0.337 0.180 92

09/16/1997 14:11 10 0.297 0.160 63

10/21/1997 13:11 10 0.376 0.230 66

11/18/1997 13:31 10 0.488 0.340 53

12/16/1997 13:21 10 0.447 0.290 75

01/13/1998 14:51 10 0.479 0.230 230

02/04/1998 13:31 10 0.306 0.150 95

03/10/1998 15:31 10 0.418 0.130 307

04/01/1998 16:01 10 0.274 0.150 44

04/14/1998 12:51 10 0.331 0.110 204

04/28/1998 12:21 10 0.194 0.070 56

05/12/1998 12:51 10 0.209 0.080 63

05/27/1998 12:41 10 0.281 0.120 108

06/09/1998 18:31 10 0.670 0.140 690

06/18/1998 13:31 10 0.404 0.150 251

06/23/1998 13:51 10 0.295 0.120 130

07/07/1998 12:51 10 0.255 0.110 83

07/21/1998 14:01 10 0.871 0.140 1,010

08/04/1998 15:11 10 0.346 0.180 106

08/12/1998 17:51 10 0.628 0.320 310

08/18/1998 12:31 10 0.423 0.240 123

09/01/1998 12:31 10 0.427 0.240 130

09/15/1998 12:31 10 0.382 0.220 94

10/15/1998 14:11 10 0.461 0.230 202

11/17/1998 15:31 10 0.621 0.300 333

12/22/1998 15:21 10 0.499 0.290 157

01/20/1999 15:01 60 0.382 0.200 128
Appendix 4 57

Appendix 4. Estimating Total Phosphorus Concentration from Orthophosphate and 

Suspended-Sediment Concentrations at USGS station number 05586100—Model Information 

and Data.—Continued

Date and time

(Central Standard Time) Method code1 Total phosphorus Orthophosphate

Suspendedsediment 

concentration

03/10/1999 15:41 10 0.347 0.140 177

04/12/1999 14:41 10 0.462 0.130 378

05/19/1999 14:51 10 0.285 0.070 201

06/08/1999 14:31 10 0.485 0.140 397

07/13/1999 14:01 10 0.384 0.170 184

08/09/1999 14:01 10 0.460 0.280 112

09/07/1999 14:51 10 0.440 0.260 115

10/12/1999 13:31 10 0.650 0.430 152

11/16/1999 14:01 10 0.398 0.240 84

12/13/1999 14:46 10 0.886 0.600 231

01/12/2000 14:16 10 0.502 0.310 127

02/08/2000 14:36 10 0.655 0.430 160

04/11/2000 15:11 10 0.419 0.210 169

05/04/2000 11:51 10 0.413 0.150 265

06/09/2000 19:11 10 0.488 0.190 314

06/29/2000 12:21 10 0.460 0.170 304

08/11/2000 12:01 10 0.545 0.270 265

09/14/2000 15:31 10 0.561 0.320 203

10/10/2000 14:31 10 0.545 0.330 160

11/15/2000 14:31 10 0.698 0.430 228

12/11/2000 14:51 10 0.495 0.280 168

02/13/2001 15:01 10 0.478 0.180 315

03/20/2001 14:01 10 0.339 0.140 165

04/25/2001 11:51 10 0.399 0.150 243

05/16/2001 15:21 10 0.353 0.170 135

06/13/2001 15:21 10 0.444 0.120 366

07/19/2001 14:31 10 0.425 0.240 126

08/22/2001 13:31 10 0.521 0.320 139

10/09/2001 12:01 10 0.466 0.260 157

12/17/2001 12:01 10 0.460 0.230 200

02/20/2002 11:51 10 0.409 0.160 241

04/10/2002 10:01 10 0.405 0.140 269

05/08/2002 11:02 10 0.310 0.090 206

06/12/2002 10:12 10 1.260 0.110 1,680

07/17/2002 10:32 10 0.425 0.200 196

08/15/2002 10:22 10 0.452 0.300 65

10/16/2002 11:02 10 0.561 0.370 116

12/16/2002 16:47 10 0.580 0.360 164

02/18/2003 11:12 10 0.524 0.360 75

04/10/2003 10:47 10 0.564 0.250 330
58 Continuous Monitoring of Sediment and Nutrients in the Illinois River at Florence, Illinois, 2012–13

Appendix 4. Estimating Total Phosphorus Concentration from Orthophosphate and 

Suspended-Sediment Concentrations at USGS station number 05586100—Model Information 

and Data.—Continued

Date and time

(Central Standard Time) Method code1 Total phosphorus Orthophosphate

Suspendedsediment 

concentration

05/16/2003 11:12 10 0.538 0.130 498

06/11/2003 14:02 10 0.377 0.130 243

07/01/2003 11:32 10 0.369 0.180 142

08/18/2003 15:17 10 0.442 0.250 136

10/15/2003 13:52 10 0.423 0.241 122

12/02/2003 12:17 10 0.436 0.193 226

02/25/2004 11:32 10 0.478 0.247 198

04/07/2004 13:02 10 0.332 0.133 166

05/13/2004 12:17 10 0.360 0.149 182

06/10/2004 11:57 10 0.351 0.144 177

07/06/2004 11:07 40 0.503 0.199 322

08/03/2004 11:47 10 0.420 0.220 153

10/19/2004 11:42 10 0.453 0.292 81

12/01/2004 11:52 40 0.470 0.217 239

02/23/2005 11:32 40 0.270 0.142 52

04/19/2005 11:52 40 0.292 0.103 155

06/01/2005 11:37 40 0.338 0.189 77

08/18/2005 12:42 40 0.752 0.583 47

10/12/2005 11:42 10 0.691 0.523 55

11/08/2005 12:02 40 0.499 0.349 54

12/21/2005 12:17 40 0.699 0.516 80

01/12/2006 12:02 40 0.668 0.483 88

02/14/2006 12:17 40 0.328 0.196 50

03/14/2006 12:47 10 0.488 0.224 255

03/28/2006 11:52 10 0.295 0.130 112

04/12/2006 12:02 10 0.406 0.186 190

04/25/2006 11:57 10 0.381 0.153 208

05/09/2006 11:47 10 0.359 0.147 184

05/23/2006 12:42 10 0.355 0.173 133

06/05/2006 12:32 10 0.454 0.189 261

06/20/2006 13:12 40 0.348 0.196 81

07/11/2006 10:52 40 0.340 0.208 47

07/25/2006 11:02 40 0.552 0.380 84

08/14/2006 12:17 40 0.553 0.379 87

08/29/2006 11:22 40 0.528 0.344 109

09/19/2006 12:07 10 0.590 0.289 304

11/06/2007 12:02 15 0.555 0.407 41

12/06/2007 11:42 15 0.757 0.565 87

02/25/2008 12:02 10 0.478 0.291 122

03/19/2008 11:32 10 0.326 0.168 95
Appendix 4 59

Appendix 4. Estimating Total Phosphorus Concentration from Orthophosphate and 

Suspended-Sediment Concentrations at USGS station number 05586100—Model Information 

and Data.—Continued

Date and time

(Central Standard Time) Method code1 Total phosphorus Orthophosphate

Suspendedsediment 

concentration

04/08/2008 11:52 10 0.254 0.116 72

04/21/2008 11:52 10 0.221 0.103 42

05/06/2008 11:42 15 0.202 0.079 53

05/19/2008 11:12 15 0.283 0.139 78

06/10/2008 11:32 15 0.390 0.193 153

06/20/2008 12:02 10 0.333 0.212 29

07/08/2008 12:02 10 0.390 0.232 85

08/19/2008 11:32 15 0.425 0.264 85

10/20/2008 11:52 10 0.399 0.200 155

12/03/2008 11:32 15 0.379 0.236 60

02/12/2009 11:52 10 0.689 0.109 775

03/03/2009 12:22 10 0.435 0.154 292

03/17/2009 11:53 10 0.258 0.112 85

04/08/2009 11:33 10 0.224 0.106 42

04/21/2009 11:33 10 0.187 0.051 79

05/05/2009 12:03 10 0.220 0.101 44

05/19/2009 12:13 10 0.243 0.093 94

06/10/2009 11:33 10 0.263 0.118 82

06/23/2009 11:43 10 0.343 0.142 167

08/11/2009 11:33 15 0.350 0.187 100

10/15/2009 11:33 10 0.521 0.290 192

10/15/2009 11:43 10 0.519 0.288 192

11/03/2009 12:03 10 0.357 0.146 183

12/08/2009 10:43 10 0.342 0.200 64

12/08/2009 10:53 10 0.340 0.199 64

01/14/2010 12:33 40 0.339 0.199 61

02/16/2010 11:33 40 0.306 0.169 61

02/16/2010 11:43 40 0.307 0.170 61

03/09/2010 11:33 10 0.414 0.170 232

03/24/2010 11:23 10 0.249 0.129 41

03/24/2010 11:33 10 0.239 0.120 41

04/06/2010 11:13 10 0.195 0.069 59

04/21/2010 11:13 10 0.213 0.058 107

04/21/2010 11:23 10 0.212 0.057 107

05/04/2010 11:13 10 0.327 0.125 172

05/04/2010 11:23 10 0.328 0.126 172

05/17/2010 11:13 10 0.833 0.094 1,030

05/17/2010 11:23 10 0.840 0.100 1,030

06/02/2010 13:13 10 0.268 0.108 108

06/02/2010 13:23 10 0.266 0.106 108
60 Continuous Monitoring of Sediment and Nutrients in the Illinois River at Florence, Illinois, 2012–13

Appendix 4. Estimating Total Phosphorus Concentration from Orthophosphate and 

Suspended-Sediment Concentrations at USGS station number 05586100—Model Information 

and Data.—Continued

Date and time

(Central Standard Time) Method code1 Total phosphorus Orthophosphate

Suspendedsediment 

concentration

06/16/2010 12:13 10 0.344 0.175 111

06/16/2010 12:23 10 0.346 0.177 111

07/07/2010 11:43 10 0.312 0.193 29

07/21/2010 12:53 10 0.676 0.099 772

08/04/2010 11:33 10 0.457 0.247 166

08/04/2010 11:43 10 0.457 0.247 166

08/17/2010 11:43 10 0.449 0.246 155

09/01/2010 11:43 15 0.488 0.297 128

10/19/2010 12:03 15 0.441 0.278 86

12/07/2010 12:48 40 0.553 0.375 94

02/22/2011 11:53 10 0.655 0.304 381

03/07/2011 12:33 10 0.342 0.152 148

03/29/2011 10:53 10 0.250 0.091 109

04/05/2011 10:33 10 0.259 0.084 135

04/18/2011 11:33 10 0.363 0.191 114

05/05/2011 10:03 10 0.238 0.114 50

05/17/2011 10:53 10 0.355 0.111 240

06/07/2011 10:43 15 0.277 0.145 57

06/21/2011 11:23 10 0.266 0.140 49

08/24/2011 11:03 15 0.462 0.299 83

10/19/2011 10:53 15 0.490 0.341 53

12/01/2011 10:43 15 0.447 0.263 121

02/24/2012 12:03 10 0.307 0.171 60

03/08/2012 12:13 15 0.468 0.285 117

03/27/2012 10:43 10 0.365 0.196 108

04/11/2012 11:13 10 0.261 0.137 46

04/24/2012 10:43 15 0.360 0.198 97

05/16/2012 11:03 10 0.452 0.188 261

05/30/2012 15:53 15 0.346 0.183 100

06/13/2012 09:13 15 0.301 0.177 40

06/27/2012 12:03 15 0.480 0.331 55

08/21/2012 11:34 10 0.758 0.580 63

02/13/2013 13:54 10 0.532 0.197 372

03/08/2013 11:44 10 0.322 0.174 79

03/21/2013 12:44 10 0.310 0.139 121

04/11/2013 10:14 10 1.426 0.095 1,970

04/24/2013 15:44 10 0.416 0.162 249

05/08/2013 09:24 10 --- 0.307 0.158 82

05/22/2013 09:14 10 0.276 0.109 119

06/05/2013 10:44 10 0.370 0.193 122
Appendix 4 61

Appendix 4. Estimating Total Phosphorus Concentration from Orthophosphate and 

Suspended-Sediment Concentrations at USGS station number 05586100—Model Information 

and Data.—Continued

Date and time

(Central Standard Time) Method code1 Total phosphorus Orthophosphate

Suspendedsediment 

concentration

06/19/2013 07:34 10 0.295 0.158 64

07/10/2013 09:14 10 0.368 0.190 124

07/22/2013 09:54 10 0.363 0.219 65

08/14/2013 10:14 10 0.324 0.200 36

11/01/2013 12:04 10 0.558 0.401 57

12/13/2013 10:44 10 0.502 0.363 34

1

Method code for sampling methods: Equal Width Increment (EWI)-10, EWI non-isokinetic-15, 

Equal Discharge Increment (EDI)-20, Single-Vertical-30, Multiple-vertical-40, Point-50, Fixed 

Single Vertical-BOX-920, not in database ---.

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For more information concerning the research in this report 

contact the

Director, Illinois Water Science Center

U.S. Geological Survey

405 N Goodwin

Urbana, IL 61801

http://il.water.usgs.gov/
Terrio and others—Continuous Monitoring of Sediment and Nutrients in the Illinois River at Florence, Illinois, 2012–13—Scientific Investigations Report 2015–5040

ISSN 2328-0328 (online) 

http://dx.doi.org/10.3133/sir20155040