M.S. Project Report Methodologies for Collecting Quality ... · The Learning Enhanced Watershed...

M.S. Project Report

Methodologies for Collecting Quality Data from a Continuous High-Frequency Environmental Monitoring System:

The Learning Enhanced Watershed Assessment System

by

Hari Raghavendar Raamanathan

Project report submitted to the faculty of the Virginia Polytechnic Institute and State University in partial fulfillment of the requirements for the degree of

Master of Science

in

Civil and Environmental Engineering

Vinod K. Lohani, Chair Randel L. Dymond Robert P. Scardina

December 9th, 2014 Blacksburg, VA

Acknowledgements

First and foremost, I would like to thank my mentor and friend, Walter McDonald who was

instrumental in the conception and writing of this report. He has spent countless hours helping me become

a better writer, engineer, professional and person.

I would like to thank my colleagues in the LEWAS Lab: Daniel Brogan, Thomas Westfall, Todd

Aronhalt, Debarati Basu, John Purviance, Aaron Bradner, Nida Syed and Darren Maczka. Each one of

them has been a vital link to the functioning and maintenance of this lab, and without whom this report

would not have been possible.

I would like to thank my committee chair and advisor, Dr. Vinod Lohani. He has been a constant

source of professional and financial support throughout my time here at Virginia Tech. His help and

advice was critical in helping me transition into a new life and career here in the United States.

To my committee member and co-advisor, Dr. Randy Dymond, I offer my sincere appreciation.

His thoughtful insights, helpful suggestions and thorough perusal have helped immensely in making this

report a more complete document.

I would like to thank my final committee member, Dr. Paolo Scardina. His genuine interest in my

project and constant encouragement were key to its completion.

I would also like thank my father Raamanathan, my mother Vijayalakshmi, my brother

Swaminath, and my close friends Hari Prakash and Shekar Sharma for their constant support and

encouragement.

Finally, I would like to acknowledge the financial support I have received from the National

Science Foundation (NSF/REU Site Grant EEC-1062860), the Institute for Critical Technology and

Applied Sciences and Virginia Tech’s Engineering Education department. Any opinions, findings, and

conclusions or recommendations expressed in this paper are those of the author and do not necessarily

reflect the views of the above organizations.

ii

Table of Contents 1. Introduction ................................................................................................................................. 1

2. Review of Literature .................................................................................................................... 2

2.1 Remote environmental monitoring ........................................................................................ 2

2.2 Continuous high-frequency monitoring ................................................................................. 3

2.3 Data quality in modern hydrologic monitoring ...................................................................... 4

3. LEWAS Field Site Description .................................................................................................... 5

4. Data Collection Methods ............................................................................................................. 9

4.1 Argonaut-SW ......................................................................................................................... 9

4.1.1 Principles of Operation ................................................................................................. 10

4.1.1.1 The Doppler Shift ...................................................................................................... 11

4.1.2 Flow calculations .......................................................................................................... 12

4.1.3 Communicating with the Argonaut-SW ....................................................................... 14

4.2 FlowTracker Handheld Acoustic Doppler Velocimeter (ADV) .......................................... 17


4.2.2 Deployment and Operation ........................................................................................... 20

4.3 Ultrasonic Level Transducer ................................................................................................ 22

4.4 Hydrolab MiniSonde 5 ......................................................................................................... 23

4.4.1 Communicating with the MS5 ...................................................................................... 26

4.5 Vaisala WXT520 Weather Transmitter ............................................................................... 27


4.6 Weathertronics Rain Gage ................................................................................................... 29

5. QA Methods ............................................................................................................................... 30

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5.1 Water Quality Data .............................................................................................................. 30

5.2 Flow Data ............................................................................................................................. 33

5.2.1 Sedimentation ............................................................................................................... 34

5.2.2 Debris ............................................................................................................................ 39

5.2.3 Beam Checks ................................................................................................................ 41

5.2.4 Validation of Index-Velocity Ratings ........................................................................... 41

5.2.5 Validation of Stage Area Ratings .................................................................................. 42

5.3 Weather Data ....................................................................................................................... 44

6. Case Studies ............................................................................................................................... 45

6.1 Unknown suspended sediments – October 16, 2014 ........................................................... 45

6.2 Unknown Acidic Impairments – October 22, 2014 ............................................................. 47

6.3 Package Rainfall-Based Event – November 2014 ............................................................... 48

7. Summary .................................................................................................................................... 51

References ...................................................................................................................................... 52

Appendix ........................................................................................................................................ 55

A.1 Instructions to Operate Argonaut ........................................................................................ 55

A.2 Instructions to Operate Ultrasonic Level Transducer ......................................................... 55

A.3 Instructions to Operate Sonde ............................................................................................. 56

A.4 Instructions to Calibrate Sonde ........................................................................................... 57

A.5 Procedure for checking beam velocity profiles ................................................................... 59

A.6 Instructions for Performing Beam Checks .......................................................................... 59

A.7 Instructions to Operate Rain Gage ...................................................................................... 62

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List of Figures

Figure 3.1 (a) Stroubles Creek Watershed and Land Use (b) LEWAS Watershed Hydraulic

Connectivity ............................................................................................................................... 6

Figure 3.2 Layout of LEWAS Field Site ............................................................................................ 7

Figure 3.3 Ultrasonic Level Transducer Installed Behind Concrete WeirA Vaisala Weather ........... 9

Figure 4.1 Sontek Argonaut-SW ...................................................................................................... 10

Figure 5.1 Parameter Distribution for LEWAS Water Quality Data for the year 2013 ................... 33

Figure 5.2 Sedimentation in Upstream Culvert June 3rd, 2013 ....................................................... 35

Figure 5.3 Sedimentation in Upstream Culvert July 1st, 2013 ......................................................... 35

Figure 5.4 LEWAS Watershed and Virginia Tech Construction ..................................................... 36

Figure 5.5 June 10th, 2013 Moss Center for the Arts Construction Site Runoff.............................. 37

Figure 5.6 Failed inlet protection ..................................................................................................... 37

Figure 5.7 (a) Sediment from construction runoff and (b) Gravel runoff on Virginia Tech campus 38

Figure 5.8 Sediment sources in watershed within town of Blacksburg ............................................ 38

Figure 5.9 Velocity Plot for (a) September 2012 and (b) November 2012 ...................................... 40

Figure 5.10 Debris on In-Stream Sonde Structure Following a January 30, 2013 Storm Event. ..... 41

Figure 5.11 Bank erosion at the LEWAS field site from summer 2009 (left) to March 2013 (right). ..

.................................................................................................................................................. 43

Figure 5.12 LEWAS field site stage-area ratings with 95% Gaussian bounds. ................................ 44

Figure 6.1 Turbidity Event Captured on October 16, 2014 at 12:32 PM ......................................... 46

Figure 6.2 The Changes in Turbidity and pH during October 16 Event .......................................... 46

Figure 6.3 Chemical Changes in Stream October 22-23 event. (a) pH & TDS; (b) ORP & Turbidity

.................................................................................................................................................. 48

Figure 6.4 November 17, 2014 Precipitation Data: (a) Cumulative Precipitation; (b) Hourly

Precipitation ............................................................................................................................ 49

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Figure 6.5 November 17, 2014 Hydrograph ..................................................................................... 49

Figure 6.6 November 17, 2014 Water Quality Data. (a) Dissolved Oxygen; (b) Turbidity; (c)

Temperature; (d) Specific Conductance ................................................................................... 50

List of Tables

Table 4.1. Argonaut Operation and Command Functions................................................................. 17

Table 4.2. Sontek FlowTracker Functions ........................................................................................ 21

Table 5.1. Water Quality Parameter Bounds ..................................................................................... 33

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1. INTRODUCTION 1

Monitoring of stormwater quality and quantity is critical to preserving and protecting our surface 2

waters and their derived uses. While several hydrologic monitoring programs have been in place for 3

decades, most existing programs are outdated in their use of equipment and methods. Recent studies have 4

shown that useful hydrologic and bio-geochemical processes can only be characterized by modern high-5

frequency monitoring and real-time observing systems (Montgomery et al., 2007). A lack of detailed 6

hydrologic data imposes severe impediments to our ability to predict and monitor a watershed’s overall 7

health, and in the framing and implementation of water directives and legislation. The time scale of many 8

hydrologic processes is on the order of minutes to hours, and understanding the linkages between 9

catchment hydrology and hydrochemistry requires measurements on a time scale consistent with these 10

processes (Kirchner et al., 2004). Continuous high-frequency water quality monitoring aims to do just this 11

by measuring various water quality and quantity parameters at a consistent temporal rate of measurement 12

of once every few seconds to once every few minutes throughout the entire monitoring period. 13

Data quality and availability from continuous high-frequency monitoring stations can be affected 14

by a number of factors including calibration and deployment procedures, data processing and handling 15

procedures, and environmental factors. It is typically the case that frequent maintenance and secondary 16

site verification procedures are needed to assure data integrity. The required frequency of these 17

procedures can vary anywhere from several days to 2 week intervals depending on the above factors. This 18

paper will focus on the data collection methodologies and quality assurance/quality control (QA/QC) 19

procedures with respect to the Learning Enhanced Watershed Assessment System (LEWAS) outdoor 20

environmental monitoring system. 21

The LEWAS is a continuous high-frequency environmental monitoring lab located in Webb 22

Branch of Stroubles Creek on Virginia Tech’s Blacksburg, Virginia campus. The lab was created as part 23

of an engineering education research project (Delgoshaei, 2012) supported by the National Science 24

Foundation (NSF) at VT. The LEWAS has various components, including water and weather monitoring 25

instruments, renewable power supply, data collection hardware, and custom data processing software 26

which are uniquely designed to provide real-time watershed monitoring data through a live data viewing 27

website (http://lewas.centers.vt.edu/dataviewer). An important research aspect of the LEWAS is the 28

collection of data at higher frequencies (1-3 minutes) than is typical for watershed monitoring. This 29

allows the LEWAS to better capture typical hydrologic responses of the watershed as well as unpredicted 30

ephemeral watershed events that would go unnoticed at less frequent sampling intervals. The LEWAS 31

currently has 5 environmental sensors measuring numerous water quality parameters, streamflow, and 32

weather parameters at varying intervals ranging from every 30 seconds to every 3 minutes. 33

The remainder of the paper is structured as follows. A review of the relevant literature in Section 34

2 will cover the contemporary technologies and data collection processes applied to continuous high-35

frequency environmental monitoring stations. The LEWAS lab and its field setup including the sensors 36

and data collection methods will be described in detail in Section 3. In Section 4, quality assurance and 37

quality control practices will be recommended for each of the sensors that are part of the LEWAS data 38

collection system. Finally, in Section 5, case studies will be presented that illustrate the utility of the high-39

frequency LEWAS and demonstrate the need for proper QA/QC procedures. 40

2. REVIEW OF LITERATURE 41

2.1 Remote environmental monitoring 42

Real-time monitoring of watersheds is an emerging field in Environmental Science and Civil 43

Engineering (Griffith, 2002). While remote sensing of weather conditions is a well-established field that 44

has been in use for decades (Prabhakara et al, 1982; Bendix, 2000), the field of remote sensing water 45

parameters started expanding only recently. Remote monitoring stations capable of wireless 46

communication of data and continuous high-resolution monitoring of multiple parameters including 47

stream or river flow rate, stage, local weather conditions, and various water quality parameters have 48

emerged in recent times in different parts of the world (O’Flynn et al., 2010). 49

2

http://lewas.centers.vt.edu/dataviewer

Despite being widely available, these electronic remote high-frequency water monitoring devices 50

are still used by only a small section of the scientific community (Falcone et al, 2010). Such measurement 51

stations can provide invaluable information about the effects of various watershed events on its overall 52

health (Delgoshaei, 2012). Perhaps the largest example of real-time watershed monitoring is provided by 53

the United States Geological Survey (USGS) which records flow and at some locations water quality 54

parameters in real-time (no shorter than 15 minutes) for nearly 15,000 continuous monitoring stations 55

across the (USGS Real-Time Water Data for the Nation. Accessed July, 2014). Remote water monitoring 56

applications have numerous advantages over traditional field sampling techniques including reduced field 57

site visits, continuous data collection, real-time transmission of data, and automated data processing, 58

while recent advances in technology allow easier high frequency continuous monitoring than previously 59

available 60

2.2 Continuous high-frequency monitoring 61

Remote data collection with contemporary sensors can be employed at high-frequencies to better 62

capture hydrologic responses of the watershed, thus allowing a full characterization of the hydrologic and 63

hydrochemical processes. High-resolution monitoring has typically been used only during rain events 64

resulting in the loss of information about unexpected events in the watershed (Deletic and Maksimovic, 65

1998). In order to understand the process linkages between catchment hydrology and the related water 66

quality and quantity parameters, continuous watershed monitoring on the time scale of the hydrologic 67

responses in small catchments is required (Kirchner et al., 2004). In the past, this was impractical due to 68

the mismatch in measurement timescales, which was measured sub-hourly, and streamwater chemistry, 69

which was measured weekly or monthly (Duan et al., 2014). This was a direct result of the time 70

consuming and expensive nature of conventional sampling and laboratory procedures for water chemistry, 71

but recent advances in in-situ autoanalysers and ion-selective electrodes have emerged as a solution 72

(Kirchner et al., 2004; Moraetis et al., 2010). 73

3

As discussed, most watershed water quantity and quality studies are based on hourly or daily 74

measurements. However, there is growing interest in monitoring water data at higher frequencies to better 75

capture hydrologic responses in a watershed (Kirchner et al., 2004). Real-time and high-frequency data 76

collection has recently been used in many applications to study water quantity concerns and is also 77

becoming increasingly important for evaluating water quality (Glasgow et al., 2004). There have been 78

many watershed studies that employ environmental sensors to collect flow and water quality data at high-79

frequency intervals of less than 30 minutes (Arnscheidt et al., 2005; Kavetski et al., 2011; Aubert et al., 80

2014) and even down to 5 minutes (Moraetis et al., 2010). These studies have demonstrated the need to 81

deploy sensors at high-frequency sampling intervals to detect certain hydrologic and hydrochemical 82

behaviors in watershed and riverine systems. 83

2.3 Data quality in modern hydrologic monitoring 84

The quality of data obtained from environmental monitoring stations is dependent on a number of 85

factors including the types of sensors used, site selection, connectivity of equipment, calibration 86

frequency and field maintenance schedules (Wagner et al., 2006). If uncorrected, errors can adversely 87

affect the value of the data for scientific applications, especially in the case of data published on the 88

internet where data would be used by investigators who are not directly familiar with the measurement 89

methods and conditions that may have caused the anomalies (Horsburgh et al., 2009a). 90

In-situ sensors, which are typically used in remote monitoring applications, occasionally 91

malfunction, some sensors are prone to fouling and drift, and data loggers and communication systems 92

can corrupt data while transmitting (Wagner et al., 2006). Fouling, drift and post-collection processing are 93

the most common sources of data errors post-deployment and can be prevented by periodic calibration, 94

maintenance of sensors and formation of standard protocols for data processing (Wagner et al., 2006). 95

The frequency of calibration and maintenance procedures varies according to the type of sensor 96

used, conditions of the monitoring site and final data-quality objectives. The performance of temperature 97

and specific conductance sensors tend to be affected the least by fouling, while Dissolved Oxygen (DO), 98

4

pH, and turbidity sensors are the most affected (Wagner et al., 2006). For sites with data-quality 99

objectives that require a high degree of accuracy, maintenance can be weekly or more often. Monitoring 100

sites with nutrient-enriched waters and moderate to high temperatures may require maintenance as 101

frequently as every third day (Wagner et al., 2006). 102

Before any sensor data can be used for most applications and analyses, they have to pass a set of 103

QA/QC procedures to ensure that anomalies and spurious data values are removed (Mourad and Bertrand-104

Krajewski, 2002). Post-collection quality assurance and quality control procedures for data typically 105

include correction of out of range values, correction for instrument fouling and drift errors, correction of 106

anomalous values, and correction of any known bias in the sensor data (Wagner et al. 2006). 107

3. LEWAS FIELD SITE DESCRIPTION 108

The LEWAS field site is located within the Stroubles Creek watershed at the outlet of the Webb 109

Branch sub-watershed, just upstream of a series of retention ponds known as the Duck Pond on the 110

Virginia Tech (VT) campus. The watershed has an area of 2.78 km2 and is highly urbanized with 111

residential and commercial development, encompassing portions of the Town of Blacksburg and the VT 112

campus. The Stroubles Creek watershed (Figure 3.1a), located in Montgomery County, Virginia, is a 113

mixed land use watershed with the headwaters in the Town of Blacksburg, followed by agricultural fields 114

and forested areas near its outlet. The watershed begins its drainage along the eastern continental divide 115

of the U.S., with water eventually draining to the New, Kanawha, Ohio, and Mississippi Rivers. Stroubles 116

Creek was chosen as the site of the lab because of its location on the VT Campus and its environmental 117

significance, as it was 303 (d) listed as impaired by the Virginia Department of Environmental Quality 118

(VDEQ) beginning in 1996 to the most recent report in 2012 (VDEQ, 2012). Stroubles Creek was found 119

to have a benthic impairment for 8 km starting at the outfall of the Duck Pond retention facility. Some of 120

the stressors of the stream include sedimentation, urban pollutants, increased development, and stream 121

channel modifications (VDEQ, 2006). 122

123

5

124

Figure 3.1 (a) Stroubles Creek Watershed and Land Use (b) LEWAS Watershed Hydraulic Connectivity 125 (credits: Walter McDonald, 2014) 126

The LEWAS lab currently has three primary environmental monitoring sensors and two 127

secondary verification sensors that monitor water quality, flow, and weather parameters at the outdoor 128

site. The Hydrolab MS-5 Sonde measures water quality parameters from within the stream channel and 129

does not have any secondary verification sensors. A SonTek Argonaut-SW Acoustic Doppler Current 130

Profiler (ADCP) measures velocities in the natural stream cross section and an Ultrasonic Level 131

Transducer measures flow depth inside the concrete culvert immediately upstream. A Vaisala Weather 132

Transmitter WXT520 measures air temperature, barometric pressure, relative humidity, precipitation and 133

wind, and a standard tipping bucket rain gauge measures precipitation at the field site. These instruments 134

are connected through underground conduits to a main control box that houses the batteries, solar power 135

regulator, and data collection hardware. The Argonaut-SW, Ultrasonic Level Transducer and Sonde are 136

installed in a natural run of the stream and the weather transmitter, solar panels, network camera, and 137

directional antenna are installed on a light pole near the site. A physical layout of the LEWAS lab 138

equipment is illustrated in Figure 3.2 139

6

140

Figure 3.2 Layout of LEWAS Field Site 141 (credits: Daniel Brogan, 2013) 142

The Hydrolab MS5 Water Quality Sonde is a multi-parameter probe that measures pH, 143

temperature, specific conductance, oxidation reduction potential (ORP), turbidity and dissolved oxygen 144

(DO). These parameters were chosen because as a composite, they give a good representation of the 145

health of the stream, and are robust enough to endure continuous high-frequency collection over an 146

extended deployment. Although phosphorous and nitrogen are important stormwater pollutants of interest 147

in the state of Virginia (VDEQ, 2013), no environmental sensors for such nutrients that can withstand the 148

rigor of a long-term continuous deployment currently exist. The Sonde is mounted at a 45 degree angle on 149

a steel frame in the center of the stream and submerged 13 cm from the bed. To protect the multiple 150

probes within the Sonde, a perforated aluminum cover surrounds the device which blocks debris while 151

allowing contact with streamflow. 152

Stage and velocity measurements are collected every 30 seconds using the Argonaut-SW ADCP. 153

The device is mounted on the bottom of the stream channel approximately 4.6 m downstream of a box 154

culvert in a straight, narrow, stream segment. The Argonaut-SW ADCP uses the Doppler shift principle to 155

deliver both stage and stream velocity profiles through the return signal of a sound pulse generated by the 156

Argonaut-SW ADCP and reflected back by particles suspended in the water (Huhta & Ward, 2003). 157

Using the stage and velocity readings from the Argonaut-SW ADCP, the index velocity method is used to 158

7

compute the flow rate (Levesque & Oberg, 2012; Rogers, 2012). This method uses an index velocity 159

rating and a stage-area rating to compute flow given a stage and velocity measurement. 160

Both the stage-area rating and index velocity ratings were established before the Argonaut SW-161

ADCP could be used to compute flow at the site. The stage-area rating was developed by using a laser 162

level and leveling rod to collect stream transect points across the cross section. The index velocity rating 163

was developed through point velocity measurements collected by a SonTek FlowTracker Handheld 164

Acoustic Doppler Velocimeter (ADV). The handheld ADV was used to collect point velocity 165

measurements across the channel cross section during various runoff events in order to capture a range of 166

flow conditions. Measurements of discharge and mean velocity were then computed by applying the 167

velocity area method (Herschy, 1993; Herschy, 1995) to point velocity measurements made by the ADV. 168

Finally, the index velocity rating was established by relating the mean stream velocities determined using 169

the ADV to the vertically averaged index velocities from the Argonaut-SW ADCP. With the index 170

velocity rating and stage-area rating established, the stage and velocity measurements from the Argonaut-171

SW ADCP can be applied to the ratings to estimate flow at the site. 172

A Global Water AQS012 ultrasonic level transducer is installed behind a weir upstream of the 173

site to provide additional flow measurements (Figure 3.3). The ultrasonic level transducer is installed 174

inside of a stilling pipe on the side of the culvert to provide continuous (every 45 seconds) stage 175

measurements. A stage-discharge rating was developed for the weir using a scaled model of the weir 176

inside of an experimental flume. Taken together, the stage-discharge rating and the stage measurements 177

provided by the ultrasonic level transducer give an estimate of discharge at the site. 178

8

179 Figure 3.3 Ultrasonic Level Transducer Installed Behind Concrete Weir 180

(credits: Hari Raamanathan, 2014) 181

A Vaisala Weather Transmitter WXT520 provides real-time measurements of air temperature, 182

barometric pressure, relative humidity, precipitation, and wind at the site. It is mounted on a light pole 183

above the solar panels that power the system and records precipitation data instantaneously, wind every 184

five seconds, and temperature, pressure, and humidity every minute. In addition, a Weathertronics 20 cm 185

tipping bucket rain gage is installed at the site to verify precipitation data collected by the weather 186

transmitter. Precipitation data is also provided by five other precipitation gages located near the watershed 187

and maintained by the Town of Blacksburg. Taken together, all three sources of on-ground precipitation 188

data are used to compute average areal rainfall volumes over the watershed for recorded storm events. 189

4. DATA COLLECTION METHODS 190

The LEWAS uses a wide range of equipment to measure and monitor critical hydrologic and 191

weather parameters such as water quality, flow, velocity and weather parameters. The different pieces of 192

equipment are described in the following sections. 193

4.1 Argonaut-SW 194

The Argonaut Shallow Water (Argonaut-SW) system, shown in Figure 4.1, is an acoustic Doppler 195

current meter manufactured by SonTek/YSI Incorporated and is designed to monitor flow in shallow 196

9

waters ranging in depth from 0.3 m (1ft) to 5 m (16ft) deep. The Argonaut-SW is designed to be deployed 197

for flow monitoring in a wide variety of settings including irrigation channels, pipes, culverts and natural 198

streams. 199

200 Figure 4.1 Sontek Argonaut-SW 201

(From: Argonaut-SW System Manual, 2009 pg. 15) 202

4.1.1 Principles of Operation 203

The Argonaut-SW has three acoustic beams (Figure 4.2). When properly bottom-mounted 204

(usually in a channel), one of these beams points straight up, and the other two point up/down stream at a 205

45-degree angle. The upward-looking beam measures stage, while the two slanted beams measure the 206

water velocity in two dimensions via the Doppler method. This water depth and velocity information is 207

then used together with the geometry of the channel to compute flow, mean velocity, and area. 208

209 Figure 4.2 Sontek Argonaut-SW Sampling Diagram 210

(From: Argonaut-SW System Manual, 2009 pg. 24) 211

10

4.1.1.1 The Doppler Shift 212

The Argonaut-SW measures the velocity of water using a physical principle called the Doppler 213

shift. This principle states that if a source of sound is moving relative to the receiver, the frequency of the 214

sound at the receiver is shifted from the transmit frequency. For a Doppler current meter, this can be 215

expressed as: 216

𝐹𝐹𝑑𝑑 = −2 𝐹𝐹0𝑉𝑉𝐶𝐶 217

where 218

Fd = Change in received frequency (Doppler shift). 219

F0 = Frequency of transmitted sound. 220

V = Represents the relative velocity between source and receiver (i.e., motion that changes the 221

distance between the two); positive V indicates that the distance from source to receiver is 222

increasing. 223

C = Speed of sound. 224

225

The Argonaut-SW is a variant of Doppler current meters called the monostatic Doppler current 226

meter and uses the same transducer as both a transmitter and a receiver. It is important to note that the 227

Argonaut-SW measures the velocities of particles in the water, and not the velocity of the water itself. 228

This means that if there is no particulate matter in the water, the SW is unable to measure velocity. 229

However, since most natural clear water has at least some particulate matter, the practical limitation of 230

clear water is not whether the SW can make velocity measurements, but what is the maximum range 231

(distance from the system) at which the SW can measure velocity. In clear water, the maximum 232

measurement range may be reduced. 233

4.1.1.2 Two-dimensional Velocity measurement 234

In its typical bottom-mounted installation, the SW measures two-dimensional (2D) velocity — 235

along-channel (horizontal) and vertical components. The SW is mounted such that the transducer axes are 236

aligned with the channel and the SW uses two transducers to measure velocity — one slanted 45° into the 237

11

flow, and one 45° away from the flow. The SW then uses the relative orientation of the transducers to 238

calculate the 2D water velocity (horizontal and vertical components) from the along-beam velocity data. 239

Beam velocities are converted to XY (Cartesian) velocities using the beam geometry, where the X 240

velocity for the SW is the along-channel water velocity and the Y velocity is the vertical water velocity 241

(typically very small). 242

4.1.1.3 Stage/Level measurement 243

The SW measures stage or level of the water by using the central transducer which has a vertical 244

beam capable of measuring between the depths of 0.10 to 5.0 m. The stage measurements are made by 245

relating the time of travel of a transmitted signal and internal calculations of the speed of sound in the 246

water at the survey site, which is primarily a function of temperature and salinity. The SW’s internal 247

temperature sensor automatically compensates for changing conditions by continually updating the sound 248

speed used for surface range calculations, but salinity is user defined (i.e., the SW does not automatically 249

adjust for salinity variations). Water level data is used to modify the measurement volume location in 250

real-time, optimizing performance of velocity and flow with changing water level. 251

4.1.2 Flow calculations 252

The SW combines water velocity data and level data with user-supplied channel geometry 253

information about the installation site to calculate flow. The SW calculates the cross-sectional area by 254

combining channel geometry with stage. The area is then multiplied by the mean channel velocity to 255

determine flow. The velocity of water changes with changes in depth, therefore the relationship between 256

the velocity measured by the SW and the mean channel velocity needs to be further explored. There are 257

two methods for determining the mean channel velocity: 258

4.1.2.1 Theoretical flow calculations 259

Theoretical flow calculations are used when no reference flow data are available; that is, only 260

channel geometry and data measured directly by the SW are available. The SW measures a vertically 261

integrated velocity over the largest possible portion of the water column, including information about the 262

12

variation of vertical velocity. A power-law velocity profile model is used by the SW, assuming a 1/6 263

power-law coefficient, and provides a velocity scaling factor that relates the SW measured velocity to the 264

mean channel velocity. The flow model is customized based on specified channel type — open channel, 265

round pipe (full/partially full), elliptical pipe (full/partially full), or closed culvert (full/partially full). 266

4.1.2.2 Index velocity calibration 267

Though the theoretical flow calculation provide a reasonably close measure of the mean channel 268

velocity, the large variations in natural channels can lead to the prior method being invalid. Periodic 269

reference measurements need to be carried out in these channels/streams, to determine the best estimate of 270

mean channel velocity using the Argonaut-SW. Discharge measurements in the channel to be measured 271

are made at a variety of water levels and flow conditions. SW water velocity data and stage data are 272

collected at the same time as reference discharge measurements. The data is analyzed to determine 273

empirically a relationship between the SW measured velocity and the mean channel velocity. The 274

following empirical relationship is then input into the SW, which outputs calibrated flow data in real time: 275

276

𝑉𝑉𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚 = 𝑉𝑉𝑖𝑖𝑚𝑚𝑖𝑖𝑚𝑚𝑖𝑖𝑖𝑖𝑚𝑚𝑖𝑖𝑖𝑖 + 𝑉𝑉𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚 ∗ (𝑉𝑉𝑚𝑚𝑠𝑠𝑠𝑠𝑖𝑖𝑚𝑚 + �𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝐶𝐶𝑠𝑠𝑚𝑚𝐶𝐶 ∗ 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 �) 277

278

where 279

Vmean = mean velocity in the channel 280

Vintercept = user-supplied* velocity offset (cm/s or ft/s) 281

Vmeas = SW measured velocity 282

Vslope = user-supplied* velocity scale factor (no units) 283

StageCoef = user-supplied* water depth coefficient (1/s) 284

Stage = measured stage (total water depth) (m or ft) 285

286

An index velocity calibration will usually supply more accurate flow data than a theoretical flow 287

calculation. This is the method utilized by the LEWAS Lab and the measurements required for this are 288

13

carried out using an Acoustic Doppler Velocimeter (ADV) which will be discussed in detail in the next 289

section on LEWAS equipment. 290

4.1.3 Communicating with the Argonaut-SW 291

Communication with the Argonaut-SW is established by means of a RS232-USB connection 292

(marked with the SonTek logo around the cable) found in the main control box at the field site. SonTek 293

has provided two different proprietary software programs to communicate with the Argonaut-SW, which 294

are the ViewArgonaut program and the SonUtils program. ViewArgonaut uses a graphical user interface 295

while SonUtils uses a direct command interface. The LEWAS Lab has primarily used the direct command 296

interface since 2012 and therefore the focus of this section will be the SonUtils program and its 297

commands. 298

Before delving into the details of Argonaut’s various deployment commands, it will be useful to 299

take a look at the factors restricting its deployment duration. The Argonaut-SW is primarily restricted in 300

its deployment duration by the internal data storage available and the battery power available. Of these 301

two, the latter is usually not of concern at the LEWAS site due to the availability of continuous power 302

either through solar cells or through grid power (Under implementation as of December 2014). The 303

limiting factor therefore is usually the Argonaut-SW’s internal data storage which is 4 MB. The lab 304

currently utilizes the internal flow and multi-cell velocity parameters in addition to the standard 305

deployment parameters of 2D velocity, water level, signal strengths, noise and ice detection. Under the 306

current setup, the SW can function for a minimum of 8.2 days when collecting all parameters listed 307

above, but this duration will vary depending on the number of velocity cells being recorded and if 308

changes are made to other parameters being measured. This duration can be calculated using the formula 309

provided below and data size information given in Section 5.5.1 of the Argonaut-SW User Manual: 310

𝐶𝐶𝑆𝑆𝐶𝐶𝑆𝑆𝐶𝐶𝐶𝐶𝑆𝑆𝐶𝐶 (𝐶𝐶𝑖𝑖 𝑑𝑑𝑆𝑆𝐶𝐶𝑑𝑑) =𝑇𝑇𝑇𝑇𝑆𝑆𝑆𝑆𝑇𝑇𝑆𝑆𝐶𝐶𝑆𝑆𝐶𝐶𝑆𝑆

𝑇𝑇𝑇𝑇𝑆𝑆𝑆𝑆𝑇𝑇 𝐵𝐵𝐶𝐶𝑆𝑆𝑆𝑆𝑑𝑑 𝐶𝐶𝑆𝑆𝑝𝑝 𝑆𝑆𝑆𝑆𝑎𝑎𝐶𝐶𝑇𝑇𝑆𝑆 ∗ 𝑆𝑆𝑆𝑆𝑎𝑎𝐶𝐶𝑇𝑇𝑆𝑆𝑑𝑑 𝐶𝐶𝑆𝑆𝑝𝑝 𝐷𝐷𝑆𝑆𝐶𝐶 311

312

14

All of the parameters mentioned above and more can be controlled and manipulated using the 313

direct command interface provided by the SonUtils program. Some of the key operating parameters for 314

the Argonaut-SW: 315

• Averaging Interval - The averaging interval determines the period of time (in seconds) that the SW 316

averages data for each sample. Settings as short as 10 seconds are allowed; however, the LEWAS 317

uses an averaging period of 30 seconds. 318

• Sampling Interval - The sampling interval sets the period (in seconds) from the start of one sample 319

to the start of the next. It must be greater than or equal to the Averaging Interval. Unless the 320

application has significant power limitations, setting the Sample Interval equal to Averaging 321

Interval is recommended to provide the best quality data. 322

• Cell Begin - This determines the vertical distance (from the top of the system) where the SW 323

begins its integrated velocity measurement. It is normally set to the minimum value (0.07 m) to 324

allow the SW to measure the maximum portion of the water column. This value is always set in 325

meters, regardless of output units of the system. 326

• Cell End - This determines the vertical distance (from the top of the system) where the SW ends 327

its integrated velocity measurement. It is normally set to the maximum value (6.00 m) to allow the 328

system to measure the maximum possible portion of the water column. When the Dynamic 329

Boundary Adjustment [DynBoundAdj] command is set to YES, the system will automatically 330

adjust the Cell End value based on the current water level. This value is always set in meters, 331

regardless of output units of the system. 332

• Dynamic Boundary Adjustment - This feature allows the SW to adjust automatically the velocity 333

measurement cell location based on water level. For standard SW applications, this value should 334

be set to YES. 335

Apart from the above operating parameters, a specific subset of operating parameters used 336

specifically when multi-cell velocity profiling is enabled (which is usually the case) are given below: 337

15

• Profiling Mode - When enabled, the profiling mode (often referred to as multi-cell profiling) lets 338

you collect a profile of velocity data from a series of range cells. This differs from the standard 339

Argonaut method of collecting data within just one range cell. 340

• Blanking Distance – Refers to the region in front of the transducers where no measurements can 341

be made. This parameter is measured as the distance from the system’s transducers to the start of 342

the first cell in the velocity profile. The blanking region is needed to give time for the transducers 343

and electronics to recover from the transmit pulse. The minimum value for the SW is 0.07 m. This 344

value is always set in meters, regardless of output units of the system. 345

• Cell Size – This determines the vertical distance from the start of one cell to the start of the next 346

in multi-cell profiling. The minimum value for the SW is 0.20 m. This value is always set in 347

meters, regardless of output units of the system. 348

• Number of Cells – This determines the number of cells to record in the multi-cell profile. The 349

minimum value is 1; the maximum value is 10. 350

Table 4.1 illustrates some of the common commands used in the deployment and operation of the 351

SW. The operation being manipulated is shown in the first column, the direct command is shown in the 352

second column within [ ] and the available arguments (i.e. possible options) are shown in the last column. 353

These commands are sufficient to operate all facets of the Argonaut-SW currently being used by the 354

LEWAS Lab. Additional details about other commands available in the SonUtils program are listed in 355

Section 5.8 and Appendix C of the Argonaut-SW User Manual. In addition, instructions for the 356

deployment and data collection process in the direct command SonUtils interface are shown in Appendix 357

A.1. 358

359 360 361 362 363 364 365

16

Table 4.1 Argonaut Operation and Command Functions 366

Operation Command Available Arguments System Wake-up [Break] -- Start Deployment [Start] -- File Name [Deployment] Alphanumeric values only e.g. Test001 Output Format [OutFormat] ENGLISH or METRIC only Internal Recorder [Recorder] ON or OFF only Averaging Interval [AvgInterval] Duration in Seconds Sampling Interval [SampleInterval] Duration in Seconds Cell Begin [CellBegin] Values in range of 0.07 to 6.0 in Meters Cell End [CellEnd] Values in range of 0.07 to 6.0 in Meters Dynamic Boundary Adj. [DynBoundAdj] YES or NO only Profiling Mode [ProfilingMode] YES or NO only Cell Size [CellSize] Values in range of 0.2 to 4.0 in Meters Number of Velocity Cells [NCells] Values in the range of 1 to 10 (unit less)

Note: Commands and arguments are not case-sensitive. 367

4.2 FlowTracker Handheld Acoustic Doppler Velocimeter (ADV) 368

The FlowTracker Handheld ADV, shown in Figure 4.3, is a portable stream velocity 369

measurement device manufactured by SonTek/YSI Incorporated. It is designed to provide rapid handheld 370

measurements of current, discharge and flow in rivers, open-channels and pipes. In the LEWAS Lab, the 371

ADV is used to provide reference stream velocity to be used in the Index-Velocity method outlined in the 372

previous section. 373


The FlowTracker Handheld ADV is identical to the ADCP in many aspects of operation 375

including the usage of the Doppler Shift principle (discussed in the previous section) and measurement of 376

multi-dimensional velocity. The ADV, similar to the ADCP, measures signals reflected by particulate 377

matter along the various beam axes to determine Doppler shifts for each receiver. This information helps 378

determine the various directional velocities in a current, and using this information the ADV can calculate 379

both 2D and 3D water velocity. The primary distinction between the two devices is that the ADV is a 380

17

bistatic Doppler current meter while the ADCP is a monostatic Doppler current meter. A diagram of the 381

FlowTracker is shown in Figure 4.3. 382

383

384 Figure 4.3 Sontek FlowTracker 385

(From: Sontek FlowTracker Technical Manual, 2007 pg. 1) 386

Bistatic Current Measurement 387

The ADV uses separate acoustic transducers for transmitting and receiving signals (Figure 4.4). 388

The signals are generated and received as narrow beams and the ADV is constructed in such a way that 389

the beams intersect at a volume of water located a fixed distance (10 cm; 4 in) from the tip of the probe, 390

ensuring an uniform sampling volume for each measurement. In order to measure the water velocity, the 391

transmitter generates a short pulse of sound at a known frequency which travels through the water along 392

the transmitter beam axis. As the pulse passes through the sampling volume, sound is reflected in all 393

directions by particulate matter (sediment, small organisms, and bubbles) and some portion of the 394

reflected energy travels back along the receiver beam axes. The reflected signal is sampled by the 395

acoustic receivers and the ADV measures the change in frequency (Doppler shift) for each receiver. The 396

Doppler shift is proportional to the velocity of the particles along the bistatic axis of the receiver and 397

18

transmitter which is located halfway between the “transmit” and “receive” axes. Knowing the relative 398

orientation of the bistatic axes allows the FlowTracker to calculate 2D or 3D water velocity. A pictorial 399

representation of this is shown in Figure 4.4. 400

401 Figure 4.4 Sontek FlowTracker Sampling Process 402 (From: Sontek FlowTracker Tech. Manual, 2007 pg. 2) 403

Due to its bistatic nature, one of the most important factors to consider while using the ADV is 404

the probe orientation. In order to receive accurate measurements of the water velocity, the ADV should be 405

oriented so the axis of the transmit transducer is roughly perpendicular to the expected direction of flow 406

(Figure 4.5). The probe should be oriented looking across the expected direction of flow (so the X-axis 407

aligns with the expected flow). ADV probes have been tested and have shown negligible flow 408

interference with the probe as much as 40-50° away from the preferred alignment. At higher angles, the 409

Flow-Tracker may see flow interference in the sampling volume. A pictorial indication of the alignment is 410

shown in Figure 4.5. 411

19

412 Figure 4.5 FlowTracker Flow Direction 413

(From: Sontek FlowTracker Technical Manual, 2007 pg. 82) 414

These readings are averaged together to give a mean velocity. The FlowTracker’s internal quality 415

control module checks for inaccurate velocity readings. A Doppler shift reading that is out of the standard 416

deviation range of the other pings (called a boundary adjustment) is omitted from the overall velocity the 417

ADV determines to be acceptable. 418

4.2.2 Deployment and Operation 419

The ADV is a handheld device, unlike the previously outlined ADCP, and therefore does not 420

require deployment per se, but rather collects data in short in-situ usage periods. Due to its handheld 421

nature, the ADV requires a handheld controller, a probe cable, a movable probe mount and power source 422

(internal or external) every time measurements are taken. The data is stored internally within the 423

controller and can later be accessed using software in a desktop or laptop computer. 424

Before each field measurement trip, two types of diagnostics need to be performed to ensure that 425

the ADV is ready for usage and is in good working condition. The two diagnostics runs can be broadly 426

classified into two categories: 427

20

• Office diagnostics – This consists primarily of a Beam Check for the ADV using methods very 428

similar to those outlined for the ADCP. As with the ADCP, the Beam Check allows us to evaluate 429

various aspects of the sensor including transmission and reception of signals, boundary errors, and 430

scattering errors. Refer to both Appendix A.6 and Section 6.5.2 of the FlowTracker Handheld 431

ADV User Manual for further details on understanding BeamCheck outputs. 432

• Field diagnostics – Field diagnostic procedures are primarily quick evaluations of various ADV 433

functions including the internal clock, temperature sensors, internal storage and battery power 434

which can be performed at the field site before measurement. These procedures require minimal 435

time and can be accessed directly from the handheld controller using a one-button test. A list of 436

functions being evaluated and access to their corresponding diagnostic test is shown in Table 4.2. 437

Further details about the range of values and error management is available in section 3.2.2 of the 438

FlowTracker Handheld ADV User Manual. 439

Table 4.1 Sontek FlowTracker Functions 440

Function Access through Handheld Controller Recorder Status Menu 2 under QC System Functions Menu (option 8) Temperature Data Menu 4 under QC System Functions Menu (option 8) Battery Data Menu 5 under QC System Functions Menu (option 8) Display Raw Data Menu 6 under QC System Functions Menu (option 8) Auto QC Test Menu 7 under QC System Functions Menu (option 8) System Clock Menu 9 under QC System Functions Menu (option 8)

441

After performing diagnostics tests, the ADV is ready to make field measurements. The process to 442

be followed for regular water velocity measurements is outlined in Appendix A.5. After data is collected, 443

mean velocity is calculated using the general purpose multi-point method outlined in Section 5.2.4 of the 444

User Manual. Finally, the total volumetric flow using the ADV is calculated using the following formula: 445

𝑄𝑄 = �1/2 �ℎ𝑚𝑚 + ℎ𝑚𝑚−1 ) (𝑤𝑤𝑚𝑚 − 𝑤𝑤𝑚𝑚−1) 12

(𝑣𝑣𝑚𝑚 + 𝑣𝑣𝑚𝑚−1�

𝑚𝑚

𝑚𝑚=1

446

21

447

where 448

Q is Volumetric Flow (ft3/s). H is height of stage (ft). W is width of creek cross section (ft). V is 449

velocity of the creek (ft/s). 450

451

Finally, the ADV can be connected to an external PC by means of the communication cable (5-452

pin connector to female DB9 serial port connection). Once connected, the SonTek FlowTracker software 453

can be used to display, export and analyze data recorded by the ADV. 454

4.3 Ultrasonic Level Transducer 455

A Global Water WL450 ultrasonic level transducer is installed behind a trapezoidal concrete weir 456

located at the LEWAS field site in order to estimate flow. The weir was originally constructed in 2003, 457

but the as-built weir did not function properly. Because of this, a modification to the weir was made in 458

2014 and a stage-discharge relationship was developed for the weir in an experimental flume. Figure 4.6 459

illustrates the model weir installed in an experimental flume in the Kelso Baker lab on the Virginia Tech 460

Campus. The experiment resulted in an equation to estimate discharge at the LEWAS site based upon the 461

stage depth behind the weir. This equation, illustrated on the right side of Figure 4.6, relates the stage 462

(head) behind the weir to the discharge over the weir and can be represented as a parabolic function. 463

464 Figure 4.6 Model Weir in Experimental Flume (left) and Weir Rating Curve (right). 465

(credits: Walter McDonald, 2014) 466

y = 10.867x2 - 8.8104x + 2.3178R² = 0.9986

01020304050607080

0 1 2 3 4

Disc

harg

e Q

(cfs

)

Head (ft)

22

Once this relationship between stage and discharge was developed, the ultrasonic level transducer 467

was installed in the field. The ultrasonic level transducer needed to be placed upstream of the weir a 468

sufficient distance in order to properly measure stage behind the weir. It is recommended that the gage is 469

placed at least 4 times the maximum depth of water to flow over the weir (FAO, 1993). The gage was 470

calibrated in the lab to the maximum height of the v-notch weir which corresponds to a height (H) of 52.9 471

cm. This would mean that the gage would need to be placed a minimum of 211.6 cm behind the weir. 472

Following the recommended minimum distance calculation, the ultrasonic level transducer was installed 473

at a distance of 2.1 m behind the weir. 474

The ultrasonic level transducer was installed in a PVC pipe housing, shown in Figure 3.3, to 475

protect the signal from debris that frequently accumulates in the culvert and behind the weir. A data 476

collection cable runs outside of the culvert and into a conveyance that leads to the main control box where 477

the data recorder is located. This system allows for accurate and efficient measurements of flow from 478

baseflow until the weir becomes submerged. At the point that the weir becomes submerged, it fails to 479

function as a weir due to the loss of free flow conditions and flow estimates become much less reliable. 480

4.4 Hydrolab MiniSonde 5 481

The Hydrolab MiniSonde5 (henceforth referred to as MS5), shown in Figure 4.7, is a multi-482

parameter water quality probe manufactured by the Hach Company. The MS5 is a portable instrument 483

used for long-term in-situ monitoring or profiling applications. The MS5 has four configurable ports that 484

can include a combination of the following sensors: ammonia, chloride, chlorophyll a, rhodamine WT, 485

conductivity, depth, dissolved oxygen, nitrate, ORP, pH, temperature, total dissolved gas, turbidity, and 486

blue-green algae. 487

23

488 Figure 4.7 Hydrolab MS5 Sonde Components 489

(From: Hydrolab DS5X, DS5, and MS5 Water Quality Multiprobes User Manual, 2006 pg. 11) 490

The MS5 can be deployed in a wide range of environments including surface and groundwater 491

applications, fresh and saltwater applications, open channels, culverts and natural waterways. The MS5 is 492

limited in its deployment by environmental factors including pressure (<225 meter depth under water), 493

temperature (-5° C to 50° C) and fouling rates (temperatures >35° C and high chlorophyll content for 494

prolonged periods). The LEWAS Lab’s MS5 has a combination of sensors measuring temperature, 495

oxygen reduction potential, pH, dissolved oxygen, specific conductivity and turbidity. These parameters 496

were chosen based on a previous study (Delogoshaei, 2012) for the purpose of observing the overall 497

health of the stream water. The different parameters being measured are explained in some detail below. 498

• Temperature - The temperature of water affects some of the important physical properties and 499

characteristics of water, and can therefore serve as a great indicator of its health. Chemical and 500

biological reaction rates change with temperature, making it essential for internal sonde 501

compensations during the measurement of other parameters such as dissolved oxygen, 502

conductivity, pH and biological parameters. Temperature in the MS5 is measured by an integrated 503

enclosed temperature sensor. 504

• Oxygen Reduction Potential (ORP) - ORP or Redox Potential measures an aqueous system’s 505

capacity to either release or accept electrons from chemical reactions, which is an indication of an 506

aqueous medium ability to break down organic matter or other pollutants present in it. Though not 507

24

well understood or routinely monitored, ORP is being increasingly used as a measure of water 508

quality. ORP in the MS5 is measured using an integrated pH/ORP sensor. 509

• pH - pH is a measure of the relative amount of free hydrogen and hydroxyl ions in the water and 510

ranges in values from 0 – 14. pH of natural waters can change drastically due to natural or human 511

effect such as leaching of rock formations, polluted runoff, chemical spills, etc. pH can help make 512

useful inferences about the quality of water since pH values are important in determining its 513

ecological and derived functions. pH in the MS5 is measured using an integrated pH/ORP sensor. 514

• Dissolved Oxygen (DO) - Dissolved oxygen is a measure of the amount of oxygen in water that is 515

available for chemical reactions and for use by aquatic organisms. Insufficient dissolved oxygen 516

can lead to a wide range of negative effects on the stream ecosystem including buildup of organic 517

matter, fish/aquatic kill leading to death of organisms and buildup of pollutants in the stream 518

system. DO measurement is therefore key to understanding the relationship between watershed 519

events and stream effects. DO in the MS5 is measured by a Luminescent Dissolved Oxygen 520

(LDO) sensor. 521

• Specific Conductivity - Conductivity is a measure of the ability of water to pass an electrical 522

current. Conductivity in streams and rivers is affected primarily by the geology of the area through 523

which the water flows, but can also be affected by sudden human events such as road salting or 524

fertilizer application. Conductivity in water is affected by the presence of inorganic dissolved 525

solids such as chloride, nitrate, sulfate, and phosphate anions (ions that carry a negative charge) or 526

sodium, magnesium, calcium, iron, and aluminum cations (ions that carry a positive charge). 527

Changes in conductivity can result in a wide range of effects on aquatic life and stream chemical 528

processes. Specific conductivity in the MS5 is measured by a standard conductivity cell and 529

additionally, the Total Dissolved Solids (TDS) is calculated and displayed using the conductivity 530

readings. 531

25

• Turbidity – Turbidity, at its most basic form, is a measure of water clarity and the ability of light 532

to pass through the water column. Suspended particles in water can lead to a wide range of 533

physical effects such as higher temperature and reduced DO and ecological effects such as 534

lowering growth rates and increasing mortality rates in aquatic organisms. Suspended materials 535

can be of many types including soil particles (clay, silt, and sand), algae, plankton, microbes, and 536

other substances. The measurement of turbidity can help in understanding these effects and serve 537

as an additional indicator of water quality. Turbidity is measured by the MS5 using a self-cleaning 538

turbidity sensor. 539

4.4.1 Communicating with the MS5 540

The MS5 can communicate with an external device using a 6-pin to RS232 connector available at 541

the LEWAS field site. It is one of two RS232 connectors in the main control box and can be distinguished 542

from the Argonaut-SW’s RS232 connector by the lack of markings/logos on the cable. Additionally, a 543

RS232 – USB converter cable is required to connect to a laptop without a serial port. 544

Communication with the sonde can be established using the Hydras-3LT program (Windows 545

only) provided by Hach. Most of the functions necessary to perform pre-deployment diagnostics, operate 546

the sonde and control some of the basic settings can be accessed by means of the Log Files menu 547

(Hydras3LT | Operate Sonde | Log Files). Some of the key settings used are outlined below: 548

• Logging Interval – The logging interval for the MS5 determines the time duration between 2 549

measurements. This setting supports a wide interval ranging from 30 seconds to several hours. The 550

recommended logging interval is every 3 minutes. 551

• Sensor and Circulator Warmup – This options allows the sensors to take test readings before 552

actual measurements start and allows sensors with movable parts to adjust to current conditions 553

before the start of deployment. The sensor starts warming up before the set logging time and is 554

usually set between 3 and 5 minutes. The circulator warmup serves a similar function for the 555

26

primary circulator of the MS5 which serves to provide a homogenous measurement volume and 556

the duration is kept consistent with the sensor warmup time. 557

• Diagnostic checks – Short-term deployment of the sonde can be achieved by creating test 558

deployment log files under the Log Files menu. By choosing appropriate Starting Logging and 559

Stop Logging times, log files necessary to check the functioning of sensors and the circulator can 560

be generated. Alternatively, a real-time graph option is available under the Online Monitoring 561

menu which allows the operator to validate sensor functioning before deployment. 562

After the above operations are carried out, the sonde is ready for deployment. The sonde has a 563

sufficiently large internal storage to allow deployment for about 10 – 15 days at a 3 minute logging 564

frequency, but it is recommended that data is collected every week to ensure that the sensors are 565

performing as desired. Further information on sonde operating and data download is presented in 566

Appendix A.3. 567

4.5 Vaisala WXT520 Weather Transmitter 568

The Vaisala WXT520 Weather Transmitter (henceforth referred to as Vaisala), shown in Figure 569

4.8, is a small lightweight weather monitoring system that offers multi-parameter monitoring in one 570

package. The Vaisala measures wind speed and direction, precipitation, atmospheric pressure, 571

temperature and relative humidity. 572

573 Figure 4.8 Weathertronics WXT520 Weather Transmitter Diagram 574 (From: Vaisala Weather Transmitter WXT520 User Guide, 2010 pg. 20) 575

27


The Vaisala has three groups or modules of sensors measuring the above mentioned weather 577

parameters and their operational principles are detailed below: 578

4.5.1.1 Wind Speed and Direction Sensor 579

The wind sensor has an array of three equally spaced ultrasonic transducers on a horizontal plane. 580

Wind speed and wind directions are determined by measuring the time it takes the ultrasound to travel 581

from each transducer to the other two. The computed wind speeds are independent of altitude, 582

temperature and humidity, which are cancelled out when the transit times are measured in both directions, 583

although the individual transit times depend on these parameters. The wind speed is represented as a 584

scalar speed in selected units (m/s, kt, mph, km/h). The wind direction is expressed in degrees (°). The 585

wind direction reported by the Vaisala indicates the direction that the wind comes from. North is 586

represented as 0°, east as 90°, south as 180°, and west as 270°. Important to note is the wind speed 587

limiting factor. Wind speed (below 0.05 m/s) is a limiting factor for the Vaisala and will result in the wind 588

direction not being calculated. In these circumstances, the wind direction fields will have an INVALID 589

value. 590

4.5.1.2 Precipitation 591

The Vaisala uses the RAINCAP® Sensor 2-technology to calculate precipitation. The 592

precipitation sensor consists of a steel cover and a piezoelectric sensor mounted on the bottom surface of 593

the cover. The precipitation sensor detects the impact of individual raindrops. The signals from the impact 594

are proportional to the volume of the drops. Hence, the signal of each drop can be converted directly to 595

accumulated rainfall. Advanced noise filtering technique is used to filter out signals originating from 596

other sources than raindrops. The measured parameters are accumulated rainfall, rain current and peak 597

intensity, and the duration of a rain event. The sensor can distinguish hail from rain, but unlike rainfall 598

measurement, hail parameters are cumulative measurements rather than event based. 599

28

4.5.1.3 Pressure – Temperature – Humidity 600

The Vaisala uses an integrated module containing separate sensors measuring atmospheric 601

pressure, temperature and humidity. The measurement principle of the pressure, temperature, and 602

humidity sensors is based on an advanced Resistor – Capacitor (RC) oscillator and two reference 603

capacitors against which the capacitance of the sensors is continuously measured. The microprocessor of 604

the transmitter performs compensation for the temperature dependency of the pressure and humidity 605

sensors. 606

4.6 Weathertronics Rain Gage 607

The Weathertronics Rain Gage (henceforth referred to as gage) is a standard tipping bucket rain 608

gage used to measure rainfall volume and/or rate. The gage has a very straightforward principle of 609

operation. Precipitation enters the small machined funnel inside the instrument and is directed to one of 610

two tipping buckets. When one bucket fills, its weight tips the other into position, while simultaneously 611

emptying the first. Each tip, representing 0.25 mm / 0.01 inches of rainfall, results in a contact closure 612

event in the mercury switch contained in the gage. The gage requires basic maintenance in the form 613

cleaning the sieve/net present at the top of the funnel periodically to remove debris, calibration of the 614

gage every 6 months and replacement of internal power (CR2477N battery) every 12 months. 615

616

The gage is fitted with a Rainlog Data Logger which records the number of contact closures in its 617

internal memory which can later be used to understand the cumulative rainfall over 5-minute periods. 618

Communication with the Rainlog Data Logger (housed inside the rain gage) can be established using a 619

RS-232 to USB connector and the data can be accessed using the RL-Loader software. Data can be 620

downloaded using the “Download” button present in the RL-Loader home screen and can be exported to 621

Excel form using the “Save As.” button. Once storage capacity on the Data Logger is near 75% capacity, 622

the data should be formatted to make space for new data. 623

29

5. QA METHODS 624

Given that the LEWAS is one of a few such systems across the world, the LEWAS Lab 625

constantly faces new operational challenges and works towards remedying existing ones. These 626

challenges are primarily a result of the physical environment where the LEWAS operates and the 627

extended high-frequency sensor deployment requirements. If left unmanaged, these issues can result in 628

degradation of data quality or even result in unavailability of data for prolonged periods. In order to 629

maintain good data quality and availability, the LEWAS has in-place management practices for the 630

different sensors and uses supplemental information such as data on stormwater network infrastructure, 631

land use, precipitation, cross-section profiles and velocity profiles to verify the quality of collected data. 632

In the following sections, the data quality issues, causatives, and the suggested management practices for 633

each of the LEWAS’ primary monitoring operations are discussed. 634

5.1 Water Quality Data 635

Data quality issues in the multi-parameter Hydrolab MS5 probe, which include bad data and data 636

gaps, occur as a result of either systematic errors, such as sensor drift and component failures, or 637

environment-induced stressors, such as clogging, fouling and debris collection. Management of these 638

issues is primarily in the form of calibration and maintenance practices. Calibration is usually carried out 639

when: 640

• Noticeable fouling has occurred (site-specific). 641

• Measured values do not match those of a known calibrated standard; measured values deviate 642

from 95% confidence intervals (presented below) over a prolonged period. 643

• Adding or removing certain components for different applications (e.g., the circulator) or when 644

replacing components (e.g., the Teflon junction of the pH reference electrode). 645

The calibration and maintenance frequency of the MS5 is dependent on the field conditions at the 646

deployment site and usage frequency. In conditions promoting high biological activity such as high 647

chlorophyll content and high temperature, the sonde may require cleaning and calibration at a frequency 648

30

as high as every 3 days. High usage and data collection frequency can lead to faster drift of values. 649

Typically, enclosed sensors such as temperature and ORP drift at a much lower rate compared to exposed 650

sensors such as pH and conductivity. Despite the varying rates of drift, given that the MS5 is an 651

integrated unit, it is typically recommended to calibrate all sensors at the same time; therefore, the 652

calibration frequency of the sonde is dictated by the sensor with the shortest calibration frequency. 653

While the multiprobe can be calibrated at the deployment site, it is recommended to calibrate it at 654

a laboratory facility with ready access to deionized water, calibration aids and cleaning tools. The 655

calibration of the sonde requires access to a standard set of calibration tools (usually housed in the blue 656

calibration box/kit present at the lab). This kit consists of: 657

• A 9-pin - RS232 connector cable 658

• RS232 – USB converter cable 659

• A computer with Hydras-3LT program installed 660

• 4 pH, 7 pH and 10 pH Standards 661

• Zobell’s Solution (ORP Standard) 662

• Conductivity Standard 663

• StabCal Turbidity Standard 664

• Delicate task wipes and cleaning solution (mild soap or toothpaste) 665

Important considerations to keep in mind while calibrating are to choose standards whose values 666

are close to field values and to make sure that sensors are rinsed with the appropriate calibration standards 667

and deionized water before each sensor is calibrated. Both these factors can cause major deviations in 668

sensor readings and data quality. 669

The purpose for calibrating the water quality Sonde is to ensure that the data that is collected is of 670

high quality. The instruments within the Sonde may drift over time and it is imperative that the Sonde be 671

calibrated often (every 3-4) weeks so that quality data is maintained. If the parameters drift too far then it 672

will affect all of the data that has been collected during the calibration window in which it occurs. Good 673

data plus bad data equals all bad data when it comes to statistical time series of continuous water quality 674

data (Bennett, 2014). 675

31

While calibration of the instruments is important to ensure that the data is of the highest quality, 676

this does not always ensure that the sensors will be functioning properly at all times. There could be many 677

internal or external factors that cause the data to be of low quality such as debris, sedimentation or 678

equipment failure. To create an alert system that indicates when abnormal or significantly deviant data 679

occur, a proper understanding of the distribution of the data must be known. Figure 5.1 illustrates the 680

distribution of common parameters that are tested at the LEWAS site. Each of the six primary parameters 681

are plotted separately with the left side of the figure showing the frequency distributions of the parameters 682

and the right side of the figure showing the respective box-and-whisker plots. A box-and-whisker plot is a 683

convenient way of representing data using its quartiles. The “box” shows the first quartile (lower bound 684

of the box), the third quartile (upper bound) and the median (middle band), while the “whiskers” on both 685

ends show data that is within 1.5 times the range between the first and third quartiles. Any data that is not 686

within the box-and-whisker plot are marked as outliers. These plots illustrate the distribution of data for a 687

complete year, including base flow and precipitation based event data. Table 5.1 illustrates the upper and 688

lower 2.5% bounds of the data for the different water quality parameters as well as the median values. 689

The 2.5% bounds be used to determine when to flag data for inspection for possible errors. It is probable 690

that data beyond these bounds will be indications of an event and not be the results of erroneous data, but 691

such an indication is also important for the LEWAS lab. 692

693

32

694

695

696 Figure 5.1 Parameter Distribution for LEWAS Water Quality Data over 1 year 697

Table 5.1 Water Quality Parameter Bounds 698

Parameter 2.5% value 97.5% value Median value Temp (°C) 7.44 20.57 15.07 pH 7.45 8.41 8.08 SpCond (uS/cm) 229 1271 734 TurbSC (NTU) 0 96.4 1.1 ORP (mV) 300 667 562 DO(%Sat) 0.4 110 92.2

699

5.2 Flow Data 700

Flow data is often subject to external factors that affect the quality of the data such as 701

sedimentation, debris, and cross sectional changes. The following section outlines a few of the external 702

33

and internal factors that affect the reliability of the data and procedures for which these factors can be 703

addressed. 704

5.2.1 Sedimentation 705

The LEWAS site experiences a significant amount of sediment that is transported through the 706

upstream stormwater network and is deposited at the site. This sedimentation has caused a change in 707

channel shape, flow characteristics, and has interfered with monitoring equipment. In order for the 708

LEWAS lab to deliver valid data, sedimentation should be addressed and limited in such a way that it 709

does not significantly affect the accuracy of flow data. This section contains the observations of 710

sedimentation over the past year and investigations into possible causes of sedimentation as well as 711

recommendations for mitigation. 712

Significant amounts of sedimentation have been observed entering the stream from the upstream 713

stormwater network during the past year. Figure 5.2 illustrates the sediment deposition in the upstream 714

culvert on June 3rd, 2013. Significant sediment deposits have occurred both behind and in front of the 715

weir structure. An island of sediment can be observed during base flow as illustrated in Figure 5.2; the 716

depth of this location was not measured directly but was believed to be between 1.5 – 2 feet in height. 717

Previous measurements of sediment height in the culvert during April 2013 found sediment depositions as 718

much as 1.15 feet. Student researchers observed that the sediment continued to extend far back into the 719

culvert. 720

721

34

722 Figure 5.2 Sedimentation in Upstream Culvert June 3rd, 2013 723


The sedimentation in the culvert changes over time due to high flows during storm events. Figure 725

5.3 illustrates the sedimentation in the culvert on July 1st, 2013. Comparing the area behind the weir, it is 726

clear that the island of sediment that was there just a month before is completely gone and the bottom of 727

the culvert is exposed. 728

729 Figure 5.3 Sedimentation in Upstream Culvert July 1st, 2013 730


The Webb Branch of Stroubles Creek is a highly urbanized watershed with a high level of 732

construction. Some of the construction projects taking place during the time of this study include the 733

construction of the Moss Center for the Arts (Area 1 in Figure 5.4), Goodwin Hall (Area 2 in Figure 5.4) 734

35

and The Edge apartments (Area 3 in Figure 5.4). Road, parking lot and driveway gravel as well as 735

construction runoff could all contribute to sedimentation at the site during storm events. To investigate 736

these sources, student researchers of the lab toured the watershed during storm events to attempt to 737

identify sources of runoff. 738

739 Figure 5.4 LEWAS Watershed and Virginia Tech Construction 740


During a rain event on June 10th, 2013 a member of the lab took pictures of runoff from the new 742

arts building construction site, given as construction site 1 in Figure 5.4. The pictures in Figure 5.5 743

illustrate how the silt fence meant to reduce sediment runoff has been compromised. Sediment is clearly 744

running off the construction site and into the stormwater network. It should be noted that this side of the 745

construction site does not runoff into the stormwater network at the LEWAS site, however it is entering 746

Stroubles Creek and the Duck Pond via the stormwater network under the drill field. It is an indication of 747

the kind of sedimentation that can be expected from the other sites. 748

749

36

750 Figure 5.5 June 10th, 2013 Moss Center for the Arts Construction Site Runoff 751


To investigate possible sources of sediment runoff into Webb Branch, student researchers toured 753

the watershed on June 18th, 2013. All major roads in the watershed were explored and areas with high 754

levels of sedimentation, or where large amounts of sediment could clearly be seen entering the stormwater 755

network were photographed. Figure 5.6 demonstrates an inlet protection structure that had been washed 756

into the inlet and was no longer functioning. The image on the far right of Figure 5.6 shows sediment that 757

had been washed into the stormwater inlet and on its way eventually to the LEWAS site and the Duck 758

Pond. 759

760 Figure 5.6 Failed inlet protection 761 (credits: Walter McDonald, 2013) 762

37

763 Figure 5.7 (a) Sediment from construction runoff and (b) Gravel runoff on Virginia Tech campus 764


Sediment from construction site 2 (Goodwin Hall ) is illustrated in Figure 5.7 (a). Even though 766

there is a silt fence surrounding the construction site, sediment can be seen outside of the silt fence with 767

runoff from the site. In addition to construction sites on the Virginia Tech campus, sediment runoff occurs 768

from areas where gravel is used as a ground surface. Figure 5.7 (b) is a good example of a gravel surface 769

contributing a significant amount of gravel to the stormwater network. 770

Major sources of sediment in the watershed from within the Town of Blacksburg and within the 771

Webb Branch watershed appeared to be from gravel parking lots, roads, and driveways. Figure 5.8 shows 772

pictures taken from North Main Street, Giles Road, Patrick Henry Drive, and Progress Street indicating 773

that sediment and gravel from these sources runoff into the stormwater network. 774

775 Figure 5.8 Sediment sources in watershed within town of Blacksburg 776


38

This sedimentation has profound effects on the quality of the flow data at the site. The sediment 778

has covered the Argonaut sensor on occasions, preventing it from being able to collect data. Sediment can 779

also clog the opening to the ultrasonic level transducer housing preventing accurate flow measurements. 780

As discussed, sedimentation can also accumulate behind the weir, affecting the performance of the weir 781

itself. The sediment could also change the shape of the channel around the Argonaut, rendering the stage-782

area rating invalid. 783

To ensure collection of higher quality data, routine maintenance should include removal of 784

sediment that accumulates over or around the Argonaut. In addition, significant sediment accumulating 785

behind the weir should be removed. Finally, routine index velocity and stage area rating checks should be 786

performed as outlined in sections 5.2.4 and 5.2.5 to ensure their reliability. 787

5.2.2 Debris 788

In addition to sedimentation, debris can affect the data by accumulating around the sensors or 789

behind the weir. In the case of the Argonaut, debris can accumulate along the channel, around the Sonde, 790

or over the Argonaut affecting the return signal that the device sends out to detect stage and velocities. 791

Debris can also accumulate behind the weir affecting its functionality or around the ultrasonic level 792

transducers affecting the stage of the water near the device. 793

To ensure that debris does not negatively affect data measurements, routine maintenance should 794

include the removal of debris behind the weir or around the sensors. Additionally, trips to remove debris 795

should be conducted immediately after large flow events, as these events are the major contributor to 796

debris accumulation at the site. The following discussion illustrates the impact that debris can have on the 797

Argonaut readings. 798

The bed mounted Argonaut-SW ADCP is often covered by sediment during high flow events and 799

can catch debris such as rocks, plastics and other garbage. These occurrences can cause error in flow 800

measurements by blanketing the flow sensor and changing the characteristics of the flow within the 801

stream. Figure 5.9 illustrates the scatter of velocities in the downstream and vertical directions when there 802

is substantial debris around the sensors. Figure 5.9a represents the velocities during September 2012 803

39

when the stream section surrounding the Argonaut-SW ADCP was clear of debris and Figure 5.9b 804

represents the velocities during November 2012 when debris had accumulated near the Argonaut-SW 805

ADCP. In periods where there is debris surrounding the sensor, velocity measurements are skewed in 806

both the vertical direction and the direction of flow. When this occurs, velocity data from the Argonaut-807

SW ADCP is no longer reliable and cannot be used to estimate flow. These findings clearly indicate that 808

debris must be removed whenever possible to safeguard the quality of flow data. 809

810 Figure 5.9 Velocity Plot for (a) September 2012 and (b) November 2012 811

(credits: Daniel Brogan, 2012) 812

Similarly, the water quality Sonde often collects large clumps of vegetative or man-made debris 813

on the mounting frame and around the aluminum casing during high flows, as illustrated in Figure 5.10. 814

Debris or sediment that becomes lodged on the sensor frame have an impact on the accuracy of the water 815

quality readings. To ensure the accuracy and reliability of the data, the LEWAS team conducts 816

maintenance visits on a weekly basis and after high-flow events. On an average, about five man hours are 817

needed every week to maintain the site. 818

40

819 Figure 5.10 Debris on In-Stream Sonde Structure Following a January 30, 2013 Storm Event. 820

(credits: Thomas Westfall, 2013) 821

5.2.3 Beam Checks 822

The effects that sedimentation and debris have on the Argonaut is that it interferes with the sound 823

waves that the sensor sends out in sensing stage and velocity. To ensure that there is no physical 824

disturbance to the pulses sent out by the Argonaut, a beam check can be performed. Beam checks should 825

be performed each time data is collected from the field site in order to ensure that the Argonaut is 826

configured properly. Beam checks ensure that beam velocities returned from the three sensors on the 827

Argonaut are of the proper magnitude and direction. Results from beam checks can indicate if there are 828

any physical interferences by checking the amplitude of each of the 3 acoustic beams. Further discussion 829

of beam checks and instructions on how to perform a beam check with the Argonaut can be found in 830

Appendix A.5. 831

5.2.4 Validation of Index-Velocity Ratings 832

The ADCP uses the Doppler shift principle to deliver both stage and stream velocity profiles 833

through the return signal of a sound pulse generated by the Argonaut and reflected back by particles 834

suspended in the water (Huhta & Ward 2003). Using the stage and velocity readings from the ADCP, the 835

index-velocity method is used to compute the flow rate. This method uses an index-velocity rating and a 836

stage-area rating to compute flow given a stage and velocity measurement. 837

41

Point velocity measurements from the Sontek FlowTracker Handheld ADV (Acoustic Doppler 838

Velocimeter) were used to develop the index velocity rating. The handheld ADV was deployed to collect 839

point velocity measurements across the channel cross section during various runoff events in order to 840

capture a range of flow conditions. Measurements of discharge and mean velocity were then computed by 841

applying the velocity area method (Herschy 1995) to point velocity measurements made by the ADV. An 842

index velocity rating was established by relating the mean stream velocities determined using the ADV to 843

the vertically averaged index velocities from the ADCP. The stage-area rating used to compute the flow 844

rate was developed by using a laser level and leveling rod to collect stream transect points across the cross 845

section. This rating curve relates the stage to cross-sectional area using the cross-sectional profile of the 846

stream. 847

5.2.5 Validation of Stage Area Ratings 848

The stage-area rating that is developed at the site is used as input into the flow computations for 849

the Argonaut. External forces such as sedimentation and erosion can alter the cross section of the natural 850

channel where the Argonaut is located and therefore alter the stage-area rating. Because of this, it is 851

recommended that the stage-area rating be validated every 3-6 months to ensure its validity. The 852

following discussion highlights the severity of the issue and the changes in the stage-area rating that have 853

occurred over time. 854

The Webb Branch watershed has a high percentage of impermeable surface coverage, causing 855

most of the runoff from rain events to enter the stream quickly. This leads to the stream flowing with 856

higher velocities than would normally be seen in a stream of this size, putting a high degree of stress on 857

the banks and causing more severe erosion than expected. The flow volume in Webb Branch ranges from 858

0.1 m3/s at base flow to more than 8 m3/s during major storm events. Due to the intense flashiness of the 859

stream during rainstorms and the poor placement of riprap during bank construction, the bank of Webb 860

Branch erodes easily relative to a natural stream bank with a sufficient flood plain. Figure 5.11 shows one 861

example of bank erosion at the LEWAS site. This figure also shows the riprap that was used to create 862

stream banks. This bank erosion has caused a number of problems, most notably the frequent alteration of 863

42

flow direction at the monitoring site due to migrating gravel beds and riprap boulders falling into the 864

stream and altering flow patterns. Multiple times per year a single large storm event will significantly 865

alter the flow direction at the monitoring site. 866

867 Figure 5.11 Bank erosion at the LEWAS field site from summer 2009 (left) to March 2013 (right). 868

(credits: Daniel Brogan, 2013) 869

The constant variations in the stream shape require frequent monitoring. Volume flow 870

calculations at the LEWAS site are based on the velocity-area method. The flow meter, used to measure 871

flow velocity and direction, was positioned and calibrated while the banks held a certain structure; since 872

then, bank shape has changed along with flow direction (Rogers, 2012). This has caused the need for re-873

surveying the stream cross-section on a regular basis every 2 months. To address this issue, options have 874

been discussed such as fortifying the banks at the site to prevent future collapse and/or using alternate 875

methods to measure stage and velocity. Gaussian statistics applied to four stream cross section surveys 876

during a two year period, produced a two sigma (95%) cross-section area, and error within ±25% for 877

stages above 1.0 ft, within ±16% for stages above 1.6 ft and within ±12% for stages between 3.0 ft and 878

5.2 ft (Figure 5.12). Prior to analysis, four cross-sections were aligned vertically using fixed reference 879

points. This alignment showed that the bottom of the stream, i.e. the zero-stage reference, changed as 880

much as 0.3 ft from one survey to the next, highlighting the need to have a fixed elevation reference for 881

long-term monitoring. 882

43

883 Figure 5.12 LEWAS field site stage-area ratings with 95% Gaussian bounds. 884


5.3 Weather Data 886

Recording quality weather data, and in particular precipitation measurements, is important in 887

order to quantify the sources of flow at the site. The weather transmitter at the LEWAS site measures 888

precipitation by detecting vibrations from the impact of raindrops proportional to the volume of the drops. 889

This method lends itself to errors in accuracy due to deviations in spatial variations, debris and wind. To 890

verify the precipitation readings, the measurements from the weather station should be compared against 891

NOAA and town precipitation gages located in the Town of Blacksburg and greater New River Valley 892

region. Additionally, precipitation data should compared against the tipping bucket rain gage installed at 893

the site. In the event that precipitation volumes between the weather station and tipping bucket are 894

significantly different, precipitation readings are checked for anomalies and adjusted. 895

44

6. CASE STUDIES 896

There is considerable investment in money, time, and expertise required to operate a continuous, 897

high-frequency environmental monitoring station such as the LEWAS. Sensors must be methodically 898

placed, calibrated, and maintained to ensure accurate and reliable data. Just as important, data must be 899

checked for quality assurance on a strict schedule. However, a continuous, high-frequency system such as 900

the LEWAS can capture data and events in a watershed that are not possible with grab samples or 901

composite sampling systems. Many acute natural or man-made watershed events occur unexpectedly and 902

can only be captured if there are continuous sensors deployed at all times. Without continuous 903

monitoring, many events may be unnoticed by watershed stakeholders, however, these events still impact 904

the ecosystem. Without knowledge of these types of acute events, watershed managers are unaware of 905

critical information when making decisions. The following case studies illustrate the advantage of 906

continuous high-frequency monitoring of watersheds. 907

The power of the LEWAS to capture unique datasets is illustrated in the following three case 908

studies. In each of these studies, the events are unexpected and would have been completely unnoticed 909

had the LEWAS sensors not been in place. The first case study captures a spike in turbidity within the 910

stream from an unknown source, which causes a rapid change in multiple water quality parameters. The 911

second case study captures an event in which pH suddenly drops in the stream due to an unknown 912

pollution event. Both case studies illustrate how the LEWAS can capture events that are most likely 913

human-induced direct pollution. Finally, the third case study presents a package-storm of a rainfall-based 914

event that was captured by flow, water quality, and rainfall sensors at the LEWAS site, and illustrates 915

how continuous data can capture detailed information about rainfall-based watershed events. 916

6.1 Unknown suspended sediments – October 16, 2014 917

The first case study occurred on October 16, 2014 when a sudden appearance of a light colored 918

suspended sediment occurred in the stream. Figure 6.1 illustrates the suspended light color material that 919

occurred in the stream as captured by student researchers at 12:32 PM. It is clear from the figure that there 920

45

is significant pollution that has been discharged into the stream, as it is completely saturated with 921

suspended material. 922

923

Figure 6.1 Turbidity Event Captured on October 16, 2014 at 12:32 PM 924 (credits: Todd Aronhalt, 2014) 925

Figure 6.2 shows the changes in Turbidity and pH during this event. A data gap occurs around 926

12:30 PM due to the system being taken offline to collect data. Turbidity, as expected when considering 927

the cloudiness of the water, rises significantly up to almost 100 NTU. Note that pH rises to almost 12, 928

indicating extreme basic conditions. This is alarming considering that freshwater fish will typically die at 929

any pH above 10 (Utah State, Accessed December 2014). 930

931 Figure 6.2 The Changes in Turbidity and pH during October 16 Event 932

(credits: Walter McDonald/Hari Raamanathan, 2014) 933

0

20

40

60

80

100

7

8

9

10

11

12

8:24 9:36 10:48 12:00 13:12 14:24 15:36 16:48 18:00 19:12 20:24 21:36

Turb

idity

[NTU

]

pH

Turbidity and pH on 10-16-2014

pH Turbidity

46

There was no precipitation during this event, indicating that it was not a result of overland runoff 934

and is most likely the result of human-induced pollutants. Within the watershed during this period, there 935

was construction occurring at The Edge apartments highlighted in Figure 5.4 (Area 3). It is possible that 936

during this time there was concrete-wash that was discharged into the stream. The chemical composition 937

of concrete could have possibly caused the changes in water quality that were captured at the LEWAS. 938

However, there is not direct proof of the pollutant source, further highlighting the difficulty in assigning 939

causal effects to acute pollutant events. Should the cause be linked to construction, this could influence 940

future construction inspection policies in order to prevent future occurrences. Only continuous high-941

frequency monitoring can capture quick, acute events like this that cause changes in the water chemistry 942

of a stream to the extent at which freshwater life would not be able to live. Without the LEWAS system in 943

place, there would be no knowledge of this event, and no chance to make policy changes to prevent future 944

occurrences. 945

6.2 Unknown Acidic Impairments – October 22, 2014 946

The second case study further illustrates the power of the LEWAS system in capturing acute 947

events in the watershed that would go completely unnoticed. Similar to the first case study, this event did 948

not coincide with rainfall and was thus most likely the result of direct human pollutants. However, unlike 949

the first case study, where a casual observer would have noticed that the stream was highly turbid and 950

thus something unusual was occurring, this event did not have any visual evidence. In fact, without the 951

sensors in place it could be reasonably concluded that this event would have no chance of being 952

uncovered, thus emphasizing the need for continuous monitoring in urban watersheds to understand the 953

full range of urban impacts to watersheds. 954

This event took place on October 22-23 and is illustrated in Figure 6.3a, where the pH drops 955

significantly below 4 as the TDS in the stream rises to over 1.5. In addition, Figure 6.3b illustrates how 956

the ORP rises 1.5 times its baseflow level and turbidity rises, although only by 3 NTUs, an unnoticeable 957

amount by a casual observer. This indicates that some sort of dissolved acidic compound was discharged 958

47

into the stream causing a significant impairment. As with the first case study, the most significant impact 959

is to the pH with it dropping below 4, which is a fatal level as all freshwater fish die at pH levels under 4 960

will kill most fish (Utah State, Accessed December 2014). 961

962 Figure 6.3 Chemical Changes in Stream October 22-23 event. (a) pH & TDS; (b) ORP & Turbidity 963


The source of this event is more difficult to determine as there is not a visible pollutant during 965

this period that could be visually traced. Therefore, determining the cause of this event is complicated. It 966

is possible that commercial enterprises within the watershed, such as an auto-repair shop, could be 967

illegally dumping unwanted chemicals such as used car oil into the stream. Another possibility could be 968

discharges of coal ash from a coal power plant on the Virginia Tech campus that discharges water into the 969

stream. Events such as this, that are invisible to the human eye, perhaps pose the greatest threat to the 970

health of streams because they are unseen and unnoticed. However, with a system such as the LEWAS, 971

these events can be captured and brought to the attention of watershed managers and stakeholders. 972

6.3 Package Rainfall-Based Event – November 2014 973

The third and final case study illustrates the ability of the LEWAS system to capture flow, water 974

quality, and rainfall data in high-frequencies. This data provides a complete story of a rainfall-based event 975

through high-frequency data that captures the movement and chemistry of the water. Figure 6.4 represents 976

the precipitation that fell as captured by a tipping bucket rain gage at the LEWAS site on November 17, 977

2014. Both cumulative (Figure 6.4a) and hourly rainfall graphs (Figure 6.4b) illustrate the temporal 978

distribution of the rainfall during this event. Over 1 inch of rain fell within 6 hours with a peak intensity 979

near the end of the storm. 980

0

1

2

0

5

10

10/22/2014 21:00 10/23/2014 3:00 10/23/2014 9:00

TDS

(g/l)

pH

(a)

pH TDS [g/l]

0

10

20

0

500

1000

10/22/2014 21:00 10/23/2014 3:00 10/23/2014 9:00

Turb

idity

(NTU

)

ORP

(mV)

(b)

ORP (mV) Turbidity (NTU)

48

981 Figure 6.4 November 17, 2014 Precipitation Data: (a) Cumulative Precipitation; (b) Hourly Precipitation 982


The resulting hydrograph from this event is illustrated in Figure 6.5. This data is derived from the 984

ultrasonic level transducer that is installed behind the weir at the site. The flow at the site reached an 985

estimated 42 cfs and had multiple peaks. This could be due to the limited measurement ability of the 986

ultrasonic level transducer as it is limited in its ability to capture high flows due to its location within the 987

culvert and the sensors blanking distance. However, the full extent of the hydrograph is captured which 988

would not be possible with less frequent sampling intervals. Finally, the runoff coefficient for the event, 989

calculated using rainfall data recorded by the rain gage, was estimated at 0.18. 990

991 Figure 6.5 November 17, 2014 Hydrograph 992


0.00

0.20

0.40

0.60

0.80

1.00

1.20

21:36 2:24 7:12 12:00

Cum

Pre

cip

(in)

Time (hr)

(a)

0.000.050.100.150.200.250.300.35

4:00 5:00 6:00 7:00 8:00 9:00 10:00

Rain

fall

(in)

Time (hr)

0

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11/17/201402:20:42

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11/17/201411:54:27

11/17/201415:05:42

11/17/201418:16:57

11/17/201421:28:12

11/18/201400:39:27

Estim

ated

Flo

w (C

FS)

49

Figure 6.6 further illustrates the unique ability of a continuous high frequency environmental 994

monitoring lab to capture the full extent of event characteristics through the water quality data captured 995

by a Sonde at the site. Figure 6.6a shows the change in dissolved oxygen at the site, which slightly 996

increased as the flow increased at the site. Figure 6.6b illustrates the turbidity in the stream throughout the 997

event. It is interesting that at the beginning of the event there was a large and sudden spike in turbidity, 998

most likely representing the “first flush” when the initial sediments and materials are washed off of the 999

hard surfaces within the watershed. Figure 6.6c and 6.6d illustrate the temperature and specific 1000

conductance respectively which both decrease as the flow increases. 1001

1002

1003

1004 Figure 6.6 November 17, 2014 Water Quality Data. (a) Dissolved Oxygen; (b) Turbidity; 1005

(c) Temperature; (d) Specific Conductance 1006 (credits: Walter McDonald/Hari Raamanathan, 2014) 1007

All three of these case studies demonstrate the value of a continuous high-frequency 1008

environmental monitoring station in capturing unpredicted acute events in the watershed. The first two 1009

10

10.5

11

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12.5

0:00:00 12:00:00 0:00:00

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50

case studies show how a system such as the LEWAS, which is deployed continuously year-round, can 1010

capture non-precipitation events that occur in the watershed. Without a system like the LEWAS, events 1011

such as these would go completely unnoticed and would not fall into consideration when planning 1012

watershed management strategies. The last case study illustrates the ability of the LEWAS to capture the 1013

full characterization of precipitation-based events that occur in the watershed. Without continuous high-1014

frequency monitoring, components of the pollutographs such as the rise in turbidity from the first flush 1015

would not be captured. Overall, these case studies highlight the novel research capabilities of the LEWAS 1016

system. 1017

7. SUMMARY 1018

Our evolving understanding of watershed-scale hydrochemical processes in the past two decades 1019

has resulted in an increasing focus and interest in continuous high-frequency hydrologic monitoring. 1020

Systems capable of measuring water quality and flow parameters at the timescale of hydrologic responses 1021

are becoming more common. The Learning Enhanced Watershed Assessment System (LEWAS) is one 1022

among a handful of such systems and has served an important role in helping understand the benefits and 1023

the limitations of high-frequency hydrologic monitoring. The novel nature of the method, volume of data 1024

collected, equipment deployment periods, and field conditions all increase the chances for errors in data 1025

quality. This necessitates robust data assurance practices in the form of standardized maintenance, 1026

calibration, and data auditing procedures to ensure good data. 1027

Working towards that goal, this paper has presented a detailed overview of data collection 1028

methods used in the LEWAS and their underlying principles, a look into the factors limiting their data 1029

quality, and detailed guidelines for optimal field procedures, maintenance, and error handling. In addition 1030

to this, the paper has outlined three case studies aimed at highlighting the value of continuous high-1031

frequency monitoring and the importance of secondary measurement systems, which are key quality 1032

assurance tools. 1033

51

REFERENCES 1034

Arnscheidt, J., McGrogan, H., and McCormick, S., 2005. High-Resolution Phosphorus Transfers at the 1035

Catchment Scale: The Hidden Importance of Non-Storm Transfers. Hydrology and Earth System 1036

Sciences (HESS) 9(6): 685-691. DOI: 10.5194/hess-9-685-2005. 1037

Aubert, A. Kirchner, J., Gascuel-Odoux, C., Faucheux, M., Gruau, G., and Mérot, P., 2014. Fractal Water 1038

Quality Fluctuations Spanning the Periodic Table in an Intensively Farmed Watershed. 1039

Environmental Science & Technology 48(2): 930-937. DOI: 10.1021/es403723r. 1040

Bendix J., 2000. Precipitation Dynamics in Ecuador and Northern Peru during the 1991/1992 El Nino: A 1041

Remote Sensing Perspective. International Journal of Remote Sensing 21:533-548. 1042

Bennett, M., 2014. “USGS Virginia Water Science Center: Current Research and Future Directions” 1043

Lecture at Virginia Tech, Blacksburg, VA., November 19, 2014. 1044

Deletic A. B. and Maksimovic C. T., Sep 1998. Evaluation Of Water Quality Factors In Storm Runoff 1045

From Paved Areas. Journal of Environmental Engineering 124: 869-879. 1046

Delgoshaei P., 2012. Design and Implementation of a Real-Time Environmental Monitoring Lab with 1047

Applications in Sustainability Education. Doctor of Philosophy dissertation. 1048

Duan, S., Powell, R.T., Bianchi, T.S., 2014. High Frequency Measurement of Nitrate Concentration in the 1049

Lower Mississippi River, USA. Journal of Hydrology, 519, pp. 376 – 386. 1050

Falcone J. A., Carlisle D. M., Wolock D. M., Meador M. R., 2010. GAGES: A Stream Gage Database for 1051

Evaluating Natural and Altered Flow Conditions in the Conterminous United States. Ecology 1052

92:621. 1053

FAO (Food and Agricultural Organization of the United Nations), (1993). Structures for Water Control 1054

and Distribution (Irrigation Water Management Training Manual). FAO, pp. 29-37. 1055

Glasgow H.B., Burkholdera J.M., Reeda R.E., Lewitusb A.J., Kleinmana J.E., March 2004. Real-Time 1056

Remote Monitoring of Water Quality: A Review of Current Applications, and Advancements in 1057

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Sensor, Telemetry, and Computing Technologies. Journal of Experimental Marine Biology and 1058

Ecology, Volume 300, Issues 1–2 409:448. 1059

Griffith, J. A., July 2002. Geographic Techniques and Recent Applications of Remote Sensing to 1060

Landscape-Water Quality Studies. Water, Air, and Soil Pollution Vol 138:181-197. 1061

Herschy, R., 1993. The Velocity-Area Method. Flow Measurement and Instrumentation, 4(1): 7–10. DOI: 1062

10.1016/0955-5986(93)90004-3. 1063

Herschy, R., 1995. Streamflow Measurement. Taylor & Francis, Oxford, United Kingdom. ISBN: 1064

0419194908 9780419194903. 1065

Huhta, C. and Ward, C., 2003. Flow Measurements Using an Upward-Looking Argonaut-SW Doppler 1066

Current Meter. Proc. IEE/OES Conference on Current Measurement Technology, San Diego, 1067

California, pp. 35-39. 1068

Kavetski, D., Fenicia, F., and Clark, M. P., 2011. Impact of Temporal Data Resolution on Parameter 1069

Inference and Model Identification in Conceptual Hydrological Modeling: Insights from an 1070

Experimental Catchment. Water Resources Research, 47(5). DOI: 10.1029/2010WR009525. 1071

Kirchner, J.W., Feng, X., Neal, C., Robson, A.J., 2004. The Fine Structure of Water-quality Dynamics: 1072

The (High-frequency) Wave of the Future. Hydrological Processes, Vol. 18 Issue 7, pp. 1353–1073

1359. 1074

Levesque, V.A, and Oberg, K.A., 2012. Computing Discharge Using the Index Velocity Method. 1075

Techniques and Methods 3-A23, U.S. Department of the Interior, U.S. Geological Survey, 1076

Reston, VA. http://pubs.usgs.gov/tm/3a23. 1077

Montgomery, J.L., Harmon, T., Kaiser, W., Sanderson, A., Haas, C.N., Hooper, R., Minsker, B., Schnoor, 1078

J., Clesceri, N.L., Graham, W., Brezonik, P., 2007. The WATERS Network: An Integrated 1079

Environmental Observatory Network for Water Research. Environmental Science and 1080

Technology, 41 (19) (2007), pp. 6642–6647 1081

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Moraetis, D., Efstathiou, D., Stamati, F., Tzoraki, O., Nikolaidis, N.P., Schnoor, J.L., Vozinakis, K., 1082

2010. High-Frequency Monitoring For the Identification of Hydrological and Bio-Geochemical 1083

Processes in a Mediterranean River Basin. Journal of Hydrology, 389, pp. 127–136. 1084

Mourad M., and Bertrand-Krajewski J.-L., 2002. A Method for Automatic Validation of Long Time 1085

Series of Data in Urban Hydrology. Water Science & Technology, Vol 45 No 4-5 pp 263–270 1086

Oberg K., and Mueller, D.S., 2007. Validation of Streamflow Measurements Made with Acoustic 1087

Doppler Current Profilers. Journal of Hydraulic Engineering, 133(12): 1421-1432. DOI: 1088

10.1061/ASCE0733-9429. 1089

O’Flynn, C., Regan, F., Lawlor, A., Wallace, J., Torres, J., and O’Mathuna, C., 2010. Experiences and 1090

Recommendations in Deploying a Real-Time, Water Quality Monitoring System. Measurement 1091

Science and Technology, 21 124004. 1092

Prabhakara, C., Chang, H. D., Chang, A. T. C., January 1982. Remote Sensing of Precipitable Water over 1093

the Oceans from Nimbus 7 Microwave Measurement. American Meteorological Society 21:59-1094

68. 1095

Rogers, M, 2012. The Determination of Stream Discharge at the LEWAS Site on the Virginia Tech 1096

Campus. M.S. Thesis, Virginia Polytechnic and State University. 1097

U.S Geological Survey, Real-Time Water Data for the Nation. http://waterdata.usgs.gov/nwis/rt. 1098

Accessed July, 2014. 1099

Utah State University Water Quality, Page: What’s in your pH?, 1100

http://extension.usu.edu/waterquality/htm/whats-in-your-water/ph: Accessed December 2014. 1101

Virginia Department of Environmental Quality (VDEQ), 2006. Upper Stroubles Creek Watershed TMDL 1102

Implementation Plan Montgomery County, Virginia. VT-BSE Document No. 2005-0013, 1103

Blacksburg, Virginia. 1104

Virginia Department of Environmental Quality (VDEQ), 2012. 305(b)/303(d) Water Quality Assessment 1105

Integrated Report. Richmond, VA. 1106

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Wagner, R.J., Boulger Jr., R.W., Oblinger, C.J., Smith, B.A., 2006. Guidelines and Standard Procedures 1107

for Continuous Water-quality Monitors: Station Operation, Record Computation, and Data 1108

Reporting. U.S. Geological Survey Techniques and Methods 1–D3. 1109

APPENDIX 1110

A.1 Instructions to Operate Argonaut 1111

SonUtils allows you direct control over the command process of deploying the Argonaut and should be 1112 used at all times to deploy: 1113 1114

1) Start→All Programs → Sontek Software → SonUtils4 1115 2) Select the Communications Port you are using. If you are connected to the front left USB port 1116

it should be COM7 and COM7 should already be selected. 1117 3) Select Connect or press Ctrl+D 1118 4) Select Break (This causes a break in the deployment and commands and will stop the data 1119

collection from the previous deployment.) If you have not previously deployed data that you 1120 need to collect skip step 5 and proceed to step 6. (This is usually not the case) 1121

5) Select Recorder 1122 6) Proceed through recorder prompt and download the data to a folder location that you create 1123

with the name as the date you are collecting data. 1124 7) Select Show Deploy. This will show the deployment settings for the Argonaut. You can set the 1125

start date by typing the date in the format “Date YYYY:MM:DD”. You can set the start time 1126 by typing in the time in the format “Time HH:MM:SS”. You can also change the name of the 1127 file by typing in “Deployment NAME” 1128

8) Deploy the Argonaut. There are two ways to do this. The first is to set the Date and Time of 1129 deployment. This will automatically command the Argonaut to deploy at this time. The second 1130 is to run the command “Deploy”. The second method is preferable. 1131

9) Select Disconnect 1132 1133

A.2 Instructions to Operate Ultrasonic Level Transducer 1134

1) Connect the USB 2.0 B-type cable to the data logger device for the ultrasonic level transducer 1135 and connect to any USB port on an external laptop. 1136

2) Open the Global Logger II program found under Start | Programs | Global Logger II. 1137 3) Select “Global Logger USB Device” from the drop down menu under the direct connection 1138

menu. Set the Baud Rate to 38400. 1139 4) Once a connection is established, click on the Get History button in the data menu. The option to 1140

download the level data in .csv format will appear. Choose to save it in a directory of your 1141 choice. 1142 Once the data is saved, erase it from the data logger to make space for new data by click erase 1143 history button. This will erase all data on the data logger. Disconnect from the data logger. 1144

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A.3 Instructions to Operate Sonde 1145

1) Connect Sonde to computer 1146 2) Open Hydras 3LT and wait for scan for Sondes to finish 1147 3) Double click on Hydrolab MS5 Sondes in the “Connected Sondes” box 1148 4) Go to LOG FILES tab 1149 5) In the bottom left, click “Create” 1150 6) Enter the name you would like the file to have (typically dates of deployment) 1151 7) ENSURE CORRECT START TIME AND DATE 1152 8) ENSURE CORRECT LOGGING INTERVAL (3 min) (format is HH:MM:SS) 1153 9) Sensor warmup and circulator warmup should stay at 2 min 1154 10) Leave audio ON 1155 11) Record 12 parameters: 1156

a. Temp (deg C) 1157 b. pH (units) 1158 c. ORP (mV) 1159 d. SpCond (uS/cm) 1160 e. TDS (g/L) 1161 f. DO (% sat) 1162 g. DO (mg/L) 1163 h. Turbidity (NTU) 1164 i. Internal battery (Volts) 1165 j. External battery (Volts) 1166 k. Internal battery (% left) 1167 l. External bettery (% left) 1168

1169 This is the order the parameters appear on the list to select from. It is important to keep these in order to 1170 make analyzation easier; when the data is downloaded the same parameters will appear in the same 1171 columns and will not have to be moved around each time. 1172 1173

12) Click SAVE SETTINGS in top right 1174 13) Click ENABLE in the bottom left 1175 14) The Sonde can now be unplugged and deployed. Beeping will occur 2 min before deployment 1176

while the sensor warms up. 1177 1178 Sonde data acquisition 1179 1180

1) Connect Sonde to computer 1181 2) Open Hydras 3 LT 1182 3) Double click on Hydrolab MS5 Sonde and wait for program to warm up 1183 4) Open the LOG FILES tab 1184 5) In the drop-down box by log file, select the file you wish to download 1185 6) In the bottom right, click download (this can take a while depending on file size) 1186 7) Once the file opens, click File and select Export Excel 1187 8) Save the excel file as the original file name 1188 9) Close Hydras 3LT and Excel and disconnect Sonde 1189

1190

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A.4 Instructions to Calibrate Sonde 1191

TEMPERATURE CALIBRATION INSTRUCTION 1192 1) Temperature probe is a thermistor and not calibrated by the consumer 1193

1194 pH CALIBRATION INSTRUCTION 1195

1) Connect Sonde to computer 1196 2) Open Hydras 3LT 1197 3) Select Calibration tab 1198 4) Select pH (units) 1199 5) Rinse sensors with tap water and dry 1200 6) Attach storage cup to Sonde 1201 7) Fill storage cup 25% with pH 7 solution, cover and shake for 6 seconds 1202 8) Empty and refill with pH 7 solution, covering sensors, and wait for readings to stabilize 1203 9) RECORD SONDE VALUE IN EXCEL DRIFT MONITORING FILE BEFORE 1204

CALIBRATING 1205 10) Enter 7.00 into box and click “Calibrate” 1206 11) Empty solution, rinse sensor with water, and dry 1207 12) Fill storage cup 25% with pH 10 solution, cover and shake for 6 seconds 1208 13) Empty and refill with pH 10 solution, covering sensors, and wait for readings to stabilize 1209 14) RECORD SONDE VALUE INTO EXCEL DRIFT MONITORING FILE BEFORE 1210

CALIBRATING 1211 15) Enter 10.00 into the box and click calibrate 1212 16) Empty solution, rinse sensor with water, and dry 1213 17) Fill storage cup 25% with pH 4 solution, cover and shake for 6 seconds 1214 18) Empty and refill with pH 4 solution, covering sensors, and wait for readings to stabilize 1215 19) RECORD SONDE VALUE INTO EXCEL DRIFT MONITORING FILE (DO NOT 1216

CALIBRATE) 1217 20) Empty storage cup, rinse sensor with tap water, and dry 1218

1219 1220 CONDUCTIVITY CALIBRATION 1221

1) Connect Sonde to computer 1222 2) Open Hydras 3LT 1223 3) Select Calibration tab 1224 4) Select SpCond (uS/cm) 1225 5) Rinse sensors with DEINONIZED water and dry thoroughly (especially inside of conductivity 1226

sensor) 1227 6) Enter 0 into the box and click “Calibrate” 1228 7) Attach storage cup and fill 25% with conductivity standard solution 1229 8) Cover and shake for 10 seconds 1230 9) Empty, refill with standard solution to cover sensors, and wait for readings to stabilize 1231 10) RECORD SONDE VALUE IN EXCEL DRIFT MONITORING FILE BEFORE 1232

CALIBRATING 1233 11) Enter labeled value of standard into box and click “Calibrate” 1234 12) Empty storage cup, rinse sensor with tap water, and dry 1235

1236 TURBIDITY CALIBRATION INSTRUCTIONS 1237

1) Connect Sonde to computer 1238 2) Open Hydras 3LT 1239 3) Select Calibration tab 1240

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4) With sensors pointed up, fill storage cup with 75% DEIONIZED water 1241 5) Screw on storage cap and turn over Sonde 1242 6) Click on “Turbidity (Rev)” tab, enter “1”, and click “Calibrate” (wiper should rotate once to 1243

remove air bubbles) 1244 7) Click on “Turbidity (NTU)” tab 1245

a) In “Turbidity Point” enter 1 1246 b) In “Turbidity NTU” enter .1-.3 (depending on estimated cleanliness of sensors) 1247

8) When readings stabilize, click “Calibrate” 1248 9) Empty DI water and dry sensors 1249 10) Gently swirl NTU standard solution for 1-2 minutes (DO NOT SHAKE) 1250 11) Fill storage cup 25%, cover, and shake for 10 seconds 1251 12) Empty, refill 75%, cover and gently turn Sonde over 1252 13) Click on “Turbidity (Rev)” tab, enter “1”, and click “Calibrate” (wiper should rotate once to 1253

remove air bubbles) 1254 14) Click on “Turbidity (NTU)” tab 1255

a) In “Turbidity Point” enter 2 1256 b) In “Turbidity NTU” enter labeled value of standard solution 1257

15) Wait for readings to stabilize 1258 16) RECORD SONDE VALUE IN EXCEL DRIFT MONITORING FILE BEFORE 1259

CALIBRATING 1260 17) Once readings have stabilized, click “Calibrate” 1261 18) Empty storage cup, rinse sensor with tap water, and dry 1262

1263 OXIDATION/REDUCTION POTENTIAL CALIBRATION 1264

1) Connect Sonde to computer 1265 2) Open Hydras 3LT 1266 3) Select Calibration tab 1267 4) Select ORP ( 1268 5) Rinse sensors with DI water and dry 1269 6) Fill storage cup 25% with Zobell’s Solution 1270 7) Shake for 6 seconds and discard solution 1271 8) Refill storage cup with Zobell’s solution until pH sensor and reference are covered 1272 9) Wait one minute for reading to stabilize 1273 10) RECORD SONDE VALUE IN EXCEL DRIFT MONITORING FILE BEFORE 1274

CALIBRATING 1275 11) When readings are stable, enter value from table below 1276

1277 Temp (deg C mV (Zobell's) 10 461 15 450 20 439 25 428 30 417

1278 12) Click “calibrate” 1279 13) Empty storage cup, rinse sensor with tap water, and dry 1280

1281 CLARK CELL (DO) CALIBRATION INSTRUCTIONS 1282

1) Connect Sonde to computer 1283 2) Open Hydras 3LT 1284

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3) Select Calibration tab 1285 4) Select DO (% sat) 1286 5) Rinse sensors with clean water 1287 6) With sensors pointing up, fill storage cup with water just until it reaches the black “O” ring 1288

securing the permeable membrane 1289 7) Remove any drops from the top of the membrane 1290 8) Place storage cap on storage cup UPSIDE DOWN 1291 9) Wait for values to stabilize, enter current barometric pressure (www.weather.gov) 1292 10) RECORD SONDE VALUE IN EXCEL DRIFT MONITORING FILE BEFORE 1293

CALIBRATING 1294 11) Once readings have stabilized, click “Calibrate” 1295 12) Empty storage cup, rinse sensor with tap water, and dry 1296

1297 NOTE: Sonde should be stored in TAP WATER ONLY. Storing the Sonde in DI water can damage 1298 sensors. 1299

1300 A.5 Procedure for checking beam velocity profiles 1301

1302 1) Open the program “ViewArgonaut” 1303 2) Click on Processing 1304 3) Go to File – Open and select the data you wish to view. (Argonaut data is found on the field 1305

notebook in Documents – 01 Data – 01 Argonaut Data) Once the data is selected it should come 1306 up on the screen as shown. 1307

4) Go to Processing – Velocity Coordinate System – Beam or click the beam button. 1308 5) Make sure direction of flow and cross sectional direction velocities are on by ensuring a red plot 1309

line (Beam 1) and blue plot line (Beam 2) are visible. If not click the red or blue beam velocity 1310 button. 1311

6) If there is a storm zoom into the velocities during the storm event. If not zoom into a sufficient 1312 sample size to see the differences in velocities between beam 1 and beam 2. 1313

7) Check to ensure that the beams follow a general trend of being opposite in sign but equal in 1314 magnitude. If they are not document the irregularity and report it to the LEWAS group. 1315

8) Print the plot to pdf by going to File – Print. 1316 Save the pdf on scholar with the date of the velocities captured. (ex: “BeamVelocities 1317 2013_08_12 to 2013_08_22”) 1318 1319

A.6 Instructions for Performing Beam Checks 1320

The Argonaut-SW measured beam velocities can be used to determine if the measurement device 1321

is properly aligned in the channel. Because there is documented change is cross sectional shape and 1322

direction of flow in the channel throughout the past few years this should be checked continually. The 1323

illustration in Figure A.1 depicts a sidelooker (LEWAS is upwards looking) in both an acceptable and 1324

unacceptable alignment. The velocity field that each beam is measuring should be homogenous or 1325

essentially the same. Figure A.1A shows an acceptable orientation where the direction of flow is the same 1326

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http://www.weather.gov/

for the upstream velocity (v2) as it is in the downstream velocity (v1). Figure A.1B shows an improper 1327

orientation where the velocity measured by beam V1 is very close to zero because it is oriented almost 1328

perpendicular to the direction of flow. 1329

1330 Figure A.1 Plan view schematic of sidelooking ADVM (A) properly aligned to measure downstream and 1331

cross-stream velocity and (B) improperly aligned to measure downstream and cross-section veolocity. 1332 (Argonaut-SW System Manual, 2009) 1333

The beam velocities of a properly oriented ADVM will be equal in magnitude but opposite in 1334

direction. An example of a properly aligned ADVM is given in Fig. A2A. The two beam velocities while 1335

not exactly the same magnitude at all times are for the most part follow the same trend in the opposite 1336

direction. An example of an improperly aligned ADVM is given in Fig. A2B. The beam 2 velocity clearly 1337

follows a flat trend line, indicating that the ADVM is improperly aligned and corresponding 1338

approximately to the orientation in Fig. A1B. 1339

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1340 Figure A.2 Beam velocity data from (A) a properly aligned sidelooker ADVM and (B) an improperly 1341

aligned sidelooker ADVM. 1342 (Argonaut-SW System Manual, 2009) 1343

Performing and Analyzing a Beam Check 1344

“Review of beam checks and other available data is essential to the selection and (or) 1345

modification of the measurement volume for index velocity measurements with ADVMs and the ongoing 1346

quality assurance of the index velocity data.” (USGS 2012) A beam check is a plot of the signal 1347

amplitude for each of the three Argonaut acoustic beams. Figure A.2 illustrates the desired features of a 1348

beam check. The beams from the Argonaut should gradually decay in amplitude as the distance increases 1349

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and should not fall below the instrument noise level. “Beam checks will often indicate some reduction in 1350

signal strength related to biofouling, debris, sediment accumulation, or ice accumulation.” 1351

1352

A.7 Instructions to Operate Rain Gage 1353

Setting up the Rain Logger 1354 1355

1. Power the Rain Logger by inserting the batteries, the LED on the logger should light up and stay 1356 lit for 8 seconds. It will then flash once every 8 seconds thereafter. If this does not happen then 1357 check that the batteries are fresh and inserted correctly. 1358

2. Connect the Rain Logger to a laptop computer through a serial cable. 1359 3. Run RL-Loader on the laptop and click on the Auto detect button. The program will then scan the 1360

ports on the laptop for the logger. When the logger is found the Info window will be updated. 1361 4. Set the loggers ID by clicking on the ID. 1362 5. Clear all logged data by clicking the Clear button.This will also set the RainLog's clock to the PC 1363

time. 1364 6. Disconnect the serial cable and place the logger securely inside the tipping bucket precipitation 1365

gage. 1366 1367 Extracting Data from the Rain Logger 1368 1369

1. Remove the RainLog from the collector and connect it to a laptop. 1370 2. Run RL-Loader and click on the Auto Detect button. 1371 3. When the logger is found click on the Download button. The data will then be downloaded from 1372

the logger. When the data is loaded entries will appear in the text window and the graph will be 1373 updated. 1374

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