Use of Continuous Monitors and Autosamplers to 

Predict Unmeasured Water-Quality Constituents in 

Tributaries of the Tualatin River, Oregon

Scientific Investigations Report 2010–5008

Prepared in cooperation with Clean Water Services

U.S. Department of the Interior

U.S. Geological Survey
Cover: Deployed autosampler and continuous monitoring station at Rock Creek near Woll 

Pond Way, in the Tualatin River basin, Oregon. (Photograph taken by Chauncey Anderson, 

U.S. Geological Survey, 2003.)

Back cover: Autosampler deployed inside a protective enclosure at Rock Creek near Woll 

Pond Way, Tualatin River basin, Oregon. (Photograph taken by Chauncey Anderson, U.S. 

Geological Survey, 2003.)
Use of Continuous Monitors and Autosamplers to 

Predict Unmeasured Water-Quality Constituents in 

Tributaries of the Tualatin River, Oregon

By Chauncey W. Anderson and Stewart A. Rounds

Prepared in cooperation with Clean Water Services 

Scientific Investigations Report 2010–5008

U.S. Department of the Interior

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

KEN SALAZAR, Secretary

U.S. Geological Survey

Marcia K. McNutt, Director

U.S. Geological Survey, Reston, Virginia: 2010

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Suggested citation:

Anderson, C.W., and Rounds, S.A., 2010, Use of continuous monitors and autosamplers to predict unmeasured waterquality constituents in tributaries of the Tualatin River, Oregon: U.S. Geological Survey Scientific Investigations Report 

2010–5008, 76 p.
iii

Contents

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

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

Purpose and Scope ..............................................................................................................................4

Study Area Description........................................................................................................................5

Methods...........................................................................................................................................................7

Data Sources.........................................................................................................................................7

Monitors ........................................................................................................................................8

Autosamplers................................................................................................................................9

Autosampler Quality Assurance ....................................................................................10

Clean Water Services Laboratory...........................................................................................14

Laboratory Quality Assurance........................................................................................14

Data Aggregation................................................................................................................................15

Regression Models.............................................................................................................................20

Relations Between Continuous Monitor Data and Selected Water-Quality Constituents ..............24

Autosampler Deployment Dates and Conditions ..........................................................................24

Fanno Creek at Durham Road...........................................................................................................25

Autosampler Data......................................................................................................................25

Total Suspended Solids....................................................................................................26

Total Phosphorus .......................................................................................................................31

Escherichia coli Bacteria .........................................................................................................35

Dairy Creek at Highway 8 ..................................................................................................................39

Autosampler Data......................................................................................................................39

Total Suspended Solids.............................................................................................................42

Total Phosphorus .......................................................................................................................45

Escherichia coli Bacteria ........................................................................................................50

Non-Target Sites .................................................................................................................................53

Discussion.....................................................................................................................................................55

Using the Regression Equations ......................................................................................................56

Suggestions for Future Study ...........................................................................................................56

Site Considerations ...................................................................................................................57

Autosamplers..............................................................................................................................57

Water Sample Collection..........................................................................................................58

Model Development and Selection .......................................................................................59

Conclusion.....................................................................................................................................................60

Acknowledgments.......................................................................................................................................60

References Cited..........................................................................................................................................61

Appendix A. Data Tables............................................................................................................................63
iv

Figures

Figure 1. Map showing Tualatin River basin, Oregon ............................................. 2

Figure 2. Map showing locations of sites selected for development of regression models 

for unmeasured water-quality constituents in the Tualatin River basin, Oregon, 

2002–04 ....................................................................................... 6

Figure 3. Graphs showing flow duration curves with samples collected from each data 

source for Scenario 1, Scenario 2, and Scenario 3, for Fanno Creek near 

Durham, Oregon, water years 2001–07 ................................................... 17

Figure 4. Graphs showing probability that specific conductances and turbidities during 

autosampling were exceeded at Fanno Creek at Durham, Oregon, May 2002 to 

September 2004.............................................................................. 25

Figure 5. Diagrams showing matrixes of scatter plots of calibration data using 

Scenario 1 and Scenario 3 datasets, from Fanno Creek near Durham, Oregon...... 27

Figure 6. Graphs showing time series of predicted concentrations of total suspended 

solids at Fanno Creek for Model 4, with prediction intervals and calibration 

and validation data during 2002–03, during storm 1, and during storms 2 and 3, 

Tualatin River basin, Oregon ............................................................... 30

Figure 7. Graph showing comparison of concentrations of measured and predicted total 

suspended solids from Model 4, using the calibration dataset from Scenario 3, 

Fanno Creek near Durham, Oregon ...................................................... 31

Figure 8. Graphs showing time series of predicted total phosphorus concentrations at 

Fanno Creek from Model 5, with prediction intervals, calibration, and validation 

data during 2002–03, during storm 1, and during storms 2 and 3, Tualatin River 

basin, Oregon .............................................................................. 34

Figure 9. Graph showing comparison of concentrations of measured and predicted total 

phosphorus from Model 5, using the calibration dataset from Scenario 3, Fanno 

Creek near Durham, Oregon ............................................................... 35

Figure 10. Graphs showing time series of predicted Escherichia coli bacteria counts at 

Fanno Creek from Model 3, with prediction intervals, calibration, and validation data 

during 2002–03, during storm 1, and during storms 2 and 3, Tualatin River 

basin, Oregon .............................................................................. 38

Figure 11. Graph showing comparison of concentrations of measured and predicted 

Escherichia coli bacteria counts from Model 3, using the calibration dataset 

from Scenario 3, Fanno Creek near Durham, Oregon ................................. 39

Figure 12. Diagrams showing matrixes of scatter plots of calibration data from Dairy 

Creek at Highway 8 near Hillsboro, Oregon, using Scenario 1 and Scenario 2 

datasets....................................................................................... 40

Figure 13. Graphs showing probability that specific conductances and turbidities were 

exceeded at Dairy Creek at Highway 8 near Hillsboro, Oregon, during May 2002 

to September 2004 ........................................................................ 41

Figure 14. Graphs showing time series of predicted total suspended solid concentrations 

at Dairy Creek at Highway 8 near Hillsboro, from Models 1 and 5, with 

prediction intervals, calibration, and validation data during 2002–04, during 

storm 1, and during storm 2, Tualatin River basin, Oregon.............................. 44

Figure 15. Graph showing comparison of predicted and measured concentrations of total 

suspended solids from Model 5 for Dairy Creek at Highway 8 near Hillsboro, 

Oregon ....................................................................................... 45
v

Figures—Continued

Figure 16. Graphs showing time series of predicted total phosphorus concentrations at 

Dairy Creek at Highway 8 near Hillsboro, from Models 1, 5, and 7, with prediction 

intervals, calibration, and validation data during 2002–04, during storm 1, and 

during storm 2, Tualatin River basin, Oregon............................................. 48

Figure 17. Graphs showing comparison of measured and predicted total phosphorus 

concentrations at Dairy Creek at Highway 8 near Hillsboro, from Scenario 1, 

Model 1, and Scenario 2, Model 5, Tualatin River basin, Oregon ..................... 49

Figure 18. Graphs showing time series of predicted Escherichia coli bacteria 

concentrations at Dairy Creek at Highway 8 near Hillsboro, from Models 4, 5, 

and 7b, with prediction intervals, calibration, and validation data during 2002–04 , 

during storm 1, and during storm 2, Tualatin River basin, Oregon ..................... 52

Figure 19. Graphs showing comparison of measured and predicted Escherichia coli

bacteria counts using Model 4 and Model 5, Dairy Creek at Highway 8 near 

Hillsboro, Oregon ........................................................................... 53

Tables

Table 1. Sampling sites and characteristics in tributaries of the Tualatin River basin, 

Oregon, 2002–04 ........................................................................... 7

Table 2. U.S. Geological Survey guidelines for placement of autosampler intake ............ 9

Table 3. Results of laboratory quality-assurance tests of autosamplers ..................... 11

Table 4. Quality-assurance tests comparing samples collected using the autosampler 

with samples collected using depth- and width-integrating techniques, and 

testing carryover between sequential samples, Fanno and Dairy Creeks, 

Oregon ....................................................................................... 13

Table 5. Constituents analyzed from water samples collected during stormflows,

Tualatin River tributaries, Oregon, May 2002 to September 2004 ..................... 14

Table 6. Datasets showing laboratory sample results and summary statistics for 

scenarios used for calibration and validation of regression models with 

continuous monitors at Fanno Creek, Durham, Oregon................................. 16

Table 7. Datasets showing laboratory sample results and summary statistics for 

scenarios used for calibration and validation of regression models with 

continuous monitors at Dairy Creek at Highway 8 near Hillsboro, Oregon ......... 19

Table 8. Goodness-of-fit statistics used for evaluation of regression model predictions ... 23

Table 9. Dates of autosampling and peak storm discharges at Tualatin River tributary 

sites, Oregon, spring 2002 to autumn 2003................................................ 24

Table 10. Preliminary model statistics for correlation of total suspended solids with 

continuous parameters at Fanno Creek near Durham, Oregon ........................ 28

Table 11. Preliminary model statistics for correlation of total phosphorus with continuous 

parameters at Fanno Creek near Durham, Oregon .................................... 32

Table 12. Preliminary model statistics for correlation of Escherichia coli bacteria with 

continuous parameters at Fanno Creek near Durham, Oregon ........................ 37

Table 13. Preliminary model statistics for correlation of total suspended solids with 

continuous parameters at Dairy Creek at Highway 8 near Hillsboro, Oregon, 

2002–04 ....................................................................................... 43
vi

Tables—Continued

Conversion Factors and Datums

Conversion Factors

Inch/Pound to SI

Multiply By To obtain

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

foot (ft) 0.3048 meter (m)

inch (in.) 2.54 centimeter (cm)

mile (mi) 1.609 kilometer (km)

square mile (mi2) 2.590 square kilometer (km2)

SI to Inch/Pound

Multiply By To obtain

liter (L) 0.2642 gallon (gal)

milliliter (mL) 0.033814 ounce, fluid (fl. oz)

millimeter (mm) 0.03937 inch (in.)

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

°F=(1.8×°C)+32.

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

25 °C).

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

or micrograms per liter (μg/L).

Datums

Horizontal coordinate information is referenced to either the North American Datum of 1927 

(NAD 27) or 1983 (NAD 83) as noted in the text.

Table 14. Preliminary model statistics for correlation of total phosphorus with continuous 

parameters at Dairy Creek at Highway 8 near Hillsboro, Oregon, 2002–04 ......... 46

Table 15. Preliminary model statistics for correlation of Escherichia coli bacteria with 

continuous parameters at Dairy Creek at Highway 8 near Hillsboro, Oregon, 

2002–04 ....................................................................................... 51

Table 16. Preliminary functional model forms for total suspended solids, total 

phosphorus, and Escherichia coli bacteria at non-target tributary sites in the 

Tualatin River basin, Oregon, 2002–04 ................................................... 54
vii

Abbreviations and Acronyms

BCF bias corrections factor

Ci concentration from a depth- and width-integrated sample

Cp concentration from a pumped sampler

Cs ambient stream concentration at the point of intake

CWS Clean Water Services

E. coli Escherichia coli 

FNU formazin nephelometric units

NWIS National Water Information System

OWRD Oregon Water Resources Department

ORWSC U. S. Geological Survey Oregon Water Science Center

R2 coefficient of determination

RMSE root mean square error

SRS standard reference samples

SSC suspended sediment concentration

TMDL Total Maximum Daily Load 

TKN total Kjeldahl nitrogen

TP total phosphorus

TSS total suspended solids

USGS U.S. Geological Survey

VIF variance inflation factor

VIFcrit critical level for VIF calculated from the model’s adjusted-R2
viii

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Use of Continuous Monitors and Autosamplers to 

Predict Unmeasured Water-Quality Constituents in 

Tributaries of the Tualatin River, Oregon

By Chauncey W. Anderson and Stewart A. Rounds

Abstract

Management of water quality in streams of the United 

States is becoming increasingly complex as regulators seek 

to control aquatic pollution and ecological problems through 

Total Maximum Daily Load programs that target reductions 

in the concentrations of certain constituents. Sediment, 

nutrients, and bacteria, for example, are constituents that 

regulators target for reduction nationally and in the Tualatin 

River basin, Oregon. These constituents require laboratory 

analysis of discrete samples for definitive determinations of 

concentrations in streams. Recent technological advances in 

the nearly continuous, in situ monitoring of related waterquality parameters has fostered the use of these parameters 

as surrogates for the labor intensive, laboratory-analyzed 

constituents. Although these correlative techniques have been 

successful in large rivers, it was unclear whether they could 

be applied successfully in tributaries of the Tualatin River, 

primarily because these streams tend to be small, have rapid 

hydrologic response to rainfall and high streamflow variability, 

and may contain unique sources of sediment, nutrients, and 

bacteria. 

This report evaluates the feasibility of developing 

correlative regression models for predicting dependent 

variables (concentrations of total suspended solids, total 

phosphorus, and Escherichia coli bacteria) in two Tualatin 

River basin streams: one draining highly urbanized land 

(Fanno Creek near Durham, Oregon) and one draining rural 

agricultural land (Dairy Creek at Highway 8 near Hillsboro, 

Oregon), during 2002–04. An important difference between 

these two streams is their response to storm runoff; Fanno 

Creek has a relatively rapid response due to extensive 

upstream impervious areas and Dairy Creek has a relatively 

slow response because of the large amount of undeveloped 

upstream land. Four other stream sites also were evaluated, 

but in less detail. Potential explanatory variables included 

continuously monitored streamflow (discharge), stream 

stage, specific conductance, turbidity, and time (to account 

for seasonal processes). Preliminary multiple-regression 

models were identified using stepwise regression and 

Mallow’s Cp, which maximizes regression correlation 

coefficients and accounts for the loss of additional degrees of 

freedom when extra explanatory variables are used. Several 

data scenarios were created and evaluated for each site to 

assess the representativeness of existing monitoring data 

and autosampler-derived data, and to assess the utility of 

the available data to develop robust predictive models. The 

goodness‐of‐fit of candidate predictive models was assessed 

with diagnostic statistics from validation exercises that 

compared predictions against a subset of the available data.

The regression modeling met with mixed success. 

Functional model forms that have a high likelihood of success 

were identified for most (but not all) dependent variables at 

each site, but there were limitations in the available datasets, 

notably the lack of samples from high-flows. These limitations 

increase the uncertainty in the predictions of the models and 

suggest that the models are not yet ready for use in assessing 

these streams, particularly under high-flow conditions, 

without additional data collection and recalibration of model 

coefficients. Nonetheless, the results reveal opportunities to 

use existing resources more efficiently. Baseline conditions are 

well represented in the available data, and, for the most part, 

the models reproduced these conditions well. Future sampling 

might therefore focus on high flow conditions, without much 

loss of ability to characterize the baseline. Seasonal cycles, 

as represented by trigonometric functions of time, were not 

significant in the evaluated models, perhaps because the 

baseline conditions are well characterized in the datasets or 

because the other explanatory variables indirectly incorporate 

seasonal aspects. Multicollinearity among independent 

variables was minimal and had little effect on model selection 

or the value of model coefficients. 
2 Continuous Monitors and Autosamplers Used to Predict Water-Quality Constituents, Tributaries of Tualatin River, Oregon

Automated unattended samplers were used to supplement 

the monitoring data used in this study, and a detailed quality 

assurance program was used to assess the accuracy and 

representativeness of samples collected using autosamplers. 

Care must be taken to avoid serial correlation among samples 

when autosamplers are used to collect multiple samples 

within individual storms. However, the results showed that 

autosamplers can provide high-quality data from small 

streams during storm-runoff conditions, thereby offering a 

cost-effective and convenient means of augmenting manually 

collected samples and collecting samples at high flows that 

otherwise might be missed by existing monitoring programs. 

Introduction

Since the early 1990s, the quality of water and ecological 

health of tributaries to the Tualatin River in northwestern 

Oregon (fig. 1) have been the subject of heightened concern 

from resource managers, regulators, and citizen groups. The 

small urban and agricultural streams on the eastern side of 

the basin are known to have water-quality problems, but the 

magnitude, duration, seasonality, and short- and long-term 

trends for those concerns have not been well characterized. 

Aspects of those problems have been studied, including 

low-flow phosphorus and bacteria levels (McCarthy, 2000), 

123°30'00 123°15'00 123°00'00 122°45'00 122°30'00

45°45'00

45°37'30"

45°30'00

45°22'30"

0 10

10

Miles

0 Kilometers

EXPLANATION

Base modified from U.S. Geological Survey and Metro digital data sets (1:24,000)

Projection: Oregon Lambert, North American Datum 1983

Designated Urban Growth Boundary (2001)

Basin boundary

Wastewater treatment facility

OREGON

Study

Area

Lake

Oswego

Tualatin

Sherwood

PORTLAND

Tigard

Beaverton

Hillsboro

Cornelius

Forest

Grove

ROCK

CREEK

DURHAM

WILLAMETTE

TUALATIN

RIVER

RIVER

RIVER

COLUMBIA

Hagg

Lake 217

26

WASHINGTON

COLUMBIA

TILLAMOOK

YAMHILL

CLACKAMAS

MULTNOMAH

East

Fork

Dairy

Gales

Creek

Scoggins

Creek

McKay

Creek

Rock

Creek

TUALATIN MOUNTAINS

CHEHALEM MOUNTAINS

COAST RANGE

Fanno

Creek

5

5

5

205

205

84

Figure 1. Tualatin River basin, Oregon.
Introduction 3

storm-related variations in nutrient and bacteria concentrations 

(Anderson and Rounds, 2003), and the levels of trace metals 

and organochlorine pesticides in fish tissue and sediment 

(Bonn, 1999). Issues of high water temperature, excessive 

bacteria levels, high phosphorus concentrations, and low 

dissolved oxygen concentrations were cited as particular 

problems requiring attention in Tualatin River tributaries 

in the 2001 revision of the Total Maximum Daily Load 

(TMDL) regulations for the basin (Oregon Department of 

Environmental Quality, 2001). Increased monitoring and 

additional studies have helped to fill gaps in our understanding 

of the dynamics of water quality in these streams. The 

characterization of the short-term dynamics, long-term trends, 

and spatial variations of water quality in these systems, 

however, probably will require the use of new approaches.

The use of submersible instruments that simultaneously 

measure and log multiple water-quality parameters in situ 

is growing rapidly in the Pacific Northwest and nationally. 

Such instruments can collect data at regular intervals and for 

long periods without human intervention, thereby providing 

opportunities for increased data collection at reduced costs. 

These instruments often are referred to as continuous 

monitors because they can be operated continuously for long 

periods. Data from continuous monitors can be used for 

many purposes, including (1) documentation of routine or 

event-based environmental conditions in a drainage basin, 

(2) detection of daily and seasonal variations and long-term 

trends in water quality, (3) calibration and validation of 

numerical models, (4) feedback for regulatory and resource 

management systems, and (5) surrogate measurements for 

the calculation of concentrations or loads of suspended 

sediment (Gray and Glysson, 2003; Uhrich and Bragg, 

2003) or other constituents (Christensen and others, 2000). 

Monitored parameters typically include water temperature, 

specific conductance, pH, and, increasingly, dissolved oxygen, 

turbidity, and chlorophyll. Many other types of sensors are 

under development. 

Despite the advantages of these continuous monitors, 

many constituents of interest to regulators and resource 

managers still cannot be directly measured by such 

technology. For example, streams in the Pacific Northwest 

often are managed for their concentrations of suspended 

sediment (or total suspended solids), various nutrients 

(nitrogen and phosphorus species), or bacterial pathogens 

(such as Escherichia coli [E. coli] as an indicator of bacterial 

pathogens). No routine and direct in situ measurements can 

be done for these constituents at environmentally relevant 

concentrations by currently available commercial instruments. 

Such analyses, therefore, must be made in a laboratory using 

discreet samples collected from the stream. 

Data from continuous monitors, however, sometimes can 

provide an indication of the concentrations of unmeasured 

constituents. For example, turbidity in water often is directly 

dependent on suspended sediment concentration (Lewis, 1996; 

Anderson and Rounds, 2003; Gray and Glysson, 2003; Uhrich 

and Bragg, 2003); therefore, turbidity data from continuous 

monitors can be used to estimate a time series of suspended 

sediment concentration. Christensen and others (2000) used 

data from continuous monitors in Kansas streams to calculate 

instantaneous concentrations and loads of alkalinity, dissolved 

solids, total suspended solids (TSS), chloride, sulfate, atrazine, 

and fecal coliform bacteria. Site-specific regressions between 

monitored parameters and the results of discrete water samples 

were derived for these constituents, and the regressions 

were then applied to long-term monitor records at the study 

sites to estimate a time series of constituent concentrations. 

By combining these concentration estimates with discharge 

information, constituent loads also can be estimated. For 

example, Uhrich and Bragg (2003), Anderson (2007), and 

Bragg and others (2007) performed similar calculations using 

continuous records of turbidity and discharge to estimate 

suspended sediment concentrations and loads in the North 

Santiam and McKenzie Rivers, respectively, in western 

Oregon. 

To use continuous monitors to develop robust statistical 

models for sampled water-quality constituents, independent 

samples representing a broad range of conditions (high- and 

low-flow and seasonal warm/cold or spring/summer/autumn/

winter) are needed at each site. Clean Water Services, the 

primary wastewater and stormwater management utility in 

the urban areas of Washington County, Oregon, has been 

collecting routine water-quality samples at many sites in the 

Tualatin River basin for more than 20 years. Most samples 

collected, however, represented low- or base-flow conditions, 

and were not targeted for storms. The U.S. Geological Survey 

(USGS) has collected data for many years and for various 

purposes at Fanno Creek near Durham, including during a 

few storms, but these data also are of limited scope. Data 

from these two sources were used to evaluate the potential 

regression models for this study. 

Like continuous monitors, automatic samplers (referred 

to as autosamplers in this report) can collect water samples at 

night, during storms, or at specific intervals without the need 

for human operators. An autosampler can collect multiple 

samples (typically as many as 24) before it must be restocked 

with empty bottles. The autosampler also can be refrigerated 

or stocked with ice to minimize sample degradation. After 

collection, samples from the autosamplers (or autosamples) 

are retrieved and analyzed at a laboratory for the water-quality 

constituents of interest. Autosamplers can be programmed 

to collect samples at prescribed intervals of time or flow, 

and can be triggered by specific conditions. Used together, 

a continuous monitor can trigger an autosampler during an 

event (for example, when conditions exceed some threshold 

measured by the monitor) and can thereby document 

water-quality conditions in the stream at the time of sample 

collection.
4 Continuous Monitors and Autosamplers Used to Predict Water-Quality Constituents, Tributaries of Tualatin River, Oregon

Continuous monitors and autosamplers offer many 

advantages over manual sampling, including the potential to 

collect many samples and large amounts of data during a short 

time, when the number of sites is large, if the sites are remote, 

or if the sites are difficult or inconvenient to access (such as 

at night, on weekends, or under hazardous conditions). These 

advantages are particularly useful when trying to characterize 

short-term variations in stream conditions during storms at 

multiple sites. Collecting an adequate number of samples at 

multiple sites during a storm with a crew of technicians can be 

inefficient and expensive compared to the use of remote and 

automated instruments, if they can accomplish the same tasks. 

Despite these advantages, continuous monitors and 

autosamplers are subject to mechanical malfunction, sampling 

bias, or both, and require a certain degree of quality control to 

assure that the resulting data are accurate and representative 

of stream conditions. The quality control issues include the 

degree to which measurements or samples collected at one 

location in the stream by the autosampler represent conditions 

throughout the stream cross section, measurement bias 

because of fouling or sensor drift of deployed monitors, the 

possibility of carryover contamination because autosampler 

tubing was not completely cleaned, and the potential for 

exceeding prescribed sample holding times or temperatures in 

autosamplers. These issues must be addressed to ensure proper 

use of this technology.

This report uses correlative techniques that have been 

shown to work with relative success in various geologic 

regions (Christensen and others, 2000; Lietz and Debiak, 

2005; Rasmussen and others, 2008), although the rivers 

studied typically have been larger than the Tualatin River 

tributaries. Application of these techniques was attempted in 

small Pacific Northwest streams that have large changes in 

characteristics between low and high flow, and in agricultural 

and urban areas. As part of a long-term scientific collaboration 

between the USGS and Clean Water Services, this study 

evaluated the quantity and attributes of data that are necessary 

to build useful predictive models for such streams. 

Purpose and Scope

The purpose of this report is to evaluate the use of 

continuous monitors and autosamplers to collect representative 

and accurate water samples over a range of stream conditions, 

and to construct and demonstrate the use of preliminary 

predictive statistical models of unmeasured water-quality 

constituents in selected tributaries to the Tualatin River. 

Specifically, the objectives were to 

1. Evaluate the use of autosamplers for unattended sampling 

in conjunction with continuous monitors and evaluate the 

quality of autosamples;

2. Develop preliminary regression models to predict the 

concentrations of selected water-quality constituents using 

concurrent data from continuous monitors, and evaluate 

the robustness and accuracy of those models;

3. Evaluate the adequacy of available laboratory data 

to augment autosampler-derived data for developing 

regression models that estimate constituent concentrations 

and loads;

4. Use the regression models to predict and evaluate time 

series concentrations and to develop uncertainty estimates 

for modeled constituent concentrations from historical 

continuous monitor data at the same sites; and 

5. Identify potential changes to sampling strategies that 

would allow future monitoring efforts to improve the 

regression models developed.

Sites on six selected tributaries in the Tualatin River 

basin were studied from June 2002 through December 2003. 

The sites represented a range of upstream land uses, from 

intense urban development to rural agricultural and forested 

areas. Continuous, in situ monitors recorded stage, streamflow, 

water temperature, specific conductance, dissolved oxygen, 

pH, and turbidity. Autosamples were analyzed for a suite of 

nutrients (nitrogen and phosphorus species), total suspended 

solids, chloride, and bacteria (E. coli) over a range of 

stream conditions. Approximately 48 discrete autosamples 

were collected at each site over the course of two or three 

storm events. The model-building process was augmented 

by additional data from USGS and Clean Water Services 

databases, covering the study period 2002–07.

Full development of example models was limited to 

two target sites—Fanno Creek near Durham Road, and Dairy 

Creek at Highway 8 near Hillsboro. Data from USGS and 

Clean Water Services databases were used to augment the 

autosampler data. The additional USGS and Clean Water 
Introduction 5

Services data were concurrent with the dates of monitor 

deployment and, together with the continuous monitor data, 

were used to evaluate calibration and validation scenarios for 

these sites. For the remaining four non-target sites, preliminary 

model forms were identified but no additional data exploration 

was performed. Data were compiled for all samples collected 

using continuous monitors and autosamplers, but model 

development was limited to three whole-water constituents 

of primary interest to Clean Water Services and other local 

regulatory and resource-management agencies, specifically 

total suspended solids (TSS), total phosphorus (TP), and 

E. coli bacteria.

Study Area Description

The Tualatin River is a major tributary to the Willamette 

River near Portland in northwestern Oregon (fig. 1). The 

characteristics of the Tualatin River basin have been described 

in several reports, including those by Kelly and others (1999) 

and Rounds and Wood (2001). The basin has undergone 

rapid urbanization since the late 1980s and is now home to 

about half a million people, mainly in the central and eastern 

part of the basin and within the urban growth boundary of 

the Portland metropolitan area. Beyond the urban growth 

boundary, the fertile soil of the valley floor supports a wide 

variety of agricultural activities. The Coast Range Mountains 

to the west are densely forested and are a source for water 

supply and lumber production.

In an attempt to improve water quality in the Tualatin 

River and address specific issues related to algal growth 

and periodic high pH and low dissolved oxygen conditions, 

TMDLs for ammonia and phosphorus were set for the Tualatin 

River and its major tributaries in 1988 (Oregon Department 

of Environmental Quality, 1997), but primarily focused on 

the main stem Tualatin River. After the establishment of the 

TMDLs, studies of water quality in the main stem of the 

Tualatin River have highlighted the role of its tributaries 

as sources of TMDL constituents and oxygen-depleting 

substances to the main stem (Kelly, 1997). Results from the 

previous studies indicated that more information is needed on 

these constituents in the tributaries, and updated methods are 

needed to document their concentrations and delivery to the 

main stem during storm runoff periods.

 When the Tualatin River TMDLs were revised in 2001, 

the tributaries received greater attention. New TMDLs for 

water temperature, bacteria, and oxygen-depleting substances 

were created, and modified limits on ammonia and phosphorus 

were retained (Oregon Department of Environmental Quality, 

2001). Although the tributaries certainly affect the quality of 

water in the Tualatin River to some degree, the 2001 TMDLs 

demonstrated that the water quality and ecological health of 

the tributaries also was important.

Some river and tributary issues, such as high water 

temperature, algal growth, high pH, and low dissolved oxygen 

occur mainly during summer and autumn low-flow conditions, 

although high bacteria levels tend to be most problematic 

during storm events. An adequate characterization of the 

water-quality and ecological issues in the tributaries must 

include a good understanding of system behavior under a 

wide variety of conditions and time scales, and should address 

issues related to nutrients, bacteria, suspended solids, and 

other TMDL-related parameters. This study was designed to 

use continuous monitors to estimate some of these quantities, 

such as TP, TSS, and E. coli bacteria, and thereby aid in 

developing a better understanding of the dynamics of these 

parameters and how they affect stream quality.

Sites representing the broad range of land uses and 

hydrology in the basin were selected for development of 

regression models. The largest tributaries of the Tualatin 

River include Gales Creek (mainly forested), Dairy Creek 

(largely agricultural), Rock Creek (mixed urban), and Fanno 

Creek (urban). These creeks account for a large fraction of 

the drainage in the Tualatin River basin. A site on Beaverton 

Creek, a tributary to Rock Creek, has a large amount of 

upstream commercial and urban land use. Chicken Creek, a 

small tributary to the Tualatin River, was included because it 

drains a rapidly expanding urban and rural-residential area in 

the southern part of the basin. The locations and characteristics 

of these sites are shown in figure 2 and table 1. These creeks 

have water-quality problems that would benefit from further 

characterization.
6 Continuous Monitors and Autosamplers Used to Predict Water-Quality Constituents, Tributaries of Tualatin River, Oregon

tac09-0396_fig02

Dairy Creek

123°04' 123°00' 122°56' 122°52' 122°48' 122°44' 122°40'

45°32'

45°28'

45°24'

OREGON

Study

Area

0 5

5

Miles

0 Kilometers

6

2

1a

1b

4

3

5

Lake

Oswego

Durham

Tualatin

Portland

King

City

Tigard

Beaverton

Farmington

Hillsboro Cornelius

Forest

Grove

Dilley

WILLAMETTE

RIVER

TUALATIN RIVER

TUALATIN

RIVER

Rock

Beaverton

Creek

Creek

Creek

Fanno

Gales

Creek

Summer Creek

217

26

5

5

5

205

EXPLANATION

Target sampling site and map number 1a

Non-target sampling site and map number 3

1a Fanno Creek at Durham City Park

1b Fanno Creek at Durham Road

2 Dairy Creek at Highway 8

3 Beaverton Creek at SW 170th Avenue

4 Chicken Creek at Scholls-Sherwood Highway

5 Rock Creek at Woll Pond Way near Hillsboro

6 Gales Creek at Old Highway 47

Base modified from U.S. Geological Survey and Metro digital data sets (1:24,000)

Projection: Oregon Lambert, North American Datum 1983

Designated Urban Growth Boundary (2001)

Figure 2. Locations of sites selected for development of regression models for unmeasured water-quality constituents 

in the Tualatin River basin, Oregon, 2002–04.
Methods 7

Methods

Data Sources

Data used for the regression models were obtained from 

four primary sources. Continuous streamflow data were 

obtained from stream-gaging stations (table 1) operated by 

USGS or the Oregon Water Resources Department (OWRD). 

Continuous data for field parameters (specific conductance 

and turbidity) were obtained from monitors operated by USGS 

at each site (table 1) for the study period or longer, although 

the monitors were removed at some sites during high flow 

in winter. Water-quality data for sampled constituents such 

as TSS, nutrients, and E. coli bacteria were obtained from 

autosamplers deployed during the study period (appendix A)

and from historical datasets maintained by USGS (2001–07) 

and Clean Water Services (2001–04). 

USGS data primarily were available for the Fanno Creek 

at Durham Road site and were collected for various purposes; 

most high-flow water-quality data available for the Fanno 

Creek site were from the USGS historical database. Analyses 

included nutrients, suspended sediment, trace and major 

elements, and dissolved pesticides; however, only suspended 

sediment and TP were used for this report. Microbiological 

sampling, including E. coli bacteria, generally was not done by 

USGS during this period. Clean Water Services collects waterquality samples at least monthly at each of the study sites, and 

sometimes weekly, as part of its ambient monitoring program; 

however, high-flow periods are not specifically targeted and 

typically are under-represented in the Clean Water Services 

database. Clean Water Services sample analyses routinely 

include TSS, nutrients, and E. coli bacteria, among others. 

For the Dairy Creek site, Clean Water Services ambient 

monitoring data are the only available historical data, and are 

primarily from monthly samples. In addition, the available 

explanatory data are limited at this site because the continuous 

monitor was not deployed during winter until 2004–05. 

Finally, under certain conditions, streamflow at Dairy Creek 

can be affected by backwater from the Tualatin River, a 

situation that might invalidate any correlations established for 

unhindered flow conditions. 

Table 1. Sampling sites and characteristics in tributaries of the Tualatin River basin, Oregon, 2002–04.

[Target sites are those where data analysis included data from autosampler deployment plus historical data from U.S. Geological Survey and Clean Water 

Services databases, and model development included more extensive calibration and validation processes. Non-target sites were sites included in this study 

but where data analysis was limited to preliminary identification of model forms based on analysis of autosampler data alone. The site at Fanno Creek was 

moved from Durham City Park to Durham Road in 2003. Latitude and Longitude, in degrees, minutes, seconds, are based on North American Datum of 1927 

(NAD 27). Streamflow data source: Stream-flow data were obtained from U.S. Geological Survey (USGS) or Oregon Water Resources Department (OWRD) 

gaging stations. Data from on-site gaging stations were used directly; data from upstream gaging stations were routed and travel times estimated as described in 

text. Drainage areas are from StreamStats (http://water.usgs.gov/osw/streamstats/index.html). Abbreviations: mi, mile; mi2, square mile]

Station name Map 

No.

Station

identification 

No.

Latitude Longitude Streamflow 

data source

Drainage 

area

(mi2

)

Primary 

upstream 

land use

Monitor deployment dates

Target sites

Fanno Creek at Durham 

City Park

1a 452348122454701 45°23′49′′ 122°45′43′′ USGS (0.25 mi

upstream)

31.7 Urban Spring–autumn, 2001–02

Fanno Creek at Durham 

Road

1b 14206950 45°24’13” 122°45’13” USGS 31.5 Urban Continuous, September 2002–

current

Dairy Creek at Highway 8 2 453113123003501 45°31′13′′ 123°00′35′′ OWRD 229 Agricultural Spring-autumn, 2001–03; 

Continuous, 2004–current

Non-target sites

Beaverton Creek at SW 

170th Avenue

3 453004122510301 45°30′04′′ 122°51′03′′ Routed from

upstream OWRD

gaging stations

22.8 Urban Spring–autumn, 2001–03; 

Continuous, 2004–current

Chicken Creek at SchollsSherwood Highway4 452230122512201 45°22′30′′ 122°51′22′′ OWRD 15.3 Urban and

agricultural

Spring–autumn, 2001–current

Rock Creek at Woll Pond 

Way near Hillsboro

5 453104122551201 45°31′04′′ 122°55′12′′ Routed from

upstream OWRD

gaging stations

65 Urban Spring–autumn, 2001–03

Gales Creek at Old 

Highway 47

6 453040123065201 45°30′40′′ 123°06′52′′ OWRD 74.7 Forested Spring–autumn, 2001–06; 

Continuous, 2007–current
8 Continuous Monitors and Autosamplers Used to Predict Water-Quality Constituents, Tributaries of Tualatin River, Oregon

Monitors

Continuous water-quality monitors were operated 

according to standard USGS protocols (Wagner and others, 

2006). All monitors were the same, a YSI Environmental 

model 6920 multiparameter sonde equipped with probes to 

measure water temperature, specific conductance, turbidity, 

pH, and dissolved oxygen. Turbidity probes were YSI 

model 6026 probes, with the data reported in Formazin 

Nephelometric Units, or FNU (Anderson, 2004). Deployed 

monitors were cleaned and calibrated regularly, typically 

at 2-week intervals, and corrections due to cleaning and 

calibration were recorded. Data from the monitors were loaded 

into the USGS database and corrected to account for the 

effects of biofouling and sensor calibration drift according to 

procedures outlined by Wagner and others (2006). 

Each monitor was deployed in a 6-in. diameter PVC pipe 

mounted vertically on a steel post midstream at a height of 

approximately 6 in. to 1 ft above the streambed, with a locking 

cap for protection. The PVC pipe was perforated generously 

at the bottom to allow free circulation of stream water around 

the probes. Data were collected hourly. Periodically, and 

at a range of streamflows, the cross-sectional variation of 

monitor parameters was examined by making instantaneous 

measurements in a transect with a calibrated multiparameter 

instrument, and comparing the results to those logged by 

the monitor. The observed cross-sectional variability never 

exceeded the allowed calibration tolerances of the instruments; 

therefore, it was not necessary to adjust the monitor data to 

account for observed cross-sectional variations.

Values for field parameters used in the regressions 

(specific conductance and turbidity) were obtained from 

the USGS continuous monitors rather than the Clean Water 

Services database when possible, for two reasons. Primarily, 

for making predictions of water-quality constituents during 

unsampled periods, the monitor data (and stream gages) are 

the only available source of independent variables. Therefore, 

the data used for constructing regressions should be collected 

in the same manner and be as internally consistent as the data 

used for making predictions. Secondly, turbidity data are 

known to be highly dependent on the optical configuration 

of the probe and potentially even the instrument model used 

(Anderson, 2004); therefore, consistency in long-term data 

collection methods is a critical factor when using turbidity as 

a surrogate for other parameters. For these reasons, the USGS 

has used the same models of turbidity probes throughout 

the monitoring network in the Tualatin River basin since 

their installation. Clean Water Services field data are from 

similar instruments, but calibration techniques and data 

management (especially policies on shifting data according 

to calibration errors) are different from those used by USGS. 

Furthermore, the Clean Water Services laboratory uses a bench 

top meter to measure turbidity, which is likely to produce 

different results than the USGS monitors because of critical 

methodological differences (Anderson, 2004). Nonetheless, 

for some periods, particularly at the Dairy Creek site where 

the USGS monitor was removed each winter during 2002–04, 

data from the continuous monitors were unavailable and Clean 

Water Services data were occasionally used to calibrate the 

regression models. 

For purposes unrelated to this study, the monitoring 

site in Fanno Creek was moved in 2003 from Durham City 

Park (fig. 2, site 1a) about 0.25 mi upstream to Durham Road 

(fig. 2; site 1b). Monitor and autosampler data were from 

the Durham City Park site until January 10, 2003, and from 

the Durham Road site thereafter. The potential influence of 

moving this station on development and interpretation of the 

regression models is discussed in the “Relations Between 

Continuous Monitor Data and Selected Water-Quality 

Constituents” section.

Streamflow was continuously recorded at some sites 

(see table 1), either by USGS (Rantz and others, 1982) or by 

OWRD, according to standard USGS methods. The Dairy 

Creek site at Highway 8, which is about 2 mi from its junction 

with the Tualatin River, is susceptible to backwater from the 

Tualatin River during high flows in winter. Oregon Water 

Resources Department (ORWD) considers the stage-discharge 

rating at this site to be unreliable at a stage greater than about 

10 ft (D. Hedin, Oregon Water Resources Department, written 

commun., July 2008), although the rating may be reliable at 

stages as high as 15–16 ft when flows in the Tualatin River 

are not high. OWRD does not provide streamflow records 

for stages greater than 10 ft at this site. At the non-target 

sites Rock Creek and Beaverton Creek, which were ungaged, 

streamflow records at the monitor site were reconstructed 

by simple summation and routing of upstream, recorded 

discharges. Travel times from upstream sites were estimated 

by examining streamflow data at upstream sites combined with 

monitor data (especially turbidity and specific conductance) 

during storms to determine the timing of discharge peaks. The 

difference in timing of the peaks was used to linearly adjust 

upstream discharges to represent flow at the downstream sites. 

Attempting to simulate the discharge record during storms 

at the Beaverton and Rock Creek sites in this manner (that 

is, without a more extensive hydrologic modeling approach) 

exposed difficulties in the use of discharge as an independent 

variable for developing predictive regression models at 

ungaged sites, contributing to these sites’ consideration as 

non-target rather than target sites. 
Methods 9

Autosamplers

Autosamplers were operated as temporary installations 

for the duration of each storm or sampling event. 

Autosamplers used were ISCO, Inc., Model 6712 portable 

samplers, equipped with level sensors. Samplers were placed 

in a secure, level position on the streambank adjacent to the 

continuous monitors. Where possible, the samplers were 

housed in portable, locking fiberglass enclosures. Each 

sampler included a peristaltic pump to draw water from the 

stream through 3/8-in. inner-diameter vinyl tubing. Together 

with a communications cable from the water-quality monitor, 

this tubing was anchored to concrete blocks along the 

streambed. The intake tubing was positioned following USGS 

guidelines as summarized by G.D. Glysson, U.S. Geological 

Survey, written commun., 2009, and shown in table 2, 

except for items 3–5, which could not be determined with 

available resources for the study sites. The mouth of the vinyl 

tubing was secured to the perforated section of the monitor 

casing, oriented along the direction of flow and pointing 

downstream, an orientation that has been shown to minimize 

adverse sampling effects for pumping samplers (Winterstein, 

1986). Because of pumping constraints, efforts were made 

to minimize the length of tubing between the monitor and 

the autosampler, typically 12 to 25 ft, with resultant vertical 

heads between 2 and 10 ft. Complete elimination of dips in 

the tubing that might trap heavy sediment particles was not 

possible; however, an effort was made to minimize the dips in 

the tubing.

Each autosampler was configured with a carousel 

holding twenty-four 1-L polyethylene bottles. Prior to each 

deployment, the vinyl tubing and polyethylene bottles were 

cleaned with hot tap water and phosphorus-free detergent and 

thoroughly rinsed with deionized water. Upon deployment, the 

middle of the carousel in the autosampler was loaded with ice. 

Once deployed, samplers were visited at least once daily to 

check on the operation of the monitor and the sampler, and to 

change sample bottles, batteries, or ice, as necessary. 

Table 2. U.S. Geological Survey guidelines for placement of autosampler intake.

[From G.D. Glysson, U.S. Geological Survey, written commun., 2009. Abbreviations: mm, millimeter]

1. Select a stable cross section of reasonably uniform depth and width to maximize the stability of the relation between concentration at a 

point and the mean concentration in the cross section. This guideline is of primary importance in the decision to use an automatic sampler 

in a given situation; if a reasonably stable relation between the sample-point concentration and mean cross-section concentration cannot 

be attained by the following outlined steps, the sampler should not be installed and an alternate location should be considered. If banks are 

unstable and the sampler has to be installed in that location, install the intake on the cutting side of the channel so that the intake will not be 

buried.

2. Consider only the part of the vertical that could be sampled using a standard U.S Geological Survey depth- or point-integrating sampler, 

excluding the unsampled zone, because data collected with a depth- or point-integrating sampler will be used to calibrate the pumping 

sampler. (See Edwards and Glysson, 1999, fig. 1.)

3. Determine, if possible, the depth of the point of mean concentration in each vertical for each size class of particles finer than 0.250 mm, 

from a series of carefully collected point-integrated samples.

4. Determine, if possible, the mean depth of occurrence of the mean concentration in each vertical for all particles finer than 0.250 mm.

5. Use the mean depth of occurrence of the mean concentration in the cross section as a reference depth for placement of the intake.

6. Adjust the depth location of the intake to avoid interference by dune migration or contamination by bed material.

7. Adjust the depth location of the intake to ensure submergence at all times.

8. Locate the intake laterally in the streamflow at a distance far enough from the bank to eliminate any possible bank effects.

9. Place the intake in a zone of high velocity and turbulence to improve distribution by mixing, reduce possible deposition on or near the 

intake, and provide for rapid removal of any particles disturbed during the purge cycle. Avoid placing the intake in an eddy, as it will 

probably not be representative of the water in the cross section.

10. Consideration must be given to placing the intake and tubing in a place so that they will be protected during high streamflows.
10 Continuous Monitors and Autosamplers Used to Predict Water-Quality Constituents, Tributaries of Tualatin River, Oregon

The water-quality monitor and a separate water-level 

sensor were interfaced with the autosampler’s programmable 

computer. The ISCO water-level sensor used a pressure gage 

to sense the back pressure on air bubbled slowly through a 

small diameter tubing, the mouth of which was anchored to a 

fixed position in the stream. The water-level sensors proved 

to be unreliable and ultimately were used only for qualitative 

purposes to verify the timing of the streamflow peak rather 

than as a trigger for sampling or for depth data that could 

be used for correlations. Therefore, the autosamplers were 

programmed to use only turbidity data from the continuous 

monitor and were interrogated at 5-minute intervals, to 

trigger the sampling. Turbidity was considered the most 

reliable indicator that the stream was responding to a storm; 

an increase in turbidity of 10–15 FNU typically was used as 

the threshold for beginning sampling. Once triggered, the 

samplers were programmed to collect samples hourly, with 

a maximum of 24 bottles, and to record the monitor data and 

the time when each sample was collected. Prior to collecting 

each sample, the autosampler purged the vinyl sample tubing 

with air to remove any residual water and sediment, then 

performed three complete stream-water rinses of the line 

between the stream-end of the tubing and the liquid detector at 

the peristaltic pump head. Upon successful sample collection, 

the numbered bottles were retrieved and transported on ice 

to the Clean Water Services water-quality laboratory. The 

position of each bottle in the carousel was recorded, and 

the sampling data (timing of sample collection, water level, 

and water-quality data from the continuous monitor) were 

downloaded from the autosampler. Autosampler data from this 

study are reported in appendix A.

Autosampler Quality Assurance

Several issues potentially affecting the quality of data 

from autosampler-collected samples were identified and 

investigated at the beginning of the study. These included 

possible cross-contamination of samples from the vinyl 

autosampler tubing, which could not be washed between 

individual samples, and the degree to which samples collected 

by the autosampler at a point in the stream were comparable 

to those collected by standard USGS depth- and widthintegrating and ultra-clean sampling protocols (Horowitz and 

others, 1994; Edwards and Glysson, 1999; G.D. Glysson, U.S. 

Geological Survey, written commun., 2009) .

Cross-contamination initially was assessed in the 

laboratory by manually directing the sampler to collect 

a sequence of samples (three replicates each) from vats 

containing the following materials:

1. Clean distilled and deionized water,

2. Mixture of tap water and suspended soil,

3. Clean deionized water,

4. Deionized water with a high-nutrient synthetic standard, 

prepared by the USGS Oregon Water Science Center 

(previously described by Anderson and Rounds [2003, 

appendix A]), and

5. Deionized water with a low-nutrient synthetic standard.

The resuspended mixture of tap water and soil (step 2 above)

could not be uniformly mixed; therefore, results were not 

expected to be precise for analyses affected by particulates 

in water, such as TP. The point of using the soil mixture 

was to evaluate the extent of carryover of particulates to the 

subsequent deionized water samples. Samples from this series 

of tests were analyzed for nutrients (whole and filtered water) 

and chloride (filtered). For the synthetic standard samples, 

these tests also functioned as an evaluation of accuracy and 

precision in the sampling-analysis process. 

The results of this series of tests generally were good 

and indicated that carryover of contamination from sample 

to sample using autosamplers with appropriately cleaned 

and maintained equipment was minimal, and could not be 

distinguished from background laboratory contamination 

levels (table 3). A low level of contamination of blank water 

by soluble reactive phosphorus was detected in laboratory 

blank samples shown in table 3 (test 1, 0.009 mg/L as P) and 

in two of three initial blank tests through the autosamplers 

(test 4, 0.007 mg/L as P). Only one sample indicated a 

small carryover of suspended material, as measured by 

total Kjeldahl nitrogen (TKN), a low concentration (test 6, 

0.055 mg/L as N) just above the detection limit, in the first 

blank deionized water replicate following the soil mixtures. 

Considering that environmental concentrations of nutrients 

in storm runoff in the study streams were expected to be 

approximately 5–10 times higher than any contamination 

level detected in these tests, it was determined that neither 

contamination nor carryover was a major problem from the 

autosampler configuration.
Methods

11

Table 3. Results of laboratory quality-assurance tests of autosamplers.[Results in bold indicate potential contamination or carryover. Results in italics represent statistics or values not derived from the laboratory. Abbreviations: DI, distilled, deionized water; SRS, Standard Reference Sample; <, less than; N/A, not applicable]

Test 

sequence Sampling method Purpose Medium Replicate No.

Total 

suspended 

solids 

(mg/L)

Ammoniumnitrogen 

(mg/L as N)

Total 

Kjeldahl 

nitrogen

(mg/L as N)

Total 

phosphorus 

(mg/L as P)

Soluble 

reactive 

phosphorus 

(mg/L as P)

Chloride 

(mg/L) Comments

1 Direct pour (no autosampler) Test of lab 

cleanliness

DI 1 N/A < 0.01 < 0.05 < 0.025 0.009 < 0.25 Tests 1-3 are analysis of 

source materials, for comparison with results of samples collected with the autosampler in tests 4,6,7,8.

2 < 0.01 < 0.05 < 0.025 < 0.005 < 0.25

Median < 0.01 < 0.05 < 0.025 0.007 < 0.25

2 Direct pour (no autosampler) Test of lab 

accuracy

SRS, low N/A N/A 0.608 0.292 0.073 0.038 5.6

Expected value N/A 0.592 0.592 0.072 0.037 5.3

3 Direct pour (no autosampler) Test of lab 

accuracy

SRS, high N/A N/A 1.03 0.732 0.127 0.061 6.2

Expected value N/A 0.986 0.986 0.156 0.051 5.9

4 Autosampler Test of initial 

cleaning

DI blank 1 N/A < 0.01 < 0.05 < 0.025 < 0.005 < 0.25

2 < 0.01 < 0.05 < 0.025 0.007 < 0.25

3 < 0.01 < 0.05 < 0.025 0.007 < 0.25

Median < 0.01 < 0.05 < 0.025 0.007 < 0.25Standard Deviation 0.000 0.000 0.000 0.004 0.0

5 Autosampler Establish dirty 

tubing

Water + dirt 1 61 0.299 0.837 0.306 0.139 3.6 Quantity of dirt undefined. 

Sampled from bucket, mixed by hand.

2 57 0.297 0.976 0.301 0.132 3.6

3 48 0.299 0.96 0.294 0.133 3.6

Median 57 0.299 0.96 0.301 0.133 3.6Standard Deviation 6.7 0.001 0.076 0.006 0.004 0.0

6 Autosampler Test of cross 

contamination

DI blank 1 < 0.2 < 0.01 0.055 < 0.025 < 0.005 < 0.25 Tests carryover from high 

TSS samples in Test 5. 2 < 0.2 < 0.01 < 0.05 < 0.025 < 0.005 < 0.25

3 N/A < 0.01 < 0.05 < 0.025 < 0.005 < 0.25

Median <0.2 <0.01 < 0.05 <0.025 <0.005 <0.25Standard Deviation 0.0 0.000 0.032 0.000 0.000 0.0

7 Autosampler Test of dilution SRS, high 1 N/A 1.02 0.943 0.141 0.053 6.2 Tests high SRS and 

possibility of dilution when sampling low concentration sources before high concentration sources.

2 1.02 0.947 0.138 0.054 6.2

3 1.04 0.964 0.143 0.055 6.2

Expected value 0.986 0.986 0.156 0.051 5.9Median 1.02 0.947 0.141 0.054 6.2Standard Deviation 0.012 0.011 0.003 0.001 0.0

8 Autosampler Test of cross 

contamination

SRS, low 1 N/A 0.616 0.591 0.069 0.039 5.6 Tests low SRS and 

possibility of cross contamination when sampling highconcentration sources before low concentration sources.

2 0.62 0.609 0.069 0.039 5.6

3 0.626 0.603 0.068 0.045 5.6

Expected value 0.592 0.592 0.072 0.037 5.3Median 0.62 0.603 0.069 0.039 5.6

Standard Deviation 0.005 0.009 0.001 0.003 0.0
12 Continuous Monitors and Autosamplers Used to Predict Water-Quality Constituents, Tributaries of Tualatin River, Oregon

For the most part, results from the synthetic standard 

samples were within expected ranges. However, TKN 

concentrations in the low- and high-level synthetic standard 

samples (tests 2 and 3) seemed to be biased low by about 

25 to 50 percent and the TP concentration in the high-level 

synthetic standard (Test 3) was almost 20 percent lower than 

expected. The synthetic standard tests were not repeated 

for this study, but they are repeated monthly as part of an 

ongoing quality-assurance program between the USGS 

and Clean Water Services. For 2002–03, TKN analysis of 

synthetic standard and spiked river water samples by Clean 

Water Services consistently had recoveries of 90–100 percent 

compared to expected values, indicating that the low 

recoveries for TKN shown in table 3 were anomalous. For 

TP, recoveries from the USGS-Clean Water Services Quality 

Assurance program during 2002–03 tended to be lower 

than for TKN, about 80–90 percent, but also were relatively 

consistent. 

For field deployments of autosamplers, the determination 

of cross-section coefficients (also known as a box coefficient) 

is used to evaluate how point concentrations derived from an 

autosampler compare to depth- and width-integrated samples 

from across the range of streamflow conditions at the site 

(G.D. Glysson, U.S. Geological Survey, written commun., 

2009). The coefficients also can be used to make corrections 

to autosampler data, if necessary. The box coefficient is 

calculated as the Ci/Cp, where Ci is a concentration derived 

from depth- and width-integrated sampling techniques, and 

Cp is the concentration from a pumping sampler. If the crosssection coefficient is near 1 for a given hydrologic condition, 

then no adjustment in concentrations is advised. 

Box coefficients and carryover through autosampling 

were assessed at base flow in Fanno Creek through a 

comparison of replicate stream-water samples collected using 

the autosampler, with samples collected using depth- and 

width-integrating techniques according to USGS protocols 

(table 4). An additional comparison was completed at Dairy 

Creek during mid-winter storm sampling. 

Some minor variations were observed in the 

determinations of autosampler box coefficients, but these were 

within analytical uncertainty. For example, the calculated 

box coefficient for TSS at Fanno Creek is 0.75; however, 

upon closer examination this primarily may be a result of 

laboratory variability. The triplicate cross-sectional samples 

(taken as aliquots from a churn splitter from a single depthand width-integrated sampling) had moderate variation in the 

reported laboratory TSS concentrations (standard deviation 

2.5), resulting in a median total TSS of 3 mg/L, whereas 

the replicate autosampler values were identical at a similar 

concentration (4 mg/L). E. coli bacteria counts showed the 

largest variability and the lowest box coefficient; however, 

bacteria counts also are known for their variability using 

current techniques and the differences shown here are not 

of concern at the low levels observed. Similarly, results of 

autosampler and equal width increment samples at Dairy 

Creek during a storm sampling in November 2003 were within 

8 percent for all constituents and are indistinguishable from 

analytical variability. Adjustment of autosampler data by 

the box coefficient is not warranted by data in table 4 or by 

comparison with laboratory analytical uncertainty. However, 

additional comparisons at higher discharge (and higher 

suspended sediment concentrations) still are warranted to 

verify these findings under varying conditions and at different 

sampling sites. 

To further evaluate carryover, the streambed was stirred 

to suspend sediments upstream of the autosampler intake, 

and samples were collected from within that plume and after 

the plume had passed or settled (approximately 15 minutes), 

as turbidity at the monitor returned to its baseline value. As 

expected, samples from the period when the streambed was 

being disturbed (sequence numbers 8–10, table 4) showed 

a high degree of variability, which is not a concern because 

the disturbance was essentially random. More importantly, 

after settling samples for all constituents (sequence numbers 

11–13, table 4) were not much different from those prior to the 

disturbance of the streambed. 
Methods

13

Table 4. Quality-assurance tests comparing samples collected using the autosampler with samples collected using depth- and width-integrating techniques, and testing carryover between sequential samples, Fanno and Dairy Creeks, Oregon.[The cross-section coefficient (or box coefficient) is determined as Ci/Cp, where Cp is the concentration from the pumping sampler and Ci is the integrated, cross sectional concentration, collected according 

to U.S. Geological Survey sampling protocols. Abbreviations: E. coli, Escherichia coli bacteria; AS, autosampler; Rep, replicate; FC, Fanno Creek near Durham; EWI, equal width increment; DI, distilled, 

deionized water; DC, Dairy Creek at Highway 8; <, less than; E, estimated; mg/L, milligram per liter; N, nitrogen, P, phosphorus; mL, milliliter]

Description and time of 

sample collection

Test

sequence 

No.

Location, date

Total 

suspended 

solids 

(mg/L)

Ammoniumnitrogen 

(mg/L as N)

Total 

Kjeldahl 

nitrogen

(mg/L as N)

Nitrate 

(mg/L as 

N)

Total 

phosphorus 

(mg/L as P)

Soluble 

reactive 

phosphorus 

(mg/L as P)

Chloride 

(mg/L)

E. coli 

(colonies 

per 100 

mL)

Comments

AS, Native Rep No. 1 11:30 1 FC, 06-06-2002 4 0.037 0.310 0.545 0.126 0.086 13.0 270 Native

AS, Native Rep No. 2 11:35 2 4 0.036 0.247 0.537 0.123 0.082 13.1 390

Median (Cp ) 4 0.037 0.279 0.541 0.125 0.084 13.1 330Standard Deviation 0 0.001 0.045 0.005 0.002 0.003 0.071 84.9

EWI, Native Rep No. 1, 11:40 4 FC, 06-06-2002 6 0.040 0.279 0.547 0.128 0.079 13.3 190 Comparison of 

autosampler with equal-widthincrement sample

EWI, Native Rep No. 2, 11:40 5 1 0.041 0.311 0.544 0.131 0.086 13.3 230

EWI, Native Rep No. 3, 11:40 6 3 0.040 0.291 0.542 0.127 0.081 13.3 240

Median (Ci) 3 0.040 0.291 0.544 0.128 0.081 13.3 230Standard Deviation 2.5 0.001 0.016 0.002 0.002 0.004 0.000 26.5

  Cross-section coefficient 0.75 1.10 1.04 1.01 1.03 0.96 1.02 0.70

AS, DI Blank, after Natives, 11:45 7 FC, 06-06-2002 1 <0.01 < 0.05 0.008 < 0.025 < 0.005 < 0.25 < 1 Test for carryover

AS, Kicked up turbidity, Rep No. 1, 12:00 8 FC, 06-06-2002 76 0.035 0.474 0.544 0.204 0.084 13.2 240 Tests dilution (from DI 

blank in sample 7) AS, Kicked up turbidity, Rep No. 2, 12:02 9 147 0.036 0.452 0.542 0.276 0.084 13.2 330

AS, Kicked up turbidity, Rep No. 3, 12:05 10 28 0.035 0.287 0.539 0.152 0.085 13.1 190

Median 76 0.035 0.452 0.542 0.204 0.084 13.2 240Standard Deviation 59.9 0.001 0.102 0.003 0.062 0.003 0.058 70.9

AS, Settled turbidity, 12:14 11 FC, 06-06-2002 5 0.035 0.19 0.543 0.117 0.081 13.2 260 Test for carryover

AS, Settled turbidity, 12:15 12 3 0.034 0.312 0.545 0.127 0.077 13.3 250

AS, Settled turbidity, 12:16 13 5 0.035 0.268 0.542 0.125 0.081 13.5 360

Median 5 0.035 0.268 0.543 0.125 0.081 13.3 260Standard Deviation 1.2 0.001 0.062 0.001 0.005 0.002 0.153 60.8

AS, Native Water, 11:26 14 DC, 11-20-2003 13.0 E 0.018 0.478 0.700 0.144 0.052 6.42 190 Calculation of Cp at 

Dairy Creek EWI, Native Water, 11:30 15 12.0 E 0.017 0.501 0.702 0.151 0.055 6.43 180

  Cross-section coefficient 0.92 0.94 1.05 1.00 1.05 1.06 1.00 0.95
14 Continuous Monitors and Autosamplers Used to Predict Water-Quality Constituents, Tributaries of Tualatin River, Oregon

Clean Water Services Laboratory

Autosampler-derived water samples were analyzed 

by Clean Water Services at their water-quality laboratory 

in Hillsboro, Oregon. Samples were delivered immediately 

after retrieval from the autosamplers and subsampled for the 

indicated constituents (table 5). The analyzed constituents 

consisted of nutrients, suspended solids, bacteria, and 

chloride, several of which are regulated by the TMDL (Oregon 

Department of Environmental Quality, 2001). E. coli bacteria 

were analyzed immediately upon sample delivery to the Clean 

Water Services laboratory. Analyses for total and dissolved 

nutrients and TSS were started within 1–3 days of sample 

delivery, well within allowable holding times. Analytical 

methods and reporting limits are indicated in table 5.

Laboratory Quality Assurance

The Clean Water Services water-quality laboratory has a 

rigorous internal quality-assurance program. The laboratory 

also participates in the USGS national Standard Reference 

Sample (SRS) program, a national interlaboratory comparison 

study (see http://bqs.usgs.gov/srs/). Results from many 

years of participation in the SRS program have shown that 

the Clean Water Services laboratory consistently produces 

results that are sufficiently accurate for the parameters in 

this and other studies (bacteria are not included in the SRS). 

During 2002–05, Clean Water Services laboratory results for 

TP samples across a broad range of nominal concentrations 

(0.085–1.35 mg/L) were biased low by about -2 to -8 percent 

in 13 of 17 samples, and biased high by about 0 to 5 percent 

in 4 samples. Likewise, results from the TKN samples were 

biased low by about -1.5 to -16 percent in 8 of 10 samples, and 

Table 5. Constituents analyzed from water samples collected during stormflows,Tualatin River tributaries, 

Oregon, May 2002 to September 2004.

[Method number: EPA, U.S. Environmental Protection Agency (1993); SM, Standard Methods (American Public Health 

Association, 1992). Abbreviations: STORET, U.S. Environmental Protection Agency’s data Storage and Retrieval system; 

E. coli, Escherichia coli; mg/L, milligram per liter; mL, milliliter]

Parameter 

(abbreviation)

STORET 

code Units Method number

Reporting

level Analyzing laboratory

Total suspended solids (TSS) 530 mg/L EPA 160.2 0.2 Clean Water Services

Ammonia nitrogen (NH3-N) 608 mg/L EPA 350.1 0.01 Clean Water Services

Total Kjeldahl nitrogen (TKN) 625 mg/L EPA 351.2 0.1 Clean Water Services

Nitrate plus nitrite nitrogen (NO3-N) 631 mg/L EPA 353.1 0.01 Clean Water Services

Total phosphorus (TP) 665 mg/L EPA 365.4 0.025 Clean Water Services

Soluble reactive phosphorus (SRP) 671 mg/L EPA 365.1 0.005 Clean Water Services

Chloride (Cl-

) 941 mg/L EPA 300.3 A 0.1 Clean Water Services

E. coli bacteria 31648 (100 mL)-1 SM 9213 D 1 Clean Water Services

biased slightly high (as much as 2.5 percent) in the remainder. 

Furthermore, the Clean Water Services laboratory methods 

and protocols have been reviewed by the USGS Branch of 

Quality Systems and were determined to be suitable. The 

Clean Water Services laboratory also participates in an annual 

Tualatin River basin Interlaboratory Comparison Study, which 

includes all laboratories that routinely analyze water samples 

for government agencies in the Tualatin River basin. 

On the basis of standard samples from both the Oregon 

Water Science Center (ORWSC) and national programs, TP 

data from Clean Water Services used in this study most likely 

were biased slightly low, but generally were consistent. The 

data, therefore, were unadjusted and considered adequate for 

the purposes of the study. When data from USGS and Clean 

Water Services laboratories are used together, any potential 

differences are reflected in the statistical uncertainty for the 

individual correlations. Ultimately, TKN was not included in 

the regression analysis, and any potential bias therefore was 

not relevant to the results of the study.

Field methods in use by Clean Water Services are 

similar to those used by USGS, including the collection of 

samples using depth- and width-integrating techniques and 

the use of churn splitters for subsampling; therefore, the 

respective laboratory methods are the most likely sources of 

any differences between the two datasets. Previous studies 

(Horowitz and others, 1994; Gray and others, 2000) have 

demonstrated that analysis of total suspended solids (TSS), 

the analytical technique used by Clean Water Services and 

many other agencies, is often biased low compared to the 

analysis for suspended sediment concentration (SSC), as 

practiced by USGS. The difference in results between the 

methods is primarily attributed to subsampling; specifically, 
Methods 15

the SSC method includes measurement of sediment in the 

entire sample, but the TSS method measures sediment 

amounts in a subsample removed from the original sample 

bottle. The subsampling process can underestimate sediment 

concentrations, especially if sand concentrations are high or 

flocculation occurs, because these particles settle quickly in 

a sample bottle, and obtaining a representative subsample is 

difficult (Gray and others, 2000). No data are available for 

direct comparison of SSC and TSS in the USGS and Clean 

Water Services databases, respectively. For this report, the 

TSS and SSC data were combined without adjustment, and 

the variability introduced by the two methods is therefore 

incorporated into the regression results. 

Data Aggregation

The intent of this report is to use the available datasets 

for model development and calibration, and to compare the 

most promising model forms and their resulting coefficients, 

to determine the likelihood that suitable predictive models 

can be used to understand transport and loading of the 

indicated constituents (TSS, TP, E. coli bacteria) at the 

Fanno and Dairy Creek sites. The data were aggregated into 

data scenarios, to evaluate (1) model calibration using the 

autosampler-only dataset, and model validation using available 

Clean Water Services data; (2) model calibration with the 

autosampler data plus the available historical USGS data, and 

model validation with Clean Water Services data; and (3) a 

combined dataset using data from all sources for calibration, 

while retaining independent data for model validation. 

Details of these scenarios for Fanno Creek at Durham are 

shown in table 6 and figure 3. During the model construction 

process, if the input variables identified as contributing the 

most information remained similar regardless of the scenario 

used, and likewise if the regression coefficient values 

remained moderately consistent for a given predicted variable 

regardless of scenario, then it could be concluded that the 

models were relatively robust and could be used reliably until 

additional data are collected that can be used to refine those 

models. On the other hand, if the use of different datasets 

resulted in widely varying model forms and coefficients, or 

goodness‐of‐fit diagnostic statistics that differ greatly, then 

additional and targeted data most likely are needed prior to 

development of useful models. All independent variables 

(specific conductance and turbidity data plus streamflow or 

stage) were consistently taken from the same data sources for 

all scenarios and model runs. Data from continuous monitors 

came from USGS, and streamflow or stage data came from 

USGS for Fanno Creek and from OWRD for Dairy Creek at 

Highway 8.

Duration curves are commonly used in hydrologic 

studies to document the range of conditions measured at 

a site during an indicated period, including the frequency 

and magnitude of certain conditions. These curves depict 

cumulative distributions of all measurements during the 

study period, and show the percentage of time during the 

study period that specific values for the constituent were 

equaled or exceeded. Although typically used for discharge 

records, duration curves have increasingly been constructed 

for water quality constituents for which high density data can 

be collected, including continuous monitoring data such as 

those collected in this study (Rasmussen and others, 2008). 

Duration curves (fig. 3) for selected continuous parameters 

used in the regressions provide information about the 

relative magnitude of the parameter values during sampling, 

compared to the full range measured at the site during the 

study period. For example, if a given turbidity associated with 

a particular sample was exceeded only 5 percent of the time 

during the study period, then that measurement represents 

a relatively high turbidity; however, if the turbidity of a 

sample was exceeded 50 percent of the time or more often, 

then that sample represents average or low-flow conditions, 

respectively. 

During the early phases of model development, it was 

determined that exclusive use of the autosampler-derived 

data (Scenario 1) to construct predictive statistical models 

was flawed because of correlation issues. The regression 

process assumes that input data points are truly independent; 

however, data collected over a single hydrograph are not 

completely independent of one another. For example, as 

streamflow increases during a storm, samples collected 

sequentially are more likely to be similar to each other than 

samples collected during different storms or under completely 

different sampling conditions. This serial correlation is a 

problem when using the Scenario 1 (autosampler-only) 

dataset for model building purposes. Methods to account for 

serial correlation are available, such as introducing a lag in 

the data to reduce the interdependence of individual samples 

(Helsel and Hirsch, 1992); however, these methods were not 

used in this study because it was recognized that, whether 

or not a lag was introduced to account for serial correlation, 

the Scenario 1 datasets would be insufficient for regression 

modeling because the range of stream conditions encompassed 

by the autosampler deployments was limited (fig. 3A). As an 

example, the peak discharge sampled by the autosamplers at 

Fanno Creek was about 134 ft3/s (table 6); although discharges 

during the study period were as much as 780 ft3/s (fig. 3) 

and historical high flows occasionally have been greater than 

1,000 ft3/s. For the purposes of simplicity and to focus on 

the larger study objectives, serial correlation was therefore 

ignored in the analysis of Scenario 1 and 2; instead, the 

Scenario 3 dataset was created to avoid serial correlation (see 

below). When appropriate, the potential influence of serial 

correlation on model development based on Scenario 1 and 

Scenario 2 is discussed. 
16 Continuous Monitors and Autosamplers Used to Predict Water-Quality Constituents, Tributaries of Tualatin River, Oregon Table 6. Datasets showing laboratory sample results and summary statistics for scenarios used for calibration and validation of regression models with continuous monitors at Fanno Creek, Durham, Oregon. [Discharge, turbidity, and specific conductance data were from records at stream gages and continuous monitors. Occasional Clean Water Services samples were collected at higher discharges than shown here, but continuous monitors were not deployed at the time so samples could not be used for this study. Note that USGS uses suspended sediment concentration (SSC) rather than total suspended solids (TSS). SSC and TSS data were combined without adjustment for Scenario 2 and Scenario 3. Abbreviations: FNU, Formazin Nephelometric Unit; n, number of samples (numbers for individual models may vary where missing values or outliers were removed); NWIS, National Water Information System water quality database; USGS, U.S. Geological Survey; ft3/s, cubic foot per second; μS/cm, microsiemens per centimeter; mg/L, milligram per liter; No./100 mL, colony count per 100 milliliters] Scenario and summary statistic Calibration Validation Discharge (ft3/s) Turbidity (FNU) Specific conductance (μS/cm) Total suspended solids (mg/L) Total phosphorus (mg/L) E. coli (No. /100 mL) Total suspended solids (mg/L) Total phosphorus (mg/L) E. coli (No. /100 mL) Scenario 1 Autosampler (2002–03), n = 54 Clean Water Services ambient monitoring (2001–04), n = 98 Minimum 7.1 5.7 110.0 4.0 0.08 180 2.0 0.08 56 Maximum 134.4 131 245.0 196.0 0.47 10,000 260.0 0.60 12,000 Mean 65.7 47 159.5 51.0 0.19 2,829 16.9 0.15 763 Median 63.9 38 146.0 37.1 0.15 1,300 7.2 0.14 325 Standard deviation 31.0 30 34.7 45.6 0.10 3,174 34.8 0.08 2,020 Scenario 2 Autosampler (2002–03) + USGS NWIS (2001–07), n = 131 Clean Water Services ambient monitoring (2001–04), n = 98 Minimum 1.12 1.4 63.8 0.5 0.08 180 2.0 0.08 56 Maximum 505.0 131 394.0 242.0 0.47 10,000 260.0 0.60 12,000 Mean 49.1 28 187.2 29.9 0.15 2,829 16.9 0.15 763 Median 33.2 17 182.2 12.5 0.14 1,300 7.2 0.14 325 Standard deviation 65.0 29 52.7 40.9 0.07 3,174 34.8 0.08 2,020 Scenario 3 Autosampler peak discharge samples, USGS NWIS high flow 2001–07, Clean Water Services first monthly + high flow, n = 96 Autosampler non-peak discharge samples, USGS non-high flow, Clean Water Services remaining monthly and non-high flow, n = 80 Minimum 1.1 3.9 63.8 2.0 0.08 67 0.5 0.08 56 Maximum 505.0 131 281.0 242.0 0.47 6,300 260.0 0.60 10,000 Mean 57.9 25 177.5 26.3 0.14 824 29.6 0.17 1,565 Median 35.4 17 168.0 13.5 0.13 345 10.0 0.14 520 Standard deviation 80.7 26 48.8 39.4 0.05 1,428 40.7 0.09 2,546
Methods 17

tac09-0396_fig03

1

10

100

1,000

1

10

100

1,000

0.01 0.1 1 10 30 50 70 90 99 99.9

DISCHARGE, IN CUBIC FEET PER SECOND

PERCENT OF TIME INDICATED DISCHARGE IS EXCEEDED

Discharge record, 

 water years 2001–07 

Autosamplers

Clean Water Services

U.S. Geological Survey

A. Scenario 1

C. Scenario 3

1

10

100

1,000

B. Scenario 2

Figure 3. Flow duration curves with samples collected from each data source for (A) Scenario 1, (B) 

Scenario 2, and (C) Scenario 3, for Fanno Creek near Durham, Oregon, water years 2001–07. Samples used 

for model calibration are in orange, and samples used for model validation are in black. See table 6 for a 

description of the data aggregation into Scenarios 1, 2, and 3.
18 Continuous Monitors and Autosamplers Used to Predict Water-Quality Constituents, Tributaries of Tualatin River, Oregon

In Scenario 2, Fanno Creek autosampler data are 

augmented with additional data collected by the USGS 

for water years 2001-07 (table 6 and fig. 3B); validation 

is done with Clean Water Services data as in Scenario 1. 

Scenario 2 provides an example of one relatively simple 

method to aggregate data when multiple sources are 

available. Serial correlation between autosampler-derived 

data remains an issue in Scenario 2, although the influence 

of serial correlation on the outcome is reduced by the 

additional data. USGS data were collected for various 

purposes, including routine monitoring (USGS National 

Water-Quality Assessment Program, http://water.usgs.gov/

nawqa/) and other, more targeted studies (McCarthy, 2000; 

Anderson and Rounds, 2003), and are stored in the USGS 

National Water Information System (NWIS) database (http://

nwis.waterdata.usgs.gov/or/nwis/qwdata). The USGS and 

Clean Water Services routine monitoring designs do not 

target specific flow conditions, and the resulting dataset is 

primarily composed of base-flow (non-storm) samples 

during all months; high-flow samples are present only when 

storm events coincided with scheduled sampling events. 

At least one USGS study (Anderson and Rounds, 2003) did 

focus on high-flow and runoff conditions, thus providing 

several samples at discharges greater than those sampled by 

the deployed autosamplers. Additionally, occasional USGS 

and Clean Water Services samples were collected prior to 

2002 at higher discharges, but continuous monitors were not 

deployed at the time so the results could not be used for this 

study.

Scenario 3 was created specifically to minimize the stated 

problems in the Scenario 1 and 2 datasets. To avoid the serial 

correlation bias, Scenario 3 used only the autosampler data 

collected during peak flow in each individual storm sampled 

(that is, one sample per storm); to minimize base-flow bias, a 

subset of the routine Clean Water Services ambient monitoring 

data was used with selected high-flow data from the USGS 

and Clean Water Services historical datasets. From the routine 

Clean Water Services data, only the first data point in each 

month was included, which reduced the number of base-flow 

samples but still represented seasonal patterns; sometimes 

these samples represented moderate storm runoff, although 

most samples were collected at relatively low flows. For high 

flow, any samples that were potentially representative of 

storm response were desired; thus, high flow was determined 

as any data point with an associated discharge greater than 

the 25th percentile value for that month, as defined from 

monthly flow‐duration statistics computed from the NWIS 

database for the period of record at Fanno Creek near Durham 

(USGS stream-gaging station 14206950), October 2000–

September 2007. This strategy allowed Scenario 3 to capture 

summer and spring storm responses while minimizing 

samples corresponding to low flows. Because of the 

paucity of high-flow samples, most available samples were 

used for calibration; however, a few were retained for 

validation (fig. 3C).

For Scenario 1, the highest discharges sampled for model 

calibration were exceeded about 10 percent of the time from 

water year 2001 to 2007 (fig. 3). The Scenario 1 validation 

dataset (in black) has three samples at slightly higher 

discharges (exceeded about 7–9 percent of the time). By far 

the bulk of the samples were at discharges exceeded about 

10–70 percent of the time, during base-flow to moderate storm 

runoff. The addition of data from USGS in Scenario 2, and 

re-aggregation to use a broad range of the samples from Clean 

Water Services’ database in Scenario 3, added successively 

greater numbers of samples from higher discharges, or those 

exceeded less than 1 to about 10 percent of the time. However, 

even in Scenario 3 only a few of these higher flow samples 

were available, and some were needed for model validation, 

whereas in each scenario large numbers of samples were 

collected during relatively low-flow conditions, when the 

respective discharges were frequently exceeded. 

Any potential contamination detected during the 

autosampler quality-assurance tests (table 3) was well 

below most of the sample concentrations included in the 

aggregated datasets. For example, although soluble reactive 

phosphorus was detected in blank water at 0.007–0.009 mg/L, 

the minimum TP concentrations in Scenarios 1, 2, and 3 

were 0.08 mg/L, and the medians ranged from 0.13 to 0.15 

mg/L (table 6). Maximum ambient concentrations for model 

calibration were as much as 0.47 mg/L. Even if contamination 

at less than 0.01 mg/L is pervasive, its effect on the model 

formulation is likely small. 

For the site at Dairy Creek near Highway 8, data were 

similarly aggregated into scenarios; however, no historical, 

independent data from USGS were available, so only two 

scenarios were evaluated. These and other differences in 

the input data are described in the section on Dairy Creek 

model results. Duration curves for samples used in the Dairy 

Creek analysis are not shown because the Dairy Creek data 

aggregation process was less complex than for Fanno Creek, 

and potential problems with backwater effects on discharge 

would complicate the construction of duration curves. 

Dairy Creek at Highway 8 is sampled routinely by Clean 

Water Services but not by USGS, with the exception of the 

autosampler deployments in autumn 2003. For that reason, 

the available data for calibration and validation of regression 

models from 2002 to 2004 are more limited than at Fanno 

Creek (table 7). Scenario 1 was derived in the same way as 

for Fanno Creek, using the autosampler data for calibration 

and Clean Water Services data for validation. However, 

for calibration, Scenario 2 used the Clean Water Services 

samples at high stage, the first routine Clean Water Services 

samples from each month, and the peak discharge samples 

collected from the two autosamplers during autumn storms. 
Methods

19

Table 7. Datasets showing laboratory sample results and summary statistics for scenarios used for calibration and validation of regression models with continuous monitors at Dairy Creek at Highway 8 near Hillsboro, Oregon.[Discharge, turbidity, and specific conductance data were from records at stream gages and continuous monitors. Occasional Clean Water Services samples were collected at higher discharges than shown here, but continuous monitors were not installed at the time so samples could not be used for this study. Abbreviations: FNU, Formazin Nephelometric Unit; E. coli, Escherichia coli bacteria; n, number of samples (numbers for individual models may vary where missing values or outliers were removed); ft3/s, cubic foot per second; μS/cm, microsiemens per centimeter; mg/L, milligram per liter; No./100 mL, colony 

count per 100 milliliters]

Scenario and 

summary statistic

Calibration Validation

Discharge 

(ft3/s)

Turbidity 

(FNU)

Specific 

conductance 

(μS/cm)

Total 

suspended 

solids 

(mg/L)

Total 

phosphorus 

(mg/L)

E. coli

(No. /100 mL)

Total 

suspended 

solids 

(mg/L)

Total 

phosphorus 

(mg/L)

E. coli

(No. /100 mL)

Scenario 1 Autosampler (2002–03), n = 38 Clean Water Services ambient monitoring (2001–04), n = 116

Minimum 38.7 5.7 117.0 5.0 0.09 40 2.8 0.05 28

Maximum 158.7 30 141.0 31.0 0.23 950 38.0 0.29 1,800

Mean 90.6 14 127.9 13.3 0.14 331 11.9 0.12 286

Median 92.4 15 124.5 11.5 0.14 260 11.2 0.11 185

Standard deviation 34.7 6 7.2 6.7 0.04 256 5.4 0.04 302

Scenario 2 Autosampler peak discharge samples, Clean Water Services first monthly + high flow, n = 42 Autosampler non-peak discharge samples, Clean Water Services remaining monthly and non-high flow, n = 113

Minimum 16.0 3.2 67.0 2.8 0.06 28 2.8 0.05 31

Maximum 705.0 16.2 190.0 38.0 0.21 1,500 31.0 0.29 1,800

Mean 137.1 10.0 108.3 12.8 0.11 256 12.1 0.13 314

Median 74.8 9.6 102.0 12.4 0.11 110 11.2 0.13 225

Standard deviation 162.2 3.4 25.8 6.4 0.04 329 5.5 0.04 277
20 Continuous Monitors and Autosamplers Used to Predict Water-Quality Constituents, Tributaries of Tualatin River, Oregon

Validation data for Scenario 2 used the remaining Clean Water 

Services monitoring data combined with the autosampler data 

from times other than peak discharge. This formulation of 

Scenario 2 datasets differs from Scenario 2 used for Fanno 

Creek (table 6), where USGS data along with the autosampler 

data were used for calibration in Scenario 2. Furthermore, no 

Scenario 3 dataset was warranted for Dairy Creek.

Having minimized the potential serial correlation and 

base-flow bias problems, the Scenario 3 dataset from Fanno 

Creek is presumed the most likely to produce robust regression 

models. This dataset includes high-flow data from USGS, 

monthly and high-flow data from Clean Water Services, and 

the peak discharge samples collected by the autosamplers. 

Two major limitations in the compilation of data, however, 

result from using historical data rather than data collected 

specifically for this study. First, few samples were collected 

during storm and high-flow conditions, which not only 

reduces the size and range of the available dataset for model 

calibration but also the available data for model validation. 

Second, for the purposes of this exercise, laboratory data from 

all sources were compiled together.

Regression Models

Several methods were used to evaluate potential 

regression models, with the intent that any models described 

herein are examples of the types of models that could be 

useful for predictive purposes, even if they currently lack 

sufficient data for either calibration or validation purposes. 

The functional form of the models is 

1 2

1 2

( , ,.. ),

where

is the dependent variable,

, ,.. are explanatory variables, and

the notation

() indicates that is a function of the 

indicated explanatory variables.

n

n

y f x x x

y

x x x

f y

= (1)

If initial correlation attempts look promising, then the results 

are given in tables for a specified parameter that show model 

coefficients and regression statistics for regression equations 

of the form:

1 2 n

1 2

... ,

where

, ,... are regression coefficients, 

 is an error term,or intercept, and 

, , ,... are as already described. n

y ax bx mx

a b m

y x x x

= + + + ε

ε

(2)

The dependent variables (y) are the predicted concentrations 

of water-quality constituents from laboratory analysis, 

such as TSS, TP, or E. coli bacteria, and the independent or 

explanatory variables are the continuously measured data 

such as streamflow, stage, specific conductance, or turbidity. 

Residual plots were generated during the regression process 

(SAS Institute, 1989) to help determine the degree of 

homoscedasticity (homogeneity in the variance) and identify 

outliers in the datasets.

Log transformation, which sometimes allows more robust 

regression predictions, was performed on independent and 

dependent variables and these transformed variables were 

evaluated for utility in making predictions. Log transformation 

can provide better homoscedasticity and result in more 

symmetric datasets with normal residuals (Gray and others, 

2000). When regression models are developed with data that 

violate assumptions of normality and homoscedasticity, the 

models are less likely to apply over the range of expected 

conditions for the site, and large prediction errors may occur. 

Rasmussen and others (2009) recommend log transformations 

for development of estimated suspended sediment 

concentrations and loads as a function of continuous turbidity 

and (or) discharge data, and this approach has been used with 

success for suspended sediment and other selected variables 

in streams in Kansas (Rasmussen and others, 2008), Oregon 

(Uhrich and Bragg, 2003; Anderson, 2007), and Florida (Lietz 

and Debiak, 2005). 

Some constituents may be affected by seasonal 

considerations that explicitly need to be included in the 

regression modeling. For example, nutrient concentrations 

in surface waters might be partially dependent on water 

temperature and its effects on biological processes, riparian 

plant growth and its ability to reduce erosion, or even the 

amount of daylight hours and its effects on algal production. 

Similarly, bacterial growth in streams (E. coli bacteria, in 

this study) is generally considered tightly coupled with 

water temperature, among other factors. Even TSS could 

have a seasonal component if factors such as the effect of 

riparian vegetation on erosion or seasonal rainfall patterns are 

important. Although the continuously measured parameters 

used in this study (discharge, turbidity, specific conductance) 

inherently incorporate these seasonal fluctuations, seasonality 

was also explored in the regression modeling with sine and 

cosine transformations of the sample date. The following two 

terms were evaluated as additional explanatory variables:

*sin( *2*pi / 365.25), and

*cos( *2*pi / 365.25),

where

and are regression coefficients similar to , ,

and in equation 2, and

is in decimal days of the year, with 365.25

representing the averag

c date

d date

c d a b

m

date

e number of days

in a year.
Methods 21

These two terms must be used together to capture and express 

an annual periodic cycle with an unknown phase offset. Using 

only the sine or cosine term without the offset is less likely to 

capture a periodic signal in the data. However, the sine and 

cosine terms also could cause an interaction with the other 

independent variables; therefore, the model building is done 

with and without the sine and cosine terms, and the presence 

of such interactions is then detected using an F-test (Helsel 

and Hirsch, 1992; R. Hirsch, U.S. Geological Survey, written 

commun., December 2008). Sine and cosine terms were tested 

in regression models for TSS, TP, and E. coli bacteria for 

the data scenarios that are presumed the most robust input 

calibration data at the respective sites; that is, Scenario 3 for 

Fanno Creek and Scenario 2 for Dairy Creek. 

Data used in this study were initially examined 

graphically for patterns between potential explanatory 

variables and the dependent variables. Some patterns that 

were observed included the presence of bimodal distributions 

or possible outliers that might affect regressions among the 

constituents, and correlations (either positive or negative) that 

might be indicative of predictive signals. Because of their 

potentially large effect on the regression statistics, outliers 

were defined as any data points lying more than three times 

the interquartile range beyond the 25th and 75th percentile 

values for a particular constituent (Lewis, 1996; Uhrich and 

Bragg, 2003; Lietz and Debiak, 2005; Rasmussen and others, 

2008), and investigated for possible data coding problems, 

field or laboratory irregularities, or other documented issues 

that might explain their abnormality. If documented problems 

could not be corrected, the data were excluded from regression 

calculations, whereas the data were retained if all available 

information confirmed the sample integrity.

Model building was initially performed with backward, 

stepwise, linear regressions (Helsel and Hirsch, 1992; SAS 

Institute, 1989), with an alpha value of 0.05, using either 

the original or log-transformed data, whichever provided 

the best fit. When stepwise-regression selected independent 

variables that were surrogates for each other (for example, 

untransformed and log-transformed versions of the same 

variable, or stage and streamflow), one variable was removed 

and the stepwise process was repeated. However, stepwise 

regression algorithms tend to continue adding explanatory 

variables until the coefficient of determination (R2) is 

maximized, whether or not the added variables actually 

provide useful information, and can create models that are 

overfitted (Burnham and Anderson, 2002). Therefore, the 

initial stepwise regressions were used only as a starting point 

to evaluate additional model forms using reduced sets of 

explanatory variables. Subsequent iterations were performed 

to minimize Mallow’s Cp (SAS Institute, 1989; Draper and 

Smith, 1998), and used the adjusted-R2, which penalizes 

additional variables, as a model selection scheme. This process 

was similar to a “Best-Subsets” regression (Draper and Smith, 

1998), although less formal. 

One challenge when using stepwise regression or other 

algorithms to select the best correlation was exploring the 

use of variables in their native units and log transformed 

forms. Although one might want to evaluate native and 

transformed variables, inclusion of the forms together 

introduces opportunities for significant cross correlation 

or multicollinearity; software programs that automatically 

perform such algorithms are usually incapable of 

distinguishing between variables that are truly independent 

and those that are transformed versions of another variable. 

The process of model selection by necessity, therefore, was 

iterative and ultimately was reduced to using log-transformed 

dependent variables to minimize the possibility that the 

resulting predicted values would be negative, while evaluating 

native and transformed versions of the independent variables 

using the methods discussed previously. 

Interactions between independent variables, such as 

occurs if one variable is dependent on another, can reduce 

the reliability of correlation coefficients (Draper and Smith, 

1998), and can contribute to overfitting of regression models 

(SAS Institute, 1989). The net result tends to be an increase 

in the standard errors of the independent variables, an effect 

that is minimized with increased observations. One measure of 

multicollinearity is the Variance Inflation Factor (VIF), which 

measures the degree to which the variance of the coefficient 

of determination for a particular variable is increased because 

of interdependence between that variable and others in a 

particular model. The VIF is calculated as

2

1

2

1

1/(1- ) ,

where

is the coefficient of determination for the

regression of the th independent variable

on all other independent variables

(Draper and Smith, 1998).

R

R

i

(3)

The value of the VIFs are dependent solely on the interactions 

of the independent variables with each other. Thus, VIFs for 

a set of independent variables can vary according to datasets 

used, or in this study, according to scenarios. Likewise, 

the same dataset may be used in regressions for different 

dependent variables, and the VIFs would be identical for each 

identical grouping of independent variables; regressions with 

only one variable have no interactions and, therefore, no VIF 

is applicable. 
22 Continuous Monitors and Autosamplers Used to Predict Water-Quality Constituents, Tributaries of Tualatin River, Oregon

The acceptable magnitude of a VIF is dependent on the 

objectives of a specific study. Several rules-of-thumb for VIFs 

are sometimes given, and tend to range from greater than 

0.2 to less than 10 (Helsel and Hirsch; SAS Institute), but 

variables with VIFs exceeding these levels may still be useful 

in a model if they have a low p value. Alternately, a critical 

value for a maximum acceptable VIF (referred to hereafter as 

VIFcrit) for an equation can be calculated by substituting the 

overall coefficient of determination of the model (R2, or in this 

study, adjusted-R2) for 2 Ri in equation 3 (SAS Institute, 1989). 

If the result is smaller than any of the VIFs of any variable 

in the equation, then multicollinearity may have contributed 

to the inclusion of that variable in the model, although 

consideration of the significance of that variable (p value) 

in the model remains important. A low adjusted-R2, as in 

equation 3, will result in a low VIFcrit and reduce the apparent 

level of interaction that is allowed among model variables. In 

this study, VIFs were obtained as output from the statistical 

software (SAS Institute, 1989). 

When log-transformed dependent variables are included 

in regression models, a transformation bias can be introduced 

when the results are converted back to native units for making 

predictions. In these cases, a bias correction factor, or BCF 

(Helsel and Hirsch, 1992) is necessary; the BCF is multiplied 

by the value of the predicted dependent variable after the BCF 

is transformed back into native units by taking the antilog. 

That is, 

 ́ *10 ,

where

 ́is the final, predicted value, untransformed into

native units, and 

y is the value of the log-transformed dependent 

variable as calculated in equation 2.

y y BCF

y

= (4)

Duan’s BCF is the average of the residuals of the dependent 

variable in the regression dataset; when the dependent variable 

was log transformed, the antilog of the residual was taken 

before averaging to determine the BCF. Likewise, when logtransformation was used for prediction, the lower and upper 

95 percent prediction interval values (SAS Institute, 1989; 

Helsel and Hirsch, 1992) also were converted to native units 

with the antilog, and these were corrected using the same BCF 

as the predicted dependent variables. 

For the Fanno Creek and Dairy Creek sites, predicted 

hourly concentrations and their 95 percent prediction intervals 

were computed for selected water-quality constituents using 

regression models, and using the indicated hourly monitor and 

streamflow records as independent variables. Predictions were 

evaluated against the available validation data (tables 6 and 

7) by interpolating the hourly predictions to the time of the 

validation samples, and then comparing the resulting values 

to the validation samples using a series of goodness-of-fit 

statistics (table 8). This validation exercise for the regression 

models provides an independent measure of the quality of 

the predictions for the dependent variables and could assist 

in the decision about which model is the most robust. Not all 

goodness-of-fit metrics in table 8 are shown in subsequent 

tables of model results, due to space constraints, but all were 

used in evaluation of model performance. The available input 

datasets and resulting regression models were not adequate for 

making predictions for the non-target sites (table 1), and only 

the preferred model forms, without the supporting regression 

coefficients, are presented to provide an indication of the most 

important independent variables to consider for monitoring.

Where regression results seem to provide a reasonable 

starting point for future modeling, several model forms are 

shown along with their respective coefficients, diagnostic 

statistics, and selected goodness-of-fit statistics. Diagnostic 

statistics include the adjusted-R2 and the root mean square 

error (RMSE) of the regression. The RMSE assesses the 

typical error between predicted and observed values. As the 

root mean square is equal to the square of the mean plus the 

square of the standard deviation, then if the mean error is zero 

(no bias), the RMSE is equal to the standard deviation of the 

errors. The Nash-Sutcliffe Coefficient (Nash and Sutcliffe, 

1970), otherwise known as the Coefficient of Model-Fit 

Efficiency, is one of the goodness-of-fit statistics computed 

for these models and commonly is used for assessing the 

accuracy of hydrologic models. Imbalance in the model 

residuals is assessed by examining the number of negative and 

positive differences between a model’s predicted results and 

the comparable laboratory values; a sign test can be used to 

estimate the likelihood that the residuals were random in the 

positive or negative directions. 

Results using several models illustrate the potential 

explanatory variables and transformations of variables that 

could be used and the effects of using different input datasets. 

More detailed regression, neural network, or autoregressive 

models could be built and would be useful for comparison. 

This study, however, is meant to be a proof of concept rather 

than a definitive model building exercise. Simple multiple 

linear regressions should be sufficient to determine whether 

adequate information is present in the monitor data to predict 

TP, TSS, and E. coli bacteria in Fanno and Dairy Creeks.
Methods

23

Table 8. Goodness-of-fit statistics used for evaluation of regression model predictions.[Abbreviations: NA, not applicable; n, number of samples]

Description of test Acceptable range Explanation

Number of points compared NA; more points Number of validation data points with comparable predicted values from the regression. 

Analogous to ‘n’ in regression model, determines degrees of freedom.

Mean error (ME) Near zero—exact range depends on constituent Average error between predicted values and laboratory values in validation data set—a 

good measure of bias.

Mean absolute relative error 

(percent)

0–50 Average absolute difference between predicted and laboratory value as normalized to 

laboratory values.

Root mean square error (RMSE) Depends on constituent; a number closer to zero is 

better

A measure of the square of the mean plus the square of the standard deviation. If the 

mean error is zero, then the RMSE is equal to the standard deviation of the errors—a good measure of the magnitude of the typical error of the prediction. A high R2 with a 

poor fit based on the RMSE is possible if the range of the data is large. 

Coefficient of determination (R2) Approximately 0.6–1.0, although user defined. Analogous to coefficient of determination for regression, based on differences of 

predicted and known values of independent variables.

Nash-Sutcliffe coefficient Approximately 0.6–1.0, although user defined. Also called the Coefficient of Model Fit Efficiency—it is the proportion of variance in 

the measured values that is explained by the predicted values, and is a more rigorous fit statistic than the coefficient of determination. A value of 1.0 is a perfect fit. A value of 0 indicates that the model predictions are only as accurate as the mean of the observed data. Anything less than zero means that the observed mean is a better predictor than the model. However, note that the value of this coefficient is highly dependent on the available validation data. If validation data are insufficient to characterize the response 

variable, this coefficient may under-represent the true fit of the model.

Number of negative differences Similar to number of positive differences Number of predicted values that are less than the relevant laboratory value.Number of positive differences Similar to number of negative differences Number of predicted values that are greater than the relevant laboratory value.Probability from sign test > Typically greater than 0.05 Using the sign test on the residuals, this is the statistical probability that the number of 

positive and negative differences could have resulted if the errors were truly random in direction.

z-statistic from sign test < Typically less than 1.96 Using the sign test on the residuals, the z-statistic provides a statistical measure that 

determines whether the number of positive and negative differences could have resulted if the errors were random in direction.
24 Continuous Monitors and Autosamplers Used to Predict Water-Quality Constituents, Tributaries of Tualatin River, Oregon

Relations Between Continuous 

Monitor Data and Selected 

Water-Quality Constituents

Autosampler Deployment Dates and Conditions 

Streams were sampled with autosamplers during storms 

from spring 2002 through autumn 2003 (table 9). Because 

of differences in hydrologic characteristics and responses 

among sites, inconsistent spatial extent of storms, and resource 

availability, only a few streams were sampled during any 

individual storm. To obtain the desired number of samples 

(approximately 48–50) covering reasonably broad ranges of 

field parameters and to develop robust regression models, 

some streams were sampled during more storms than others. 

For example, Rock, Chicken, and Fanno Creeks were sampled 

during three storms each, whereas Gales Creek was only 

sampled during one storm. 

In some cases, the storms sampled by autosamplers at 

individual sites represented different seasonal conditions. 

For example, Beaverton and Rock Creeks were both sampled 

in summer (June) and autumn (December). This difference 

in season helped increase the range of field and laboratory 

constituent values obtained, which ordinarily would be useful 

for deriving strong correlations. For some field values at some 

sites, however, the same temporal differences also resulted 

in bimodal distributions that likely reduced the quality of the 

autosampler-derived regression models. For example, specific 

conductance at some sites was less variable during individual 

storms than between seasons, which abnormally skewed 

regressions that relied on specific conductance at those sites. 

For this reason, where bimodal distributions were observed in 

the initial graphical analysis, those parameters were removed 

from consideration for regressions at the respective locations. 

For the most part, bimodal distributions occurred primarily 

at the non-target sites, whereas the autosampler deployments 

at Fanno Creek were primarily during late spring in 2002 and 

2003, and the deployments at Dairy Creek occurred in autumn 

2003 (table 9) possibly serving to narrow the resulting range 

of constituent values. 

In the following sections, figures are provided to show 

the percentage of time during the study period that the field 

values measured during the autosampler deployments were 

exceeded. Monitors were installed primarily during the late 

spring, summer, and early autumn because of reduced access 

when winter flows were high. Therefore, the data used to 

determine the percentage of time a given value was exceeded 

do not include the typically higher-discharge in winter, when 

it could be expected that, on average, specific conductance 

generally would be lower but also highly variable, and 

turbidities would be higher than during the months of monitor 

deployment. 

Table 9. Dates of autosampling and peak storm discharges at 

Tualatin River tributary sites, Oregon, spring 2002 to autumn 2003.

[Abbreviations: ft3/s, cubic feet per second]

Stream Sampling dates

Peak storm 

discharge 

(ft3

/s)

Beaverton Creek June 28–30, 2002 207

December 10–12, 2002 207

Chicken Creek June 17, 2003 12.2

May 8, 2003 22

May 17–18, 2003 27

Dairy Creek October 9, 2003 42

November 19–20, 2003 158

Fanno Creek June 17–18, 2002 94

May 4–5, 2003 134

May 8–9, 2003 118

Gales Creek November 17–20, 2003 245

Rock Creek June 28–30, 2002 180

December 10–12, 2002 372

September 5–9, 2003 70

The Fanno and Dairy Creek sites were sampled during 

relatively moderate storms during the study period (fig. 3), 

resulting in a smaller but sometimes bimodal range for specific 

conductance and turbidity. Overall, the range of physical 

conditions encountered while sampling was somewhat 

narrow. Discharges increased moderately during storms but 

mostly did not represent the highest peaks that commonly 

occur during some years; likewise, turbidity and other field 

parameters showed only moderate ranges during the sampled 

storms. Caution must be exercised when using regression 

equations from this analysis if conditions are outside the range 

documented during this study (tables 6 and 7). Extrapolation 

of regression equations beyond the bounds of the data used 

to formulate them is considered a potentially large source 

of error and is not recommended (Helsel and Hirsch, 1992). 

To a certain extent, the validation datasets in this study 

allow evaluation of the error introduced when the regression 

models are applied to conditions beyond the range of the 

input datasets. However, the validation datasets are limited 

in the range of conditions encompassed and therefore do not 

provide much additional information about the adequacy of 

the regression models to address many of the higher flow 

conditions. 
Relations Between Continuous Monitor Data and Selected Water-Quality Constituents 25

Figure 4. Probability that (A) specific conductances and (B) turbidities during autosampling were exceeded 

at Fanno Creek at Durham, Oregon, May 2002 to September 2004. 

Probability

Autosampler

PERCENT OF TIME INDICATED VALUES WERE EXCEEDED

SPECIFIC CONDUCTANCE, 

IN MICROSIEMENS PER CENTIMETER

A.

B.

0 10 20 30 40 50 60 70 80 90 100

0 10 20 30 40 50 60 70 80 90 100

1

10

100

1,000

50

100

150

200

250

300

350

400

450

500

0

TURBIDITY, IN FORMAZIN 

NEPHELOMETRIC UNITS

Fanno Creek at Durham Road

Autosampler Data

The storms were sampled by autosamplers at Fanno 

Creek in late spring or early summer. The sampled peak 

discharges (94–134 ft3/s, table 9) covered a narrow range of 

potential discharges for this site (fig. 3); although storms of 

this size are fairly representative for May–June in most years, 

peak discharges exceeded these amounts at least 15 times on 

other dates during the study period (http://waterdata.usgs.

gov/or/nwis/, accessed November 17, 2005). Fanno Creek is 

in a highly urbanized basin and responds quickly to rainfall 

making it a challenge to anticipate and react to storms to 

collect high-flow samples. This situation is indicative of the 

need for automatic sampling and increases the likelihood that 

extreme events will not be adequately sampled. 

Several critical constituents exhibited less variability 

during individual autosampler storm events than between 

sampling events. This resulted in bimodal data distributions of 

the autosampler data, as illustrated for specific conductance, 

in figure 4. Data points tended to be clustered into small 

groups, and generally represented average conditions rather 

than the rarely occurring extremes usually indicative of storm 

conditions. 
26 Continuous Monitors and Autosamplers Used to Predict Water-Quality Constituents, Tributaries of Tualatin River, Oregon

Specific conductance measured during sample collection, 

for example, was exceeded from about 25 to more than 

95 percent of the time during the study period. The duration 

curves for specific conductance are different than for turbidity 

because the sources and mechanisms affecting the two are 

different. During base flow, specific conductance at Fanno 

Creek is relatively high (>200 μS/cm) but rainfall is dilute 

(about 0–20 μS/cm) so dilution by rain can cause a large range 

in responses. Turbidity caused by suspended particles may 

come from upland sources transporting particles to the stream, 

or from within the stream from erosion or resuspension. 

Turbidity values measured during autosampler deployment 

were more indicative of the higher and more continuous range 

during the study than were specific conductances, and were 

exceeded only about 2 to 34 percent of the time during the 

entire study period. Model results from conditions of specific 

conductances, and turbidities beyond those actually measured 

during the samplings in the Scenario 1–3 datasets (table 6) 

cannot be verified. 

The relations between Fanno Creek field parameters and 

laboratory sample results from the autosampler deployments 

and the Scenario 3 dataset show some useful patterns (fig. 5). 

In figure 5A, the symbols for a given storm and constituent 

combination sometimes show different patterns indicating 

the differences between storms. Possible linear relations for 

the autosampler dataset are indicated for combinations of 

turbidity or discharge with TSS, TP, and E. coli bacteria. In the 

larger dataset represented by Scenario 3, linear relations are 

indicated between turbidity and TSS, TP, and E. coli bacteria. 

Several other potential relations are indicated, particularly for 

discharge and TSS or TP; however, considerable scatter is also 

apparent. 

The occasional bimodal data distributions among 

storms and the narrow range of peak flows sampled (figs. 3, 

4, and 5) indicate that the autosampler-derived data may be 

inadequate to develop robust regression models between 

field parameters and laboratory sample results for Fanno 

Creek near Durham. Patterns in the autosampler-derived 

data, however, also support the possibility that such models 

might be constructed with a more comprehensive dataset. The 

main limitations of the autosampler data, beyond any serial 

correlation issues, are that they do not represent all seasons 

or the high flow conditions, and that some constituent data 

are bimodal. The incorporation of USGS-NWIS and Clean 

Water Services ambient monitoring datasets into Scenario 2 

and 3 datasets for Fanno Creek, in addition to the autosampler 

data, was an attempt to overcome these limitations (table 6). 

Outliers observed in the scatter plots were removed to prevent 

unacceptable leverage on the regression computations. 

Total Suspended Solids

Several regression models for TSS at Fanno Creek 

near Durham are listed and characterized in table 10. The 

preferred models produced with each scenario included 

turbidity and specific conductance as explanatory variables. 

Discharge (or stage, as a surrogate for discharge) was included 

as an explanatory variable for Scenarios 1 and 2, but added 

little information in Model 5, as compared to Model 4, in 

Scenario 3. The values of the model coefficients for turbidity 

(0.01 and 0.009) and specific conductance (-0.003 and -0.003) 

did not change much in Scenario 3, whether or not discharge 

(Q) was included, nor was there any substantive change 

in the BCF or adjusted-R2. Sine and cosine terms were not 

significant (p>0.05) for the models, indicating that seasonality 

was either unimportant or was already captured by the 

continuously monitored variables; these terms are therefore 

not shown in table 10.

Log transformation of the dependent variable was 

especially helpful for producing estimated TSS concentrations 

using continuous monitor data, despite requiring the use of 

a BCF when transforming the estimated values into normal, 

non-logarithmic space. In Scenario 1, the coefficients of 

determination for log-transformed (Model 1, adjusted 

R2 = 0.936) and non-transformed (Model 2, adjusted 

R2 = 0.956) TSS are good. However, many non-transformed 

values of TSS predicted from continuous monitor data using 

Model 2 (not shown) were negative, particularly outside of the 

specific calibration period of the autosampler storms; negative 

predicted TSS values are an unacceptable outcome and render 

Model 2 unusable. In subsequent regression calculations, 

the log-transformed values of TSS were always used for the 

dependent variable. 

The use of different data scenarios for developing 

regression models met with mixed success but illustrates 

the need for more comprehensive input data. As expected, 

Scenario 1, using only the autosampler data, produced 

regressions with high adjusted-R2 (> 0.90), most likely a 

result of serial correlation in the autosampler-only data 

and a small range of environmental conditions sampled; 

however, the Scenario 1 regressions also had relatively large 

mean error and validation RMSE values, and non-randomly 

signed residuals from the sign test, when compared with the 

broader validation dataset. Model 3 (Scenario 2), combining 

autosampler data with USGS-NWIS historical data, had 

similar calibration statistics to Model 6 (Scenario 3) but 

still may have been affected by serial correlation in the 

autosampler data. Nonetheless, from the validation process 

for Model 3, the mean error was intermediate (although 

indicating a high bias rather than a low bias) and the z-statistic 
Relations Between Continuous Monitor Data and Selected Water-Quality Constituents 27

tac09-0396_fig05CONTINUOUSLY MEASURED CONSTITUENTS

LABORATORY CONSTITUENTS

B. Scenario 3: 

SPECIFIC CONDUCTANCE,

IN MICROSIEMENS PER 

CENTIMETER

TURBIDITY,

IN FORMAZIN

NEPHELOMETRIC

UNITS

DISCHARGE,

IN CUBIC FEET

PER SECOND

A. Scenario 1: ( Storm 1, Storm 2, Storm 3)

SPECIFIC CONDUCTANCE,

IN MICROSIEMENS PER 

CENTIMETER

TURBIDITY,

IN FORMAZIN

NEPHELOMETRIC

UNITS

DISCHARGE,

IN CUBIC FEET

PER SECOND

TOTAL

PHOSPHORUS

TOTAL

SUSPENDED

SOLIDS

E. coli 

BACTERIA

Figure 5. Matrixes of scatter plots of calibration data using (A) Scenario 1 and (B) 

Scenario 3 datasets, from Fanno Creek near Durham, Oregon. Scenario 1 data were 

from autosampler deployments: storm 1, June 17–18, 2002; storm 2, May 4–5, 2003; 

storm 3, May 8–9, 2003. Data sources for Scenario 3 include U.S. Geological Survey 

(USGS) historical data, Clean Water Services ambient monitoring, and autosamplers, 

from 2001 to 2007. All Escherichia coli (E. coli) bacteria data in (B) are from the Clean 

Water Services ambient monitoring program because no E. coli bacteria data were 

collected by USGS. Units for total suspended solids and total phosphorus are in 

milligrams per liter, and units for E. coli bacteria are in colonies per 100 millimeters.
28 Continuous Monitors and Autosamplers Used to Predict Water-Quality Constituents, Tributaries of Tualatin River, Oregon Table 10. Preliminary model statistics for correlation of total suspended solids with continuous parameters at Fanno Creek near Durham, Oregon. [Regression models are of the form TSS = a*Turb+ b*

Q + c*SC + d, where a, b, and c are model coefficients and d is the intercept, Turb, Q, and SC are the explanatory variables turbidity (in Formazin 

Nephelometric Units), discharge (in cubic feet per second), and specific conductance (in microsiemens per centimeter), respectively, and TSS is the dependent variable, total suspended solids, in milligrams per liter. In some models, as indicated by the model form column, the dependent or explanatory variables were log transformed. In some models Stage (stream stage, in feet) was used instead of 

Q, and its 

coefficient is shown in the ‘b’ column. Where TSS is log transformed, a bias transformation factor (BCF; Duan, 1983) is multiplied by 10(logTSS) to get the final value. RMSE values are in milligrams per 

liter. The maximum Variance Inflation Factor (VIF) indicates the largest VIF obtained for any one variable in the correlation. Abbreviations: n, number of samples; Adj.-

R2, adjusted 

R2, a coefficient of 

determination, which adjusts for degrees of freedom and penalizes the use of too many explanatory variables; f, a function of indicated constituents; log, base 10 logarithm; RMSE, root mean square error; USGS, U.S. Geological Survey; –, not included in the regression]

Model No. 

and form

Model calibration

Model validation—Goodness-of-fit evaluation

Value of coefficient, when used Correlation statistics

a b c d BCF n Adj.-

R2 Maximum VIF Mean error Validation RMSE Coefficient of determination Nash- Sutcliffe coefficient z-statistic from sign test

Scenario 1 Calibration data set—Autosamplers only Validation data set—Clean Water Services ambient monitoring data

1. logTSS=f(logTurb, logStage, logSC) 1.19 2.56 2.1 -6.09 1.03 54 0.936 2.8 -1.9 12.9 0.83 0.78 5.35

2. TSS=f(Turb, logStage, logSC) 1.32 132.1 156.6 -414.6 – 54 0.956 2.4 -8.5 168 0.71 0.58 5.95Scenario 2 Calibration data set—Complete autosampler storm samples + USGS 2001–07 Validation data set—Clean Water Services ambient monitoring data

3. logTSS=f(Turb, log

Q, logSC) 0.011 0.432 0.49 -0.834 1.1 131 0.848 14.1 4.1 38.6 0.83 -0.94 1.51

Scenario 3 Calibration data set—Peak autosampler plus first monthly and high flow 

monitoring samples from USGS and Clean Water Services datasets

Validation data set—Remaining monthly low flow USGS + Clean 

Water Services ambient monitoring + non-peak autosampler data

4. logTSS=f(Turb, SC) 0.01 – -0.003 1.42 1.06 96 0.885 1.7 -0.58 8.44 0.04 0.02 1.67

5. logTSS=f(Turb, Q, SC) 0.01 0.0006 -0.003 1.35 1.05 96 0.885 2.2 -0.52 8.42 0.04 0.03 2.12

6. logTSS=f(Turb, log

Q, logSC) 0.009 0.224 -0.5 1.69 1.04 96 0.897 4.4 -0.85 30.8 0.05 0.04 1

1 Exceeds a threshold VIF value, calculated as {1/(1– (Adj.-R2)} and indicates possible multicollinearity.
Relations Between Continuous Monitor Data and Selected Water-Quality Constituents 29

from the sign test was considerably lower than the models 

from Scenario 1; also, the coefficient of determination (0.83) 

was the among the highest of all models. Despite the more 

randomly signed residuals, the Nash-Sutcliffe coefficient 

for Scenario 2 indicates that the predictive power of Model 

3 may be worse than using the mean of the laboratory data. 

For Scenario 3 (which uses high‐flow data from USGS and 

Clean Water Services, with monthly Clean Water Services 

ambient monitoring data and the peak discharge samples from 

the autosampler deployment), the regression coefficients, 

correlation statistics, and validation statistics were similar with 

or without discharge (Models 4 and 5). Model 6 was evaluated 

to test the importance of log transformation of the explanatory 

variables in Scenario 3, but this transformation increased 

the mean error and RMSE for the validation statistics. All 

Scenario 3 models had poor coefficients of determination 

(<0.1) and Nash-Sutcliffe coefficients (<0.1), suggesting that 

they did not reproduce the validation data well, and that the 

means of the validation data would provide estimates that were 

as good or better than the model estimates. However, because 

the validation data are heavily weighted towards base‐flow 

conditions, and the objective of the modeling exercise is 

primarily to predict the high constituent concentrations during 

stormflows, these coefficients probably do not adequately 

reflect the utility of the model.

Model 5 produced diagnostic statistics equivalent to 

Model 4 but used an extra variable, discharge (Q), indicating 

that Model 5 probably is overfitted and therefore less robust 

(Helsel and Hirsch, 1992), and that Model 4 may be the most 

appropriate functional form given the available datasets. 

Conversely, specific conductance has little physical relevance 

to TSS other than as a surrogate for discharge, yet it was 

an important variable in all models. VIF values were less 

than 5 for all independent variables in the models shown; 

however, the largest VIF for Model 3 (for logQ) exceeded the 

VIFcrit. LogQ was highly significant in the model (p <0.0001, 

not shown); whereas logSC was only slightly significant 

(p = 0.07). The inclusion of discharge as an independent 

variable may be needed to represent mid-winter, high-flow 

conditions. Specific conductance was a significant (p<0.05) 

model coefficient in all other models. The relatively low 

VIFcrit for Model 3 may be a reflection of the Scenario 2 

dataset and the model’s relatively low adjusted-R2.

Plotting a time series of the measured TSS concentrations 

against those predicted using the regression models allows 

the overall results of the model to be evaluated qualitatively. 

Individual data from the Scenario 3 calibration and validation 

datasets and the results from spring 2002 until summer 2003 

including the 95 percent prediction interval of Model 4, are 

shown in figure 6. This period includes the two storms when 

the autosampler was deployed (table 9), and encompasses 

January 2003 when the station was moved from Durham City 

Park to Durham Road (fig. 6). Upon a cursory inspection, 

the model seems to predict slightly lower baseline TSS 

concentrations prior to moving the station. However, this 

period also predominantly encompasses spring–autumn, 2002, 

with naturally lower discharges; whereas the period after 

January 2003 is predominantly winter, characterized by higher 

discharges, so it is reasonable to expect higher baseline TSS 

concentrations in the winter. Calibration and verification data, 

which (except for the autosampler data) were collected at the 

Durham Road site, also show this shift, indicating that moving 

the station had a negligible effect on model calibration and 

predictions. 

Although Scenario 3 demonstrates the type of dataset 

that may be most appropriate for developing robust regression 

models (that is, data that are independent, year-round, and 

include high-flow samples), validation of results from Models 

4, 5, and 6 is hampered because few high-flow samples are 

included in either the calibration or validation datasets. Many 

high TSS concentrations are predicted but few calibration 

or validation data points are present during the high TSS 

events for comparison (fig. 6). Model 4 appears to be the 

most robust model for TSS at Fanno Creek, on the basis 

of up-front assumptions about the value of the different 

potential calibration datasets, and on the results in table 10

(the relatively low coefficient of determination [0.04] from 

the validation dataset notwithstanding). Visually, figure 6 

shows that the storm-related predictions are relatively accurate 

for the moderate-sized storms represented in the available 

datasets. The log-scale used on the y-axis in figure 6 can cause 

a misperception in the magnitude of errors: during base-flow 

conditions, the model appears to slightly overpredict TSS 

concentrations; however, the actual errors are small compared 

to those at higher concentrations. Validation of these models 

at higher concentrations cannot be accomplished with the 

available data. 

Comparing the measured and predicted values directly 

provides additional perspective into the uncertainties and 

limitations of the available datasets and models (fig. 7). That 

comparison, using results from Model 4, shows that the 

indicated prediction interval spans a range of almost an order 

of magnitude (~0.75 log units) for a given measured value. 

Available measured-TSS data are relatively well represented 

up to about 102 (or 100) mg/L, with a few additional samples 

at slightly higher concentrations up to about 102.5 (or 

316) mg/L. 
30 Continuous Monitors and Autosamplers Used to Predict Water-Quality Constituents, Tributaries of Tualatin River, Oregon Figure 6. Time series of predicted concentrations of total suspended solids (TSS) at Fanno Creek for Model 4, with prediction intervals and calibration and validation data (A) during 2002–03, (B) during storm 1, and (C) during storms 2 and 3, Tualatin River basin, Oregon. Discharge is shown to indicate timing of events.

tac09-0396_fig06

11-04-2002 2-12-2003 5-23-2003

1

10

100

1,000

 6-15-2002 6-20-2002 6-25-2002 6-30-2002 7-05-2002

DATE

 4-29-2003 5-04-2003 5-09-2003 5-14-2003

DATE

1

10

100

1,000

 4-18-2002 7-27-2002

TOTAL SUSPENDED SOLIDS,

IN MILLIGRAMS PER LITER

TOTAL SUSPENDED SOLIDS,

IN MILLIGRAMS PER LITER

TOTAL SUSPENDED SOLIDS,

IN MILLIGRAMS PER LITER

DISCHARGE, IN CUBIC FEET PER SECOND

DISCHARGE, IN CUBIC FEET PER SECOND

DISCHARGE, IN CUBIC FEET PER SECOND

Prediction interval, logTSS=f(turbidity, specific conductance), Model 4Predicted TSS, logTSS=f(turbidity, specific conductance), Model 4

Discharge

Validation data, Scenario 3Calibration data, Scenario 3

C. B.

A.

1

10

100

1,000 1,000

100101

1,000100101

10,0001,000100101

EXPLANATION
Relations Between Continuous Monitor Data and Selected Water-Quality Constituents 31

tac09-0396_fig07

LOGARITHM OF PREDICTED TOTAL 

SUSPENDED SOLIDS CONCENTRATION

LOGARITHM OF MEASURED TOTAL 

SUSPENDED SOLIDS CONCENTRATION

0.0

0.5

1.0

1.5

2.0

2.5

3.0

0.0 0.5 1.0 1.5 2.0 2.5 3.0

Predicted concentration

95 percent prediction 

 intervals

Figure 7. Comparison of concentrations of measured and predicted 

total suspended solids from Model 4, using the calibration dataset from 

Scenario 3, Fanno Creek near Durham, Oregon. Native reporting units 

for total suspended solids concentrations prior to log transformation 

were in milligrams per liter.

Total Phosphorus

Example regression models for TP at the Fanno Creek 

site are shown in table 11. As for TSS, initial results for 

TP produced many negative predictions when TP was 

not log transformed (Scenario 1, Model 3), despite the 

adjusted-R2 being relatively high (0.905). Bias correction 

factors for all models with log-transformation of TP (that 

is, all except Model 3) were similar, ranging from 1.01 

to 1.03. Log transformation of explanatory variables was 

evaluated in Model 2 and does not seem to provide any 

benefit. The resulting adjusted-R2 is slightly lower than for 

non-transformation of the same variables (Model 1), and the 

regression RMSE is slightly larger. Validation statistics for 

Model 2 also are poorer than for Model 1. Sine and cosine 

terms were not significant, indicating that no seasonal cycles 

were present that were not already expressed by the other 

independent variables.

Adjusted-R2 values for Scenario 1 (calibration 

using autosampler data only) generally were higher 

than for Scenarios 2 and 3, which may be an artifact of 

serial correlation in the autosampler data, and fewer data 

representing the range of variability from different seasons 

or more high-flow events. Although turbidity seems to be 

the most directly linear predictor of TP when examined 

graphically from the autosampler data alone (fig. 5), 

specific conductance also was repeatedly an important 

independent variable in the regression process (table 11). 
32 Continuous Monitors and Autosamplers Used to Predict Water-Quality Constituents, Tributaries of Tualatin River, Oregon Table 11. Preliminary model statistics for correlation of total phosphorus with continuous parameters at Fanno Creek near Durham, Oregon. [Regression models are of the form TP = a*Turb+ b*

Q + c*SC + d, where a, b, and c are regression coefficients and d is the intercept, Turb, Q, and SC are the explanatory variables turbidity (in 

Formazin Nephelometric Units), discharge (in cubic feet per second), and specific conductance (in microsiemens per centimeter), respectively, and TP is the dependent variable, total phosphorus concentration, in milligrams per liter. In some models, as indicated by the model form column, the dependent or explanatory variables were log transformed. Where TP is log transformed, a bias 

transformation factor (BCF; Duan, 1983) is multiplied by 10(logTP) to get the final value. RMSE values are in milligrams per liter. The maximum Variance Inflation Factor (VIF) indicates the largest VIF 

obtained for any one variable in the correlation. Abbreviations: n, number of samples; Adj.-

R2, adjusted 

R2, a coefficient of determination, which adjusts for degrees of freedom and penalizes the use of 

too many explanatory variables; f, a function of indicated constituents; log, base 10 logarithm; RMSE, root mean square error; USGS, U.S. Geological Survey; –, not included in the regression]

Model No. 

and form

Model calibration

Model validation – Goodness-of-fit evaluation

Value of coefficient, when used Correlation statistics

a b c d BCF n Adj.-

R2 Maximum VIF Mean error Validation RMSE Coefficient of determination Nash - Sutcliffe coefficient z-statistic from sign test

Scenario 1 Calibration data set— Autosamplers only Validation data set— Clean Water Services ambient monitoring data1. logTP=f(Turb, SC) 0.007 – 0.003 -1.63 1.01 54 0.893 1.3 -0.003 0.075 0.24 -0.1 1.1

2. logTP=f(logTurb, logSC) 0.752 – 2.05 -6.47 1.02 54 0.813 1.7 -0.039 0.092 0.05 -0.67 5.2

3. TP=f(Turb, SC) 0.004 – 0.001 -0.2 – 54 0.905 1.3 -0.007 0.072 0.20 -0.01 1.3Scenario 2 Calibration data set— Complete autosampler storm samples + USGS 2001–07 Validation data set— Clean Water Services ambient monitoring data

4. logTP=f(Turb, SC) 0.006 – 0.002 -1.34 1.03 140 0.645 1.7 -0.008 0.064 0.29 0.19 1.7

Scenario 3 Calibration data set— Peak autosampler plus first monthly and high flow monitoring samples from USGS and Clean Water Services datasets Validation data set—remaining monthly low flow USGS + Clean Water Services ambient monitoring + non-peak autosampler data

5. logTP=f(Turb, SC) 0.005 – 0.001 -1.19 1.02 99 0.575 11.6 -0.02 0.067 <0.01 -0.14 3.1

6. logTP=f(Turb, Q, SC) 0.005 -0.00003 0.001 -1.18 1.02 95 0.582 12.0 -0.021 0.067 <0.01 -0.16 4

1 Exceeds a critical threshold VIF value (VIFcrit), calculated as {1/(1– (Adj.-R2)} and indicates possible multicollinearity.
Relations Between Continuous Monitor Data and Selected Water-Quality Constituents 33

Discharge, which often is significantly correlated with 

turbidity and TSS as well as TP, was evaluated for Fanno 

Creek in the scenario using high-flow data (Scenario 3, 

Model 6). Model results using discharge were not noticeably 

better than those in Model 5 without it, and its inclusion in 

models would likely result in overfitting. Although Model 5 

is presumed, like Model 4 for TSS, the most robust because 

the input dataset is the most comprehensive and the least 

affected by serial correlation, the model’s correlation and 

validation statistics were relatively poor. In particular, the 

coefficients of determination for the initial model calibration 

(adjusted-R2 = 0.575) and for the validation (<0.01)

indicate substantial room for improvement, although the 

validation dataset may not have a large enough range in TP 

to be useful for an R2 determination. Turbidity and specific 

conductance in Model 5 have VIF values exceeding the 

VIFcrit, but both variables are highly significant in the model 

(p <0.0001) indicating that the VIFcrit which is low because 

of the low adjusted-R2, does not accurately reflect the severity 

of multicollinearity. In Model 6, discharge is not a significant 

variable (p = 0.89, not shown) and seems to contribute to 

multicollinearity problems. 

The predicted results from Model 5 were biased low 

compared to the validation dataset in Scenario 3, which 

could be a result of the large amount of calibration data 

representing baseline rather than high-flow conditions. These 

goodness‐of‐fit statistics, like those for TSS at the Fanno 

Creek site, are a measure primarily of the base flow rather 

than high-flow conditions, due to a relative lack of high-flow 

data for comparison. The model, therefore, is not necessarily 

as poor as the goodness-of-fit statistics might indicate, and 

the model predictions during storms probably still have some 

value.

Models 1, 4, and 5 are of the form logTP= f (Turb, SC), 

using datasets from Scenarios 1, 2, and 3, and their respective 

model coefficients and BCFs are relatively similar (table 11). 

The same is true of their model validation statistics; although 

the coefficient of determination and the Nash-Sutcliffe 

coefficient are the best for Model 4, no model’s validation 

statistics are particularly good. Although the VIFs indicate 

possible multicollinearity in Model 5, this is a result of the 

model’s adjusted-R2 being relatively low. It is, therefore, 

reasonable that this model form is appropriate for TP at Fanno 

Creek near Durham, but that gathering larger, more applicable 

datasets with high-flow samples will result in more refined 

model coefficients (the values for a, c, d, and BCF in table 11) 

rather than changing the functional form of the model. 

Despite the relatively poor correlation and validation 

statistics mentioned above, the predicted results from Model 5 

appear to capture the overall pattern of TP concentrations 

in Fanno Creek reasonably well (fig. 8), especially at low 

flow conditions. Most observed values are well within the 

95 percent prediction interval for the model, despite often 

being separated from the model’s prediction line. A few high 

spikes in concentration are predicted during winter events 

(fig. 8A) when concentrations may reach 10 mg/L or higher; 

however, the accuracy of these spikes cannot be evaluated 

because no samples representing those events were available. 

The model also achieved a reasonable representation of TP 

during storms when the autosamplers were in use, June 2002 

and May 2003. 

Baseline conditions in the data are well represented in 

the model, with relatively constant average concentrations 

about 0.1–0.15 mg/L. Observed concentrations during 

summer 2002 were slightly higher than the predicted values, 

whereas in Winter 2002–03 they were slightly lower than 

those predicted, but the differences are within the uncertainty 

range indicated by the prediction interval. These differences 

are unrelated to the relocation of the continuous monitor 

from Durham City Park (site 1a in fig. 2) to Durham Road 

(site 1b in fig. 2), because that relocation would only affect 

the predicted concentrations rather than the observed. Instead, 

with generally higher discharges during winter samplings, the 

TP concentrations may be diluted during certain conditions 

such as the falling limb of storm hydrographs as reported 

by Anderson and Rounds (2003). With the exception of the 

autosampler data, most data in the Scenario 3 calibration and 

validation datasets were collected without regard to storm 

conditions and many were collected immediately after storm 

discharge peaks. Discharge is not included as an independent 

variable in Model 5, and generally was not significant in the 

candidate models (table 11). Future formulations of a model 

for TP at Fanno Creek may be enhanced by evaluating a model 

calibrated specifically for winter periods. 

A comparison of measured and predicted TP 

concentrations illustrates that most of the available data 

occupy a relatively narrow range of TP concentrations, from 

about 10-1.2 to 10-0.8 (or about 0.06 to 0.16) mg/L, with only a 

few at higher concentrations being represented (fig. 9).
34 Continuous Monitors and Autosamplers Used to Predict Water-Quality Constituents, Tributaries of Tualatin River, Oregon Figure 8. Time series of predicted total phosphorus (TP) concentrations at Fanno Creek from Model 5, with prediction intervals, calibration, and validation data (A) during 2002–03, (B) during storm 1, and (C) during storms 2 and 3, Tualatin River basin, Oregon. Discharge is shown to indicate timing of events. tac09-0396_fig08

TOTAL PHOSPHORUS CONCENTRATION,

IN MILLIGRAMS PER LITER

TOTAL PHOSPHORUS CONCENTRATION,

IN MILLIGRAMS PER LITER

TOTAL PHOSPHORUS CONCENTRATION,

IN MILLIGRAMS PER LITER

 4-18-2002 7-27-2002 11-04-2002 2-12-2003 5-23-2003

0.01

0.1

1

10

Prediction interval, logTP=f(turbidity,specific conductance), Model 5

Predicted TP, logTP=f(turbidity,specific conductance), Model 5

Validation data, Scenario 3Calibration data, Scenario 3

 6-15-2002 6-20-2002 6-25-2002 6-30-2002 7-05-2002

DATE

0.01

0.1

1

 4-29-2003 5-04-2003 5-09-2003 5-14-2003

DATE

0.01

0.1

1 1,000

100101

1,000100101

DISCHARGE,

IN CUBIC FEET PER SECOND

DISCHARGE,

IN CUBIC FEET PER SECOND

DISCHARGE,

IN CUBIC FEET PER SECOND

Discharge

A.

B. C.

EXPLANATION

1,000100101
Relations Between Continuous Monitor Data and Selected Water-Quality Constituents 35

tac09-0396_fig09

-1.4

-1.2

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

-1.2 -1.0 -0.8 -0.6 -0.4 -0.2 0.0

LOGARITHM OF PREDICTED TOTAL 

PHOSHORUS CONCENTRATION 

LOGARITHM OF MEASURED TOTAL

 PHOSPHORUS CONCENTRATION

Predicted concentration

95 percent prediction 

 intervals

Figure 9. Comparison of concentrations of measured and predicted 

total phosphorus from Model 5, using the calibration dataset from 

Scenario 3, Fanno Creek near Durham, Oregon. Native reporting units 

for total phosphorus concentrations prior to log transformation were 

in milligrams per liter.

Escherichia coli Bacteria

Model results for E. coli bacteria were moderately 

successful (table 12), with several model forms having similar 

coefficients and reasonably strong adjusted-R2 values

(0.586–0.713). No data on E. coli bacteria were available from 

the USGS databases, so the data for calibration were available 

for Scenarios 1 and 3 only, modified by the lack of USGS data. 

Models 1–5 used log-transformation of E. coli bacteria counts, 

and each model used turbidity as an explanatory variable. In 

Models 1, 3, 4, and 5, in which turbidity is not transformed, 

the regression coefficients for turbidity (0.014–0.016) and 

the intercepts (2.17–2.53) vary only slightly. The addition 

of discharge as an explanatory variable, whether or not it 

was transformed, was not particularly useful, as evidenced 

by the lack of changes in the coefficients for turbidity, 

minor increases in the models’ adjusted-R2, and substantial 

increases in validation RMSE when discharge was added. 

Bias correction factors were all relatively large, particularly 

for the Scenario 3 datasets (about 1.4–1.5), indicating 

substantial negative bias in the uncorrected values. Once 

again, sine and cosine terms were not significant, so no 

models using them are shown. Goodness-of-fit statistics 

indicate relatively large uncertainty in the predicted values 

compared to the validation data, with a large (negative) 

mean error and RMSE values measured that mostly are 

about 1,000 or more for the Scenario 3 dataset. Model 6 
36 Continuous Monitors and Autosamplers Used to Predict Water-Quality Constituents, Tributaries of Tualatin River, Oregon

had the highest adjusted-R2 for calibration in Scenario 3, 

and the coefficient of determination and Nash‐Sutcliffe 

coefficient were high (0.69 and 0.69, respectively). However, 

the explanatory and dependent variables were untransformed, 

and many predicted values during the modeled 2002–03 time 

period were negative, rendering Model 6 unusable for general 

purposes. Regression coefficients for the independent variables 

and intercept in Model 6 were 2–3 orders of magnitude higher 

than those in the other models, which is an artifact of the lack 

of log transformation. 

All models with more than one independent variable were 

possibly affected by multicollinearity, despite maximum VIF 

values that were less than 3, again reflecting the relatively low 

adjusted-R2 values for the models. In each case, the statistical 

significance of the discharge term was poor (p = 0.003, 0.438, 

0.233, and 0.0025 for Models 2, 4, 5, and 6, respectively). 

Thus, the addition of discharge increased multicollinearity 

without an offsetting gain in model confidence. 

Given the results from table 12, with previous 

assumptions that Scenario 3 represents the most appropriate 

input data available, Model 3 then represents the presumed 

best available model for E. coli bacteria. Predicted data from 

Model 3, together with the 95 percent prediction interval and 

the calibration and validation datasets are shown in figure 10. 

The prediction interval is substantially larger for models 

predicting E. coli bacteria than for TSS or TP, spanning almost 

2 orders of magnitude. The model predicts baseline E. coli 

bacteria counts of about 300 colonies/100 mL during summer 

2002, which is close to the single-sample water quality 

standard of 406 colonies/100 mL. It also predicts closer to 

400–500 colonies/100 mL during Winter 2003, overpredicting 

most calibration and validation samples and indicating 

that baseline predictions may have little utility. The model 

predicts numerous peaks of 10,000–100,000 colonies/100 mL, 

and mirrors the pattern of turbidity and discharge in Fanno 

Creek. Because most Clean Water Services monitoring 

data are from relatively low-flow conditions, few data are 

available to confirm these high counts, although the model 

accounts reasonably well for the variability observed over 

the hydrographs during storms sampled by the autosamplers 

in June 2002 and May 2003 (fig. 10B and C). Quantitation 

of E. coli bacteria at concentrations greater than about 

1,000 colonies/100 mL is not routinely done by the Clean 

Water Services laboratory owing to the difficulty of 

differentiating tightly packed colonies grown on agar, or the 

nonconservative nature of large dilutions for bacterial growth 

(J. Miller, Clean Water Services, oral commun., June 2008). 

For that reason, obtaining reliable data from storms to 

calibrate or validate these or subsequent E. coli bacteria

models may be difficult.

Prediction intervals for E. coli bacteria from Model 3 

range almost 2 orders of magnitude (fig. 11). Measured 

data (that is, samples) are predominantly at low E. coli

bacteria counts, with only a few from counts greater than 

103 (1,000) colonies/100 mL. The model captures some 

trends in the measured bacteria concentrations, showing 

that the explanatory variable (turbidity) has some predictive 

information, but the uncertainty is large enough that this 

particular model has limited application until a better dataset 

becomes available. On the other hand, if use of the model 

were limited to predicting periods when bacterial counts 

exceed a threshold value such as 1,000 colonies/100 mL, 

rather than quantifying the actual peak values, model 3 might 

be adequate. 
Relations Between Continuous Monitor Data and Selected Water-Quality Constituents 37

Table 12. Preliminary model statistics for correlation of Escherichia coli bacteria with continuous parameters at Fanno Creek near Durham, Oregon.[No Scenario 2 data sets were available because USGS did not collect E. coli data during the study period. Regression models are of the form E. coli = a*Turb+ b*

Q + c*SC + d, where a, b, and c are 

regression coefficients and d is the intercept, Turb, Q, and SC are the explanatory variables turbidity (in Formazin Nephelometric Units), discharge (in cubic feet per second), and specific conductance 

(in microsiemens per centimeter), respectively, and E. coli is the dependent variable, E. coli bacteria counts, in colonies per 100 milliliters. In some models, as indicated by the model form column, the 

dependent or explanatory variables were log transformed. Where E. coli is log transformed, a bias transformation factor (BCF; Duan, 1983) is multiplied by 10(logE. coli) to get the final value. RMSE 

values are in colonies per 100 mL. The maximum Variance Inflation Factor (VIF) indicates the largest VIF obtained for any one variable in the correlation. Abbreviations: n, number of samples; Adj.-

R2, adjusted 

R2, a coefficient of determination which adjusts for degrees of freedom and penalizes the use of too many explanatory variables; f, a function of indicated constituents; E. coli, 

Escherichia coli bacteria; log, base 10 logarithm; RMSE, root mean square error; USGS, U.S. Geological Survey; –, not included in the regression]

Model No. 

and form

Model calibration

Model validation – Goodness-of-fit evaluation

Value of coefficient, when used Correlation statistics

a b c d BCF n Adj.-

R2 Maximum VIF Mean error Validation RMSE Coefficient of determination Nash - Sutcliffe coefficient z-statistic from sign test

Scenario 1 Calibration data set—Autosamplers only Validation data set—Clean Water Services ambient monitoring data1. logE. coli=f(Turb) 0.014 – – 2.53 1.22 54 0.692 – 528.3 196.6 0.50 -2.2 5.45

2. logE. coli=f(logTurb, log

Q) 1.69 -0.702 – 1.76 1.19 54 0.713 12.7 219.2 1,760 0.36 0.25 2.09

Scenario 3 Calibration data set—Peak autosampler plus first monthly and high flow monitoring samples from Clean Water Services dataset Validation data set—Remaining monthly low flow Clean Water Services ambient monitoring + non-peak autosampler data

3. logE. coli=f(Turb) 0.014 – – 2.17 1.53 40 0.590 – -454 996 0.31 0.16 1.86

4. logE. coli=f(Turb, Q) 0.016 -0.002 – 2.21 1.42 40 0.586 12.6 -407 2,470 0.31 0.07 2.06

5. logE. coli=f(Turb, log

Q) 0.016 -0.18 – 2.39 1.43 40 0.595 11.7 -410 1,077 0.28 0.004 2.06

6. E. coli=f(Turb,Q) 103.7 -28.4 – -2.39 – 40 0.7 12.3 -81 1,425 0.69 0.69 1.07

1Exceeds a threshold VIF value, calculated as {1/(1– (Adj.-R2)} and indicates possible multicollinearity. 
38 Continuous Monitors and Autosamplers Used to Predict Water-Quality Constituents, Tributaries of Tualatin River, Oregon

tac09-0396_fig10

 7-27-2002 11-04-2002

C.

 4-18-2002 2-12-2003 5-23-2003

1,000100101

10

100

1,000

10,000

100,000

10

100

1,000

10,000

100,000

COUNTS, E. coli 

IN COLONIES PER 100 MILLILITER

COUNTS, E. coli 

IN COLONIES PER 100 MILLILITER

COUNTS, E. coli 

IN COLONIES PER 100 MILLILITER

10

100

1,000

10,000

100,000

 6-15-2002 6-20-2002 6-25-2002 6-30-2002 7-05-2002 4-29-2003 5-04-2003 5-09-2003 5-14-2003

A.

B.

DATE DATE

95 percent prediction interval, LogEcoli=f(Turbidity), Model 3

Predicted E. coli, logEcoli=f(Turbidity), Model 3

Discharge

Validation data, Scenario 2 Calibration data, Scenario 2 

1,000100101

DISCHARGE, IN CUBIC FEET PER SECOND

DISCHARGE, IN CUBIC FEET PER SECOND

1,000100101

DISCHARGE, IN CUBIC FEET PER SECOND

EXPLANATION

Figure 10. Time series of predicted Escherichia coli (E. coli) bacteria counts at Fanno Creek from Model 3, with prediction intervals, 

calibration, and validation data (A) during 2002–03, (B) during storm 1, and (C) during storms 2 and 3, Tualatin River basin, Oregon. Discharge is shown to indicate timing of events.
Relations Between Continuous Monitor Data and Selected Water-Quality Constituents 39

Figure 11. Comparison of concentrations of measured and predicted 

Escherichia coli (E. coli) bacteria counts from Model 3, using the 

calibration dataset from Scenario 3, Fanno Creek near Durham, Oregon. 

Native reporting units for Escherichia coli bacteria counts prior to log 

transformation were in colonies per 100 milliliters.

tac09-0396_fig11

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

5.5

1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5

LOGARITHM OF PREDICTED E. coli COUNTS

LOGARITHM OF MEASURED E. coli COUNTS

Predicted concentration

95 percent prediction 

 intervals

Dairy Creek at Highway 8

Autosampler Data

The response of Dairy Creek to storm runoff is different 

than that of many other streams included in this study. The 

Dairy Creek basin is predominately agricultural and is 

relatively insensitive to runoff from small- to medium-sized 

storms unless antecedent rainfall is high, a characteristic that is 

likely related to the small amount of impervious land upstream 

of the sampling site and the relatively low surrounding 

topographic relief. Prolonged dry conditions during summer 

cause streamflow to recede and several consecutive days to 

weeks of rain are usually required for streamflow to increase.

Once the soils are well saturated in autumn, streamflow 

in Dairy Creek tends to increase rapidly to relatively high 

levels and remains sustained for long periods during the winter 

until conditions begin to dry in late spring. Also potentially 

contributing to the hydrological and chemical response of the 

drainage basin, about 36 percent of the agricultural land in 

the Dairy Creek basin uses subsurface drainage or tile drains 

(U.S. Department of Agriculture, 1995). Tile drains are used 

where soil drainage is poor, allowing cultivation on lands that 

might otherwise preclude agricultural activities. However, tile 

drains also can provide an effective route for preferential flow 

of water and solutes to streams, speeding hydrologic response 

and reducing chemical transformations such as uptake, 

adhesion, or degradation (Stone and Wilson, 2006). The actual 

effect of tile drains was not evaluated directly in this study. 

During the study period, the continuous monitor at Dairy 

Creek was deployed seasonally. The monitor was installed 

in spring when stage receded to allow wading, and removed 

in the autumn when stage was expected to become high. 

Backwater from the Tualatin River was sometimes the cause 

of high stages that limited access to the creek. As a result 

of the winter high stages, neither continuous monitor nor 

discharge data are available at Dairy Creek during the winter 

and early spring months (about November–May), limiting 

the ability to make predictions during those periods. Stream 

stage, which was the only continuously recorded parameter 

during the winter months in the study period, can be a useful 

surrogate for discharge. 

Individual storms were sampled during October 

2003 by autosamplers at Dairy Creek, with relatively little 

antecedent rainfall, and November 2003, with slightly wetter 

antecedent conditions than the October storm. The pattern 

of events sampled resulted in streamflow and water-quality 

conditions that were different between storms (although the 

stage during storms remained less than 10 ft). Therefore, 

data for discharge, stage, turbidity, and specific conductance 

had bimodal distributions that were dominated by storm-tostorm differences when all samples were included (fig. 12A). 

Specific conductance, in particular, showed little variability 

during the October 2003 storm, and varied only by about 

5 percent during the November 2003 storm. In contrast to 

the autosampler data, the Clean Water Services ambient 

monitoring dataset includes numerous samples with stages 

greater than 10 ft (maximum 22.42 ft) during 2002-04. 
40 Continuous Monitors and Autosamplers Used to Predict Water-Quality Constituents, Tributaries of Tualatin River, Oregon

tac09-0396_fig12

LABORATORY CONSTITUENTS

CONTINUOUSLY MEASURED PARAMETERS

STAGE, IN FEET

TOTAL

SUSPENDED

SOLIDS

TOTAL

PHOSPHORUS

E. coli 

BACTERIA

B. Scenario 2: Peak autosampler flow samples and 

Clean Water Services Ambient Monitoring

A. Scenario 1: Autosampler ( Storm 1, Storm 2)

STAGE, IN FEET

SPECIFIC CONDUCTANCE,

IN MICROSIEMENS PER 

CENTIMETER

TURBIDITY,

IN FORMAZIN

NEPHELOMETRIC

UNITS

DISCHARGE,

IN CUBIC FEET

PER SECOND

SPECIFIC CONDUCTANCE,

IN MICROSIEMENS PER 

CENTIMETER

TURBIDITY,

IN FORMAZIN

NEPHELOMETRIC

UNITS

DISCHARGE,

IN CUBIC FEET

PER SECOND

Figure 12. Matrixes of scatter plots of calibration data from Dairy Creek 

at Highway 8 near Hillsboro, Oregon, using (A) Scenario 1 and (B) Scenario 

2 datasets. Scenario 1 data were from autosampler deployments: storm 1, 

October 9, 2003; storm 2, November 19–20, 2003. Data sources for Scenario 

2 include Clean Water Services ambient monitoring and autosamplers. 

Units for total suspended solids and total phosphorus are in milligrams per 

liter, and units for Escherichia coli (E. coli) bacteria are in colonies per 100 

milliliter.
Relations Between Continuous Monitor Data and Selected Water-Quality Constituents 41

Linear relationships between turbidity and TSS were evident 

in both the autosampler-only and larger combined (Scenario 2) 

datasets; however, few other constituent pairs have apparent 

linear relations at the Dairy Creek site. 

The sampling during storm 1 was triggered by an 

individual turbidity value (32 FNU) from the continuous 

monitor that was greater than the autosampler threshold 

value used to initiate sampling (25 FNU); however, turbidity 

values were less than 10 FNU in most subsequent samples. 

A slight increase in stream stage accompanied this storm and 

the samples were retained for analysis, despite the relatively 

modest overall storm response. Consequently, the sampler 

had been removed for cleaning before a larger storm several 

days later, which may have produced a broader range of 

values for field parameters and laboratory constituents. Soils 

0 10 20 30 40 50 60 70 80 90 100

Probability

Autosampler

1

10

100

TURBIDITY, IN FORMAZIN 

NEPHELOMETRIC UNITS

B.

SPECIFIC CONDUCTANCE, 

IN MICROSIEMENS PER CENTIMETER

0

50

100

150

200

250

300

A.

PERCENT OF TIME INDICATED VALUES WERE EXCEEDED

Figure 13. Probability that (A) specific conductances and (B) turbidities were exceeded at Dairy Creek at 

Highway 8 near Hillsboro, Oregon, during May 2002 to September 2004.

in the drainage basin were apparently well saturated by the 

November 2003 storm, and stream stage increased to levels 

that were too high to collect samples.

Specific conductance during autosampler deployments 

was representative of mostly average conditions, ranging 

from 117 to 140 μS/cm, values that were exceeded about 

40–70 percent of the time during the study period (fig. 13A). 

The sampled turbidity data represented a broader range of 

conditions at Dairy Creek, ranging from about 6 to 30 FNU, 

values that were exceeded between 1 and almost 90 percent of 

the time during the study period (fig. 13B). Recall, however, 

that the dataset used to determine these exceedances was 

derived from monitoring data that did not include winter 

high‐flow conditions. 
42 Continuous Monitors and Autosamplers Used to Predict Water-Quality Constituents, Tributaries of Tualatin River, Oregon

Total Suspended Solids

Models for the autosampler-only data and the Clean 

Water Services ambient monitoring data were selected for 

high flow and the first routine samples from each month 

(the presumed best calibration data available) (table 13). 

Adjusted-R2 values were similar in models from Scenarios 1 

and 2 (0.695–0.758). However, coefficients of determination 

for the goodness-of-fit validations were poor, less than 0.2 

for all models. Bias correction factors were 1.02–1.03 for all 

models. Seasonal factors evaluated by inclusion of sine and 

cosine terms were insignificant and models using them are not 

shown. 

Turbidity was an important explanatory variable for 

all models in both scenarios. In Scenario 1, the addition of 

discharge as an independent variable caused small increases 

in the adjusted R2 in Models 2 and 4 over Models 1 and 3. 

However, the addition of discharge generally increased the 

error when predicted values were compared to the validation 

dataset. The addition of discharge also increased the level of 

multicollinearity, with VIFs for both turbidity and discharge 

exceeding the calculated VIFcrit in Model 4. In contrast, the 

addition of discharge to Scenario 2 models had little effect on 

the calibration of the models, provided only a minor benefit to 

the models’ validation, and incurred possible multicollinearity 

in Model 6. High stages were not experienced during the 

autosampler deployments so discharge data were available and 

meaningful for all the Scenario 1 samples. However, stages 

greater than 10 ft were recorded for several samples in the 

Scenario 2 dataset. No discharge data were available for these 

samples from high stages, which explains the lack of benefit of 

discharge as an explanatory variable for TSS in Models 6 and 

8. 

The inclusion of specific conductance data was not 

statistically significant for any Scenario 1 or 2 models, and 

reduced the fit in almost all cases. The model coefficients for 

turbidity were the same in Models 1 and 3 (a=0.025), and 

in Models 2 and 4 (a=0.019), regardless of the addition of 

specific conductance, with similar effects in Scenario 2. 

Scenario 2 may represent the most robust input datasets 

available for Dairy Creek, and Models 5 or 6, therefore, may 

represent reasonable initial models for TSS, although their 

respective goodness-of-fit statistics were poor. Models 5 

and 6 provide similar results (hence, Model 6 is not shown 

in figure 14), and capture the baseline conditions (about 

10 mg/L) moderately well for some periods in each summer 

during 2002–04. Model 1, derived from autosampler-only data 

and, therefore, limited by the range of conditions observed 

and by serial-correlation issues, overestimates the baseline 

conditions more than Models 5 or 6, especially when the 

actual TSS values drop to less than about 8 mg/L. Model 1 

also has much greater variability and higher peak values than 

Models 5 or 6. 

Results from the Scenario 1 and Scenario 2 models 

indicate that the most robust model form for TSS at Dairy 

Creek will probably be logTSS= f (Turbidity), although the 

addition of discharge (or stage) may be beneficial, especially 

at high discharges. Backwater issues will make discharge a 

difficult variable to use in the winter. Although stage data 

remain accurate during backwater conditions, the presence 

of these conditions still may require development of separate 

models for free-flowing and backwater conditions. Assuming 

that Scenario 2 uses more representative and thorough datasets 

than Scenario 1, the presumed best model for TSS at Dairy 

Creek, given the available data, is currently Model 5. Model 5 

may appear to underestimate TSS during base flow (fig. 14), 

but this is an artifact of the superposition of the Model 1 

results onto the graph, where the Model 5 line is obscured by 

the Model 1 line. The base-flow calibration and validation 

data are relatively well represented by the Model 5 results, 

and furthermore they are well within the 95 percent prediction 

interval for Model 5. Given this, Model 5 may perform 

adequately during summer.

A comparison of predicted and measured TSS 

concentrations from Model 5 (fig. 15) further illustrates 

the predominance in the Scenario 2 dataset by samples 

at relatively low TSS concentrations. Most measured 

concentrations were in the range of 100.6 (or about 4.0) to 101.5

(or about 32) mg/L, Importantly, this comparison also reveals 

the relatively large uncertainty of the model, with prediction 

intervals that encompass about a full order of magnitude. The 

clustering of predicted values at about 101.0 (or about 10) mg/L

despite a moderate range in measured values likely is an 

indication that a model based on turbidity alone is insensitive 

to some of the factors contributing to raised TSS, and that 

inclusion of other independent variables such as stage or 

discharge or separation of models based on a stage threshold 

such as 10 ft will be beneficial. 
Relations Between Continuous Monitor Data and Selected Water-Quality Constituents 43

Table 13. Preliminary model statistics for correlation of total suspended solids with continuous parameters at Dairy Creek at Highway 8 near Hillsboro, Oregon, 2002–04.[Regression models are of the form TSS = a*Turb+ b*

Q + c*SC + d, where a, b, and c are model coefficients and d is the intercept, Turb, Q, and SC are the explanatory variables turbidity (in Formazin 

Nephelometric Units), discharge (in cubic feet per second), and specific conductance (in microsiemens per centimeter), respectively, and TSS is the dependent variable, total suspended solids, in milligrams per liter. Where TSS is log transformed, a bias transformation factor (BCF; Duan, 1983) is multiplied by 10(logTSS) to get the final value. The maximum Variance Inflation Factor (VIF) 

indicates the largest VIF obtained for any one variable in the correlation. Abbreviations: n, number of samples; Adj.-

R2, adjusted 

R2, a coefficient of determination which adjusts for degrees of freedom 

and penalizes the use of too many explanatory variables; f, a function of indicated constituents; log, base 10 logarithm; RMSE, root mean square error; –, not included in the regression]

Model No. 

and form

Model calibration

Model validation—Goodness-of-fit evaluation

Value of coefficient, when used Correlation statistics

a b c d BCF n Adj.-

R2 Maximum VIF Mean error Validation RMSE Coefficient of determination Nash - Sutcliffe coefficient z-statistic from sign test

Scenario 1 Calibration data set— Autosamplers only Validation data set— Clean Water Services ambient monitoring data1. logTSS=f(Turb) 0.025 – – 0.709 1.03 38 0.704 – -2.2 5 0.07 -0.16 3.52. logTSS=f(Turb, Q) 0.019 0.002 – 0.642 1.02 38 0.745 2.1 -3.9 6.1 0.10 -0.57 5.23. logTSS=f(Turb, SC) 0.025 – 0.0009 0.696 1.03 38 0.695 1.4 -2.2 5 0.06 -0.17 3.5

4. logTSS=f(Turb, Q, SC) 0.019 0.002 0.0009 0.524 1.02 38 0.738 12.3 -4 6.3 0.03 -0.64 5

Scenario 2 Calibration data set— Peak autosampler plus first monthly and high flow monitoring samples from Clean Water Services dataset Validation data set—Remaining monthly Clean Water Services ambient monitoring + non-peak autosampler data

5. logTSS=f(Turb) 0.051 – – 0.415 1.02 20 0.758 – 0.07 4.6 0.06 0.06 2.5

6. logTSS=f(Turb, Q) 0.051 0.0001 – 0.396 1.02 20 0.757 12.7 -0.49 4.9 0.13 0.08 1

7. logTSS=f(Turb, SC) 0.053 – 0.0007 0.304 1.02 20 0.754 1.1 -1.4 4.6 0.14 0.04 1.3

8. logTSS=f(Turb, Q, SC) 0.054 0.0002 0.0009 0.251 1.02 20 0.758 1.3 -1.5 4.9 0.19 0.11 1.6

1Exceeds a threshold VIF value, calculated as {1/(1– (Adj.-R2)} and indicates possible multicollinearity. 
44 Continuous Monitors and Autosamplers Used to Predict Water-Quality Constituents, Tributaries of Tualatin River, Oregon

tac09-0396_fig14

12-30-2001 4-09-2002 7-18-2002 10-26-2002 9-25-2004 1-03-2005

1

10

100

TOTAL SUSPENDED SOLIDS CONCENTRATION,

IN MILLIGRAMS PER LITER

TOTAL SUSPENDED SOLIDS CONCENTRATION,

IN MILLIGRAMS PER LITER

TOTAL SUSPENDED SOLIDS CONCENTRATION,

IN MILLIGRAMS PER LITER

95 percent prediction interval, Model 5logTSS=ƒ(Turbidity), Model 5, Scenario 2logTSS=ƒ(Turbidity), Model 1, Scenario 1

Stage

Validation data, Scenario 2Calibration data, Scenario 2

100101

STAGE, IN FEET

STAGE, IN FEET

STAGE, IN FEET

10-07-2003 10-08-2003 10-09-2003 10-10-2003 10-11-2003 10-12-2003

DATE

1

10

100

1,000

1

10

100

11-17-2003 11-19-2003 11-21-2003

DATE

100101

A.

B. C.

EXPLANATION

 2-03-2003 5-14-2003 8-22-2003 11-30-2003 3-09-2004 6-17-2004

1,000100101

Figure 14. Time series of predicted total suspended solid (TSS) concentrations at Dairy Creek at Highway 8 near Hillsboro, from Models 

1 and 5, with prediction intervals, calibration, and validation data (A) during 2002–04, (B) during storm 1, and (C) during storm 2, Tualatin 

River basin, Oregon. Winter conditions are not predicted because of the lack of available monitor and discharge data.
Relations Between Continuous Monitor Data and Selected Water-Quality Constituents 45

tac09-0396_fig15

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

2.2

2.4

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4

LOGARITHM OF PREDICTED TOTAL 

SUSPENDED SOLIDS CONCENTRATION

LOGARITHM OF MEASURED TOTAL

SUSPENDED SOLIDS CONCENTRATION

Predicted concentration

95 percent prediction 

 intervals

Figure 15. Comparison of predicted and measured concentrations of 

total suspended solids from Model 5 for Dairy Creek at Highway 8 near 

Hillsboro, Oregon. Native reporting units for total suspended solids 

concentrations prior to log transformation were in milligrams per liter.

Total Phosphorus

Models for TP at Dairy Creek from Scenario 1 were 

primarily dependent on turbidity, with discharge and specific 

conductance playing a lesser role. Using the Scenario 2 

data, however, each model includes specific conductance 

which exerts a stronger role than either turbidity or discharge 

(table 14), and which may be a result of the more expansive 

range of specific conductances encompassed by the Scenario 

2 dataset. Sine and cosine terms again were insignificant, 

suggesting that seasonal considerations were unimportant or 

already incorporated with the other independent variables. 

Coefficients of determination (adjusted-R2) for calibration 

of Scenario 1, Models 1 and 2, were substantially better than 

those from all other models examined, regardless of input 

datasets, potentially owing to serial correlation. Coefficients 

for turbidity in Scenario 1 ranged from 0.012 to 0.021, and 

coefficients for discharge and specific conductance were 

an order of magnitude less, varying little between models 

where they were used. Bias correction factors were relatively 

low, ranging from 1.01 to 1.04 among all models and both 

scenarios. 

In Scenario 2, coefficients for specific conductance were 

essentially unchanged (0.003–0.004) between models, and 

were slightly less than one half the value of the respective 

Scenario 1 models (0.009). The number of observations (n) 

in the Scenario 2 models were, for the most part, fewer than 

those used in the Fanno Creek models (Scenarios 2 and 3, 

table 11). The number of observations was less because the 

Scenario 2 dataset includes many samples from mid-winter 

during 2002–04, when stage in Dairy Creek was greater than 

10 ft (discharge unavailable), and (or) the continuous monitor 

at the Dairy Creek site had been removed for the winter. The 

use of stage as an explanatory variable in Scenario 2, in place 

of discharge, allowed the inclusion of eight additional samples 

in the calibration dataset but resulted in a lower adjusted-R2

for the model. 

On the basis of calibration and validation statistics, no 

model from either scenario is strong enough for predictive 

purposes. Mean errors are relatively small, especially for 

Scenario 2 models. Possible multicollinearity was indicated 

for Models 1, 3, and 8, although the maximum VIFs were 

relatively low. Coefficients of determination for 

the model validation exercise were highest (0.55) for Model 1, 

and were otherwise poor (<0.1–0.38) for all other models. 
46 Continuous Monitors and Autosamplers Used to Predict Water-Quality Constituents, Tributaries of Tualatin River, Oregon Table 14. Preliminary model statistics for correlation of total phosphorus with continuous parameters at Dairy Creek at Highway 8 near Hillsboro, Oregon, 2002–04. [Regression models are of the form TP = a*Turb+ b*

Q + c*SC + d, where a, b, and c are model coefficients and d is the intercept, Turb, Q, and SC are the explanatory variables turbidity (in Formazin 

Nephelometric Units), discharge (in cubic feet per second), and specific conductance (in microsiemens per centimeter), respectively, and TP is the dependent variable, total phosphorus, in milligrams per liter. Where TP is log transformed, a bias transformation factor (BCF; Duan, 1983) is multiplied by 10(logTP) to get the final value. Data sets for correlation included: peak discharge samples from 

the autosampler deployment, plus monthly and high flow monitoring samples from Clean 

Water Services databases. Stage is used in place of discharge with the Clean Water Services data because 

discharge data are not provided by Oregon Water Resources Department when stage is greater than 10 feet. RMSE values are in milligrams per liter. The maximum Variance Inflation Factor (VIF) indicates the largest VIF obtained for any one variable in the correlation. Abbreviations: n, number of samples; Adj.-

R2, adjusted 

R2, a coefficient of determination which adjusts for degrees of 

freedom and penalizes the use of too many explanatory variables; f, a function of indicated constituents; log, base 10 logarithm; RMSE, root mean square error; –, not included in the regression]

Model No. 

and form

Model calibration

Model validation – Goodness-of-fit evaluation

Value of coefficient, when used Correlation statistics

a b c d BCF n Adj.-

R 2 Maximum VIF Mean error Validation RMSE Coefficient of determination Nash - Sutcliffe coefficient z-statistic from sign test

Scenario 1 Calibration data set— Autosamplers only Validation data set— Clean Water Services ambient monitoring data

1. logTP=f(Turb, Q, SC) 0.021 -0.0008 0.009 -2.26 1.01 37 0.808 12.9 0.023 0.06 0.55 -2.1 0.49

2. logTP=f(Turb, SC) 0.018 – 0.009 -2.32 1.01 37 0.788 1.3 0.015 0.1 0.38 -4.9 1.8

3. logTP=f(Turb, Q) 0.017 -0.001 – -1 1.01 37 0.535 12.7 0.025 0.05 0.08 -0.86 3.5

4. logTP=f(Turb) 0.012 – – -1.03 1.02 37 0.503 – 0.005 0.04 0.00 -0.09 2

Scenario 2 Calibration data set— Peak autosampler plus first monthly and high flow monitoring samples from Clean Water Services dataset Validation data set—Remaining monthly Clean Water Services ambient monitoring + non-peak autosampler data5. logTP=f(SC) – – 0.003 -1.35 1.01 19 0.557 – -0.001 0.04 0.27 0.26 2.2

6. logTP=f(Turb, SC) -0.012 – 0.004 -1.32 1.01 30 0.711 1.0 -0.0006 0.04 0.15 0.07 3.2

7. logTP=f (Q, SC)

2 – -0.00007 0.004 -1.44 1.02 31 0.683 1.1 -0.0008 0.03 0.18 0.14 3.2

8. logTP=f (Stage, SC)

3 – 0.004 0.004 -1.45 1.04 39 0.375 11.4 -0.002 0.03 0.20 0.17 1.6

9. logTP=f(Turb, Stage, SC) -0.006 -0.007 0.003 -1.22 1.01 19 0.533 1.2 -0.002 0.04 0.04 -0.1 2.9

1 Exceeds a threshold VIF value, calculated as {1/(1– (Adj.-R2)} and indicates possible multicollinearity. 

2 High discharge (and accompanying samples) excluded, where stage greater than 10 feet.3 High stage data available. Note increase in number of samples. Residuals wide-spread for high stage samples.
Relations Between Continuous Monitor Data and Selected Water-Quality Constituents 47

Many of the Nash-Sutcliffe coefficients were near zero 

or negative (max=0.26), indicating that the means of the 

laboratory data may be as good a predictor as the models 

derived from it. The goodness‐of‐fit statistics are more 

reflective of base flow than high-flow conditions because of 

the paucity of high-flow data, especially for the validation 

dataset; therefore, model performance could not be properly 

evaluated. Additional data would be needed to refine and 

evaluate the models.

The predicted results of Scenario 2 models for TP at 

Dairy Creek captured the general seasonal pattern of summer 

baseline concentrations relatively well, but did not appear 

to capture the shorter term variability associated with events 

or other factors (fig. 16). Although Scenario 2 is assumed to 

represent a more robust calibration scheme than Scenario 1, 

Model 1 results tracked better with the laboratory data from 

the Storm 2 hydrograph than either Model 5 or Model 7, and 

may better represent the range of variability experienced under 

normal conditions. Model 1 also predicted high storm peaks 

of TP, sometimes exceeding 1 mg/L, but the accuracy of these 

predictions could not be evaluated. Results from Model 8, 

which incorporate stage rather than discharge, are not shown 

in figure 16 because they were almost identical to those of 

Model 7. Likewise, Model 6 (not shown in figure 16), the 

highest ranked model from Scenario 2 that used turbidity, did 

not capture the Storm 2 increases in TP and produced only 

minor variations compared to Models 5–8. 

Comparisons of measured and predicted TP values for 

Model 1 have less variability and stay within the prediction 

intervals better than the values for Model 5 (fig. 17). Model 

6 results were similar to those from Model 5. However, 

Model 1 used input data from Scenario 1 and Model 5 used 

input data from Scenario 2, so the two models are not directly 

comparable. 

Generally, Scenario 1 models, particularly Model 1, 

were slightly more useful than those from Scenario 2 for 

estimating TP concentrations at Dairy Creek, but no model 

provided acceptably accurate predictions using the available 

datasets. Model 1 may overestimate variability in stream TP 

concentrations, but could be useful for understanding the 

overall pattern of TP resulting from changes in streamflow, 

turbidity, or specific conductance. However, it must be 

stressed that maximum TP concentrations encompassed by the 

Scenario 1 input data were less than 0.25 mg/L, so the model 

cannot be relied upon for predictions of concentrations greater 

than 0.25 mg/L. Furthermore, the reliance on discharge will 

be a limitation during high stages unless backwater conditions 

are comprehensively understood at the Dairy Creek site at 

Highway 8. The inclusion of specific conductance in almost 

every model implies that much of the TP in Dairy Creek 

may come from dissolved sources or may be associated with 

the movement of solutes in the basin, which is consistent 

with known groundwater inputs of dissolved phosphorus to 

Tualatin River basin streams during summer. Alternatively, 

because specific conductance is sometimes correlated with 

discharge, its presence in the models might also reflect erosion 

and solute sources, including phosphorus, at higher flows.
48 Continuous Monitors and Autosamplers Used to Predict Water-Quality Constituents, Tributaries of Tualatin River, Oregon

tac09-0396_fig16

95 percent prediction interval, Model 5logTP=ƒ(Turbidity, Specific Conductance), Model 1logTP=ƒ(Specific Conductance), Model 5

logTP=ƒ(Discharge, Specific Conductance), Model 7

Stage

Validation data, Scenario 2

Calibration data, Scenario 2

12-30-2001 7-18-2002 9-25-2004

1.000.10

0.01

TOTAL PHOSPHORUS CONCENTRATION,

IN MILLIGRAMS PER LITER

TOTAL PHOSPHORUS CONCENTRATION,

IN MILLIGRAMS PER LITER

TOTAL PHOSPHORUS CONCENTRATION,

IN MILLIGRAMS PER LITER

100101

STAGE, IN FEET

STAGE, IN FEET

STAGE, IN FEET

10-07-2003 10-08-2003 10-09-2003 10-10-2003 10-11-2003 10-12-2003

DATE

0.300.250.200.150.100.05

0.300.250.200.150.100.05

11-17-2003 11-18-2003 11-19-2003 11-20-2003 11-21-2003 11-22-2003

DATE

5.00.0

5.00.0

10.0

A.

B. C.

EXPLANATION

 2-03-2003 8-22-2003 3-09-2004

10.0

Figure 16. Time series of predicted total phosphorus concentrations at Dairy Creek at Highway 8 near Hillsboro, from Models 1, 5, and 7, with 

prediction intervals, calibration, and validation data (A) during 2002–04, (B) during storm 1, and (C) during storm 2, Tualatin River basin, Oregon. Winter conditions are not predicted because of the lack of available monitor and discharge data.
Relations Between Continuous Monitor Data and Selected Water-Quality Constituents 49

tac09-0396_fig17

Predicted concentration

95 percent prediction 

 intervals

A.

B.

-1.4 -1.2 -1.0 -0.8 -0.6 -0.4 -0.2 0.0

-1.4

-1.2

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

-1.4

-1.2

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

LOGARITHM OF PREDICTED TOTAL PHOSPHORUS CONCENTRATION

LOGARITHM OF MEASURED TOTAL PHOSPHORUS CONCENTRATION

Figure 17. Comparison of measured and predicted total phosphorus

concentrations at Dairy Creek at Highway 8 near Hillsboro, from (A) 

Scenario 1, Model 1, and (B) Scenario 2, Model 5, Tualatin River basin, 

Oregon. Native reporting units for total phosphorus concentrations 

prior to log transformation were in milligrams per liter.
50 Continuous Monitors and Autosamplers Used to Predict Water-Quality Constituents, Tributaries of Tualatin River, Oregon

Escherichia coli Bacteria 

Models for E. coli bacteria at Dairy Creek were 

primarily functions of specific conductance; only Model 4 

in Scenario 1, using the autosampler data, did not include 

specific conductance (table 15). Furthermore, seasonal 

aspects were unimportant, with sine and cosine terms again 

being insignificant. The coefficients for specific conductance 

varied little between the individual models within a specified 

scenario, ranging from -0.025 to -0.029 for Scenario 1 models, 

and from 0.011 to 0.013 for Scenario 2 models. Coefficients 

for specific conductance were negative in Scenario 1 and 

positive in Scenario 2, suggesting that the response of E. coli

bacteria during the storms sampled by autosamplers in autumn 

2003 was different than in the long term Scenario 2 dataset. 

Multicollinearity, which can result in coefficients with signs 

different than expected, may have contributed to the results of 

Models 7b or 8, despite the low maximum VIFs of 1.4 and 1.2, 

respectively. The low VIFcrit values for these models reflect 

the poor adjusted-R2 values for the Scenario 2 dataset—all 

VIF values are well below the general rule-of-thumb values 

sometimes used by other investigators (Helsel and Hirsch, 

1992); likewise the Condition Index (not shown), an alternate 

measure of multicolinearity (Draper and Smith, 1998) was 

about 50 for Models 1 and 3 but less than 20 for Models 6 and 

7, in the range previously described as acceptable (Draper and 

Smith, 1988).

Although bacteria often are associated with particles in 

streams, E. coli bacteria regression models resulting from 

this study were only a function of turbidity in a few cases, 

primarily from Scenario 1. Log transformation introduced 

substantial bias when predicting E. coli bacteria, resulting 

in BCF values for Scenario 2 models from 1.29 to 1.45, 

indicating corrections of about 29 to 45 percent. Scenario 1 

BCFs were lower (1.05–1.12) than those in Scenario 2, 

but remain mostly higher than for TP (table 14) and TSS 

(table 13). Comparatively high BCFs were also determined for 

E. coli bacteria models for Fanno Creek (table 12).

Adjusted-R2 values for calibration of Scenario 1 

models for E. coli bacteria were substantially greater than 

for Scenario 2 models, which was also the case for models 

for TSS and TP. In contrast, model validation statistics, 

particularly the coefficients of determination and the NashSutcliffe coefficients, were all poor. None of the models’ 

validation statistics were within the optimal ranges for errors 

generated by the prediction of E. coli bacteria counts. The 

highest Nash-Sutcliffe coefficient was only 0.21 for the 

Scenario 2 model using stage instead of discharge (Model 7b) 

as an independent variable, and all the coefficients of 

determination were less than or equal to 0.1. 

The negative coefficients for specific conductance from 

the Scenario 1 models were opposite in direction to those 

from Scenario 2. As a consequence, the pattern from models 

using specific conductance in Scenario 1 also were opposite in 

direction to those from Scenario 2. All models overestimated 

bacteria counts in storm 1 (fig. 18). Data collection for storm 1 

preceded the largest stream response by about 1 day, although 

Model 4, which was a function of turbidity only, predicted 

bacteria counts that were closest to the measured values. 

Model 1 (not shown), with a negative coefficient for specific 

conductance, predicted summer E. coli counts that were below 

the baseline and were not realistic. Model 4 also performed 

better during storm 2 than either Scenario 2 model shown 

(fig. 18C), mimicking the temporary increases in bacteria 

counts during the storm. Models 5 and 7b were selected 

for plotting because they represented the best Scenario 2 

calibration model according to the Mallow’s Cp selection 

scheme and the best validation according to the Nash-Sutcliffe 

coefficients, respectively. Both models performed almost 

identically; the line from either model obscures the other in 

figure 18, reflecting the influence of specific conductance as an 

independent variable. 

Comparison of measured and predicted values for E. coli

bacteria models show considerable variation and generally 

poor predictions, especially by Model 5 (fig. 19B). Uncertainty 

around Model 5 was greater than for Model 4, particularly at 

low and high bacteria counts. Neither model demonstrated 

acceptable abilities to reproduce the measured values; all 

models should be considered preliminary. It is possible that 

the sources and dynamics of E. coli bacteria in Dairy Creek 

cannot be well characterized by variables such as flow, stage, 

specific conductance, and turbidity. If so, then this approach 

of using continuous monitors to estimate E. coli bacteria

concentrations in Dairy Creek may fail. More data are needed 

to make such a conclusion.

Given the model calibration and validation statistics, 

and the performance of the models at predicting time series 

data and reproducing the original measured values used in the 

correlations, reliable predictions for E. coli bacteria at Dairy 

Creek at Highway 8 is a possibility, but several challenges 

remain. Although the continuous monitor at that site was 

converted to a permanent installation in 2004, the potential 

for backwater at stages greater than 10 ft during winter causes 

several concerns. Stage instead of discharge may be required 

as an explanatory variable, and separate models may be 

needed for winter and for summer. E. coli bacteria (and other 

constituents) may respond differently to backwater conditions 

than to unimpeded flow conditions, including potential 

issues of particle settling or upstream sources. One potential 

problem with backwater, the introduction of water from the 

downstream receiving waters at the sampling location, does 

not occur at the Highway 8 site (C. Beaman, Oregon Water 

Resources Department, written commun., April 22, 2009). 
Relations Between Continuous Monitor Data and Selected Water-Quality Constituents 51

Table 15. Preliminary model statistics for correlation of Escherichia coli bacteria with continuous parameters at Dairy Creek at Highway 8 near Hillsboro, Oregon, 

2002–04.

[Regression models are of the form E. coli = a*Turb+ b*

Q + c*SC + d, where a, b, and c are model coefficients and d is the intercept, Turb, Q, and SC are the explanatory variables turbidity (in 

Formazin Nephelometric Units), discharge (in cubic feet per second), and specific conductance (in microsiemens per centimeter), respectively, and E. coli is the dependent variable, in colonies per 100 milliliters. Where E. coli is log transformed, a bias transformation factor (BCF; Duan, 1983) is multiplied by 10(logE. coli ) to get the final value. Model 7b was not included in the model selection scheme 

because stage and discharge are surrogates for one another, but model 7b was built separately to evaluate the use of stage as an independent variable to compensate for backwater effects on discharge at the Dairy Creek site. RMSE values are in colonies per 100 milliliters. The maximum Variance Inflation Factor (VIF) indicates the largest VIF obtained for any one variable in the correlation. 

Abbreviations: E. coli, Esherichia coli bacteria; n, number of samples; Adj.-

R2, adjusted 

R2, a coefficient of determination which adjusts for degrees of freedom and penalizes the use of too many 

explanatory variables; f, a function of indicated constituents; log, base 10 logarithm; RMSE, root mean square error; NA, VIFs are not applicable because only one independent variable was used]

Model No. 

and form

Model calibration

Model validation—Goodness-of-fit evaluation

Value of coefficient, when used Correlation statistics

a b c d BCF n Adj.-

R2 Maximum VIF Mean error Validation RMSE Coefficient of determination Nash - Sutcliffe coefficient z-statistic fromsign test

Scenario 1 Calibration data set—Autosamplers only Validation data set—Clean Water Services ambient monitoring data

1. logE. coli =f(Turb, SC) 0.035 – -0.025 5.14 1.05 37 0.871 1.3 185 694 0.10 -4.1 0.31

2. logE. coli =f(Turb, Q, SC) 0.036 -0.0001 -0.025 5.15 1.05 37 0.867 2.9 118 735 0.09 -3.3 0.63

3. logE. coli =f(

Q, SC) – 0.005 -0.029 5.71 1.12 38 0.715 1.2 11 653 0.10 -2.4 1.1

4. logE. coli =f(Turb) 0.049 – – 1.69 1.1 37 0.695 NA -122 331 0.01 -0.15 0.93

Scenario 2 Calibration data set—Peak autosampler plus first monthly and high flow monitoring samples from Clean Water Services dataset Validation data set—Remaining monthly Clean Water Services ambient monitoring + non-peak autosampler data

5. logE. coli =f(SC) – – 0.013 0.713 1.29 17 0.342 NA 94 567 0.02 -3.2 1.2

6. logE. coli =f(Turb, SC) 0.017 – 0.013 0.497 1.45 17 0.309 1.0 161 667 0.03 -4.8 2.4

7a. logE. coli =f(

Q, SC) – -0.0004 0.011 0.981 1.37 16 0.378 1.2 19 329 0.02 0.13 1.8

7b. logE. coli =f(Stage, SC) – -0.0001 0.011 0.828 1.36 32 0.391 11.4 -2 331 0.02 0.21 0.9

8. logE. coli =f(Turb, Q, SC) 0.017 0.00001 0.013 0.494 1.45 17 0.256 11.2 71 338 0.07 -0.26 2

1 Exceeds a threshold VIF value, calculated as {1/(1– (Adj.-R2)} and indicates possible multicollinearity. 
52 Continuous Monitors and Autosamplers Used to Predict Water-Quality Constituents, Tributaries of Tualatin River, Oregon Figure 18. Time series of predicted Escherichia coli (E. coli) bacteria concentrations at Dairy Creek at Highway 8 near Hillsboro, from Models 4, 5, and 7b, with prediction intervals, calibration, and validation data (A) during 2002–04 , (B) during storm 1, and (C) during storm 2, Tualatin River basin, Oregon. Winter conditions are not predicted because of the lack of available monitor and discharge data.

tac09-0396_fig18

4-27-2002 11-24-2002 11-13-2004

10

100

1,000

10,000

100,000

1,000,000

 BACTERIA COUNTS, E. coli

IN COLONIES PER 100 MILLILITERS

 BACTERIA COUNTS, E. coli

IN COLONIES PER 100 MILLILITERS

Prediction interval, Model 5

LogEcoli=ƒ(Turbidity, Specific Conductance), Model 4LogEcoli=ƒ(Stage, Specific Conductance), Model 7b, Scenario 2LogEcoli=ƒ(Specific Conductance), Model 5

Calibration data, Scenario 2

Validation data, Scenario 2

100101

STAGE, IN FEET

STAGE, IN FEET

Stage

10

100

1,000

10,000

 BACTERIA COUNTS, E. coli

IN COLONIES PER 100 MILLILITERS

10

100

1,000

10,000 10

1

STAGE, IN FEET

1

A.

B. C.

EXPLANATION

10-07-2003 10-08-2003 10-09-2003 10-10-2003 10-11-2003 10-12-2003

DATE

11-17-2003 11-18-2003 11-19-2003 11-20-2003 11-21-2003 11-22-2003 11-23-2003

DATE

4-22-2003 11-19-2003 4-16-2004

10
Relations Between Continuous Monitor Data and Selected Water-Quality Constituents 53

tac09-0396_fig19

Predicted concentration

95 percent prediction 

 intervals

LOGARITHM OF PREDICTED E. coli COUNTS

LOGARITHM OF MEASURED E. coli COUNTS

A.

B.

1.0 5.1 2.0 5.2 3.0 5.3

1.0

1.5

2.0

2.5

3.0

3.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5

Figure 19. Comparison of measured and predicted Escherichia coli

(E. coli) bacteria counts using (A) Model 4 and (B) Model 5, Dairy Creek 

at Highway 8 near Hillsboro, Oregon. Native reporting units for E. coli

bacteria counts prior to log transformation were in colonies per 100 

milliliters.

Non-Target Sites

Preliminary model forms were identified for TSS, TP, and 

E. coli bacteria at the non-target sites using autosampler-only 

data (table 16) and minimization of Mallow’s Cp. Other 

than removal of outliers that cannot be resolved, and logtransformation of dependent variables, no attempt was made 

to verify homoscedasticity, use additional data, compensate 

for autocorrelation, or otherwise optimize the models. Model 

coefficients and adjusted-R2 values are not shown because 

the objective of this exercise was to evaluate the likelihood 

that models that are more robust could be developed if data 

representative of the range of environmental conditions at 

these sites become available. 
54 Continuous Monitors and Autosamplers Used to Predict Water-Quality Constituents, Tributaries of Tualatin River, Oregon

Table 16. Preliminary functional model forms for total suspended solids, total phosphorus, and Escherichia coli bacteria at non-target 

tributary sites in the Tualatin River basin, Oregon, 2002–04.

[Model forms are based on autosampler-only data. If the adjusted-R2 of the optimum model, based on minimization of Mallow’s Cp, was greater than 0.5, 

then the functional form of the optimal model is shown, without model coefficients. If the adjusted-R2 was less than 0.5, then no model is shown and the result 

is listed as N/A (not applicable). Turb, Q, and SC are the explanatory variables of turbidity (in Formazin Nephelometric Units), discharge (in cubic feet per 

second), and specific conductance (in microsiemens per centimeter), respectively. Abbreviations: TSS, total suspended solids; TP, total phosphorus; E. coli, 

Escherichia coli bacteria; log, base 10 logarithm; f, a function of indicated constituents]

Site

Functional model form

TSS TP E. coli

Beaverton Creek at SW 170th Ave. logTSS=f(Turb,SC,Q) logTP=f(Turb,SC,Q) N/A

Chicken Creek at Scholls-Sherwood Highway logTSS=f(Turb,SC,Q) logTP=f(Turb,SC,Q) N/A

Rock Creek at Woll Pond Way near Hillsboro logTSS=f(Turb,SC,Q) logTP=f(SC,Q) N/A

Gales Creek at Old Highway 47 logTSS=f(Turb,SC,Q) logTP=f(Turb,SC,Q) logE. coli=f(Turb,SC,Q)

Results in table 16 indicate a high probability that robust 

regression models can be developed for TSS and TP at all 

non-target sites, but that E. coli bacteria may be difficult to 

predict at sites other than Gales Creek at Old Highway 47. In 

almost all other cases, models of the form logX= f (Turb, SC,

Q), where X is the dependent variable, may be constructed and 

would provide acceptable predictions. For the Rock Creek site, 

a regression model with specific conductance and discharge 

may be sufficient to predict logTP. When E. coli bacteria 

model results are not applicable, the adjusted-R2 values of the 

functional models were much lower than 0.5 as indicated in 

table 16; most were less than 0.1, and the available data were 

insufficient for predicting E. coli bacteria. The adjusted-R2

values for all other indicated models were greater than 0.7, 

which in some cases indicate even stronger correlations than 

the models for Fanno and Dairy Creeks. However, some of the 

limitations of the available data and the stream responses at 

the non-target sites should be considered:

1. Discharge (and stage) was not continuously measured 

at either the Rock Creek or Beaverton Creek sites, so 

instantaneous values at those sites were reconstructed by 

simple routing of upstream discharges at existing stream 

gages. Considerable error likely was inherent in the 

timing and magnitude of the resulting hourly estimates 

of the storm hydrographs, especially considering the 

dynamic and variable nature of stream responses to 

different storms.

2. No large storms were sampled at Chicken Creek, where 

the relatively undeveloped drainage basin muted the 

stream response to storms. Indeed, the stream response 

may represent an increase in groundwater input after 

storms rather than direct runoff, as indicated by minimal 

increases in turbidity and increases (rather than the 

expected decreases) in specific conductance. Nonetheless, 

Chicken Creek does, at times, respond to large storms, 

and warrants future study. 

3. Storm responses at the Gales Creek site were more muted 

than at several of the other sites and had lower flows 

than originally intended, and the range of values of the 

potential explanatory variables (turbidity, discharge, and 

specific conductance) was small. 

4. The Rock Creek at Woll Pond Way site was just 

downstream of an anomalous sediment source, where the 

streambank was observed to episodically calve into the 

river during high flow and cause short-term pulses of high 

turbidity that may have been poorly mixed. It is likely that 

the turbidity and sediment response at this site were not 

necessarily reflective of larger drainage basin processes. 

Subsequent to this study, the monitor at the Rock Creek 

site was moved downstream to a bridge crossing and 

reinstalled with a more permanent (all-season) design, so 

any attempt to predict water quality in Rock Creek would 

benefit from collection of data at the new site. 

Given these limitations, additional efforts to further refine 

the model results at the non-target sites are not warranted 

without additional data collection specifically from high flow 

conditions and during several seasonal periods. Nonetheless, 

preliminary results in this study indicate that reasonably robust 

models for some constituents can be developed if appropriate 

data become available. 
Discussion 55

Discussion

The use of continuously measured parameters as 

surrogates for unmeasured constituents, including real-time 

applications, has increased in recent years. Rasmussen and 

others (2008) published regression models for 19 constituents, 

including whole-water and dissolved solutes such as various 

major ions (calcium, magnesium, and sulfate) nutrients, 

and bacteria (bacteria, fecal coliform, and enterococcus) at 

5 sites in Kansas. The program in Kansas has been successful 

enough that results of the regression predictions are posted 

online in real time, and coefficients of determination for the 

regression models mostly range from about 0.6 to greater 

than 0.9. However, the streams in Kansas, which are known 

to consistently carry appreciable sediment loads and to have 

relatively steady flow, are different from Tualatin River 

tributaries in Oregon. In the Tualatin River basin, most 

streams are considerably smaller than the streams monitored in 

Kansas and many have rapid, short-term responses to rainfall 

runoff (due to their highly urbanized upstream land uses) or 

highly variable streamflows (given the prolonged dry climate 

in summer and prolonged wet periods in winter). It was not 

clear, therefore, that the modeling approaches taken in Kansas 

and elsewhere could be successfully applied in the Tualatin 

River basin. 

Given the uncertainty over application of the predictive 

regression techniques to conditions in the Tualatin River 

tributaries, this study followed a proof-of-concept approach. 

Regression models and predictions were developed for this 

report as examples of the type of results that could be obtained 

for selected Tualatin River tributaries, if additional data were 

collected to better represent the range of conditions at those 

sites. Although all the sites (target and non-target) in the 

study are considered important for management of non-point 

runoff, the target sites (on Fanno Creek and Dairy Creek) 

were selected for detailed analysis because they represent land 

use types (urban and agricultural, respectively) of interest 

from a management standpoint. Furthermore, additional 

stream-chemistry data were available for these sites from 

Clean Water Services (and for Fanno Creek, from USGS) 

beyond the temporary autosampler deployments used in this 

study. However, the resulting regression models for these 

sites are considered preliminary because the available data 

do not adequately represent the range of conditions expected 

at these sites, particularly high flows that often lead to high 

concentrations and loads. Therefore, several models are shown 

and discussed for each site, any of which could, with sufficient 

additional data, become the most useful model form for 

predicting the indicated parameter. Water-quality constituents 

associated with suspended particulates, notably TSS, TP, 

and E. coli bacteria, were modeled (as dependent variables) 

because they are the constituents of greatest interest to local 

regulators and resource managers, and because they may be 

most effectively modified through land use management, 

whereas dissolved constituents such as nitrate or phosphate 

may be more controlled by groundwater and microbiological 

processes. 

Neither historical dataset (from Clean Water Services 

and USGS) was originally collected for the purposes used in 

this report, so neither dataset represented optimal input data 

for calibration or validation of regression models. High-flow 

conditions, which in particular cause high concentrations and 

loads of TSS, TP, and E. coli bacteria, are under-represented. 

The regression models and coefficients discussed in this 

report, therefore, are considered examples or starting points 

for future modeling efforts. Furthermore, the aggregation 

of data from multiple laboratories that was done for Fanno 

Creek introduced additional uncertainty, particularly for 

predictions of TSS, for which the input calibration and 

validation datasets used a combination of TSS analysis from 

Clean Water Services and suspended sediment concentration 

analysis from USGS laboratories. No data were available upon 

which to base any adjustment of USGS suspended sediment 

concentration data to compare with the TSS analysis by Clean 

Water Services. Any inherent differences were incorporated 

into the regression model uncertainties and the magnitude of 

the prediction intervals. 

Several sites in the Tualatin River basin continuously 

measure streamflow and stream stage, and water‐quality 

monitors collect temperature, specific conductance, dissolved 

oxygen, pH, and turbidity data, and, at some sites, chlorophyll 

a (see http://or.water.usgs.gov/tualatin/monitors/). Of these, 

specific conductance, turbidity, and streamflow most directly 

indicate short-term physical changes in the stream that may 

result in water-quality changes, and are the most likely 

candidates to be used as surrogates for water-sample chemical 

data. Seasonality also was explored with the incorporation of 

sine and cosine transformations of sample date. 

Most results from the regressions for the highly urbanized 

Fanno Creek site were consistent with findings from Anderson 

and Rounds (2003), who determined that TSS from three 

sites in Fanno Creek was significantly correlated with several 

parameters, including discharge, turbidity, and total dissolved 

solids (a surrogate for specific conductance). Similarly, TP 

was significantly correlated with total dissolved solids and 

turbidity, and E. coli bacteria was significantly correlated 

with turbidity. In this study, models for TSS, TP, and E. coli

bacteria were primarily a function of turbidity, with discharge 

and specific conductance typically having various influences 

on the models. Despite sometimes impressive adjusted-R2

values for model calibration, the goodness-of-fit statistics 

from the model validation exercise generally reflected poor 
56 Continuous Monitors and Autosamplers Used to Predict Water-Quality Constituents, Tributaries of Tualatin River, Oregon

agreement with the validation datasets. This poor agreement 

with the validation datasets was particularly true for the 

Nash‐Sutcliffe coefficient, a measure of model errors that 

is more stringent than the coefficient of determination for 

the model. The validation datasets in this study, however, 

are primarily composed of low-flow samples and do not 

adequately evaluate the response of the models to high flows, 

so the actual fit during these conditions is unknown. 

Results from Dairy Creek at Highway 8, with primarily 

undeveloped or agricultural land upstream, were equivocal 

and likely reflect the limited dataset. No data were available 

from routine or sporadic studies by the USGS (unlike at 

Fanno Creek), and no winter data were available for any 

of the independent variables except stage during 2002–04. 

Furthermore, backwater conditions at Dairy Creek during 

late autumn and winter may have a major effect on any 

correlations that involve discharge and may necessitate 

separate models for free-flowing as opposed to backwater 

flow regimes at that site. Using discharge as an independent 

variable at these high stages would require a different 

method of stream gaging (for example, measuring stream 

velocity and deriving a rating for velocity with discharge). 

Velocity also may be a useful independent variable at this 

site. The slower velocities associated with backwater produce 

much less turbulence than the faster velocities associated 

with unimpeded streamflow thus affecting the quantity 

of suspended sediment and other particulates. Therefore, 

correlations of streamflow, turbidity, or specific conductance 

with TSS, TP, E. coli bacteria, and other parameters likely 

would be different under backwater conditions. The effect of 

backwater on correlations with dissolved constituents such as 

orthophosphorus or nitrate-nitrogen may depend on its effect 

on different sources such as groundwater discharge at high 

stage. For example, redox or other conditions in temporarily 

saturated soils could cause changes in the release of nutrients, 

dissolved minerals, or dissolved organic carbon (which 

could affect turbidity and specific conductance) if backwater 

conditions are prolonged.

The lack of significance of sine and cosine terms in 

any of the regression models may indicate that the other 

independent variables inherently capture most of the seasonal 

signal contained in the data. To the extent that seasonal 

processes such as riparian or upland growth would affect 

runoff patterns, the continuous records of turbidity, specific 

conductance, and discharge also should reflect these factors 

and may more directly measure the indirect seasonal patterns. 

Additionally, Dairy Creek, with its larger upstream area of 

agricultural land use and pervious surfaces, generally would 

be more susceptible to seasonal patterns than Fanno Creek; 

however, regression modeling was less successful overall 

at this site than at Fanno Creek. The lack of appropriate 

high‐flow data and difficulties with backwater may have 

caused problems with the regression-based models at the 

Dairy Creek site that masked sine- or cosine-dependent 

seasonal patterns. Finally, dissolved constituents that are 

more directly functions of biological processing may be more 

likely than TSS and TP to exhibit seasonal fluctuations (for 

example, nitrate production from nitrification, or dissolved 

orthophosphate uptake during primary production; Anderson 

and Rounds, 2003). For future modeling efforts, particularly 

those involving dissolved chemical species, seasonal 

aspects should be evaluated using sine and cosine terms as 

independent variables. 

Using the Regression Equations

Assuming future development of regression models 

is successful for some constituents at sites in the Tualatin 

River basin, the models can be used in several ways. The 

primary expected use is to evaluate peak concentrations in 

the modeled streams in response to hydrologic events, and 

thereby anticipate related water-quality effects or conditions 

in the mainstem Tualatin River. To fully evaluate effects 

on the Tualatin River, constituent loads (in mass per year) 

exported from the tributaries should be calculated or estimated 

as well; a simple matter if the monitoring station and a 

streamflow gaging station are located together (for example, 

Uhrich and Bragg, 2003). Regulators may compare predicted 

concentrations with benchmarks such as regulatory criteria 

or TMDL-based requirements, but model uncertainty (for 

example, the range of possible concentrations indicated by 

prediction intervals around a given predicted value) should 

be considered in any such comparison. Water managers may 

also wish to evaluate predicted concentrations as a potential 

response of stream restoration or other land-use management 

changes in the drainage basin. After several years of data 

collection and iterations of model calibrations, it may be 

possible to use such models to detect trends in water quality 

over time. If model coefficients or functional forms change 

consistently over time in ways that are insensitive to simple 

increases in the number of samples available for calibration, 

then such changes could indicate new or reduced sources, 

steady changes in the association of a particular constituent 

with another, or other process-based changes in the drainage 

basin. For example, model forms at a site that are constant for 

TSS but that have declining coefficients for turbidity in a TP 

model could indicate that the source of TP has changed and 

may be less dependent on suspended particulates.

Suggestions for Future Study

The initial plan for this study was to use autosamplers, 

deployed in conjunction with, and potentially triggered by, 

continuous monitors to collect high-density data over the 

course of several storms at individual sites, six of which had 

been identified in the larger tributaries to the Tualatin River. 
Discussion 57

These deployments would provide data spanning a broad 

range of environmental conditions and that could be used to 

establish initial regression models. The planned sampling 

was limited, however, by available resources such that only 

two sites could be sampled for each storm; furthermore, the 

sampled storms proved to be relatively small and did not 

produce the desired range of hydrologic responses in the 

sampled streams. Additional constraints were imposed by 

limited, seasonal deployments of the continuous monitors at 

some sites, resulting in fewer continuously monitored data 

being collected with autosamplers at the highest streamflows. 

Streamflow or stage was not directly available at two sites 

(Beaverton and Rock Creeks), and was subject to backwater 

at another site (Dairy Creek at Highway 8), so only a few 

measurements were available for use as a surrogate for those 

sites. 

Site Considerations

To build on the findings from this study, several 

considerations could improve data collection procedures and 

help select locations at which success would be most likely. 

These include

1. Permanent or long-term installations of equipment such 

as streamflow and stream-stage sensors, continuous 

monitors, and (or) autosamplers, during all seasons 

and streamflow conditions, and maintenance of 

stage-discharge rating curves and electronic databases 

for streamflow and continuous monitors under high and 

low streamflow conditions. When streamflow cannot 

be directly monitored at a site, estimation of discharge 

from upstream or nearby gaging stations requires a more 

thorough approach than the simple summation and routing 

that was used at some non-target sites in this study; 

2. Availability of telemetry or other remote communication 

with monitors and autosamplers to enhance the quality 

of monitor data, reduce downtime, anticipate stream 

conditions that might result in autosamplers triggering, 

and allow determination of the status of sample collection. 

Such communication, together with currently available 

database software, allows the real-time display of 

calculated concentrations, loads, and prediction intervals 

in other locations around the Nation (for example, see 

http://nrtwq.usgs.gov/ks/);

3. Avoidance of backwater conditions that may render 

regression models inapplicable under certain situations; 

alternatively, the development of models that apply 

seasonally or under specific streamflow conditions;

4. Avoidance of local influences that do not adequately 

represent drainage basin conditions, but which may exert 

disproportionately strong influence on water quality at 

the sampling or monitoring sites, such as nearby tributary 

inputs, localized erosion or other sediment sources, point 

sources, or impoundments.

Samples were collected from Fanno and Dairy Creeks 

and several additional sites. Regression models were 

not explored for these additional sites, however, because 

insufficient storm data were available for 2002-03, or other 

individual considerations, and because water managers were 

more interested in Fanno and Dairy Creek. Chicken Creek did 

not respond readily to storms during the period of study, and 

seemed to have a groundwater-dominated hydrologic response 

that resulted in low concentrations of suspended materials. 

Likewise, Gales Creek at Old Highway 47, with a primarily 

forested upstream drainage basin, did not show a substantive 

hydrologic response to storms during periods of monitor 

deployment. Beaverton and Rock Creeks did not have stream 

gages at the same sites where the monitors and autosamplers 

were deployed, and efforts at simple mass-balance routing 

of streamflow from upstream gaging stations, including 

tributaries, were unsuccessful for the purposes of this study. 

Within a short distance upstream of the Rock Creek sampling 

site, a streambank was actively eroding during high-flows, 

and that episodic contribution of suspended sediment may 

not have been representative of upstream sediment and 

chemistry sources. Although these streams may be subjects 

of future studies, careful selection of sampling locations 

and equipment installation will be needed to provide data of 

sufficient seasonality and quality for successful development 

of regression models. 

Autosamplers

Autosamplers allow the collection of unattended samples 

during inconvenient times or unsafe conditions and the 

collection of time-series samples over the course of a storm 

hydrograph. However, autosamplers are expensive and require 

maintenance (for example, for intake clogging, battery and 

ice replacement, and programming). They also have sampling 

reliability issues (for example, inadvertent triggering when the 

stream hydrograph does not match the desired pattern, [that 

is, false starts], or conversely not triggering when the stream 

response should have dictated the desired sampling), and 

quality assurance concerns. As a result, although autosamplers 

can change the types and frequency of samples collected and 

make certain sample-collection schedules logistically possible, 

they do not necessarily reduce the expense of sampling. 

Finally, the use of multiple samples collected during a few 

storm hydrographs for regression modeling may result in 

serial correlation issues, artificially inflating the value of 

coefficients-of-determination (R2) for regression models, 

and indicating a level of model robustness that may not 

be warranted.
58 Continuous Monitors and Autosamplers Used to Predict Water-Quality Constituents, Tributaries of Tualatin River, Oregon

Despite these issues, autosamplers can be highly useful 

for developing a robust dataset for refining the regression 

models started in this study. Primary uses for autosamplers 

could include

1. Unattended sampling at nights, weekends, or other 

situations that are difficult to sample manually;

2. Sampling in streams with rapid hydrological responses, 

when it may be difficult to get to the site before the peak 

discharge; 

3. Collecting enough samples during a storm hydrograph, 

together with continuous monitor or streamflow data, 

to allow screening for key samples for laboratory 

submission, on the basis of peak discharge or turbidity 

values; 

4. Collecting samples at multiple sites during a single storm, 

if enough autosamplers are available for deployment; 

5. Collecting samples from locations that are inconvenient or 

unsafe for human sampling, such as manholes or culverts; 

and 

6. Exploring within-storm variability of selected waterquality constituents, such as comparing constituent 

concentrations as streamflow increases and decreases 

during storms.

Quality assurance data collected for this study indicate that 

autosamplers can be used for collection of representative 

samples in Tualatin River basin streams, but additional tests 

during high streamflow conditions and at additional sites are 

warranted. Appropriate tests include evaluation of sample 

holding times, especially for bacteria and during warm 

weather, additional determination of cross section coefficients 

at high flows and at various sampling sites, and additional tests 

of equipment contamination or carryover when sample tubing 

has been deployed for extended periods. 

Water Sample Collection

Historical data from USGS and Clean Water Services 

databases were helpful for evaluating whether or not useful 

regression models can be developed for Fanno Creek, 

Dairy Creek, and elsewhere. These databases extended the 

range of conditions represented in the models, increased the 

number of samples and thereby the degrees-of-freedom of 

the regressions, supported the use of several scenarios of data 

aggregation to better understand the constraints of available 

data, and allowed validation of the developed models with 

independent data not used for model calibration. The historical 

data were collected to meet other objectives, however, and 

therefore were not as useful in this study for predictive 

purposes as might be desired. The primary limitation was the 

lack of samples collected during storms or other high-flow 

periods. Clean Water Services data were collected in a routine 

manner as part of an established ambient monitoring program, 

during which samples were sometimes collected during 

storm runoff, but collection was not designed specifically for 

those conditions. USGS data collection at Fanno Creek was 

mostly routine, although several additional high-flow samples 

had been collected as part of other studies (see Anderson 

and Rounds, 2003). No historical USGS data that could be 

used for this study were available from Dairy Creek near 

Highway 8. Also, USGS data did not include E. coli bacteria 

so regression models for bacteria had fewer samples to use. 

Additional uncertainty may have been introduced to the Fanno 

Creek analysis by combining analytical results from USGS 

and Clean Water Services databases, representing different 

laboratory methodologies—most likely for the suspended 

sediment concentration and total suspended solids data, which 

could not be compared because of a lack of available data 

from concurrent samplings. 

To build on the models initiated in this report, and 

to develop robust regressions that can be useful for 

understanding concentrations and (or) loading of waterquality constituents, additional high-flow samples are needed 

to extend the range of conditions represented. The baseline 

conditions are well represented in the available data (for 

example, figure 3), and probably can be easily predicted. 

The models discussed in this report do a reasonable job of 

predicting a range of baseline conditions, especially for Fanno 

Creek, such that routine sample collection could theoretically 

be scaled back (regulatory considerations aside) with minimal 

loss of understanding of stream conditions. 

Although redesigning the Clean Water Services ambient 

monitoring program is beyond the scope of this report, the 

simple addition of several samples each year from high flow 

conditions would allow the model results from this study 

to be revisited and improved upon, particularly if those 

samples included the most extreme conditions. If the use of 

surrogates for predictive modeling as outlined here were the 

sole objective of a modified sampling plan, at least for selected 

sites, it might include elements such as 
Discussion 59

1. Reducing the routine sampling frequency at each site 

to twice-monthly intervals, especially during low-flow 

periods; 

2. Installing autosamplers that are designed to capture 

instantaneous (not flow-weighted) samples during storms, 

thus allowing the selection of samples for laboratory 

analysis (based on streamflow or turbidity, for example), 

and sampling during weekends and evenings, ideally with 

remote interrogation or activation capabilities;

3. Sampling of selected storms manually, particularly the 

most extreme events each year, with an added focus 

on collecting cross sectional data for evaluating the 

representativeness of the autosampler’s intake location; 

4. Evaluating analytical procedures, especially for E. coli

bacteria, to ensure that resulting data will meet the needs 

of model development; 

5. Developing predictive models for other stream 

constituents, such as chlorophyll, dissolved 

orthophosphate, or nitrogen either as total nitrogen, 

nitrate-nitrogen, or ammonia-nitrogen; and

6. Considering additional independent variables such as 

continuously monitored water temperature, optical 

measurements of chlorophyll or ultraviolet fluorescence, 

or local precipitation data.

Model Development and Selection 

Log transformation of dependent variables was an 

important step in the development of most predictive models 

for this study, an approach that is similar to those from other 

studies. Distributions of many environmental parameters 

are log-normal in nature, so such transformations often are 

consistent with stream processes (Helsel and Hirsch, 1992). 

Additionally, log transformation has the practical benefit 

of eliminating negative results when model predictions are 

converted back to normal units. However, log transformation 

can introduce a bias that needs to be corrected. Duan’s Bias 

Correction Factor (Duan, 1983), or BCF, which converts 

the logarithmic residuals from the regression process 

into normal space and then averages them, has been used 

frequently in recent studies (Uhrich and Bragg, 2003; 

Anderson, 2007; Rasmussen and others, 2008) and is used 

by the USGS as part of a national protocol for surrogate 

prediction by regression models (Rasmussen and others, 

2009). Other steps in the maintenance of data and regression 

modeling include graphical evaluation of the relations 

between independent and dependent variables (for example, 

figure 5), removal of outliers (if careful attempts to resolve 

them were unsuccessful), and evaluation of residuals for 

homoscedasticity (constant variance across the range of data; 

Rasmussen and others, 2009).

When predicting surrogate concentrations using 

regression models such as those presented in this report, model 

uncertainty should be considered. In this study, 95 percent 

prediction intervals are included in figures showing time-series 

based on the continuously measured independent variables (for 

example, figure 6). For log-transformed dependent variables, 

lower and upper prediction interval values initially were in 

logarithmic terms and needed the bias correction applied 

after conversion to normal units in the same way as the actual 

predicted independent values. Although many models did not 

perform well for predicting the exact value of the independent 

variables in the validation datasets, the prediction intervals 

almost always encompassed the validation data. It is therefore 

important to display the range covered by the lower and upper 

prediction intervals, and equally important for water managers 

to consider that the actual concentration of an unmeasured 

constituent could fall anywhere within the portrayed interval 

range.

In this report, regression models were compared against 

an independent validation dataset through the determination 

of several goodness-of-fit statistics (table 8), including RMSE, 

a coefficient of determination analogous to the model’s 

R2, and the Nash-Sutcliffe coefficient, which measures the 

contribution of the predicted values to the measured variance. 

This approach appears somewhat unique among recent studies 

that predict surrogate values from continuous monitors, but 

provides a critical assessment of the model’s performance 

against an independent dataset. The available datasets for 

model validation in this study were not adequate to assess 

model performance at the high values, most of which was 

during stormflow periods, so the goodness-of-fit statistics 

presented in this report would change if the same models were 

evaluated with a more complete dataset. 

The use of continuous parameters such as discharge, 

stage, turbidity, or specific conductance for estimation of 

additional constituents has inherent risks of multicollinearity, 

wherein the independent variables are correlated, potentially 

causing spurious regression results or confounding the values 

of regression coefficients. Multicollinearity is a potential 

risk because many of the physical processes that affect these 

continuous parameters, such as discharge and turbidity, are 

related. Multicollinearity can be measured through the use of 
60 Continuous Monitors and Autosamplers Used to Predict Water-Quality Constituents, Tributaries of Tualatin River, Oregon

variance inflation factors (VIFs), which provide a measure of 

the independence of the individual variables, and generally 

is reduced with increasing numbers of samples (Draper 

and Smith, 1998). Assessment of multicollinearity is not 

straightforward, however, and must be done in conjunction 

with specific study objectives. In this study, VIFs were all less 

than general rules-of-thumb (that is, less than 5–10) that are 

sometimes cited (Helsel and Hirsch, 1992). Critical VIFs were 

calculated according to the SAS Institute’s method (1989) by 

using the model’s adjusted-R2 in equation 3, and compared 

with the VIFs for the individual parameters. As a result, 

multicollinearity was potentially indicated for at least one or 

two models for each estimated parameter at each site because 

the respective adjusted-R2 values were low. The collection of 

additional data to increase the sample size and the number of 

values (constituent data) measured during storm conditions 

would be expected to reduce the likelihood of multicollinearity 

among independent variables in future studies. 

Model selection schemes initially used backward 

stepwise regression to identify potential explanatory variables, 

followed by the use of Mallow’s Cp to reduce the likelihood 

of producing models that were overfitted (Helsel and Hirsch, 

1992). The Best Subsets algorithm, which is widely available 

in many statistical software packages, makes formal use of 

Mallow’s Cp along with alternating inclusion and exclusion 

of independent variables to achieve a parsimonious model 

(Draper and Smith, 1998). Future work on model selection 

also could benefit from a more broad-based selection scheme 

that uses all the information available from regression 

statistics to identify the most robust and parsimonious 

models, and to minimize the use of extraneous independent 

variables. An example model selection metric that could be 

useful is Akaike’s Information Criterion, or AIC (Burnham 

and Anderson, 2002), which provides numerous scores and 

weights to identify the best model from a suite of potential 

regression models. Regardless of which of these schemes is 

used, however, the process can be expected to be iterative, 

particularly with exploration of transformation schemes and 

evaluation of seasonal variation by using sine and cosine 

transformations, while concurrently watching for potential 

problems with multicollinearity and serial correlation. 

Conclusion

For the water-quality constituents in the small 

Tualatin River basin streams presented in this report, 

automatic samplers were capable of collecting unbiased and 

representative stream samples. No major cross-contamination 

issues from sample to sample were observed. Sufficient care 

certainly must be exercised to keep the samples on ice and 

deliver them to the laboratory in a timely manner, but when 

used with a good quality assurance plan, autosamplers were 

a useful component of a sampling plan. Used in conjunction 

with water-quality monitors that can trigger sampling, 

autosamplers may become an invaluable component of future 

monitoring or sampling schemes.

The use of continuously measured field parameters to 

predict constituents of regulatory interest in streams could be 

helpful for understanding the effect of management strategies 

on water quality in the Tualatin River basin. Results of this 

study indicate that the potential to develop predictive relations 

is good. Additional data, including more water-quality data 

over a broader range of conditions along with co-located 

discharge monitoring, would increase the predictive ability of 

the resulting regressions. 

These sorts of predictive regression equations may 

be used to quantify peak concentrations or annual loads 

of sediment or phosphorus moving through the system. 

The equations may be useful in suggesting certain types 

of occurrences during storms or other conditions that 

merit further study, thus aiding in our understanding of 

the water-quality dynamics of these streams. This method 

of using continuous water-quality monitors to predict the 

concentrations of unmeasured water-quality constituents is 

an underutilized technique that deserves more attention in the 

future.

This study was a reconnaissance effort to determine the 

transferability of techniques used elsewhere to tributaries 

of the Tualatin River basin. These techniques for predicting 

the unmeasured concentrations of selected water quality 

constituents from continuously monitored surrogates require 

site-specific correlations and relatively consistent upstream 

conditions. If successful, this effort could provide a foundation 

for development of more detailed and accurate correlations 

at the study locations and elsewhere, and for their use in 

near‐real time, potentially allowing evaluation of the efficacy 

of land-use and other management decisions.

Acknowledgments

Micelis Doyle, Anna Buckley, and Amy Brooks (USGS) 

all helped with data collection during this study. Two 

individuals volunteered their time: Steve Eikenas helped by 

checking on the samplers and reporting on their response to 

storms, and Mary Lindenberg spent many hours consolidating 

and making sense of the many data sources. Special thanks 

go to Jan Miller and the staff at the Clean Water Services 

Water-Quality Laboratory for their willingness to accept 

extra water samples and work long hours to ensure that those 

samples were properly analyzed. 
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Appendix A. Data Tables

63

Appendix A. Data Tables
64 Continuous Monitors and Autosamplers Used to Predict Water-Quality Constituents, Tributaries of Tualatin River, Oregon Table A1.

Water-quality and discharge data collected during sampled storms, Fanno Creek, Oregon, June 2002 and May 2003.

[Abbreviations: E. coli, Escherichia coli bacteria; oC, degrees Celsius; μS/cm, microsiemens per centimeter; mg/L, milligram per liter; FNU, formazin nephelometric unit; ft3/s, cubic foot per second; ft, foot; C/100 mL, colonies per 100 milliliters; E, estimated; <, less than]

Date

(mm-dd-yy) Time Temperature 

(oC)

Specific 

conductance 

(μS/cm)

Dissolved 

oxygen 

(mg/L)

pH Turbidity (FNU) Discharge (ft3/s) Stage (ft)

Total 

suspended 

solids 

(mg/L)

Dissolved 

ammonium 

nitrogen 

(mg/L)

Total 

Kjeldahl 

nitrogen 

(mg/L)

Nitrate 

+ nitrite 

nitrogen 

(mg/L)

Total 

phosphorus 

(mg/L)

Soluble 

reactive 

phosphorus 

(mg/L)

Dissolved 

chloride 

(mg/L)

E. coli 

(C/100mL)

Storm 1

06-17-02 14:54 16.2 245 8.4 7.6 5.7 7.1 1.86 4 E 0.01 0.31 0.53 0.13 0.08 11.8 180

15:20 16.2 245 8.4 7.6 5.9 9.0 1.91 4 < 0.01 0.28 0.52 0.13 0.08 11.7 290

15:59 16.2 241 8.5 7.7 8.4 15.3 2.06 4 < 0.01 0.38 0.52 0.16 0.08 11.3 680

16:06 16.3 240 8.5 7.7 9.0 15.3 2.06 9 < 0.01 0.34 0.52 0.15 0.08 11.4 680

16:09 16.3 240 8.5 7.7 9.0 17.8 2.11 8 < 0.01 0.38 0.51 0.15 0.08 11.3 460

16:33 16.2 243 8.5 7.6 9.8 24.2 2.22 17 < 0.01 0.39 0.52 0.17 0.08 11.4 640

17:33 16.4 228 8.2 7.6 38.2 47.0 2.55 67 0.07 0.93 0.58 0.31 0.07 10.0 2,300

18:33 16.7 199 8.0 7.5 78.4 48.5 2.57 101 0.06 1.0 0.48 0.37 0.07 11.6 4,100

19:33 16.4 186 7.9 7.5 104 76.9 2.91 138 0.16 1.4 0.60 0.42 0.06 7.4 E 6,400

20:33 16.4 132 8.1 7.4 131 94.9 3.11 175 0.10 1.3 0.47 0.47 0.06 5.9 E 6,300

21:33 16.3 139 8.1 7.4 118 89.3 3.05 160 0.07 1.2 0.44 0.43 0.05 5.7 E 6,40022:33 16.3 138 8.1 7.4 113 79.5 2.94 126 0.04 1.1 0.43 0.33 0.06 5.9 E 7,80023:33 16.4 150 7.9 7.4 73.1 75.1 2.89 88 0.03 0.93 0.45 0.31 0.06 7.0 E 10,000

06-18-02 00:33 16.4 140 7.5 7.3 82.9 72.4 2.86 85 0.08 1.1 0.48 0.30 0.05 6.4 E 8,600

01:33 16.2 157 7.7 7.4 68.5 67.2 2.80 84 0.03 0.93 0.47 0.30 0.06 7.9 E 7,700

02:33 16.2 147 7.5 7.3 77.8 64.7 2.77 79 < 0.01 0.94 0.48 0.30 0.05 7.6 E 9,200

03:33 16.2 149 7.3 7.3 71.0 63.9 2.76 71 0.03 1.1 0.52 0.28 0.06 7.0 E 8,000

04:33 16.2 138 7.3 7.3 68.9 60.6 2.72 76 0.02 0.97 0.46 0.27 0.07 6.4 E 10,000

05:33 16.1 145 7.4 7.3 71.6 56.5 2.67 75 < 0.01 0.93 0.45 0.27 0.07 7.1 E 10,000

06:33 16.1 150 7.4 7.3 65.6 54.0 2.64 72 < 0.01 0.86 0.43 0.25 0.06 7.4 E 9,900

Storm 2

05-04-03 13:25 11.9 162 9.7 7.3 20.8 108 3.24 25 <.01 0.43 0.68 0.11 5.1 420

14:25 12.1 139 9.9 7.3 70.9 134 3.53 84 0.02 0.48 0.62 0.19 0.03 4.8 2,300

15:25 12.3 110 10.1 7.2 194 127 3.45 196 0.03 1.07 0.41 0.39 0.03 3.1 4,600

16:25 12.5 123 9.9 7.2 86.6 94.9 3.09 68 0.03 0.56 0.48 0.19 0.03 3.7 4,700

17:25 12.6 138 9.9 7.2 35.7 78.6 2.90 31 0.03 0.41 0.57 0.12 0.03 4.4 2,000

18:25 12.6 144 9.9 7.2 28.6 71.6 2.82 21 E 0.01 0.34 0.59 0.11 0.03 4.5 1,400

19:25 12.7 145 9.9 7.3 23.2 63.9 2.73 17 E 0.02 0.44 0.61 0.11 0.03 4.6 1,800

20:25 12.6 148 9.9 7.3 20.9 59.8 2.67 16 E 0.02 0.32 0.62 0.10 0.03 4.9 1,200

23:25 12.4 153 9.6 7.3 19.8 51.6 2.57 13 < 0.01 0.32 0.64 0.09 0.03 5.2 380

05-05-03 02:25 12.4 162 9.6 7.3 15.6 47.0 2.51 10 E 0.02 0.27 0.67 0.09 0.04 5.7 400

05:25 12.4 163 9.4 7.3 13.5 41.0 2.43 10 0.03 0.33 0.69 0.08 0.04 5.4 E 180
Appendix A. Data Tables

65

Date

(mm-dd-yy) Time Temperature (oC) Specific conductance (μS/cm) Dissolved oxygen (mg/L) pH Turbidity (FNU) Discharge (ft3/s) Stage (ft) Total suspended solids (mg/L) Dissolved ammonium nitrogen (mg/L) Total Kjeldahl nitrogen (mg/L) Nitrate + nitrite nitrogen (mg/L) Total phosphorus (mg/L) Soluble reactive phosphorus (mg/L) Dissolved chloride (mg/L) E. coli (C/100mL)

Storm 3

05-07-03 00:29 11.5 186 10.0 7.4 37.7 29.8 2.27 9.0 0.02 0.31 0.74 0.09 0.04 7.1 260

05-08-03 01:29 11.3 179 10.0 7.4 24.9 66.4 2.76 37.2 0.02 0.34 0.77 0.12 0.04 6.8 350

02:29 11.3 176 9.9 7.4 40.6 91.2 3.05 69.2 0.05 0.55 0.63 0.18 0.03 5.5 640

03:29 10.9 154 10.1 7.4 59.8 109 3.25 78.0 0.05 0.47 0.58 0.19 0.03 5.0 2,900

04:29 11.3 158 9.8 7.4 53.0 115 3.31 66.0 0.05 0.50 0.60 0.17 0.03 5.7 1,100

05:29 11.1 151 9.8 7.3 55.2 118 3.35 73.0 0.05 0.51 0.58 0.19 0.03 4.9 2,000

06:29 10.8 145 9.9 7.3 52.0 115 3.32 61.0 0.03 0.48 0.59 0.18 0.04 5.9 940

07:29 10.7 139 9.8 7.3 75.6 108 3.24 72.0 0.03 0.56 0.59 0.20 0.04 4.4 E 1,800

08:29 10.6 139 9.7 7.3 58.0 98.5 3.13 54.0 0.03 0.49 0.59 0.18 0.04 4.2 1,300

09:29 10.5 131 9.8 7.2 46.5 89.3 3.03 37.0 0.03 0.43 0.56 0.14 0.03 3.9 1,400

10:29 10.5 133 9.8 7.2 51.2 83.9 2.97 40.0 0.02 0.43 0.60 0.13 0.03 4.2 E 1,900

11:29 10.5 134 9.8 7.3 48.9 80.3 2.92 38.0 0.03 0.41 0.58 0.13 0.04 4.2 E 1,800

12:29 10.8 138 9.8 7.3 37.7 76.0 2.87 27.0 E 0.02 0.39 0.59 0.13 0.04 4.6 1,400

13:29 11.0 140 9.8 7.3 35.1 69.8 2.80 23.0 E 0.01 0.38 0.58 0.11 0.04 4.6 1,000

14:29 11.0 140 9.9 7.2 32.8 63.1 2.72 18.0 E 0.01 0.29 0.59 0.10 0.03 4.6 1,100

15:29 11.0 141 9.9 7.2 29.7 56.5 2.63 17.0 E 0.01 0.34 0.61 0.10 0.03 4.5 900

16:29 11.2 140 9.9 7.2 29.5 50.8 2.56 16.0 < 0.01 0.36 0.60 0.10 0.03 4.3 920

17:29 11.3 140 9.9 7.2 31.0 46.2 2.50 16.0 E 0.01 0.31 0.58 0.10 0.03 4.2 960

18:29 11.4 142 9.9 7.2 29.4 43.3 2.46 18.0 < 0.01 0.32 0.59 0.10 0.03 4.4 540

19:29 11.4 143 9.8 7.2 29.4 41.0 2.43 15.0 E 0.01 0.34 0.59 0.12 0.03 4.4 800

20:29 11.4 145 9.8 7.2 28.2 38.8 2.40 15.0 E 0.02 0.31 0.59 0.09 0.04 4.4 720

21:29 11.4 146 9.8 7.2 26.1 36.6 2.37 13.0 E 0.01 0.33 0.59 0.10 0.03 4.5 680

22:29 11.4 147 9.8 7.2 25.8 35.2 2.35 12.0 E 0.02 0.29 0.59 0.09 0.04 4.6 520

23:29 11.4 149 9.7 7.2 21.2 33.8 2.33 7.0 E 0.02 0.30 0.59 0.09 0.04 4.7 640

Table A1. Water-quality and discharge data collected during sampled storms, Fanno Creek, Oregon, June 2002 and May 2003.—Continued[Abbreviations: E. coli, Escherichia coli bacteria; oC, degrees Celsius; μS/cm, microsiemens per centimeter; mg/L, milligram per liter; FNU, formazin nephelometric unit; ft3/s, cubic foot per second; ft, foot; 

C/100mL, colonies per 100 milliliters; E, estimated; <, less than]
66 Continuous Monitors and Autosamplers Used to Predict Water-Quality Constituents, Tributaries of Tualatin River, Oregon Table A2.

Water-quality data collected during sampled storms, Beaverton Creek at 170th, Oregon, June and December 2002.

[Level is a relative measure of stream depth, taken from a bubbler anchored to the streambed at an arbitrary datum for each deployment. Abbreviations: E. coli, Escherichia coli bacteria; oC, degrees Celsius; μS/cm, microsiemens per centimeter; mg/L, milligram per liter; FNU, formazin nephelometric unit; ft, foot; C/100mL, colonies per 100 milliliters; E, estimated; >, greater than; ND, no data]

Date

(mm-dd-yy) Time Temperature 

(oC)

Specific 

conductance 

(μS/cm)

Dissolved 

oxygen 

(mg/L)

pH Turbidity (FNU)

Autosampler 

bubbler 

level 

(ft)

Total 

suspended 

solids 

(mg/L)

Dissolved 

ammonium 

nitrogen

(mg/L)

Total 

Kjeldahl 

nitrogen 

(mg/L)

Nitrate 

+ nitrite 

nitrogen 

(mg/L)

Total 

phosphorus 

(mg/L)

Soluble 

reactive 

phosphorus 

(mg/L)

Dissolved 

chloride 

(mg/L)

E. coli 

(C/100mL)

Storm 1

06-28-02 15:57 18.6 216 ND 7.4 14.1 ND 10 0.05 0.51 0.49 0.18 0.09 9.6 740

16:57 18.5 212 ND 7.5 13.5 ND 12 0.06 0.61 0.48 0.20 0.10 9.9 E 1,700

17:57 18.2 202 ND 7.5 16.0 ND 19 0.08 0.75 0.51 0.21 0.08 9.4 E 2,600

18:57 18.0 174 ND 7.4 29.4 ND 34 0.11 0.92 0.57 0.22 0.07 10.5 E 8,200

19:57 17.8 136 ND 7.3 42.4 ND 57 0.11 0.99 0.62 0.24 0.08 6.5 > 10,000

20:57 17.9 134 ND 7.3 44.8 ND 65 0.09 0.99 0.59 0.27 0.09 5.9 > 10,000

21:57 17.9 130 ND 7.2 48.3 ND 73 0.09 1.03 0.53 0.28 0.08 5.7 > 10,000

22:57 17.8 124 ND 7.2 68.9 ND 71 0.09 1.02 0.55 0.29 0.09 5.5 > 16,000

23:57 17.6 118 ND 7.1 59.1 ND 71 0.08 1.03 0.55 0.31 0.09 5.2 > 16,000

06-29-02 00:57 17.5 114 ND 7.1 60.2 ND 90 0.07 1.07 0.55 0.35 0.11 5.1 > 16,000

01:57 17.1 110 ND 7.1 77.5 ND 97 0.07 1.11 0.54 0.38 0.11 4.9 > 16,000

 02:57 17.1 106 ND 7.0 91.3 ND 134 0.06 1.06 0.56 0.38 0.11 4.5 > 16,000

 03:57 17.0 106 ND 7.0 78.2 ND 136 0.06 1.02 0.57 0.37 0.13 4.5 > 16,000

 04:57 16.9 106 ND 7.0 64.5 ND 121 0.05 0.98 0.57 0.32 0.12 4.5 > 10,000

 05:57 16.8 108 ND 7.0 53.5 ND 90 0.04 0.96 0.56 0.35 0.13 4.6 > 10,000

 06:57 16.8 108 ND 6.9 52.1 ND 81 0.04 0.91 0.52 0.31 0.15 4.6 > 10,000

 07:57 16.7 110 ND 6.9 51.0 ND 55 0.04 0.85 0.48 0.30 0.15 4.6 > 10,000

 08:57 16.7 108 ND 6.9 36.2 ND 39 0.03 0.84 0.45 0.29 0.15 4.6 > 16,000

09:57 16.8 108 ND 6.9 31.7 ND 33 0.03 0.81 0.42 0.28 0.15 4.4 > 10,000

14:41 18.4 101 ND 6.9 17.9 ND 18 0.03 0.69 0.36 0.23 0.14 3.8 E 8,100

15:49 18.7 101 ND 6.9 17.0 ND 9 0.03 0.69 0.35 0.24 0.15 3.8 E 12,000

16:49 18.9 102 ND 6.9 16.5 ND 8 0.03 0.66 0.34 0.22 0.14 3.8 E 6,800

17:49 19.1 102 ND 6.9 16.2 ND 10 0.02 0.65 0.33 0.21 0.14 3.8 E 6,200

18:49 19.3 103 ND 6.9 14.5 ND 8 0.03 0.64 0.31 0.20 0.15 3.8 E 6,500

19:49 19.4 105 ND 6.9 14.4 ND 10 0.03 0.65 0.31 0.21 0.14 3.9 5,900

20:49 19.3 107 ND 6.9 13.4 ND 10 0.03 0.61 0.30 0.20 0.13 3.9 6,800

21:49 19.2 110 ND 6.9 15.1 ND 10 0.03 0.62 0.30 0.22 0.14 4.1 4,900

22:49 19.0 112 ND 6.9 13.4 ND 8 0.04 0.59 0.29 0.20 0.14 4.2 4,500

23:37 18.8 115 ND 6.9 11.9 ND 6 0.04 0.61 0.29 0.20 0.13 4.3 4,500

23:42 18.8 115 ND 6.9 13.2 ND 8 0.04 0.62 0.29 0.19 0.13 4.3 E 3,600

23:54 18.8 115 ND 6.9 13.1 ND 10 0.04 0.38 0.30 0.16 0.14 4.3 E 3,300

06-30-02 00:52 18.6 118 ND 6.9 11.5 ND 6 0.04 0.32 0.29 0.16 0.14 4.4 E 2,500

 00:57 18.6 118 ND 6.9 15.5 ND 8 0.05 0.28 0.29 0.17 0.14 4.3 E 2,200

01:00 18.6 118 ND 6.9 15.5 ND 9 0.05 E 0.67 0.29 E 0.21 0.14 4.4 E 2,600

01:07 18.5 119 ND 6.9 11.2 ND 7 0.05 E 0.68 0.29 E 0.21 0.14 4.4 E 1,600
Appendix A. Data Tables

67

Date

(mm-dd-yy) Time Temperature (oC) Specific conductance (μS/cm) Dissolved oxygen (mg/L) pH Turbidity (FNU) Autosampler bubbler level (ft) Total suspended solids (mg/L) Dissolved ammonium nitrogen (mg/L) Total Kjeldahl nitrogen (mg/L) Nitrate + nitrite nitrogen (mg/L) Total phosphorus (mg/L) Soluble reactive phosphorus (mg/L) Dissolved chloride (mg/L) E. coli (C/100mL)

Storm 2

12-10-02 15:16 7.8 103 9.1 6.9 55.5 0.21 79 0.08 0.77 0.54 0.28 0.08 6.3 E 1,100

16:16 7.8 99 8.9 6.9 63.8 0.25 85 0.08 0.75 0.54 0.30 0.09 5.8 E 1,000

17:16 7.7 101 8.7 6.9 56.6 0.28 74 0.07 0.74 0.55 0.26 0.09 6.5 E 1,300

18:16 7.7 102 8.5 6.8 47.6 0.28 54 0.07 0.62 0.55 0.24 0.10 6.6 E 1,100

20:16 7.5 101 8.2 6.8 42.4 0.24 40 0.05 0.61 0.51 0.21 0.10 6.2 E 900

21:16 7.5 100 8.1 6.8 34.7 0.23 30 0.05 0.53 0.50 0.20 0.10 6.1 E 1,200

23:16 7.4 101 7.8 6.8 30.8 0.16 21 0.04 0.53 0.49 0.19 0.09 6.0 E 1,000

12-11-02 01:16 7.3 104 7.5 6.8 25.7 0.08 15 0.04 0.47 0.50 0.18 0.09 6.1 E 1,100

05:16 7.2 114 7.2 6.8 22.0 -0.09 10 0.05 0.47 0.52 0.16 0.09 6.4 E 1,000

07:16 7.2 118 7.4 6.8 20.9 -0.14 8 0.05 0.49 0.54 0.16 0.08 6.6 E 940

10:16 7.4 123 8.8 6.9 26.8 0.05 24 0.08 0.55 0.49 0.17 0.06 7.8 E 1,100

12:16 7.7 99 9.7 6.9 52.2 0.28 72 0.07 0.64 0.42 0.24 0.08 6.5 E 830

13:41 7.8 91 9.4 6.8 81.9 0.49 166 0.05 0.82 0.49 0.33 0.06 5.5 E 1,000

15:41 8.0 82 9.0 6.8 74.5 0.73 158 0.04 0.85 0.44 0.35 0.07 4.9 E 1,000

17:41 8.2 78 8.7 6.7 70.6 0.83 116 0.04 0.67 0.42 0.28 0.08 4.5 E ,1500

19:41 8.3 78 8.4 6.7 48.2 0.66 100 0.04 0.81 0.42 0.28 0.08 4.5 1,000

21:41 8.4 80 8.1 6.7 36.4 0.73 60 0.03 0.58 0.44 0.24 0.09 4.6 1,900

22:41 8.5 82 8.0 6.7 42.3 0.65 48 0.03 0.52 0.45 0.22 0.09 4.6 1,400

23:41 8.5 83 7.8 6.7 32.7 0.65 40 0.03 0.47 0.47 0.19 0.10 4.7 E 950

12-12-02 01:41 8.7 87 7.5 6.7 27.5 0.51 29 0.03 0.48 0.49 0.21 0.10 5.0 E 750

05:41 8.9 96 6.9 6.7 25.8 0.31 19 0.04 0.45 0.55 0.18 0.09 5.5 870

07:41 8.8 101 6.7 6.7 23.7 0.22 12 0.04 0.45 0.57 0.16 0.10 5.6 900

09:41 8.8 104 6.5 6.7 21.6 0.13 11 0.04 0.45 0.57 0.16 0.09 5.7 E 430

11:41 8.9 108 6.5 6.7 18.7 0.04 11 0.04 0.42 0.57 0.14 0.10 5.8 670

Table A2. Water-quality data collected during sampled storms, Beaverton Creek at 170th, Oregon, June and December 2002.—Continued[Level is a relative measure of stream depth, taken from a bubbler anchored to the streambed at an arbitrary datum for each deployment. Abbreviations: E. coli, Escherichia coli bacteria; oC, degrees Celsius; μS/cm, microsiemens per centimeter; mg/L, milligram per liter; FNU, formazin nephelometric unit; ft, foot; C/100mL, colonies per 100 milliliters; E, estimated; >, greater than; ND, no data]
68 Continuous Monitors and Autosamplers Used to Predict Water-Quality Constituents, Tributaries of Tualatin River, Oregon Table A3.

Water-quality and discharge data collected during sampled storms, Chicken Creek at Scholls-Sherwood Highway, Oregon, June 2002 and May 2003.

[Abbreviations: FNU, formazin nephelometric unit; E. coli, Escherichia coli bacteria; oC, degrees Celsius; μS/cm, microsiemens per centimeter; ft3/s, cubic foot per second; ft, foot; mg/L, milligram per liter; 

C/100mL, colonies per 100 milliliters; E, estimated]

Date

(mm-dd-yy) Time Temperature 

(oC)

Specific 

conductance 

(μS/cm)

Dissolved 

oxygen 

(mg/L)

pH Turbidity (FNU) Discharge (ft3/s) Stage (ft)

Total 

suspended 

solids 

(mg/L)

Dissolved 

ammonium 

nitrogen

(mg/L)

Total 

Kjeldahl 

nitrogen 

(mg/L)

Nitrate 

+ nitrite 

nitrogen 

(mg/L)

Total 

phosphorus 

(mg/L)

Soluble 

reactive 

phosphorus 

(mg/L)

Dissolved 

chloride

(mg/L)

E. coli 

(C/100mL)

Storm 1

06-17-02 19:50 15.7 146 6.9 7.1 15.4 12.2 1.19 15 E 0.01 0.29 0.40 0.13 0.03 3.5 220

19:53 15.7 146 6.9 7.1 15.5 12.2 1.19 15 E 0.02 0.33 0.40 0.14 0.04 3.5 E 400

19:55 15.7 147 6.9 7.1 15.6 12.2 1.19 15 E 0.02 0.34 0.40 0.14 0.04 3.6 E 320

20:50 15.7 144 6.7 7.1 17.2 12.2 1.19 15 E 0.01 0.40 0.40 0.14 0.04 3.5 460

21:50 15.6 141 6.5 7.1 16.6 12.2 1.19 15 < 0.01 0.42 0.40 0.14 0.03 3.4 980

22:24 15.6 141 6.6 7.1 15.8 12.2 1.19 16 < 0.01 0.34 0.40 0.14 0.04 3.4 980

22:26 15.6 141 6.6 7.1 15.7 12.2 1.19 14 < 0.01 0.37 0.40 0.14 0.03 3.4 E 1,300

22:30 15.6 142 6.6 7.1 15.6 12.2 1.19 14 E 0.01 0.39 0.40 0.14 0.03 3.4 900

22:32 15.6 142 6.6 7.1 15.5 12.2 1.19 14 E 0.01 0.31 0.40 0.13 0.05 3.5 840

22:36 15.6 142 6.6 7.1 15.4 12.2 1.19 12 E 0.01 0.34 0.40 0.13 0.04 3.4 880

22:40 15.6 142 6.6 7.1 15.3 11.6 1.18 13 E 0.01 0.38 0.41 0.15 0.04 3.5 900

22:42 15.6 142 6.6 7.1 15.3 11.6 1.18 11 E 0.01 0.33 0.41 0.14 0.03 3.5 1,100

23:11 15.5 141 6.7 7.1 14.8 11.6 1.18 12 E 0.02 0.38 0.41 0.14 0.04 3.5 660

23:14 15.5 141 6.7 7.1 14.8 11.6 1.18 13 E 0.02 0.34 0.41 0.15 0.04 3.4 860

23:18 15.5 141 6.7 7.1 14.9 11.6 1.18 15 E 0.02 0.35 0.41 0.15 0.04 3.5 840

23:30 15.5 141 6.7 7.1 14.9 11.0 1.17 11 E 0.02 0.30 0.41 0.13 0.04 3.5 900

23:32 15.5 140 6.7 7.1 14.9 11.0 1.17 14 E 0.02 0.32 0.41 0.14 0.04 3.5 740

23:34 15.5 140 6.7 7.1 14.9 11.0 1.17 14 E 0.02 0.32 0.42 0.13 0.04 3.5 820

23:36 15.5 140 6.7 7.1 14.9 11.0 1.17 16 E 0.02 0.34 0.42 0.14 0.04 3.5 680

23:38 15.5 140 6.7 7.1 14.9 11.0 1.17 14 E 0.02 0.32 0.42 0.13 0.03 3.5 680

06-18-02 00:01 15.5 139 6.8 7.1 15.0 11.0 1.17 10 E 0.02 0.37 0.42 0.13 0.03 3.5 740

00:25 15.4 139 6.8 7.1 14.5 10.5 1.16 14 E 0.02 0.35 0.42 0.13 0.03 3.5 760

00:27 15.4 139 6.8 7.1 14.5 10.5 1.16 10 0.02 0.34 0.43 0.13 0.04 3.4 900

00:29 15.4 139 6.8 7.1 14.5 10.5 1.16 10 0.02 0.33 0.43 0.13 0.04 3.4 940

Storm 2

05-08-03 00:35 11.0 85 9.4 7.0 31.9 20.7 1.42 28 0.02 0.20 0.78 0.10 0.02 2.7 E 64

01:35 10.9 84 9.5 7.0 17.5 22.2 1.45 13 0.02 0.23 0.78 0.08 0.02 2.7 180

02:35 10.9 85 9.3 7.0 18.0 22.2 1.45 10 0.03 0.21 0.79 0.08 0.03 2.7 300

03:35 10.8 86 9.4 7.0 13.8 23.2 1.47 11 0.03 0.21 0.76 0.08 0.03 2.7 120
Appendix A. Data Tables

69

Date

(mm-dd-yy) Time Temperature (oC) Specific conductance (μS/cm) Dissolved oxygen (mg/L) pH Turbidity (FNU) Discharge (ft3/s) Stage (ft) Total suspended solids (mg/L) Dissolved ammonium nitrogen (mg/L) Total Kjeldahl nitrogen (mg/L) Nitrate + nitrite nitrogen (mg/L) Total phosphorus (mg/L) Soluble reactive phosphorus (mg/L) Dissolved chloride (mg/L) E. coli (C/100mL)

Storm 3

05-17-03 18:00 10.9 95 9.9 7.0 31.2 22.7 1.46 30 0.03 0.28 0.52 0.12 0.03 2.7 600

19:00 10.8 94 10.0 7.0 29.2 26.8 1.54 27 0.04 0.35 0.51 0.12 0.03 2.7 E 700

20:00 10.7 88 10.2 7.0 38.2 28.9 1.58 29 0.03 0.34 0.50 0.13 0.02 2.6 950

21:00 10.7 93 10.2 7.0 31.3 27.8 1.56 31 0.04 0.37 0.50 0.14 0.02 2.7 3,900

22:00 10.4 88 9.8 7.0 49.1 25.7 1.52 26 0.03 0.36 0.47 0.13 0.02 2.6 3,800

23:00 10.2 89 10.0 6.9 31.5 23.7 1.48 22 0.03 0.34 0.49 0.13 0.03 2.7 2,200

05-18-03 00:00 10.1 92 9.8 6.9 23.0 21.2 1.43 14 0.03 0.28 0.51 0.09 0.02 2.7 1,900

01:00 9.9 92 9.8 6.9 20.1 19.8 1.40 11 0.03 0.28 0.53 0.09 0.03 2.7 1,600

05:00 9.2 90 10.1 6.9 19.3 17.0 1.34 9 0.03 0.23 0.55 0.08 0.02 2.6 1,200

06:00 9.1 90 10.1 6.9 17.7 17.0 1.34 9 0.03 0.24 0.54 0.08 0.02 2.5 1,200

11:00 9.3 92 10.4 6.9 17.1 16.5 1.33 7 0.03 0.22 0.53 0.08 0.02 2.5 940

12:00 9.6 92 10.6 6.9 16.8 16.1 1.32 9 0.03 0.26 0.54 0.09 0.02 2.5 1,000

13:00 10.2 92 10.4 6.9 15.3 16.1 1.32 7 0.03 0.27 0.54 0.09 0.02 2.5 740

16:00 11.8 94 10.6 7.0 13.4 15.6 1.31 5 0.03 0.26 0.56 0.08 0.02 2.6 E 360

17:00 12.0 95 10.4 7.0 12.4 15.6 1.31 5 0.03 0.24 0.56 0.08 0.02 2.6 540

Table A3. Water-quality and discharge data collected during sampled storms, Chicken Creek, Oregon, June 2002 and May 2003.—Continued[Abbreviations: FNU, formazin nephelometric unit; E. coli, Escherichia coli bacteria; oC, degrees Celsius; μS/cm, microsiemens per centimeter; ft3/s, cubic foot per second; ft, foot; mg/L, milligram per liter; 

C/100mL, colonies per 100 milliliters; E, estimated]
70 Continuous Monitors and Autosamplers Used to Predict Water-Quality Constituents, Tributaries of Tualatin River, Oregon Table A4.

Water-quality and discharge data collected during sampled storms, Rock Creek at Woll Pond Way, Oregon, June and December 2002, and September 2003.

[Level is a relative measure of stream depth, taken from a bubbler anchored to the streambed at an arbitrary datum for each deployment. Abbreviations: FNU, formazin nephelometric unit; E. coli, Escherichia coli bacteria; oC, degrees Celsius; μS/cm, microsiemens per centimeter; mg/L, milligram per liter; ft, foot; ft3/s, cubic foot per second; C/100mL, colonies per 100 milliliters; E, estimated; >, greater than; ND, 

no data]

Date

(mm-dd-yy) Time Temperature 

(oC)

Specific 

conductance 

(μS/cm)

Dissolved 

oxygen 

(mg/L)

pH Turbidity (FNU)

Autosampler

bubbler

level

(ft)

Discharge

(ft3/s)

Total 

suspended 

solids 

(mg/L)

Dissolved 

ammonium 

nitrogen 

(mg/L)

Total 

Kjeldahl 

nitrogen 

(mg/L)

Nitrate 

+ nitrite 

nitrogen 

(mg/L)

Total 

phosphorus 

(mg/L)

SRP 

(mg/L)

Dissolved 

chloride 

(mg/L)

E. coli 

(C/100mL)

Storm 1

06-28-02 17:12 18.3 234 6.5 7.6 15 ND 13.3 17 E 0.01 0.52 0.77 0.26 0.17 11.7 E 1,300

17:22 18.3 236 6.5 7.6 16 ND 13.3 20 E 0.02 0.49 0.75 0.26 0.17 11.7 E 1,600

17:27 18.2 238 6.5 7.5 16 ND 13.9 20 E 0.02 0.53 0.74 0.26 0.17 11.7 E 1,700

18:17 18.2 240 6.4 7.5 22 ND 15.7 31 E 0.02 0.55 0.63 0.29 0.16 12.4 E 2,300

19:17 18.1 240 6.5 7.5 28 ND 20.6 41 0.03 0.66 0.54 0.31 0.15 13.0 E 2,400

20:17 18.0 238 6.4 7.5 37 ND 23.6 52 0.03 0.70 0.50 0.31 0.14 12.3 E 2,400

21:17 17.9 214 6.5 7.5 48 ND 30.9 64 0.02 0.82 0.44 0.33 0.14 9.8 E 5,100

22:17 17.8 194 6.7 7.5 51 ND 41.1 72 0.04 0.86 0.46 0.33 0.14 8.2 > 6,000

23:17 17.8 186 6.7 7.4 53 ND 54.1 76 0.04 0.76 0.45 0.34 0.14 8.1 > 8,000

06-29-02 00:17 17.8 184 6.7 7.4 55 ND 69.3 82 0.09 0.92 0.78 0.42 0.19 8.4 > 10,000

01:17 17.8 172 6.7 7.4 61 ND 86.6 96 0.07 0.94 0.56 0.37 0.16 7.5 > 10,000

02:17 17.7 164 6.7 7.4 66 ND 107 103 0.04 0.91 0.42 0.36 0.14 8.3 > 10,000

03:17 17.7 154 6.7 7.3 67 ND 128 96 0.04 0.91 0.41 0.34 0.12 6.9 > 10,000

04:17 17.7 156 6.8 7.3 65 ND 153 95 0.04 0.87 0.41 0.35 0.12 7.3 > 10,000

05:17 17.6 146 6.7 7.3 67 ND 172 94 0.04 0.91 0.39 0.33 0.12 7.0 > 10,000

06:17 17.6 132 6.6 7.2 70 ND 180 96 0.04 0.92 0.40 0.33 0.11 6.1 > 10,000

07:17 17.5 128 6.6 7.2 78 ND 181 95 0.04 0.91 0.40 0.33 0.12 5.8 > 7,500

15:55 18.0 136 6.4 7.1 45 ND 130 61 0.04 0.76 0.54 0.27 0.13 7.4 4,600

16:55 18.1 138 6.4 7.1 42 ND 124 49 0.04 0.86 0.50 0.26 0.13 7.4 4,400

17:55 18.2 138 6.4 7.1 41 ND 118 48 0.03 0.73 0.43 0.24 0.12 7.4 5,400

18:55 18.3 138 6.4 7.2 38 ND 112 43 0.03 0.73 0.40 0.25 0.13 7.3 3,900

19:55 18.4 138 6.4 7.2 39 ND 107 53 0.03 0.74 0.38 0.25 0.13 7.3 4,800

20:55 18.6 138 6.4 7.2 35 ND 103 39 0.03 0.65 0.37 0.25 0.13 7.3 4,700

21:55 19.6 138 6.4 7.2 31 ND 98.9 37 0.03 0.61 0.35 0.23 0.13 7.2 4,600

22:55 18.7 138 6.3 7.2 29 ND 94.0 33 0.03 0.65 0.34 0.22 0.13 7.1 3,400

23:55 18.7 138 6.3 7.2 28 ND 65.1 31 0.03 0.55 0.34 0.22 0.13 7.1 3,300

06-30-02 00:55 18.6 138 6.3 7.2 27 ND 85.1 33 0.03 0.64 0.33 0.22 0.14 7.3 2,200

01:55 18.5 140 6.3 7.2 26 ND 80.1 29 0.04 0.58 0.33 0.21 0.14 7.2 2,400

02:55 18.5 140 6.3 7.2 25 ND 75.9 30 0.03 0.55 0.32 0.21 0.14 7.2 1,600

03:55 18.4 140 6.3 7.2 22 ND 71.8 29 0.07 0.80 0.33 0.26 0.17 7.3 2,200

04:55 18.3 142 6.3 7.2 22 ND 67.9 21 0.03 0.59 0.31 0.21 0.13 7.3 E 2,100

05:55 18.3 142 6.3 7.2 23 ND 64.0 25 0.04 0.60 0.31 0.21 0.13 7.4 E 2,200
Appendix A. Data Tables

71

Date

(mm-dd-yy) Time Temperature (oC) Specific conductance (μS/cm) Dissolved oxygen (mg/L) pH Turbidity (FNU) Autosampler bubbler level (ft) Discharge (ft3/s) Total suspended solids (mg/L) Dissolved ammonium nitrogen (mg/L) Total Kjeldahl nitrogen (mg/L) Nitrate + nitrite nitrogen (mg/L) Total phosphorus (mg/L) SRP (mg/L) Dissolved chloride (mg/L) E. coli (C/100mL)

Storm 2

12-10-02 14:56 6.3 167 10.5 7.3 78 0.57 151 106 0.10 1.0 0.49 0.32 0.08 11.6 E 700

15:56 6.6 155 10.3 7.3 81 0.67 180 99 0.10 0.86 0.49 0.31 0.08 12.2 2,200

16:56 6.8 134 10.4 7.3 77 0.69 184 98 0.10 0.87 0.47 0.31 0.10 9.7 E 1,600

17:56 6.9 129 10.4 7.2 71 0.67 180 90 0.09 0.82 0.46 0.29 0.08 9.3 E 1,600

18:56 6.9 131 10.3 7.2 69 0.64 175 77 0.09 0.77 0.49 0.30 0.09 9.8 E 1,900

19:56 6.9 129 10.4 7.2 60 0.59 171 66 0.09 0.73 0.47 0.26 0.09 10.0 E 700

20:56 6.9 131 10.3 7.2 54 0.55 168 62 0.08 0.71 0.49 0.25 0.10 9.9 E 1,500

21:56 6.9 134 10.3 7.2 48 0.48 164 55 0.08 0.70 0.49 0.23 0.09 10.1 E 900

22:56 6.9 133 10.3 7.2 47 0.41 161 51 0.07 0.66 0.47 0.24 0.09 10.4 E 1,100

23:56 7.0 132 10.4 7.2 46 0.32 156 49 0.07 0.66 0.47 0.21 0.09 11.6 E 1,400

12-11-02 11:15 6.9 139 10.5 7.1 36 0.28 93 36 0.05 0.54 0.41 0.20 0.08 12.8 E 900

12:15 6.9 140 10.7 7.1 50 0.84 106 59 0.05 0.62 0.40 0.22 0.08 12.9 E 700

13:15 7.1 132 10.7 7.1 59 1.33 143 73 0.06 0.67 0.41 0.24 0.09 11.7 E 1,800

14:15 7.2 124 10.7 7.1 62 1.69 197 96 0.06 0.65 0.40 0.26 0.09 10.7 2,100

15:16 7.3 115 10.8 7.1 66 1.89 241 88 0.05 0.65 0.37 0.26 0.09 9.7 1,600

17:16 7.4 100 10.7 7.0 71 2.07 287 93 0.05 0.65 0.37 0.25 0.09 8.0 1,400

19:16 7.6 105 10.5 7.0 72 2.06 287 82 0.04 0.68 0.39 0.27 0.09 8.9 E 950

21:16 7.7 108 10.5 7.0 67 1.98 275 73 0.04 0.69 0.43 0.27 0.09 9.5 1,600

23:16 7.8 109 10.5 7.0 66 1.81 260 73 0.04 0.69 0.47 0.25 0.08 9.5 1,600

12-12-02 02:16 8.0 109 10.2 6.9 55 1.50 337 58 0.04 0.63 0.45 0.22 0.09 9.5 1,000

03:16 8.0 109 10.2 6.9 53 1.38 355 56 0.04 0.60 0.44 0.22 0.08 9.4 E 950

05:16 8.1 109 10.1 6.9 47 1.07 372 43 0.04 0.55 0.46 0.22 0.08 9.3 1,300

07:16 8.2 109 10.1 6.9 44 0.73 370 39 0.03 0.55 0.47 0.19 0.08 9.0 E 550

09:16 8.3 112 10.1 6.9 36 0.41 363 34 0.03 0.44 0.48 0.17 0.08 9.3 1200

11:16 8.3 116 10.0 6.9 31 0.11 344 25 0.03 0.46 0.48 0.17 0.09 9.9 E 700

13:16 8.4 119 10.0 6.9 27 -0.18 317 21 0.03 0.44 0.49 0.16 0.08 10.5 E 450

Table A4. Water-quality and discharge data collected during sampled storms, Rock Creek at Woll Pond Way, Oregon, June and December 2002, and September 2003.—

Continued

[Level is a relative measure of stream depth, taken from a bubbler anchored to the streambed at an arbitrary datum for each deployment. Abbreviations: FNU, formazin nephelometric unit; E. coli, Escherichia coli bacteria; oC, degrees Celsius; μS/cm, microsiemens per centimeter; mg/L, milligram per liter; ft, foot; ft3/s, cubic foot per second; C/100mL, colonies per 100 milliliters; E, estimated; >, greater than; ND, 

no data]
72 Continuous Monitors and Autosamplers Used to Predict Water-Quality Constituents, Tributaries of Tualatin River, Oregon Date (mm-dd-yy) Time Temperature 

(oC)

Specific 

conductance 

(μS/cm)

Dissolved 

oxygen 

(mg/L)

pH Turbidity (FNU)

Autosampler

bubbler

level

(ft)

Discharge

(ft3/s)

Total 

suspended 

solids 

(mg/L)

Dissolved 

ammonium 

nitrogen 

(mg/L)

Total 

Kjeldahl 

nitrogen 

(mg/L)

Nitrate 

+ nitrite 

nitrogen 

(mg/L)

Total 

phosphorus 

(mg/L)

SRP 

(mg/L)

Dissolved 

chloride 

(mg/L)

E. coli 

(C/100mL)

Storm 3

09-07-03 02:05 19.2 290 5.8 7.6 26 ND 5.24 4.0 0.03 0.37 0.50 0.25 0.19 23.1 E 100

03:05 19.0 289 5.8 7.6 20 ND 5.24 5.0 0.03 0.32 0.50 0.23 0.19 22.8 E 100

05:05 18.7 287 5.8 7.6 28 ND 5.24 5.0 0.02 0.36 0.55 0.24 0.19 21.9 > 120

08:05 18.5 284 5.9 7.6 33 ND 5.76 4.0 0.02 0.32 0.69 0.23 0.19 20.8 E 84

11:05 18.7 282 6.2 7.6 32 ND 5.68 4.0 0.03 0.36 0.58 0.25 0.20 20.1 E 120

13:05 18.9 281 6.3 7.6 33 ND 5.54 4.0 0.03 0.31 0.55 0.23 0.20 19.9 E 84

16:05 19.1 281 6.2 7.6 30 ND 5.81 5.0 0.03 0.26 0.55 0.23 0.19 19.3 E 6717:05 19.1 280 6.1 7.6 36 ND 6.03 3.0 0.03 0.32 0.55 0.25 0.20 19.1 > 100

18:05 19.0 277 6.2 7.6 43 ND 6.16 21.0 0.02 0.41 0.56 0.27 0.20 18.6 110019:05 18.9 277 6.1 7.6 44 ND 6.25 8.0 E 0.02 0.36 0.55 0.25 0.20 18.3 620

20:05 18.8 274 6.0 7.6 45 ND 6.41 8.0 0.07 0.56 0.55 0.26 0.20 17.6 550

21:05 18.7 243 5.9 7.6 56 ND 6.55 13.0 0.10 0.61 0.51 0.25 0.19 15.5 650

22:05 18.5 262 5.9 7.6 59 ND 6.83 16.0 0.04 0.52 0.50 0.28 0.20 16.8 620

23:05 18.3 262 5.8 7.6 54 ND 7.20 12.0 0.05 0.50 0.52 0.25 0.20 16.2 630

09-08-03 00:05 18.0 250 6.0 7.6 60 ND 8.10 9.0 0.05 0.53 0.54 0.25 0.19 15.2 780

18:30 17.8 246 6.2 7.6 79 ND 25.4 13.0 0.05 0.60 0.49 0.21 0.14 14.0 E 600

19:30 17.7 243 6.0 7.4 79 ND 24.8 12.0 0.05 0.62 0.55 0.18 0.13 14.0 E 1,000

20:30 17.6 239 5.7 7.4 73 ND 23.9 15.5 0.06 0.84 0.61 0.20 0.14 14.1 E 1,000

21:30 17.6 236 5.6 7.4 71 ND 23.2 18.5 0.07 0.84 0.67 0.21 0.13 14.1 E 2,200

22:30 17.5 233 5.4 7.4 81 ND 22.5 24.5 0.07 0.92 0.72 0.22 0.13 14.1 E 1,400

23:30 17.5 229 5.3 7.3 77 ND 21.8 25.0 0.08 0.95 0.75 0.21 0.12 13.7 > 3,600

09-09-03 00:30 17.5 222 5.4 7.3 79 ND 22.6 24.5 0.08 0.90 0.65 0.22 0.12 13.5 E 1,600

01:30 17.5 222 5.6 7.3 81 ND 25.8 24.0 0.07 0.85 0.57 0.21 0.12 14.1 E 2,000

02:30 17.5 216 5.7 7.3 82 ND 30.1 21.0 0.07 0.86 0.55 0.20 0.12 13.7 E 2,400

03:30 17.4 208 5.7 7.3 78 ND 31.1 20.0 0.07 1.10 0.54 0.23 0.12 13.1 E 2,000

04:30 17.2 206 5.8 7.3 82 ND 31.1 22.0 0.06 1.10 0.55 0.21 0.13 12.8 E 600

05:30 17.0 219 6.0 7.3 83 ND 29.6 33.0 0.06 0.86 0.57 0.25 0.14 12.0 E 1,000

06:30 16.6 210 6.2 7.4 90 ND 28.0 60.0 0.06 0.90 0.45 0.27 0.13 10.2 E 2,800

07:30 16.5 209 6.0 7.3 85 ND 27.2 70.0 0.05 1.00 0.46 0.30 0.13 10.3 E 3,600

08:30 16.4 191 6.2 7.3 106 ND 29.3 110.0 0.05 1.10 0.42 0.37 0.12 9.2 8,400

09:30 16.4 183 6.4 7.3 99 ND 34.2 113.0 0.06 1.10 0.40 0.33 0.12 9.4 5,600

10:30 16.3 162 6.6 7.2 99 ND 42.8 109.0 0.09 1.20 0.39 0.34 0.12 8.1 12,000

11:30 16.4 155 6.6 7.2 99 ND 53.1 112.0 0.10 0.98 0.50 0.35 0.13 8.0 12,000

12:30 16.4 143 6.8 7.2 103 ND 70.7 112.0 0.09 0.92 0.45 0.33 0.13 7.3 11,000

Table A4. Water-quality and discharge data collected during sampled storms, Rock Creek at Woll Pond Way, Oregon, June and December 2002, and September 2003.—

Continued

[Level is a relative measure of stream depth, taken from a bubbler anchored to the streambed at an arbitrary datum for each deployment. Abbreviations: FNU, formazin nephelometric unit; E. coli, Escherichia coli bacteria; oC, degrees Celsius; μS/cm, microsiemens per centimeter; mg/L, milligram per liter; ft, foot; ft3/s, cubic foot per second; C/100mL, colonies per 100 milliliters; E, estimated; >, greater than; ND, 

no data]
Appendix A. Data Tables

73

Table A5. Water-quality and discharge data collected during sampled storms, Dairy Creek at Highway 8, Oregon, October and November 2003.[Abbreviations: FNU, formazin nephelometric unit; E. coli, Escherichia coli bacteria; oC, degrees Celsius; μS/cm, microsiemens per centimeter; mg/L, milligram per liter; ft3/s, cubic foot per second; ft, foot; 

C/100mL, colonies per 100 milliliters; E, estimated; <, less than]

Date

(mm-dd-yy) Time Temperature (oC) Specific conductance (μS/cm) Dissolved oxygen (mg/L) pH Turbidity (FNU) Discharge (ft3/s) Stage (ft) Total suspended solids (mg/L) Dissolved ammonium nitrogen (mg/L) Total Kjeldahl nitrogen (mg/L) Nitrate + nitrite nitrogen (mg/L) Total phosphorus (mg/L) Soluble reactive phosphorus (mg/L) Dissolved chloride (mg/L) E. coli (C/100mL)

Storm 1

10-09-03 01:25 14.2 140 7.4 7.6 5.8 39.1 3.2 7 0.03 0.41 0.45 0.14 0.07 5.14 E 45

02:25 14.1 140 7.5 7.6 7.5 39.1 3.2 7 0.03 0.36 0.45 0.14 0.07 5.12 E 95

03:25 14.0 140 7.5 7.6 5.7 39.1 3.2 5 0.03 0.39 0.45 0.14 0.07 5.11 E 65

04:25 13.9 139 7.5 7.6 6.1 38.8 3.2 8 0.03 0.36 0.45 0.13 0.07 5.19 E 40

05:25 13.9 139 7.6 7.6 6.7 38.8 3.2 7 0.03 0.38 0.46 0.14 0.07 5.16 E 85

06:25 13.8 139 7.6 7.6 6.5 38.8 3.2 8 0.03 0.37 0.46 0.14 0.07 4.89 E 60

07:25 13.7 141 7.6 7.6 6.4 38.7 3.2 9 0.03 0.38 0.48 0.14 0.07 5.14 E 60

08:25 13.6 140 7.6 7.6 6.5 39.1 3.2 7 0.03 0.38 0.52 0.14 0.07 5.10 E 85

Storm 2

11-19-03 00:45 8.9 122 3.2 7.3 27.3 74.1 4.0 8 0.03 0.37 0.23 0.09 0.04 5.33 140

01:45 9.0 122 3.1 7.3 10.0 73.0 4.0 8 0.02 0.34 0.23 0.09 0.04 5.34 160

02:45 9.0 122 3.1 7.3 9.0 72.5 4.0 8 0.02 0.37 0.23 0.10 0.04 5.29 230

03:45 9.0 122 3.0 7.3 9.0 72.5 4.0 6 E 0.02 0.37 0.23 0.09 0.04 5.29 160

04:45 9.1 122 2.9 7.3 8.8 74.6 4.0 8 0.02 0.36 0.23 0.09 0.04 5.23 170

05:45 9.1 122 2.9 7.3 9.1 76.7 4.1 9 E 0.02 0.38 0.23 0.10 0.04 5.26 320

06:45 9.1 122 2.8 7.3 9.8 75.6 4.1 10 E 0.01 0.38 0.24 0.10 0.04 5.20 E 400

07:45 9.1 120 2.8 7.3 11.5 76.1 4.1 11 E 0.01 0.38 0.24 0.11 0.05 5.01 340

08:45 9.0 117 2.7 7.3 14.2 79.3 4.1 12 E 0.01 0.36 0.26 0.11 0.04 5.00 330

09:45 8.9 124 2.6 7.3 10.8 84.8 4.2 12 E 0.01 0.34 0.26 0.11 0.04 5.35 220

10:45 8.9 125 2.6 7.3 13.4 90.6 4.3 11 E 0.01 0.38 0.26 0.11 0.05 5.38 280

11:45 8.8 126 2.5 7.3 15.0 94.2 4.4 10 E 0.01 0.37 0.27 0.12 0.05 5.45 520

12:45 8.7 126 2.5 7.3 14.9 99.1 4.5 16 E 0.01 0.41 0.37 0.14 0.07 5.19 E 380

13:45 8.7 123 2.4 7.3 19.5 106.0 4.6 19 E 0.01 0.41 0.44 0.17 0.09 5.03 900

14:45 8.8 125 2.3 7.3 21.4 113.3 4.7 27 E 0.01 0.48 0.40 0.19 0.09 5.16 750

15:45 8.7 124 2.3 7.3 26.9 113.3 4.7 31 E 0.02 0.48 0.41 0.19 0.09 5.11 950

16:26 8.6 123 2.7 7.3 30.1 120.1 4.8 29 < 0.01 0.47 0.43 0.21 0.10 5.46 E 520

17:26 8.6 123 5.9 7.3 21.6 121.7 4.8 25 < 0.01 0.51 0.46 0.23 E 0.10 5.48 780

18:26 8.6 124 5.4 7.3 23.9 121.4 4.8 20 < 0.01 0.53 0.50 0.22 0.10 5.69 760

19:26 8.6 125 6.0 7.4 23.4 121.2 4.8 21 < 0.01 0.53 0.52 0.21 0.10 5.63 720

20:26 8.5 124 5.9 7.4 21.8 120.3 4.8 22 E 0.01 0.45 0.48 0.20 0.09 5.67 640

21:26 8.5 123 5.6 7.3 20.1 117.7 4.8 16 < 0.01 0.44 0.47 0.16 0.08 5.68 480

22:26 8.4 123 6.0 7.3 17.0 115.6 4.7 15 < 0.01 0.41 0.46 0.16 0.08 5.60 E 360

11-20-03 00:26 8.3 123 6.0 7.3 15.0 112.7 4.7 13 < 0.01 0.43 0.44 0.14 0.07 5.59 250

02:26 8.2 126 6.0 7.3 14.5 112.7 4.7 11 < 0.01 0.44 0.45 0.14 0.06 5.78 290

04:26 8.1 132 6.5 7.3 18.4 116.6 4.7 16 E 0.01 0.40 0.49 0.14 0.06 6.02 270

06:26 8.0 132 5.4 7.3 16.3 125.6 4.9 14 E 0.01 0.44 0.49 0.15 0.06 6.21 200

08:26 7.9 132 4.9 7.3 15.7 138.3 5.0 12 E 0.01 0.37 0.53 0.15 0.05 6.22 180

10:26 7.9 133 4.6 7.3 15.6 152.6 5.2 15 E 0.01 0.34 0.64 0.14 0.05 6.04 160

11:26 7.9 135 4.3 7.3 15.1 158.7 5.3 13 E 0.02 0.48 0.70 0.14 0.05 6.42 190
74 Continuous Monitors and Autosamplers Used to Predict Water-Quality Constituents, Tributaries of Tualatin River, Oregon Table A6.

Water-quality and discharge data collected during sampled storm, Gales Creek at Old Highway 47, Oregon, November 2003.

[Abbreviations: FNU, formazin nephelometric unit; E. coli, Escherichia coli bacteria; oC, degrees Celsius; μS/cm, microsiemens per centimeter; mg/L, milligram per liter; ft3/s, cubic foot per second; ft, foot; 

C/100mL, colonies per 100 milliliters; E, estimated; <, less than]

Date

(mm-dd-yy) Time Temperature 

(oC)

Specific 

conductance 

(μS/cm)

Dissolved 

oxygen 

(mg/L)

pH Turbidity (FNU) Discharge (ft3/s) Stage (ft)

Total 

suspended 

solids 

(mg/L)

Dissolved 

ammonium 

nitrogen 

(mg/L)

Total 

Kjeldahl 

nitrogen 

(mg/L)

Nitrate 

+ nitrite 

nitrogen 

(mg/L)

Total 

phosphorus 

(mg/L)

Soluble 

reactive 

phosphorus 

(mg/L)

Dissolved 

chloride

(mg/L)

E. coli 

(C/100mL)

Storm 1

11-17-03 15:04 7.9 99 11.3 7.5 24.3 180.40 3.30 24 < 0.01 0.31 0.47 0.06 0.02 5.09 560

16:04 7.9 99 11.3 7.5 18.1 180.91 3.31 46 < 0.01 0.29 0.47 0.07 0.02 5.05 600

17:04 8.0 99 11.3 7.5 18.3 179.51 3.29 45 < 0.01 0.33 0.49 0.08 0.02 5.11 220

18:04 8.1 99 11.3 7.5 13.8 176.61 3.25 41 < 0.01 0.28 0.50 0.06 0.02 5.03 240

19:04 8.2 99 11.3 7.5 17.5 173.71 3.21 38 < 0.01 0.28 0.50 0.06 0.01 5.15 280

20:04 8.2 98 11.3 7.5 14.1 170.91 3.17 34 < 0.01 0.28 0.50 0.06 0.01 5.21 420

21:04 8.3 97 11.3 7.5 13.9 168.01 3.13 26 E 0.01 0.27 0.51 0.05 0.02 5.21 210

22:04 8.4 97 11.2 7.5 11.3 165.91 3.10 18 < 0.01 0.21 0.51 E 0.05 0.02 5.18 190

23:04 8.5 98 11.2 7.5 10.8 163.90 3.07 20 < 0.01 0.22 0.51 E 0.04 0.02 5.20 190

11-18-03 00:04 8.6 97 11.1 7.5 10.7 163.20 3.06 20 < 0.01 0.24 0.51 E 0.05 0.02 5.22 200

03:04 8.7 98 11.0 7.5 12.0 161.10 3.03 15 < 0.01 0.21 0.51 E 0.04 0.01 5.15 120

04:04 8.7 98 11.0 7.5 10.8 161.10 3.03 16 < 0.01 0.2 0.52 E 0.05 0.02 4.96 110

05:04 8.8 98 10.9 7.5 11.3 162.50 3.05 16 < 0.01 0.22 0.57 E 0.05 0.02 5.12 110

06:04 8.8 98 10.9 7.5 12.4 163.39 3.06 15 < 0.01 0.2 0.54 E 0.04 0.02 5.12 180

07:04 8.8 98 10.9 7.5 12.3 163.90 3.07 18 < 0.01 0.2 0.54 E 0.04 0.01 5.20 200

08:04 8.9 98 10.9 7.5 9.0 164.89 3.08 18 < 0.01 0.21 0.56 E 0.04 0.02 5.18 150

11:04 9.1 99 10.9 7.5 9.8 168.20 3.13 14 < 0.01 0.2 0.6 E 0.04 0.02 5.15 E 130

12:10 9.3 100 10.9 7.5 37.5 168.90 3.14 12 < 0.01 0.18 0.63 E 0.04 0.02 4.87 120

13:10 9.3 101 10.8 7.5 15.7 168.90 3.14 14 E 0.01 0.19 0.67 E 0.05 0.02 4.70 140

14:10 9.4 101 10.8 7.5 11.7 169.60 3.15 15 < 0.01 0.19 0.71 E 0.04 0.02 4.90 140

15:10 9.5 101 10.8 7.5 10.4 170.40 3.16 14 < 0.01 0.2 0.65 E 0.05 0.02 4.84 100

18:10 9.7 100 10.8 7.5 18.8 170.40 3.16 18 < 0.01 0.17 0.66 0.05 0.02 4.75 120

20:10 9.9 98 10.8 7.5 10.6 168.20 3.13 17 < 0.01 0.18 0.66 E 0.04 0.02 4.63 E 95

11-19-03 03:10 10.3 96 10.5 7.5 8.1 162.50 3.05 10 < 0.01 0.17 0.56 E 0.04 0.02 4.12 320

04:10 10.3 95 10.4 7.5 15.8 162.27 3.05 14 < 0.01 0.18 0.60 E 0.05 0.02 4.15 E 11,200

05:10 10.3 95 10.4 7.5 40.0 163.20 3.06 13 E 0.01 0.17 0.54 E 0.04 0.02 4.09 290

06:10 10.2 95 10.4 7.5 1150.5 164.43 3.08 9 < 0.01 0.17 0.57 0.05 0.02 4.15 100

07:10 10.2 95 10.4 7.5 9.4 165.87 3.10 15 < 0.01 0.18 0.55 0.05 0.02 4.25 160

08:10 10.1 95 10.4 7.5 13.7 167.27 3.12 17 < 0.01 0.18 0.52 0.05 0.02 4.64 560

09:10 10.0 95 10.4 7.5 15.0 170.60 3.16 14 < 0.01 0.17 0.52 E 0.04 0.02 4.18 530

10:10 9.9 94 10.4 7.5 16.5 174.90 3.22 15 < 0.01 0.17 0.51 E 0.05 0.02 4.02 400

14:35 9.4 94 10.6 7.4 34.1 214.53 3.77 53 < 0.01 0.23 0.54 0.08 0.02 4.61 E 100

15:35 9.2 93 10.7 7.5 26.3 226.73 3.93 53 < 0.01 0.24 0.55 0.09 0.02 4.45 E 200
Appendix A. Data Tables

75

Date

(mm-dd-yy) Time Temperature (oC) Specific conductance (μS/cm) Dissolved oxygen (mg/L) pH Turbidity (FNU) Discharge (ft3/s) Stage (ft) Total suspended solids (mg/L) Dissolved ammonium nitrogen (mg/L) Total Kjeldahl nitrogen (mg/L) Nitrate + nitrite nitrogen (mg/L) Total phosphorus (mg/L) Soluble reactive phosphorus (mg/L) Dissolved chloride (mg/L) E. coli (C/100mL)

Storm 1—Continued

11-19-03 16:35 9.0 92 10.8 7.5 23.6 236.23 4.06 60 10.04 0.23 0.54 0.09 10.06 4.45 E 380

17:35 8.8 91 10.9 7.4 28.3 242.30 4.14 126 < 0.01 0.34 0.55 0.17 0.02 4.32 420

18:35 8.6 90 11.0 7.5 31.6 244.80 4.17 90 < 0.01 0.32 0.57 0.10 0.02 4.25 440

19:35 8.5 89 11.0 7.4 31.1 243.30 4.15 62 < 0.01 0.30 0.57 0.11 0.02 4.20 E 360

20:35 8.4 89 11.1 7.5 31.8 241.00 4.12 62 < 0.01 0.33 0.57 0.09 0.02 4.15 E 360

21:35 8.2 88 11.1 7.4 31.3 240.30 4.11 64 < 0.01 0.35 0.57 0.10 0.02 4.09 E 380

22:35 8.0 88 11.2 7.4 29.4 238.53 4.09 76 < 0.01 0.33 0.58 0.09 0.02 4.16 E 140

23:35 7.9 88 11.2 7.5 28.1 236.23 4.06 56 < 0.01 0.30 0.58 0.10 0.02 4.16 E 190

11-20-03 00:35 7.7 88 11.3 7.5 27.3 233.97 4.03 53 < 0.01 0.25 0.60 0.08 0.02 4.24 E 90

01:35 7.5 88 11.3 7.5 24.1 231.00 4.03 38 < 0.01 0.27 0.61 0.08 0.02 4.30 E 110

02:35 7.4 88 11.4 7.5 26.0 227.70 3.94 47 < 0.01 0.27 0.63 0.08 0.02 4.31 E 100

04:35 7.1 89 11.5 7.5 21.1 220.53 3.85 32 < 0.01 0.24 0.64 0.06 0.02 4.39 E 90

08:35 6.8 89 11.7 7.5 16.7 207.93 3.68 24 < 0.01 0.25 0.64 0.06 0.02 4.43 160

1 Outlier value not used in regressions.

Table A6. Water quality and discharge data collected during sampled storm, Gales Creek at Old Highway 47, Oregon, November 2003.—Continued[Abbreviations: FNU, formazin nephelometric unit; E. coli, Escherichia coli bacteria; oC, degrees Celsius; μS/cm, microsiemens per centimeter; mg/L, milligram per liter; ft3/s, cubic foot per second; ft, foot; 

C/100mL, colonies per 100 milliliters; E, estimated; <, less than]
76 Continuous Monitors and Autosamplers Used to Predict Water-Quality Constituents, Tributaries of Tualatin River, Oregon

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U.S. Geological Survey

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Portland, Oregon 97201

http://or.water.usgs.gov
Anderson and Rounds—Continuous Monitoring and Autosampling for Water-Quality Constituents, Tualatin River Tributaries, Oregon—Scientific Investigations Report 2010–5008