Prepared in cooperation with the U.S. Army Corps of Engineers

Use of Surrogate Technologies to Estimate Suspended 

Sediment in the Clearwater River, Idaho, and Snake River, 

Washington, 2008–10

U.S. Department of the Interior

U.S. Geological Survey

Scientific Investigations Report 2013-5052
Cover:

Background: Confluence of the Snake and Clearwater Rivers at Lewiston, Idaho, May 14, 2012. 

From left to right

Inset 1: Laser In Situ Scattering and Transmissometry (LISST)-Streamside laser diffraction instrument—Clearwater River at Spalding, Idaho, March 19, 2009.

Inset 2: Acoustic Doppler velocity meters (ADVMs)—Snake River near Anatone, Washington, March 20, 2009.

Inset 3: Nephelometric turbidity probe—Clearwater River at Spalding, Idaho, May 7, 2008.

All photographs were taken by Molly Wood, U.S. Geological Survey. 
Use of Surrogate Technologies to Estimate 

Suspended Sediment in the Clearwater River, 

Idaho, and Snake River, Washington, 2008–10

By Molly S. Wood, U.S. Geological Survey, and Gregg N. Teasdale, U.S. Army 

Corps of Engineers

Prepared in cooperation with the U.S. Army Corps of Engineers

Scientific Investigations Report 2013–5052

U.S. Department of the Interior

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

KEN SALAZAR, Secretary

U.S. Geological Survey

Suzette M. Kimball, Acting Director 

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

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Suggested citation:

Wood, M.S., and Teasdale, G.N., 2013, Use of surrogate technologies to estimate suspended sediment in the 

Clearwater River, Idaho, and Snake River, Washington, 2008–10: U.S. Geological Survey Scientific Investigations 

Report 2013-5052, 30 p.
iii

Contents

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

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

Background............................................................................................................................................3

Site Descriptions and Surrogate Instrument Configurations .................................................................4

Clearwater River Study Site................................................................................................................4

Snake River Study Site.........................................................................................................................6

Methods...........................................................................................................................................................8

Sediment Sample Collection...............................................................................................................8

Surrogate Instrument Data Corrections ...........................................................................................9

Acoustic Data Corrections.........................................................................................................9

Acoustic Beam Spreading ................................................................................................9

Acoustic Absorption by Water .......................................................................................10

Acoustic Absorption by Sediment .................................................................................11

Surrogate Model Development ........................................................................................................11

Use of Surrogate Models To Estimate Suspended Sediment...............................................................12

Clearwater River Study Site..............................................................................................................12

Snake River Study Site.......................................................................................................................18

Advantages of Acoustics over Sediment-Transport Curves in Sediment Monitoring ............21

Comparison over Short Time Scales ......................................................................................21

Comparison over Annual Time Scales ...................................................................................26

Summary and Conclusions.........................................................................................................................27

Acknowledgments.......................................................................................................................................27

References Cited..........................................................................................................................................27

Figures

1. Map showing study area and locations of sediment surrogate and U.S.

Geological Survey (USGS) streamgage sites in the Clearwater River, Idaho, and

Snake River, Washington, May 2008–September 2010...........................................................2

2. Photograph showing sediment surrogate instruments deployed at the

Clearwater River near Spalding, Idaho.....................................................................................5

3. Photographs showing sediment surrogate instruments deployed at the Snake

River near Anatone, Washington...............................................................................................6

4. Graph showing process for calculation of range-normalized acoustic

backscatter corrected for two-way transmission losses in the Clearwater River,

Idaho, and Snake River, Washington.......................................................................................10

5. Graph showing surrogate regression models for total suspended sediment,

sand, and fines concentrations based on acoustic backscatter for the

Clearwater River near Spalding, Idaho...................................................................................15

6. Graph showing measured and estimated total suspended sediment

concentrations in the Clearwater River at Spalding, Idaho, based on a surrogate

model with acoustic backscatter.............................................................................................16
iv

7. Graph showing estimated instantaneous values of total suspended-sediment

concentration for March 23–July 1, 2010, in the Clearwater River near Spalding,

Idaho, based on a surrogate model with acoustic backscatter and sedimenttransport curves developed using data from the 2008–10 and 1970s studies ..................17

8. Graph showing surrogate regression models for total suspended sediment,

sand, and fines concentrations based on acoustic backscatter for the Snake

River near Anatone, Washington.............................................................................................18

9. Graph showing measured and estimated total suspended-sediment

concentrations in the Snake River near Anatone, Washington, based on a

surrogate model with acoustic backscatter..........................................................................19

10. Graph showing estimated instantaneous values of suspended-sediment

concentration during a storm event on June 1–15, 2010, in the Snake River near

Anatone, Washington, based on a surrogate model with acoustic backscatter

and sediment transport curves developed using data from the 2008–10 and 1970s

studies ..........................................................................................................................................20

11. Boxplots showing distribution of total suspended-sediment concentration by

month in the Clearwater River at Spalding, Idaho, based on (A) a surrogate

model with 3MHz acoustic backscatter, (B) 2008–10 sediment transport curves,

and (C) 1970s sediment transport curves, May 2008–September 2010 .............................22

12. Graphs showing total suspended-sediment load by month based on a surrogate

model with acoustic backscatter and 2008–10 and 1970s sediment-transport

curves for the (A) Clearwater River at Spalding, Idaho, May 2008–September

2010, and (B) Snake River near Anatone, Washington, April 2009–September

2010 ...............................................................................................................................................24

Tables

1. Suspended-sediment and streamflow data collected in the Clearwater River,

Idaho, and Snake River, Washington, May 2008–September 2010 ......................................8

2. Surrogate model results and regression statistics for the Clearwater River at

Spalding, Idaho, May 2008–September 2010 .........................................................................13

3. Surrogate model results and regression statistics for the Snake River near

Anatone, Washington, May 2008–September 2010 ..............................................................14

4. Comparison of suspended sediment loads during selected storm events

estimated using acoustic backscatter and sediment-transport curves based on

2008–10 streamflows in the Clearwater River at Spalding, Idaho, and Snake

River near Anatone, Washington.............................................................................................25

5. Comparison of total, daily, and annual suspended sediment loads estimated

using acoustic backscatter and sediment-transport curves based on 2008–10

and 1970s streamflows in the Clearwater River at Spalding, Idaho, and Snake

River near Anatone, Washington.............................................................................................26

Figures—Continued
v

Multiply By To obtain

Length

foot (ft) 0.3048 meter (m)

mile (mi) 1.609 kilometer (km)

Area

square mile (mi2) 259.0 hectare (ha)

square mile (mi2) 2.590 square kilometer (km2) 

Flow rate

foot per second (ft/s) 0.3048 meter per second (m/s)

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

Mass

ton, short (2,000 lb) 0.9072 megagram (Mg) 

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

ton per day (ton/d) 0.9072 megagram per day (Mg/d)

ton per year (ton/yr) 0.9072 megagram per year (Mg/yr)

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

Pressure

atmosphere, standard (atm) 101.3 kilopascal (kPa)

pound-force per square inch (lbf/in2) 6.895 kilopascal (kPa)

pound per square foot (lb/ft2) 0.04788 kilopascal (kPa) 

pound per square inch (lb/in2) 6.895 kilopascal (kPa) 

Conversion Factors and Datums

Conversion Factors

Inch/Pound to SI
vi

 SI to Inch/Pound

Multiply By To obtain

Length

centimeter (cm) 0.3937 inch (in.)

meter (m) 3.281 foot (ft)

Volume

liter (L) 33.82 ounce, fluid (fl. oz)

liter (L) 2.113 pint (pt)

liter (L) 1.057 quart (qt)

liter (L) 0.2642 gallon (gal)

liter (L) 61.02 cubic inch (in3) 

Flow rate

centimeter per second (cm/s) 0.03281 foot per second (ft/s) 

centimeter per second (cm/s) 0.03281 foot per second (ft/s) 

Mass

gram (g) 0.03527 ounce, avoirdupois (oz)

kilogram (kg) 2.205 pound avoirdupois (lb)

Density

kilogram per cubic meter (kg/m3) 0.06242 pound per cubic foot (lb/ft3) 

gram per cubic centimeter (g/cm3) 62.4220 pound per cubic foot (lb/ft3) 

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

°F=(1.8×°C)+32.

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

°C=(°F-32)/1.8.

Datums

Vertical coordinate information is referenced to the North American Vertical Datum of 1988 

(NAVD 88).

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

Altitude, as used in this report, refers to distance above the vertical datum.

Conversion Factors and Datums—Continued

Conversion Factors—Continued
Abstract 

Elevated levels of fluvial sediment can reduce the 

biological productivity of aquatic systems, impair freshwater 

quality, decrease reservoir storage capacity, and decrease the 

capacity of hydraulic structures. The need to measure fluvial 

sediment has led to the development of sediment surrogate 

technologies, particularly in locations where streamflow 

alone is not a good estimator of sediment load because of 

regulated flow, load hysteresis, episodic sediment sources, and 

non-equilibrium sediment transport. An effective surrogate 

technology is low maintenance and sturdy over a range of 

hydrologic conditions, and measured variables can be modeled 

to estimate suspended-sediment concentration (SSC), load, 

and duration of elevated levels on a real-time basis. Among 

the most promising techniques is the measurement of acoustic 

backscatter strength using acoustic Doppler velocity meters 

(ADVMs) deployed in rivers. The U.S. Geological Survey, 

in cooperation with the U.S. Army Corps of Engineers, Walla 

Walla District, evaluated the use of acoustic backscatter, 

turbidity, laser diffraction, and streamflow as surrogates for 

estimating real-time SSC and loads in the Clearwater and 

Snake Rivers, which adjoin in Lewiston, Idaho, and flow into 

Lower Granite Reservoir. The study was conducted from May 

2008 to September 2010 and is part of the U.S. Army Corps 

of Engineers Lower Snake River Programmatic Sediment 

Management Plan to identify and manage sediment sources in 

basins draining into lower Snake River reservoirs.

Commercially available acoustic instruments have 

shown great promise in sediment surrogate studies because 

they require little maintenance and measure profiles of the 

surrogate parameter across a sampling volume rather than at a 

single point. The strength of acoustic backscatter theoretically 

increases as more particles are suspended in the water to 

reflect the acoustic pulse emitted by the ADVM. ADVMs of 

different frequencies (0.5, 1.5, and 3 Megahertz) were tested 

to target various sediment grain sizes. Laser diffraction and 

turbidity also were tested as surrogate technologies. Models 

between SSC and surrogate variables were developed using 

ordinary least-squares regression. Acoustic backscatter using 

the high frequency ADVM at each site was the best predictor 

of sediment, explaining 93 and 92 percent of the variability 

in SSC and matching sediment sample data within +8.6 and 

+10 percent, on average, at the Clearwater River and Snake 

River study sites, respectively. Additional surrogate models 

were developed to estimate sand and fines fractions of 

suspended sediment based on acoustic backscatter. Acoustic 

backscatter generally appears to be a better estimator of 

suspended sediment concentration and load over short 

(storm event and monthly) and long (annual) time scales 

than transport curves derived solely from the regression 

of conventional sediment measurements and streamflow. 

Changing grain sizes, the presence of organic matter, and 

aggregation of sediments in the river likely introduce some 

variability in the model between acoustic backscatter and SSC.

Introduction

The U.S. Geological Survey (USGS) Idaho Water Science 

Center, in cooperation with the U.S. Army Corps of Engineers 

(USACE), Walla Walla District, is evaluating surrogate 

technologies to estimate suspended-sediment concentrations 

(SSC) in the Clearwater River at Spalding, Idaho, and the 

Snake River near Anatone, Washington (fig. 1) to help 

quantify sediment transport to Lower Granite Reservoir 

in northern Idaho and eastern Washington. USACE is 

developing strategies for managing fluvial sediment transport 

and deposition in lower Snake River reservoirs, which 

has negatively affected navigation and flow conveyance. 

Historically, sediment deposition has been managed through 

periodic dredging of the federal navigation channel; however, 

USACE plans to identify more opportunities for controlling 

sediment by quantifying sediment sources and transport in 

contributing drainage basins, particularly the Clearwater, 

Snake, and Salmon River basins. Streamflow in the two river 

systems is partially regulated, meaning that some but not all 

of the flow is controlled by dam releases. Some flow passing 

each study site is contributed by unregulated (free-flowing) 

tributaries.

Use of Surrogate Technologies to Estimate Suspended 

Sediment in the Clearwater River, Idaho, and Snake River, 

Washington, 2008–10

By Molly S. Wood, U.S. Geological Survey, and Gregg N. Teasdale, U.S. Army Corps of Engineers
2 Surrogate Technologies to Estimate Suspended Sediment, Clearwater River, Idaho, and Snake River, Wash., 2008–10

tac13-0799_fig 01

Lapwai

South

Fork

River

Salmon

Snake

Grande

Ronde

Dworshak Reservoir Lower Granite 

Reservoir

River

Clearwater

River

River

Salmon

Salmon

River Fork

M di dle

Lapwai R

Sa ml on

River

Snake River

Potlatch

River

River North Fork

To Hells Canyon Dam

Lower Granite Dam

Dworshak

Dam

Clearwater

River

EXPLANATION

Sediment surrogate site and 

USGS streamgage

USGS streamgage site

0 25 50 75 MILES

0 25 50 75 KILOMETERS

13342500

13317000

OREGON

CANADA

MONTANA

WYOMING

UTAH NEVADA

IDAHO

WASHINGTON

Study area

OREGON

IDAHO

MONTANA

WASHINGTON

118°

119°

117°

116°

115°

114°

44°

45°

46°

47°

Potlatch

Pullman

Spalding

Anatone

Lewiston

13342500

13334300

13317000

Figure 1. Study area and locations of sediment surrogate and U.S. Geological Survey (USGS) streamgage sites in the Clearwater 

River, Idaho, and Snake River, Washington, May 2008–September 2010.
Introduction 3

The Lower Granite Lock and Dam forms the farthest 

upstream reservoir on the lower Snake River and captures 

sediment from about 27,000 mi2 of forested and agricultural 

land in the Clearwater, Salmon, and Grande Ronde River 

basins (Teasdale, 2010). Levees were constructed along the 

Snake and Clearwater Rivers in Lewiston, Idaho, to contain 

the backwater of Lower Granite Dam and provide flood 

damage reduction up to the level of the Standard Project 

Flood, which is the streamflow expected to result from the 

most severe hydrologic and meteorological conditions that 

is characteristic of the drainage basins (U.S. Army Corps 

of Engineers, 2002). Sediment deposition has reduced the 

hydraulic capacity of the levees since completion of the 

dam in 1974. Periodic dredging has been performed by the 

USACE to maintain the navigation channel and recover 

hydraulic capacity of the levee system. An Environmental 

Impact Statement (EIS) for proposed dredging action prepared 

by the USACE in 2002 was suspended in litigation and the 

USACE is now revising the EIS as part of the development 

of a comprehensive Programmatic Sediment Management 

Plan (PSMP). The PSMP-EIS is evaluating alternatives to 

dredging, including drainage basin measures that may reduce 

sediment loads and the construction of structures within the 

reservoir to promote movement of sediment through the 

confluence. Accurate measurements of sediment concentration 

and load are necessary to plan and evaluate potential sediment 

management actions and to calibrate sediment yield and 

transport models.

The USGS conducted a sediment sampling program 

in the Clearwater and Snake Rivers from 1972 to 1979 and 

developed sediment-transport curves that related streamflow 

to suspended and bedload sediment samples to calculate 

continuous records of sediment concentration and load. The 

results of the 1970s study are presented in Jones and Seitz 

(1980). One of the goals of the 2008–10 sediment sampling 

program was to determine whether the 1970s sedimenttransport curves are representative of current sedimenttransport conditions. A detailed discussion of comparisons 

between results of the 1970s and 2008–10 sampling programs 

is provided in Clark and others (2013).

This report documents the ability and limitations of using 

sediment-surrogate technologies (surrogate technologies), 

such as acoustic backscatter, laser diffraction, and turbidity, 

to estimate SSC and load on continuous, 15-min intervals in 

the Snake and Clearwater Rivers draining to Lower Granite 

Reservoir. Surrogate technologies are evaluated to determine 

whether they provide improved estimates of SSC and load in 

comparison with sediment-transport curves generated using 

streamflow and sediment data collected during the 1970s and 

2008–10 studies. Transport curves relying on streamflow as 

the explanatory variable may be poor estimators of SSC in 

these river systems, particularly during rain events, owing to 

“hysteresis” and varying sources of sediment (mostly from 

unregulated tributary inflows) that may not contribute a large 

percentage of the total flow but contribute a large amount 

of sediment. Sediment “hysteresis” means that sediment 

concentrations have different values at identical streamflow on 

the ascending and descending limbs of a hydrograph. A plot 

of streamflow and SSC during a storm event often appears to 

have a looped relation owing to hysteresis.

Additional sediment samples and surrogate data 

were collected in water year 2011 to validate the acoustic 

backscatter surrogate models described in this report; 

validation results are presented in Clark and others (2013). 

Clark and others (2013) also presents a comparison of 

suspended sediment-load estimates generated using the 

acoustic backscatter surrogate models described in this 

report and using a LOADEST (LOAD ESTimation) model, 

which is a FORTRAN (FORmula TRANslation) program for 

estimating constituent loads in streams and rivers based on 

streamflow and time variables (Runkel and others, 2004).

Background

The USGS has traditionally used streamflow as a 

surrogate to estimate instantaneous SSC and sediment 

loads based on guidelines in Porterfield (1972), Glysson 

(1987), and Nolan and others (2005). A relation is developed 

between streamflow and SSC or sediment load using log 

transformations on both variables or plotting on logarithmic 

scales. The relation, which may be linear or non-linear, is 

called a sediment-transport curve. 

Uncertainties in sediment-transport curves have led to the 

development and evaluation of more direct, in-situ surrogate 

techniques. Acoustic instruments have shown great promise 

as sediment- surrogate technologies. They are tolerant of 

biological fouling and measure profiles across a sampling 

volume rather than at a single point in the stream (Gartner and 

Gray, 2005). 

Acoustic backscatter has been used with success 

as a surrogate technology for SSC or suspended solids 

concentration in the San Francisco Bay (Gartner, 2004), 

Florida estuaries (Patino and Byrne, 2004), Colorado River 

(Topping and others, 2004, 2006), Hudson River (Wall and 

others, 2006), the Aegean Region in Turkey (Elci and others, 

2009), and subtropical estuaries in Australia (Chanson and 

others, 2008). Although the primary purpose of these types of 

acoustic instruments is to measure water velocity, additional 

measures are useful to monitor suspended-sediment transport. 

As the instrument emits an acoustic pulse into the water 

and measures the Doppler-shifted frequency of the pulse as 

it bounces off acoustic reflectors (typically assumed to be 

primarily sediment particles), the strength of the returned pulse 

(backscatter) also is measured as it returns to the instrument 

along the beam path (SonTek/Yellow Springs Instruments, 

2007). Backscatter should increase when more particles are 

present in the water. As a result, the backscatter measurement 

may be related to SSC. 
4 Surrogate Technologies to Estimate Suspended Sediment, Clearwater River, Idaho, and Snake River, Wash., 2008–10

Additional surrogate technologies have been used 

to monitor suspended-sediment transport. Turbidity has 

been successfully used as a surrogate for SSC in Kansas 

(Rasmussen and others, 2005), Oregon (Uhrich and Bragg, 

2003), and Florida (Lietz and Debiak, 2005), among other 

locations. Turbidity probes typically used in these studies emit 

a near-infrared light at 780–900 nm and measure the amount 

of light scattered at an angle of 90 degrees (Yellow Springs 

Instruments, 2011). The greater the amount of light scattered, 

the higher the turbidity reading. In theory, this should equate 

to a larger amount of suspended material in the measurement 

volume.

The concept of laser diffraction is documented in 

Agrawal and others (2008) and has been used with success 

as a sediment surrogate in the Colorado River (Topping 

and others, 2004) and in laboratory experiments (Meral, 

2008). Essentially, a laser is passed through a water sample 

and a receiving lens in the instrument focuses the light that 

is scattered by particles in the water onto a series of ring 

detectors. The detectors calculate a volumetric concentration 

of sediment in 32 size classes. Data can be converted to a mass 

concentration by multiplying the volumetric concentration by 

a known sediment density, or the volumetric concentrations 

can be used alone in a calibration with measured SSC.

Site Descriptions and Surrogate 

Instrument Configurations

Study sites were co-located with existing USGS 

streamgages to take advantage of existing infrastructure for 

mounting equipment and transmitting data and to facilitate 

computations of sediment loads. Acoustic frequencies were 

selected for this study to maximize sensitivity of backscatter 

to dominant sediment particle size (grain size) with low 

acoustic frequency for the sand-sized fraction (grain size 

between 0.63 and 2 mm) and high acoustic frequency for the 

fines fraction (grain size less than 0.63 mm) to minimize errors 

because of changing grain-size distribution, as recommended 

in Gartner (2004) and Topping and others (2004). The 

following sections describe characteristics of the two study 

sites and configuration of surrogate instruments.

Clearwater River Study Site

The Clearwater River study site is co-located with 

USGS streamgage No. 13342500 on the left streambank at 

Spalding, Idaho. Part of the streamflow passing the study site 

is regulated by Dworshak Dam located upstream of the site 

on the North Fork Clearwater River (fig. 1). The main stem 

Clearwater River is unregulated except for a few upstream 

irrigation diversions, which affect about 18 percent of the 

drainage area (U.S. Geological Survey, 2012). The site is 

equipped with a 0.5-MHz SonTekTM/YSI Argonaut-SL acoustic 

Doppler velocity meter (ADVM), a 3-MHz SonTekTM/

YSI Argonaut-SL ADVM, a Yellow Springs Instruments 

(YSITM) 6600EDS water-quality sonde with a model 6136 

nephelometric turbidity probe, and a Sequoia Scientific 

Laser In Situ Scattering and Transmissometry (LISST)-

StreamSide laser diffraction instrument (fig. 2). The YSITM 

6136 nephelometric turbidity probe used in this study emits a 

near-infrared light at 780–900 nm and measures the amount 

of light scattered at an angle of 90 degrees (Yellow Springs 

Instruments, 2011). The site also is equipped with a datalogger 

and satellite telemetry for collecting and transmitting realtime data. The ADVMs, turbidity probe, and telemetry were 

installed in May 2008; the LISST-StreamSide was installed 

in July 2008. The LISST-StreamSide is deployed inside a 

gage house, and a pump draws water from the river into 

the LISST-StreamSide optical analyzer box. The intended 

advantage of the LISST-StreamSide over other commercially 

available, in-situ laser diffraction instruments as described 

in Gray and Gartner (2010) is improved data quality through 

reduced stream contact and resulting biological fouling. 

Unforeseen configuration problems in the LISST-StreamSide, 

which resulted in poor pump operation, the formation of 

bubbles in the line, and possible condensation on internal 

lenses, prevented reliable measurements for most of the study 

period. The manufacturer of the LISST-StreamSide, Sequoia 

Scientific, is working closely with the USGS to resolve the 

problems. Further testing is needed to determine whether the 

instrument will perform as intended, once these issues are 

resolved.

The ADVMs, turbidity probe, and LISST-StreamSide 

pump intake are mounted on a 44-ft aluminum slidetrack mount that can be raised and lowered as needed to 

service equipment. The 0.5- and 3-MHz ADVMs measure 

backscatter in five discrete, equally sized cells in a horizontal 

sampling volume, at distances of 5.0–100 ft and 3.3–12 ft 

from the instrument, respectively. The sampling volume 

for each ADVM was selected based on acoustic frequency, 

abundance of acoustic reflectors along the beam path, and any 

obstructions in the beam path. The ADVMs were originally 

configured to measure backscatter in 10 cells, within a 

sampling volume twice as large as the current configuration. 

However, the sampling volume represented by the first five 

cells was determined to be optimum for developing the model 

between SSC and acoustic backscatter. In addition, data 

transfer limitations using Serial Data Interface-1200 baud 

rate (SDI-12) protocol, the communication protocol used to 

transfer data from the ADVMs to the dataloggers at both sites, 

prevents real-time display of data from more than five cells. 

As a result, only the first five cells could be practically used to 

compute real-time estimates of SSC and sediment load using 

developed surrogate models.
Site Descriptions and Surrogate Instrument Configurations 5

tac13-0799_fig02

Laser diffraction instrument

(not described in this report) 

Water-quality sonde with 

turbidity probe

0.5-MHz acoustic Doppler 

velocity meter (ADVM)

3-Mhz ADVM

Not shown: the pump and 

intake for an additional 

laser diffraction instrument 

were later installed on the 

back side of the aluminum 

plate attached to the ADVMs

Figure 2. Sediment surrogate instruments deployed at the Clearwater River near Spalding, Idaho. 

Instruments are shown pulled up the slide track mount for servicing. Not shown: the pump and intake for 

the LISST-StreamSide was later installed on the back side of the aluminum plate attached to the acoustic 

Doppler velocity meters. Photograph taken by Molly Wood, U.S. Geological Survey, May 8, 2009. 
6 Surrogate Technologies to Estimate Suspended Sediment, Clearwater River, Idaho, and Snake River, Wash., 2008–10

The ADVMs average measurements collected over 

2 min out of every 15 min. The water-quality sonde measures 

turbidity adjacent to the instrument every 15 min and is 

equipped with an automated wiper mechanism to reduce 

biological fouling on the face of the probe. The LISSTStreamSide measures volumetric SSC and grain-size 

distribution every 30 min. The sampling line for the LISSTStreamSide is flushed for 2–5 min prior to each measurement 

(duration changed during study period), and measurements are 

then averaged over 30 sec.

Snake River Study Site

The Snake River study site is co-located with USGS 

streamgage No. 13334300 on the left streambank near 

Anatone, Washington (fig. 1). Part of the streamflow passing 

the study site is regulated by numerous dams along the Snake 

River, including Hells Canyon Dam located 31 mi upstream. 

The Salmon and Grande Ronde Rivers join the Snake River 

upstream of the study site and contribute most of the sediment 

passing the site (Gregory Clark, U.S. Geological Survey, 

oral commun., 2011). The site is equipped with a 0.5-MHz 

SonTekTM/YSI Argonaut-SL ADVM, a 1.5-MHz SonTekTM/

YSI Argonaut-SL ADVM, and a YSITM 6600EDS waterquality sonde with a model 6136 turbidity probe (fig. 3).

Figure 3. Sediment surrogate instruments deployed at the Snake River near Anatone, Washington. (Photographs A and B taken 

December 15, 2008 and March 30, 2009, respectively, by Molly Wood, U.S. Geological Survey.

A. The pipe housing a Yellow Springs Instruments (YSITM) water-quality sonde with turbidity probe. 

Pipe housing a water-quality sonde with 

turbidity probe
Site Descriptions and Surrogate Instrument Configurations 7

B. The 1.5-MHz and 0.5-MHz SonTekTM/YSI acoustic Doppler velocity meters 

(ADVMs) attached to an aluminum slide track mount about 1,000 ft upstream of the 

water-quality sonde. 

Figure 3.—Continued

Like the Clearwater River study site, the Snake River site is equipped with a 

datalogger and satellite telemetry. The water-quality sonde and telemetry were 

installed in May 2008; the ADVMs were installed in April 2009. The waterquality sonde is mounted in a plastic pipe, drilled with holes to maintain hydraulic 

communication between the inside of the pipe and surrounding water, which 

extends into the river from the left bank near the gage house. The ADVMs could 

not be co-located with the streamgage and water-quality sonde because streambed 

features limited profiling across the 

channel. The ADVMs were installed 

in a more suitable measurement 

location about 1,000 ft upstream of 

the streamgage, on a 32-ft aluminum 

slide track mount that can be raised 

and lowered as needed to service the 

equipment.

The 0.5- and 1.5-MHz ADVMs 

are configured to measure backscatter 

in five discrete, equally sized cells in a 

horizontal sampling volume, 6.6–203 ft 

and 6.6–59 ft from the instrument, 

respectively. The ADVMs average 

measurements collected over 2 min 

out of every 15 min. The water-quality 

sonde measures turbidity adjacent to 

the instrument every 15 min and is 

equipped with an automated wiper 

mechanism. 

Unlike the Clearwater River site, 

the ADVMs at the Snake River site are 

direct current-powered through a solar 

panel and battery. To avoid fluctuations 

in input voltage, which is common 

at sites powered by a solar panel and 

battery, that could lead to fluctuations 

in power during transmission of the 

acoustic pulse (Craig Huhta, SonTek/

Yellow Springs Instruments, oral 

commun., 2012), both ADVMs are 

connected to a direct current voltage 

converter to maintain a constant 

voltage input to the instruments during 

measurements. The voltage converter 

changes direct current voltage from a 

solar panel and battery to a constant 

output of 13 volts to the ADVMs, to 

remove the potential uncertainty in 

backscatter measurements because of 

fluctuations in input voltage. This setup 

was deemed necessary because Wall 

and others (2006) noted that differing 

power-supply voltages supplied to 

acoustic Doppler current profilers used 

to estimate sediment in the Hudson 

River resulted in changes in transmit 

power of the acoustic pulse, which 

required corrections to the data. This 

phenomenon could cause fluctuations 

in backscatter measurements which are 

not a result of changes in SSC in the 

river. 
8 Surrogate Technologies to Estimate Suspended Sediment, Clearwater River, Idaho, and Snake River, Wash., 2008–10

Methods

The following sections describe the methods used to 

collect suspended-sediment samples, process and apply 

corrections to surrogate data, and develop surrogate models 

for the computation of continuous records of SSC and load.

Sediment Sample Collection

Suspended-sediment samples were collected using 

the equal-width-increment (EWI) sampling method (U.S. 

Geological Survey, 2006) with a cable-suspended, US D-96 

depth-integrating, isokinetic water sampler and were analyzed 

at the USGS Cascades Volcano Observatory Sediment 

Laboratory in Vancouver, Washington. Sampling was targeted 

towards the ascending limb, the peak, and the descending limb 

of the snowmelt runoff hydrograph for each river. Thirtythree EWI suspended- sediment samples were collected at 

each site during May 2008–September 2010 (table 1) and 

were analyzed for concentration, percent fines smaller than 

0.063 mm, and organic content through a loss-on-ignition test. 

A full grain-size analysis on the sand fraction was performed 

for some samples. Samples submitted for analysis were a 

composite representative of the entire cross section. 

To quantify cross-sectional variability, 10 discrete depthintegrated samples, each from a separate vertical section, 

were collected and analyzed during 4 sampling events at the 

Clearwater River site and 5 sampling events at the Snake 

River site. EWI samples should have been collected with 

each of the discrete sample subsets because the average 

concentration from the discrete sample subsets may not 

necessarily equal the EWI sample concentration. However, 

EWI samples were collected concurrently for only two of the 

discrete sample subsets at the Snake River site. When discrete 

bottles were collected without a corresponding EWI sample, 

the results from the discrete samples were averaged for use in 

the analysis. Discrete and corresponding EWI samples were 

collected at flows of 48,000 and 105,000 ft3/s at the Snake 

River site. Ratios were calculated between the EWI sample 

concentration and average concentration from discrete samples 

for these two sampling events. The ratios were 0.79 and 0.96 

for the higher and lower flow sampling events, respectively, 

meaning that in both cases the EWI sample concentration was 

less than the average concentration from discrete samples. 

Sample results from the remaining three sampling events 

in the Snake River, when a corresponding EWI sample was 

not collected, were adjusted based on these ratios. Samples 

collected at 24,600 and 55,000 ft3/s were adjusted using the 

0.96 (lower flow) ratio and a sample collected at 103,000 ft3/s 

was adjusted using the 0.79 (higher flow) ratio. For example, 

the average concentration for discrete samples collected on 

May 20, 2009, at a flow of 103,000 ft3/s was 301 mg/L. The 

concentration was adjusted to 301 mg/L × 0.79 = 237 mg/L 

to estimate what the EWI sample concentration might have 

been for that sampling event. None of the samples on the 

Clearwater River site were adjusted in this way because no 

corresponding EWI samples were collected concurrently with 

the four discrete sample subsets.

Table 1. Suspended-sediment and streamflow data collected in the Clearwater River, Idaho, and Snake River, Washington, 

May 2008–September 2010. 

[Abbreviations: USGS, U.S. Geological Survey; mg/L, milligrams per liter; ft3/s, cubic feet per second; na, not applicable]

Characteristic

USGS streamgage

Units Clearwater River 

(13342500)

Snake River

(13334300)

Number of sediment samples collected during study period 33 33 na

Mean annual streamflow, period of record1 14,710 34,450 ft3/s

Annual mean streamflow

2008 16,220 31,310 ft3/s

2009 16,040 33,080 ft3/s

2010 10,830 29,130 ft3/s

Total suspended-sediment concentration

Mean 26 70 mg/L

Median 13 40 mg/L

Ranges

Total suspended-sediment concentration 3–210 6–414 mg/L

Sand concentration 0.3–122 0.5–232 mg/L

Fines concentration 2–88 5–206 mg/L

Flows during sample collection 4,760–78,900 14,900–155,000 ft3/s

Flows during study period (May 2008–September 2010) 2,190–79,700 10,900–173,000 ft3/s

1

 Based on published period of record for streamgage, water years 1972–2010 for Clearwater River, 1958–2010 for Snake River. 
Methods 9

Surrogate Instrument Data Corrections

The surrogate technologies required varying levels of 

correction to be used in SSC estimates from the raw measured 

values. Turbidity data were corrected for calibration drift and 

fouling errors as described in Wagner and others (2006). Laser 

diffraction data were recorded by the instrument in volumetric 

concentration in microliters per liter (μL/L), which can be 

multiplied by a known or assumed particle density to obtain 

mass concentration in milligrams per liter (mg/L). In this 

investigation, it was not practical to continuously measure 

particle density, so a regression model was developed between 

LISST volumetric concentration and the mass concentration 

of the physical samples. No corrections were applied to the 

LISST volumetric concentration data.

To correct acoustic backscatter data into a more 

meaningful estimator of SSC, multiple steps are required. 

Acoustic backscatter data were corrected for (1) beam 

spreading, (2) transmission losses owing to absorption by 

water, and (3) absorption or attenuation by sediment. Methods 

for correcting acoustic backscatter data are documented 

in Flammer (1962), Urick (1975), Thevenot and Kraus 

(1993), and Gartner (2004). Methods for correcting acoustic 

backscatter data differ and can significantly change estimates 

of sediment concentration. Selection of an appropriate method 

is an important decision in the analysis of acoustic backscatter 

data. Candidate methods were reviewed and those selected for 

this study are described in the following sections.

Acoustic Data Corrections

Mass concentration of suspended sediment can be related 

to acoustic backscatter using equation (1) in exponential form:

SSC ABScorr = + + + + 10 0 1 2 ( ( β β ) (β β EVi EVn ) ... ( )) n (1)

where 

SSC is suspended-sediment concentration (mg/L)

β0 is the equation intercept, 

β1 is the regression coefficient corresponding to 

ABScorr, and

ABScorr is the range-normalized acoustic backscatter 

(ABS) corrected for two-way transmission 

losses (Thevenot and Kraus, 1993) in decibels 

(dB). 

EVi through EVn are other explanatory variables used 

in the regression, and β2 through βn are the corresponding 

regression coefficients. The regression coefficients are 

determined by regressing mass concentration measurements of 

suspended sediment with measurements of ABScorr and other 

explanatory variables during sample collection.

Backscatter data must be range-normalized or corrected 

for transmission losses through a multi-step process (fig. 4). 

Corrected acoustic backscatter, ABScorr, is calculated using a 

form of the sonar equation from Urick (1975):

ABScorr = − K E E R r w + + R R + s ( ) log ( )20 2 2 10 α α (2)

where

ABScorr is the range-normalized acoustic backscatter 

corrected for two-way transmission losses in 

dB, 

K is a scale factor used to convert uncorrected ABS 

in counts to dB, 

E is the raw amplitude of the uncorrected ABS as 

reported by the acoustic device (counts), 

 Er is the received signal strength indicator reference 

level or instrument noise floor (counts), 

R is the slant distance along the acoustic beam 

to the measurement location incorporating 

beam angle (25 degrees for SonTekTM/YSI 

ADVMs) (m), 

	 αw is the water absorption coefficient (dB/m), and 

	 αs is the sediment attenuation coefficient (dB/m).

The scale factor used to convert uncorrected ABS in 

counts to dB typically ranges from 0.35 to 0.55 according to 

Deines (1999). For SonTekTM/YSI ADVMs, the appropriate 

value for K when converting ABS from counts to dB is 

0.43 (SonTek/Yellow Springs Instruments, 2007). The term 

Er, or instrument noise floor, is specific to the ADVM and 

deployment location, and is the baseline echo measured by 

the instrument when no signal is transmitted. Local electronic 

interferences can affect Er. Er is measured automatically by 

the ADVMs used in this study immediately after a backscatter 

measurement is made. The term K(E – Er) is output from 

the SonTekTM/YSI ADVMs directly as Signal to Noise Ratio 

(SNR) in each cell, so this term was used in all calculations 

because it incorporated actual measurements of the instrument 

noise floor (fig. 4, step 1).

Acoustic Beam Spreading

Losses owing to beam spreading, represented by the 

term 20log10(R) in equation (2), are different for acoustic 

backscatter data collected near the transducer, or within a zone 

called the near-field distance. The near-field distance is defined 

by Rcritical = πrt

2/λ, where rt

 is the transducer radius (cm) and 

λ is the acoustic wavelength, or the speed of sound in water 

(cm/s) divided by the acoustic frequency (Hz). At distances 

less than Rcritical, the near-field correction for spreading loss 

is defined by Downing and others (1995) as:

Ψ =  + +  

  +   1 1 35 25 135 2 5 3 2 3 2 . ( . ) / . ( . ) . . Z Z Z Z (3)

where 

 Z = R/Rcritical and R is the slant range distance along the 

beam to the sampling volume of interest.
10 Surrogate Technologies to Estimate Suspended Sediment, Clearwater River, Idaho, and Snake River, Wash., 2008–10

tac13-0799_fig 04

0

10

20

30

40

50

60

70

80

90

100

0 10 20 30 40 50 60 70

Acoustic backscatter, in decibels

Distance along beam from acoustic Doppler velocity meter (ADVM), in feet

Step 1: Collect signal-to-noise ratio in cells (noise floor

removed and data converted to decibels)

Step 3: Further correct cell data for acoustic absorption by sediment

(attenuation) calculated using -0.5*slope of the Step 2 line. Average

corrected cell values in this line to compute sediment- and watercorrected acoustic backscatter (ABScorr) 

Step 2: Correct cell data in raw data line for beam

spreading and acoustic absorption by water 

Raw measured signal-to-noise ratio data (circles denote centroid of each cell)

Data corrected for beam spreading and acoustic absorption by water

Data corrected for beam spreading and acoustic absorption by water and sediment

EXPLANATION

Figure 4. Process for calculation of range-normalized acoustic backscatter corrected for two-way transmission losses in 

the Clearwater River, Idaho, and Snake River, Washington.

At points within the near field, the term 20log10(R) in 

equation (2) becomes 20log10(Rψ). Cell 1 centroids for the 

ADVMs in the Clearwater and Snake Rivers were greater than 

Rcritical and so were not corrected for near-field spreading 

losses. Losses owing to beam spreading were calculated 

simply using the term 20log10(R).

Acoustic Absorption by Water

The water absorption coefficient, αw, in equation (2) 

is a function of acoustic frequency, pressure, salinity, and 

temperature; and is calculated according to Schulkin and 

Marsh (1962): 

 αw t t t SAf f f f Bf f

P

= + +

− × × −

[ / ( ) / ]

[ ( . )( )] .

2 2 2 2

4 1 6 54 10 8 686

(4)

where 

	 αw is the water absorption coefficient (dB/m), 

S is salinity (practical salinity units), 

A is a constant for ionic relaxation process in sea 

water equal to 2.34×10-6, 

f t is the temperature-dependent relaxation frequency 

(kilohertz or kHz) defined as 21.9×10[6-1520/

(T+273)] , 

T is temperature (oC), 

f is the ADVM acoustic frequency (kHz), 

B is a constant for viscosity mechanism in pure 

water, defined as 3.38×10-6

, and 

 P is pressure (atmospheres or kg/cm3). 

In this analysis, the first term of the equation, SAft f 2/

(ft 

2+f 2), is assumed to be zero because salinity is negligible. 

Pressure, P, is considered 1 atmosphere because the difference 

in pressure between the elevations of the water surface at 

the sites (about 800 ft above sea level) and the depth of the 

deployed ADVMs is negligible.

The terms 20log10(R) and 2αwR in equation (2) represent 

the two-way transmission loss, or acoustic signal loss owing 

to beam spreading and acoustic absorption by water. Data 

corrected for these losses are represented by step 2 in figure 4.
Methods 11

Acoustic Absorption by Sediment

The last term in equation (2), 2αsR, represents the two-way 

transmission loss owing to absorption or attenuation by sediment, 

and ideally should be calculated based on knowledge of source 

level, target strength, ensonified volume, and mass of suspended 

material in various size classes. Attenuation of an acoustic 

signal by suspended particles consists of viscous, scattering, and 

diffraction energy loss components (Flammer, 1962). Diffraction 

losses are described in more detail in Reichel and Nachtnebel 

(1994) and are not a concern at the study site, given the frequency 

of the selected ADVMs and measured sediment concentrations and 

particle sizes. A theoretical calculation of viscous and scattering 

losses can be made based on the following equation from Urick 

(1975):

2 1 6 8 686 2 2 2 4 3 α γ γ τ s K S p = − ( ( ) { / [S + + ( ) ]}+( ) K a / )( . )(SSC)

Viscous losses Scattered losses

(5)

where 

SSC is the suspended-sediment concentration (mg/L)

2αs is the two-way transmission loss owing to attenuation 

from suspended particles (dB/m), 

K is 2π/λ, 

λ is acoustic wavelength or the speed of sound in water 

(cm/s) divided by acoustic frequency (Hz), 

S is [9/(4βap)][1+1/(βap)], β is [ω/2v]0.5, ω is 2πf, 

f is acoustic frequency (Hz), 

v is the kinematic viscosity of water (Stokes), 

ap is particle radius (cm), 

γ is the particle wet density divided by fluid density, and 

τ is 0.5+9/(4βap).

It was not always practical to measure true values for 

some of the parameters in equation (5) given the expected nonuniformity in particle shape, size, and density. Topping and 

others (2004, 2006) proposed that the acoustic absorption by 

sediment (attenuation) can be calculated based on profiles of 

acoustic backscatter corrected for spherical beam spreading and 

absorption by water. In Topping and others (2004, 2006) and 

the study reported here, 2αs

R in equation (2) was calculated 

for each cell by determining -0.5 times the slope of the line of 

K(E-Er  )+20log10(R)+2αwR (represented by the line in fig. 4, 

step 2). This value, called sediment attenuation, or αs, is then 

multiplied by 2×R (the slant range distance along the beam to the 

sampling volume of interest). ABScorr was then calculated for each 

cell according to equation (2) (fig. 4, step 3), and the average of 

ABScorr from all cells was used to relate surrogate data to sediment 

sample data.

During some brief periods of low backscatter and low SSC, 

the line representing data corrected for beam spreading and 

acoustic absorption by water (fig. 4, step 2) curved upward in cells 

4 or 5. This is not physically possible, and use of this data in the 

calculations would have resulted in erroneous estimates 

of the slope of the line, or sediment attenuation. During 

these periods, acoustic backscatter in the outer cell(s) 

may have been erroneous because it could not be 

distinguished from the instrument noise floor. When 

this occurred, these cells were discarded from the 

calculation of sediment attenuation. Only cells along 

the decreasing trend of the line representing data 

corrected for beam spreading and acoustic absorption 

by water were used to calculate sediment attenuation 

for further correction of the data in step 3 of figure 4.

Surrogate Model Development

Samples collected in 2008–10 were used to 

develop sediment-surrogate models at each site. 

Surrogate measurements (acoustic backscatter, 

turbidity, streamflow, and laser diffraction (laser 

diffraction was measured at the Clearwater River site 

but not the Snake River site) data) were averaged over 

a 1-hour period bracketing each sediment sample to 

obtain concurrent measurements for surrogate-model 

calibrations. Some samples were not included in the 

surrogate models because of intermittent equipment 

malfunctions, varying installation dates for surrogate 

instruments, and surrogate instruments being out 

of water for short periods of time during low-flow 

conditions.

Models between SSC and surrogate variables 

were developed using stepwise ordinary leastsquares regression techniques in TIBCO Spotfire S+®

statistical software (TIBCO Software Inc., 2008). Log 

transformations were performed on SSC, streamflow, 

LISST concentration, and at the Clearwater River 

site, turbidity, to improve distribution and fit prior 

to the evaluation in the regression model. Various 

transformations were evaluated on variables prior to 

use in the regressions, including the square root, cube 

root, reciprocal root, and reciprocal, as described in 

Helsel and Hirsch (2002). Use of the log transformation 

produced the best fit and most linear relations of other 

evaluated transformations. Acoustic backscatter data 

are already reported in a log-based scale and do not 

require a transformation. Regression models were 

selected based on statistical significance (p-values) of 

explanatory variables and various regression statistics, 

such as high coefficient of determination (R2), low 

standard error, constant variance and random patterns 

in residuals plots, and low relative percent difference 

(RPD) between measured and estimated SSC, as 

defined in equation (6): 

 RPD Estimated SSC Measured SSC)/

Measured SSC

= −

×

[(

] 100

 (6)
12 Surrogate Technologies to Estimate Suspended Sediment, Clearwater River, Idaho, and Snake River, Wash., 2008–10

Additional forms of the regression models developed 

for the acoustic backscatter surrogates were evaluated in 

an attempt to improve fit at high SSC. One of the forms 

evaluated was a compound regression model composed of 

two linear segments with different slopes. A breakpoint in 

acoustic backscatter between the two linear segments was 

selected based on backscatter measured during times when 

upstream tributaries contributed high SSC. For example, in 

the Clearwater River, the evaluated breakpoint was 72 dB. 

Above this breakpoint, most of the samples in the Clearwater 

River were collected during times when high SSC was 

measured in the Potlatch River (fig. 1), an upstream tributary 

to the Clearwater River. Polynomial forms of the acoustic 

backscatter models also were evaluated, which included terms 

of backscatter and backscatter squared. None of the evaluated 

forms substantially improved the overall fit of the regression 

model to the measured data nor the variance in residuals 

plots, in comparison with the simple linear relations between 

corrected acoustic backscatter and log-transformed SSC.

A nonparametric bias correction factor described in 

Duan (1983) was applied to each regression model to correct 

for bias induced by log transformation and subsequent 

retransformation of the dependent variable. Duan’s bias 

correction factor is calculated by averaging the values of 10 

to the power of each residual of the dependent variable in the 

dataset used to develop the regression model. The factor was 

used to correct each value of SSC as well as upper and lower 

95-percent confidence intervals estimated by a regression 

model. Sediment loads were calculated by multiplying 

estimates of SSC by rated streamflow at each study site.

Sediment-transport curves developed during the 1970s 

study and presented in Jones and Seitz (1980) were applied to 

streamflow data in the 2008–10 study to determine whether 

the relation between suspended sediment and streamflow 

changed between the two studies. Jones and Seitz (1980) did 

not use a bias correction factor in their equations. The original 

sediment-transport curves were not altered for the comparison 

with the 2008–10 study.

Use of Surrogate Models To Estimate 

Suspended Sediment

Stream conditions varied at each sediment-surrogate 

monitoring site. Measured SSC in the Clearwater River 

ranged from 3 to 210 mg/L, with a median of 13 mg/L, during 

the period of sample collection used for development of the 

surrogate models (May 2008–September 2010) (table 1). 

Fines content (<63 μm) ranged from 30 to 96 percent. 

In the Snake River, measured SSC ranged from 6 to 414 

mg/L, with a median SSC of 40 mg/L. Fines content ranged 

from 32 to 94 percent. Fines content at both sites typically 

decreased with increasing concentration. Samples were 

collected over nearly the full range in streamflow at both 

sites; 96 and 86 percent of the range in flow was represented 

by samples at the Clearwater River and Snake River sites, 

respectively (table 1). 

The acoustic surrogate models demonstrate the robust 

nature of acoustic technologies for use as sediment surrogates 

at the study sites. The higher frequency acoustic surrogate 

models were the best estimator of SSC of all of the evaluated 

surrogate technologies, based on regression statistics (tables 2

and 3). The ADVMs also required the least maintenance of 

the instruments evaluated; however, post-processing of the 

data was more difficult than for other surrogates. Substantial 

variability was observed in the turbidity and laser diffraction 

models, which may be due in part to cross-sectional variability 

in sediment concentration, which was verified through the 

collection of the discrete samples across each cross section as 

well as visual observations of sediment stratification during 

some site visits, typically after a runoff event. At each study 

site, tributary inflows enter the main channel on the left bank 

less than 2 mi upstream of the measurement site. During some 

storm runoff and snowmelt events, the tributaries discharge 

sediment-laden water that adjoins the left bank and persists 

downstream past the location of the surrogate equipment. 

However, turbulence induced by channel and bank features 

varied with streamflow and seemed to cause slight spatial 

variability in this zone, relative to the location of the surrogate 

instruments. This small-scale spatial variability likely resulted 

in the high variability in the calibrations of the laser diffraction 

and turbidity instruments. The ADVMs were less affected by 

this streamflow condition because they sample a larger part of 

the channel volume and capture more of the cross-sectional 

variability. The method for correcting acoustic backscatter 

for losses assumes that the suspended sediment within the 

sampling volume is relatively uniform in concentration and 

particle-size distribution, but the acoustic surrogates seem to 

be more tolerant of small amounts of spatial variability than 

the point measurements of the laser diffraction and turbidity 

instruments.

Clearwater River Study Site

Backscatter from the 3-MHz ADVM was the best 

estimator of SSC in the Clearwater River, likely because 

sediment is dominated by fine sands and silt which seems 

to be well-targeted by the high frequency ADVM (table 2). 

Surrogate models also were developed to estimate sand and 

fines concentrations separately based on 3-MHz acoustic 

backscatter. RPD was calculated between each pair of 

measured and estimated SSC values according to equation (6).
Use of Surrogate Models To Estimate Suspended Sediment 13

Table 2. Surrogate model results and regression statistics for the Clearwater River at Spalding, Idaho, May 2008–September 2010.[Sediment surrogate: ADVM, acoustic Doppler velocity meter; MHz, megahertz. Model: SSC, suspended-sediment concentration in milligrams per liter (mg/L); ABScorr, acoustic backscatter corrected 

for beam spreading and attenuation by water and sediment in decibels (dB); Turb, turbidity in Formazin Nephelometric Units (FNU); Q, streamflow in cubic feet per second (ft3/s); LISST, suspendedsediment concentration estimated by the LISST StreamSide in microliter per liter (μL/L). R2: Coefficient of determination. Average RPD: Relative percent difference. BCF: Duan’s bias correction factor. 

Abbreviation: na, not applicable]

Sediment surrogate

Number of 

samples used 

for regression

Model R2

Average 

RPD 

(percent)

Standard 

error 

(mg/L)

BCF

3-MHz ADVM backscatter 30 ( ) 0.0557 3-MHz_ABScorr -2.431 SSC 10 1.040 ×     = × 0.93 +8.6 1.34 1.040

( ) 0.0743×3-MHz_ABScorr -4.147 Sand concentration 10 1.146 =     × 0.87 +34 1.71 1.146

( ) 0.0461 3-MHz_ABScorr -2.030 Fines concentration 10 1.097 = ×     × 0.78 +19 1.58 1.097

Turbidity 30 ( ) ( ) 0.872 log Turb 0.454 SSC 10 1.202   × +   = × 0.64 +48 1.92 1.2022008–10 sediment transport curve 33 ( ) 1.142 log -3.840 SSC 10 1.284 Q ×     = × 0.54 +64 2.10 1.284

1970s sediment transport curve 1 135 1.074 SSC 0.000349 Q = × 0.52 +54 2.69 na20.5-MHz ADVM backscatter 30 ( ) 0.0064 0.5-MHz_ABScorr -0.676 SSC 10 1.916 ×     = × 0.007 +204 2.97 1.916

Laser diffraction 15 ( ) 0.0459 log(LISST) 1.011 SSC 10 1.560 × +     = × 0.003 +119 2.73 1.560

1 As published in Jones and Seitz (1980).2 Bias correction factor was not used in the computation of concentrations and loads in Jones and Seitz (1980).
14 Surrogate Technologies to Estimate Suspended Sediment, Clearwater River, Idaho, and Snake River, Wash., 2008–10 Table 3. Surrogate model results and regression statistics for the Snake River near Anatone, Washington, May 2008–September 2010.

[Sediment surrogate: ADVM, acoustic Doppler velocity meter; MHz, megahertz. Model: SSC, suspended-sediment concentration in milligrams per liter (mg/L); ABScorr, acoustic backscatter corrected 

for beam spreading and attenuation by water and sediment in decibels (dB); Turb, turbidity in Formazin nephelometric units (FNU); 

Q, streamflow in cubic feet per second (ft3/s); LISST, suspendedsediment concentration estimated by the LISST StreamSide in microliter per liter (μL/L). 

R2: Coefficient of determination. Average RPD: Relative percent difference. BCF: Duan’s bias correction factor. 

Abbreviation: na, not applicable

Sediment surrogate

Number of 

samples used 

for regression

Model R2

Average 

RPD 

(percent)

Standard 

error 

(mg/L)

BCF

1.5-MHz ADVM backscatter 22 ( ) 0.0756 1.5-MHz_ABScorr -4.676 SSC 10 1.048 ×    

= × 0.92 +10 1.39 1.048

( ) 0.105 1.5-MHz_ABScorr -7.636 Sand concentration 10 1.129

= ×     × 0.89 +24 1.73 1.129

( ) 0.0615 1.5-MHz_ABScorr -3.730 Fines concentration 10 1.084

= ×     × 0.81 +19 1.54 1.084

2008–10 sediment transport curve 33 ( ) ( ) 1.761 log -6.697 SSC 10 1.120 Q   ×  

= × 0.83 +24 1.65 1.120

1970s sediment transport curve 1 125 1.460 SSC 0.000003

Q = × 0.61 -34 2.03 na2

Turbidity 29 ( ) 0.0181 Turb 1.249 SSC 10 1.209 × +    

= × 0.70 +48 1.93 1.209

0.5-MHz ADVM backscatter 22 ( ) 0.0333 0.5-MHz_ABScorr 4.301 SSC 10 1.417 − × +    

= × 0.33 +120 2.59 1.417

1 As published in Jones and Seitz (1980).2 Bias correction factor was not used in the computation of concentrations and loads in Jones and Seitz (1980).
Use of Surrogate Models To Estimate Suspended Sediment 15

The model between 0.5 MHz acoustic backscatter and 

SSC was poor (R2=0.007) because of a problem noted in 

the instrument noise level measurements. At high flows, the 

measured noise level increased substantially and was high 

relative to the raw backscatter measurement. The 0.5-MHz 

ADVM appears to be more sensitive to electrical and other 

noise that occurs in the water at high streamflows than the 

other evaluated frequencies (Craig Huhta, SonTek/Yellow 

Springs Instruments, oral commun., 2012). Because the 

instrument noise level is subtracted from the raw backscatter 

to compute SNR, in many cases this resulted in a low SNR 

when SSC was high. For comparison, a model was developed 

between the raw backscatter from the 0.5-MHz ADVM 

(without subtracting the noise level) and SSC, resulting in 

an improved R2 of 0.89, but raw backscatter data collected 

during this period also could have been erroneous. Overall, 

the 3-MHz ADVM was still a better estimator of SSC than the 

other surrogates whether raw backscatter or SNR was used to 

develop the model.

Topping and others (2004) determined that in the 

Colorado River, the degree of sediment attenuation along the 

beam path is closely related to the fines fraction, and average 

backscatter is closely related to the sand fraction. However, 

backscatter alone was determined to be a good estimator 

of the fines and sand fractions, as well as overall SSC, in 

the Clearwater River. The model between 3-MHz acoustic 

backscatter and SSC (overall, sand, and fines) shows that a 

shift from a fines-dominated SSC to a sand-dominated SSC 

occurs around 60 mg/L or an ABScorr for the 3-MHz ADVM 

of 75 dB (fig. 5). Non-zero attenuation at low SSC, likely 

because of the presence of organic matter, created significant 

variability in the relation between attenuation and the fines 

fraction, as well as overall SSC, at low concentrations. High 

variability in the individual sand and fines models is caused by 

many physical factors of sediment load and transport including 

the magnitude of the washload component, mobility of bed 

material and armor, non-equilibrium (supply limited) transport 

Figure 5. Surrogate regression models for total suspended sediment, sand, and fines concentrations based on acoustic 

backscatter for the Clearwater River near Spalding, Idaho.

1.0

0.5

0.0

0.5

1.0

1.5

2.0

2.5

50.0 55.0 60.0 65.0 70.0 75.0 80.0 85.0

Log suspended-sediment concentration, in log milligrams per liter

Sediment- and water-corrected acoustic backscatter (ABScorr) from 3-Megahertz (MHz) acoustic Doppler velocity meter (ADVM), in decibels

Total suspended-sediment concentration

Sand concentration

Fines concentration

Total suspended sediment

log SSC = (0.0557 × 3-MHz_ABScorr)−2.431

R2 = 0.93 

Fines

log Fines = (0.0461 × 3-MHz_ABScorr)−2.030

R2 = 0.78 

Sand

log Sand = (0.0743 × 3-MHz_ABScorr)−4.147

R2 = 0.87 

EXPLANATION
16 Surrogate Technologies to Estimate Suspended Sediment, Clearwater River, Idaho, and Snake River, Wash., 2008–10

of sediment, relative magnitudes of the tributary flows, 

timing of releases of stored water for water management, and 

proximity of episodic sediment sources. The uncertainty and 

stochasticity of the relations between these factors motivated 

the USACE’s interest in the use of surrogate sediment 

measurement technology in this study.

The selected regression based on 3-MHz acoustic 

backscatter represented 93 percent of the variability in SSC 

and resulted in an average RPD between measured and 

estimated SSC of +8.6 percent. Standard error for the 3-MHz 

model was lower and variance in residuals was lower and 

more constant than for all other models indicating best fit. 

Best agreement (lowest RPD) was observed when fines were 

between about 70 and 85 percent of total SSC. Estimates of 

SSC when the sand fraction was high were not substantially 

improved by using the model with the 0.5-MHz ADVM, even 

when using a model that did not incorporate the continuously 

measured noise level. This is likely because most of the sand 

fraction is very fine and fine sand (<250 μm), which along 

with fines is well-represented by the 3-MHz ADVM.

Results of discrete samples collected to assess crosssectional variability in SSC show that inflows from the 

upstream tributary Lapwai Creek are not well mixed with 

the Clearwater River at the study site under some conditions 

of flow. Segregation of Clearwater River and Lapwai Creek 

flows is supported by hydraulic analysis and observations 

made by aerial survey (Teasdale, 2005). Standard deviation 

among discrete samples ranged from 2 mg/L at low SSC to 

24 mg/L at high SSC. Because water from Lapwai Creek 

adjoins the bank on the same side of the river as the surrogate 

instruments, they likely sample a zone of average to aboveaverage SSC relative to the entire cross section. The biased 

sampling leads to an overestimate of sediment concentration 

when this phenomenon occurs. Even with this local effect, the 

ADVMs represent cross-sectional variability better than other 

surrogates. Alternative methods to correct bias imposed by 

non-uniform flow conditions are being evaluated.

Following a transformation back to original units, 

the selected regression model for estimating SSC at the 

Clearwater River site is:

SSC = 10 [(0.0557×3-MHz_ABScorr)-2.431]×1.040 (7)

where 

SSC is the suspended-sediment 

concentration (mg/L), 

3-MHz_ABScorr is the range-normalized acoustic backscatter 

from the 3-MHz ADVM corrected for two-way 

transmission losses (dB), and 

1.040 is Duan’s bias correction factor.

Measured and estimated SSC based on the selected model 

(eq. 7) compare well but deviate at higher SSC (>100 mg/L) 

(fig. 6). The upper and lower 95-percent confidence level for 

the sample with highest concentration, 210 mg/L, plotted 

well below the value estimated by the surrogate model. RPD 

for individual observations ranged from -43 to +80 percent, 

but most of the high RPDs occurred at low SSC, when small 

differences between estimated and measured values can result 

in high percent differences. At high SSC (>100 mg/L), mean 

RPD was -33 percent, meaning that in general, the regression 

model underestimated measured SSC when high. A possible 

reason for model underestimation at high SSC is that more 

sand is transported during these periods. Sand may travel 

lower in the water column than finer materials owing to higher 

mass and may not be captured within the sampling volume 

of the ADVMs, which are installed approximately mid-depth 

in the water column. An additional source of error may have 

been that 4 of the sample concentrations (5.1, 19, 38, and 

104 mg/L) used to develop the surrogate model were averages 

of 10 concentrations of discrete samples collected across the 

cross section. However, none of these sample concentrations 

appear to be highly influential in the regression (fig. 5) so 

likely do not contribute to substantial model error. The USGS 

evaluated whether the inclusion of additional explanatory 

variables in the regression would improve estimates at high 

SSC. Some of the evaluated variables included the fraction 

Figure 6. Measured and estimated total suspended 

sediment concentrations in the Clearwater River at 

Spalding, Idaho, based on a surrogate model with 

acoustic backscatter.

0

50

100

150

200

250

0 50 100 150 200 250

Estimated suspended-sediment concentration, 

in milligrams per liter 

Measured suspended-sediment concentration, in milligrams per liter

Observation

Upper 95-percent confidence level

Lower 95-percent confidence level

EXPLANATION

1:1 Line
Use of Surrogate Models To Estimate Suspended Sediment 17

of unregulated flow passing the site, turbidity, the square of 

3-MHz_ABScorr, and ratios of attenuation and backscatter 

for the 3- and 0.5-MHz ADVMs. The fraction of unregulated 

flow term is discussed in more detail in Wood (2010). None of 

the variables substantially improved the regression statistics 

or SSC estimates in comparison with the base model using 

3-MHz_ABScorr alone.

At low SSC (<100 mg/L), mean RPD was +16 percent; 

thus the regression model generally overestimated measured 

SSC when low. Average percent organic matter was 10 percent 

at high SSC and 23 percent at low SSC. High percent organic 

matter at low SSC is a possible cause of high positive RPD 

because the ADVMs likely detect the organic matter as 

sediment. Inaccuracy in the estimates of the low SSC range 

had a negligible effect on the estimation of the magnitude 

and timing of total suspended sediment load in this study, but 

may be of importance where chronic exposure to low levels of 

contaminated sediment is the concern.

Measured and estimated SSC for March–July 2010, 

based on the selected model with 3-MHz acoustic backscatter 

as well as with transport curves developed from 2008 to 2010 

and 1970s samples, and streamflows, is presented in figure 7. 

Agreement between measured and estimated SSC is better 

for the 3-MHz ADVM than for the sediment-transport curves. 

At this site, sediment predictions based on streamflow are 

less accurate than those based on acoustic backscatter over a 

storm event. Sediment transport curves inadequately represent 

hysteresis of sediment concentration caused by the timing of 

inflows from sediment-laden tributaries and the other factors 

mentioned above.

Figure 7. Estimated instantaneous values of total suspended-sediment concentration for March 23–July 1, 2010, 

in the Clearwater River near Spalding, Idaho, based on a surrogate model with acoustic backscatter and sedimenttransport curves developed using data from the 2008–10 and 1970s studies.

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

0

20

40

60

80

100

120

140

160

180

200

Streamflow, in cubic feet per second

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

3-MHz_ABScorr Model SSC

2008–10 sediment-transport curve SSC

1970s sediment-transport curve SSC

Clearwater streamflow (at USGS streamgage 13342500)

Samples

EXPLANATION

March April May June July
18 Surrogate Technologies to Estimate Suspended Sediment, Clearwater River, Idaho, and Snake River, Wash., 2008–10

Snake River Study Site

Acoustic backscatter was shown to be as good of 

an estimator for SSC in the Snake River as it was in the 

Clearwater River, despite the shorter period of record during 

which the ADVMs were installed and fewer samples available 

for the calibration (table 3). Similar to the Clearwater River, 

the model between 0.5-MHz ADVM backscatter and SSC 

was poor owing to high noise levels at high flows resulting 

in an inverse relation between SNR and SSC. A model 

developed between the raw backscatter from the 0.5-MHz 

ADVM (without subtracting the noise level) and SSC resulted 

in an improved R2 of 0.67 but was still inferior to the model 

between the 1.5-MHz ADVM and SSC.

Discrete samples collected to assess cross-sectional 

variability in SSC show that inflows from the upstream 

tributary Grande Ronde River are not always well-mixed 

with the Snake River at the study site. Standard deviation 

among discrete samples was higher for the Snake River than 

for the Clearwater River, ranging from 15 mg/L at low SSC 

to 185 mg/L at high SSC. The surrogate instruments likely 

measure a zone of average to above-average SSC relative 

to the entire cross section because water from the Grande 

Ronde River adjoins the left bank and does not fully mix with 

the Snake River flow before the measurement site. As at the 

Clearwater River site, the ADVMs at the Snake River site are 

able to better represent cross-sectional variability than other 

surrogates.

Following a transformation back to original units, the 

selected regression model for estimating SSC at the Snake 

River site is:

SSC = 10 [(0.0756 × 1.5-MHz_ABScorr) – 4.676]×1.048 (8)

where 

SSC is the suspended-sediment concentration 

(mg/L), 

1.5-MHz_ABScorr is the range-normalized acoustic backscatter 

from the 1.5-MHz ADVM corrected for twoway transmission losses (dB), and 

1.048 is Duan’s bias correction factor.

Separate models were developed to estimate overall SSC 

as well as sand and fines fractions (fig. 8). The shift from 

a fines-dominated SSC to a sand-dominated SSC appears 

to occur at about 110 mg/L or an ABScorr for the 1.5-MHz 

ADVM of 89 dB.

Figure 8. Surrogate regression models for total suspended sediment, sand, and fines concentrations based on acoustic 

backscatter for the Snake River near Anatone, Washington.

0.5

70 75 80 85 90 95 100

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Log suspended-sediment concentration (SSC), in log milligrams per liter 

Total suspended sediment

log SSC = (0.0756 × 1.5-MHz_ABScorr) −4.676

R2 = 0.92 

Fines

log Fines = (0.0615 × 1.5-MHz_ABScorr) −3.730

R2 = 0.81

Sand

log Sand = (0.105 × 1.5-MHz_ABScorr) −7.636

R2 = 0.89

Sediment- and water-corrected acoustic backscatter (ABScorr) from 1.5-Megahertz (MHz) acoustic Doppler velocity meter (ADVM), in decibels

Total suspended-sediment concentration

Sand concentration

Fines concentration

EXPLANATION
Use of Surrogate Models To Estimate Suspended Sediment 19

In general, agreement between measured and estimated 

SSC improved as the percentage of fines increased. Similar to 

the Clearwater River, estimates of SSC when the sand fraction 

was high were not significantly improved by using the model 

with the 0.5-MHz ADVM, even when using a model that did 

not incorporate the continuously measured noise level. Based 

on a grain-size analysis of the full sand fraction conducted 

for 12 of the samples, most of the sand fraction appears to be 

very fine and fine sand (<250 μm) with some medium sand 

(<500 μm), which along with fines appears to be fairly wellrepresented by the 1.5-MHz ADVM.

Measured and estimated SSC based on the selected 

model (eq. 8) shows good agreement but some deviation at 

high SSC (fig. 9). RPD for individual observations ranged 

from -40 to +123 percent, but many of the high RPDs were 

at low SSC, when small differences between the estimated 

and measured values can result in high percent differences. 

At high SSC (>100 mg/L), mean RPD was -16 percent, 

meaning that in general, the regression model underestimated 

true SSC when high. Similar to the Clearwater River, model 

underestimation at high SSC likely occurs because more sand 

is transported during these periods, which may travel lower 

in the water column than finer materials owing to higher 

Figure 9. Measured and estimated total suspended-sediment concentrations in the Snake River near Anatone, 

Washington, based on a surrogate model with acoustic backscatter.

mass and may not be captured within the sampling volume 

of the ADVMs. Estimates at high SSC may be improved by 

collecting samples to define the degree of vertical stratification 

of sediment and by installing another ADVM at a depth 

likely to capture the zone of sand transport. Similar to the 

Clearwater River analysis, the USGS evaluated whether the 

inclusion of additional explanatory variables in the regression 

would improve estimates at high SSC. Some of the evaluated 

variables included fraction of unregulated flow passing the site 

(discussed in Wood (2010) for a preliminary analysis of the 

Snake River data), turbidity, the square of 1.5-MHz_ABScorr, 

and ratios of attenuation and backscatter for the 1.5- and 

0.5-MHz ADVMs. In the final analysis, none of the variables 

substantially improved the regression statistics or SSC 

estimates in comparison with the base model using 1.5-MHz_

ABScorr alone.

At low SSC (<100 mg/L), mean RPD was +16 percent; 

thus, the regression model generally overestimated true SSC 

when low. Similar to the Clearwater River site, percent organic 

matter at low SSC was higher (16 percent) than at high SSC 

(5 percent) and is a possible cause of high positive RPD in 

those samples. Overall, however, measured and estimated SSC 

compared well, matching on average within 10 percent.

0 

50 

100 

150 

200 

250 

300 

350 

400 

450 

0 50 100 150 200 250 300 350 400 450

Estimated suspended-sediment concentration, in milligrams per liter 

Measured suspended-sediment concentration, in milligrams per liter 

1:1 Line 

EXPLANATION

Observation 

Upper 95-percent confidence level 

Lower 95-percent confidence level 
20 Surrogate Technologies to Estimate Suspended Sediment, Clearwater River, Idaho, and Snake River, Wash., 2008–10

Measured and estimated SSC, based on the selected 

regression model as well as sediment- transport curves based 

on 2008–10 and 1970s samples and streamflows, are shown 

for a selected high flow event in June 2010 in figure 10. The 

highest SSC sample collected during the period of analysis 

(414 mg/L) was collected during this event but was not 

well-represented by any of the surrogate models. The peak in 

sediment concentration estimated by the acoustic backscatter 

model on June 3 on the ascending limb of the hydrograph 

was caused by an increase in sediment-laden inflows from 

the Salmon River. Because the increase in Salmon River flow 

was proportional to the increase in total flow at the study site, 

but sediment contributions were not, the increase in sediment 

concentrations estimated at the study site was not wellrepresented by the sediment-transport curves.

As a whole, the concentrations and loads calculated 

using the 1970s sediment-transport curve underestimate 

current sediment transport. If the estimates based on acoustic 

backscatter are assumed to be more accurate, then estimates 

using the 2008–10 sediment-transport curve underestimate 

sediment transport on the ascending limb and peak of the 

hydrograph and overestimate current sediment transport 

on the descending limb of the hydrograph. The differences 

between sediment loads estimated using sediment transport 

curves developed from 2008–10 and 1970s streamflows were 

much greater for the Snake River than the Clearwater River. 

Based on other sampling conducted by the USGS in the Snake 

River basin, the Salmon River transported more sediment 

(particularly sand) in the 2008–10 study than in the 1970s 

study (Clark and others, 2013). 

0 0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

160,000

180,000

200,000

Streamflow, in cubic feet per second

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

50 

100 

150 

200 

250 

300 

350 

400 

450 

500 

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

1.5-MHz_ABScorr Model SSC

2008–10 sediment-transport curve SSC

1970s sediment-transport curve SSC

Snake streamflow (at USGS 

streamgage 13334300)

Salmon streamflow (at USGS 

streamgage 13317000)

Samples 

EXPLANATION

June 2010

Figure 10. Estimated instantaneous values of suspended-sediment concentration during a storm event on June 

1–15, 2010, in the Snake River near Anatone, Washington, based on a surrogate model with acoustic backscatter 

and sediment transport curves developed using data from the 2008–10 and 1970s studies.
Use of Surrogate Models To Estimate Suspended Sediment 21

Advantages of Acoustics over SedimentTransport Curves in Sediment Monitoring

Use of regression models that relate measured SSC 

to streamflow is common practice in sedimentation 

engineering (Glysson, 1987; Gray and Simões, 2008) and 

is often necessary when sediment load must be predicted 

for forecast or hypothetical streamflows. In sediment load 

monitoring, SSC is often estimated on a continuous basis 

using a sediment-transport curve or linear regression, but such 

relations are often not accurate over short time scales because 

the regression prediction is for the mean SSC response. This 

is particularly true when estimating SSC for a particular storm 

event where sediment supply is the limiting factor of sediment 

transport and is not well represented by the mean response. 

These inaccuracies arise in simple univariate regression 

because the same SSC is predicted at identical streamflows 

on the ascending and descending limbs of the hydrograph, 

although the actual sediment load may be strongly hysteretic. 

In addition, streamflows in rivers that are partially regulated 

may be comprised of relatively non-turbid water management 

releases, sediment-laden tributary inflows and overland 

runoff. Under these conditions, the dominant sediment sources 

may not contribute a large percentage of flow but contribute 

most of the sediment load. It follows that a large increase 

in flow owing to a regulated flow release may not equate 

to a corresponding increase in SSC. Furthermore, sudden 

increases in SSC because of increased sediment transport from 

unregulated tributaries will not be represented by a simple 

streamflow-sediment load regression derived for the main 

river unless such events were adequately represented in the 

regression dataset. 

More complex approaches may be used to estimate 

composite load with separate regressions for each source, a 

multivariate relation (Helsel and Hirsch, 2002), or a model 

simulation. Load monitoring with ADVMs provides a more 

direct means to improve the accuracy of continuous sedimentload estimates.

The effect of tributary sediment inflow is seen in figure 7

which shows an increase in SSC estimated by acoustic 

backscatter in the Clearwater River on April 8, 2010, because 

of a storm event in the Lapwai River drainage that was 

not estimated by the 2008–10 or 1970s sediment-transport 

curves. In the Snake River in June 2010 (fig. 10), assuming 

that acoustic backscatter was the most accurate estimator of 

SSC, a small increase in flow on the ascending limb of the 

hydrograph owing to increases in flow from the Salmon River 

caused a large increase in estimated SSC that was not captured 

in 2008–10 and 1970s sediment-transport curve estimates.

The timing of sediment sample collection also has 

been traditionally targeted for capturing the peak of the 

hydrograph, which may or may not coincide with peak SSC. 

At the study sites, SSC estimated by the acoustic backscatter 

surrogate models typically peaks on the ascending limb of the 

hydrograph then decreases fairly rapidly after peak streamflow 

(figs. 7 and 10). It is rational that higher concentrations 

would be observed on the ascending limb owing to a “first 

flush” effect from overland runoff, tributary inflows, and 

resuspension of sediment from the stream channel. A surrogate 

model other than streamflow is needed to help guide sediment 

sampling efforts as well as to capture the variability in SSC 

during an event.

Comparison over Short Time Scales

To further quantify differences in suspended-sediment 

concentration and load estimates over short time scales, 

suspended-sediment loads were summed by month and for 

the duration of selected, well-defined hydrologic events. 

The range and distribution of SSC in the Clearwater River, 

analyzed by month during the period of analysis based on 

3-MHz acoustic backscatter, shows several outliers with high 

SSC because the 3-MHz surrogate model tends to capture 

short-term increases in SSC (fig. 11A). Most storm and rainon-snow events occur in April and May (including June 2010), 

and streamflow begins to decline in later June and July. Except 

for outliers, the estimated range and overall distribution in 

SSC is higher for the 2008–10 sediment-transport curves 

than for the acoustic backscatter models during the high flow 

months (April, May, June) and for months with declining 

flows (July, August) (fig. 11B). This pattern indicates that 

the 2008–10 sediment-transport curve does not represent 

conditions during individual storm events owing to hysteresis 

but, on a monthly basis, the transport curve overestimation 

of sediment on the descending limb of the hydrograph results 

in an overall net concentration that is higher than what is 

estimated using the acoustic backscatter model. Monthly 

SSC estimated by the 1970s sediment-transport curves were 

similar in pattern but slightly lower in magnitude compared 

with the 2008–10 sediment-transport curves. The 3-MHz 

surrogate model and 1970s and 2008–10 sediment-transport 

curves estimate similar concentrations during September 

through March when flows are fairly steady and storm events 

are infrequent. Monthly SSCs in the Snake River are similar 

in pattern but higher in magnitude compared to those in the 

Clearwater River.
22 Surrogate Technologies to Estimate Suspended Sediment, Clearwater River, Idaho, and Snake River, Wash., 2008–10

Figure 11. Distribution of total suspended-sediment concentration by month in the Clearwater 

River at Spalding, Idaho, based on (A) a surrogate model with 3MHz acoustic backscatter, 

(B) 2008–10 sediment transport curves, and (C) 1970s sediment transport curves, May 2008–

September 2010.

tac13-0799_fig 11ab

xxx

0

50

100

150

200

250

Suspended-sediment concentration, in milligrams per liter

A.

5948 5236 5895 5742 8147 8624 8858 8814 6999 6999 3977 5666

0

50

100

150

200

250

5948 5236 5895 5742 8147 8624 8858 8814 6999 6999 3977 5666

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

B.

30 number of 

 values

EXPLANATION 

upper adjacent

75th percentile

median

25th percentile

lower adjacent

lower outside

lower detached

upper detached

upper outside
Use of Surrogate Models To Estimate Suspended Sediment 23

tac13-0799_fig 11c Suspended-sediment concentration, in milligrams per liter

0

50

100

150

200

250

5948 5236 5895 5742 8147 8624 8858 8814 6999 6999 3977 5666

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

C.

30 number of 

 values

EXPLANATION 

upper adjacent

75th percentile

median

25th percentile

lower adjacent

lower outside

lower detached

upper detached

upper outside

Figure 11.—Continued

Monthly patterns also are evident in figure 12, which 

presents total load by month for both study sites for the 

acoustic backscatter model and 1970s and 2008–10 sedimenttransport curves. At the Clearwater River study site (fig. 12A), 

the 1970s sediment-transport curves estimate lower sediment 

loads during months when flow generally is increasing (April–

May) and higher monthly sediment loads during months when 

flow is high or generally decreasing (June–August) relative 

to the acoustic backscatter model. At the Snake River study 

site, estimates generated from the 1970s sediment- transport 

curves are much lower than those generated from the acoustic 

backscatter model and 2008–10 sediment-transport curve 

during April–June and similar to those generated from the 

acoustic backscatter model during other months.

Differences in load estimates owing to hysteresis 

were further examined by summing estimated loads over 

the ascending and descending limbs of the hydrograph 

for several well-defined hydrologic events—seven in the 

Clearwater River and six in the Snake River (table 4). Load 

estimates based on acoustic backscatter were higher on the 

ascending limb (negative percent difference) and lower on the 

descending limb (positive percent difference) than estimates 

based on the 2008–10 sediment-transport curves in all cases 

except the descending limb for two events in the Clearwater 

River. Loads for these two events were low relative to other 

events. For all events combined, load estimates based on 

acoustic backscatter were 15 and 35 percent higher on the 

ascending limb and 30 and 49 percent lower on the descending 

limb than estimates based on 2008–10 sediment-transport 

curves for the Clearwater and Snake Rivers, respectively. 

Estimated SSC was usually higher on the ascending limb 

(especially for acoustic backscatter) but total loads were 

often higher on the descending limb because of a prolonged 

recession in flow. Loads estimated by the 1970s sedimenttransport curves were not included in the storm event 

analysis because patterns are expected to be similar to the 

2008–10 sediment-transport curves. In this study, acoustic 

backscatter appears to be a better estimator of sediment for 

load monitoring than streamflow alone over short time scales 

because it (1) is not affected by hysteresis, (2) provides a 

more direct, in-situ measurement of suspended sediment, and 

(3) better represents sediment sources from a combination of 

regulated and unregulated sources.
24 Surrogate Technologies to Estimate Suspended Sediment, Clearwater River, Idaho, and Snake River, Wash., 2008–10

tac13-0799_fig 12

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

0

50,000

100,000

150,000

200,000

250,000

300,000

350,000

400,000

450,000

Suspended-sediment load, in tons per month

0

200,000

400,000

600,000

800,000

1,000,000

1,200,000

1,400,000

1,600,000

1,800,000

2,000,000

A.

B.

EXPLANATION 

3-MHz_ABScorr Model

2008–10 sediment-transport curve

1970s sediment-transport curve

EXPLANATION 

1.5-MHz_ABScorr Model

2008–10 sediment-transport curve

1970s sediment-transport curve

Figure 12. Total suspended-sediment load by month based on a surrogate 

model with acoustic backscatter and 2008–10 and 1970s sediment-transport 

curves for the (A) Clearwater River at Spalding, Idaho, May 2008–September 

2010, and (B) Snake River near Anatone, Washington, April 2009–September 

2010. 

NOTE: Each month represents the total load that occurred during that month 

within the study period. For example, the total load for May in the Clearwater 

River is the sum of loads measured in May 2008, May 2009, and May 2010.
Use of Surrogate Models To Estimate Suspended Sediment 25

Table 4. Comparison of suspended sediment loads during selected storm events estimated using acoustic backscatter and sediment-transport curves based on 2008–10 streamflows in the Clearwater River at Spalding, Idaho, and Snake River near Anatone, Washington.[Abbreviations: ABScorr, acoustic backscatter corrected for beam spreading and attenuation by water and sediment in decibels (dB); na, not applicable]

Site Event No. Event dates

Sediment load for ascending limb of hydrograph 

(tons)

Sediment load for descending limb of hydrograph 

(tons)

3-MHz_ABScorr or 1.5-

MHz_ABScorr model

2008–10 

sediment 

transport curve

Percent 

difference

3-MHz_ABScorr or 

1.5-MHz_ABScorr 

model

2008–10 

sediment 

transport curve

Percent 

difference

Clearwater River na na – All 7 events combined 172,000 148,000 -15 131,000 178,000 30

1 May 13–24, 2008 59,400 56,700 -5 13,000 18,800 36

2 November 12–30, 2008 2,070 1,120 -60 3,750 2,850 -27

3 January 1–12, 2009 2,950 790 -116 3,540 2,700 -27

4 April 21–May 1, 2009 13,000 12,900 -1 17,700 24,600 33

5 May 18–June 12, 2009 9,130 8,170 -11 51,100 62,600 20

6 May 14–26, 2010 14,700 11,200 -27 7,370 10,200 32

7 June 2–July 5, 2010 70,600 57,200 -21 34,900 56,000 46

Snake River na na –All 6 events combined 585,000 409,000 -35 762,000 1,260,000 49

1 April 18–May 3, 2009 122,000 68,500 -56 89,600 113,000 23

2 May 5–11, 2009 48,600 39,400 -21 24,400 31,200 24

3 May 18–24, 2009 98,400 84,100 -16 106,000 129,000 20

4 April 20–27, 2010 15,700 11,300 -33 12,100 15,300 23

5 May 16–30, 2010 33,000 26,600 -21 48,400 73,000 41

6 June 2–19, 2010 267,000 180,000 -39 481,000 895,000 60
26 Surrogate Technologies to Estimate Suspended Sediment, Clearwater River, Idaho, and Snake River, Wash., 2008–10

Comparison over Annual Time Scales

Total sediment loads were computed for the period of 

analysis using continuous estimates of SSC based on models 

with acoustic backscatter and sediment-transport curves 

based on 2008–10 and 1970s streamflows to determine how 

estimates compared over longer time scales (table 5). The 

period of analysis for each site was limited to the period 

when the ADVMs were deployed so that a direct comparison 

could be made between sediment-transport curves and 

acoustic surrogate models. On an annual basis, the 2008–10 

sediment-transport curves produced load estimates that 

were +27 and +26 percent different for the Clearwater and 

Snake Rivers, respectively, from load estimates based on 

acoustic backscatter, meaning that the sediment-transport 

curves estimated more sediment than acoustic backscatter. 

Annual load estimates for the Clearwater River based on the 

1970s transport curves were about 24 percent lower than 

estimates based on the 2008–10 sediment-transport curves; 

thus for a given flow, slightly more sediment was transported 

in the 2008–10 study than in the 1970s study. In the Snake 

River, load estimates based on the 1970s transport curves 

were 77 percent lower than estimates based on the 2008–10 

transport curves and 54 percent lower than estimates based 

on acoustic backscatter. For a given streamflow, much more 

sediment was transported in the 2008–10 study than in the 

1970s study in the Snake River. As stated previously, Clark 

and others (2013) noted that much more sand was transported 

in the Snake River during the 2008–10 study than in the 1970s 

study.

Overall, the acoustic backscatter model appears to be 

more accurate than sediment-transport curves over an annual 

time scale because of the patterns observed over shorter time 

scales (monthly and storm events). Based on the few samples 

collected on the receding limbs of storm hydrographs and 

during the long recession in flow from July to September, 

the sediment-transport curves overestimate sediment 

concentrations and loads during these flow conditions. As a 

result, computed annual suspended sediment is consistently 

higher for the sediment-transport curves than for the acoustic 

backscatter models.

Table 5. Comparison of total, daily, and annual suspended sediment loads estimated using acoustic backscatter and sedimenttransport curves based on 2008–10 and 1970s streamflows in the Clearwater River at Spalding, Idaho, and Snake River near Anatone, 

Washington.

[Sediment surrogate: ADVM, acoustic Doppler velocity meter; MHz, megahertz. Abbreviations: ton/d, ton per day; ton/yr, ton per year]

Site Sediment surrogate Period of analysis

Total 

sediment 

load 

(tons)

Average 

daily load 

(ton/d)

Average 

annual load 

(ton/yr)

Percent difference 

in average annual 

load between 

acoustic surrogate 

model and sediment 

transport curves 

(percent)

Clearwater River 3-MHz ADVM backscatter May 8, 2008–

September 30, 2010

 721,000 824 300,400 

2008–10 sediment transport curve 944,000 1,079 394,000 27

1970s sediment transport curve 742,000 848 309,000 3

Snake River 1.5-MHz ADVM backscatter April 2, 2009–

September 30, 2010

 2,580,000 4,720 1,720,000 

2008–10 sediment transport curve 3,350,000 6,120 2,230,000 26

1970s sediment transport curve 1,480,000 2,700 987,000 -54
References Cited 27

Summary and Conclusions

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

U.S. Army Corps of Engineers, evaluated the use of acoustic 

backscatter, turbidity, laser diffraction, and streamflow as 

surrogate technologies to estimate real-time suspendedsediment concentrations (SSC) and loads in the Clearwater 

and Snake Rivers during 2008–10. Acoustic backscatter, 

measured using acoustic Doppler velocity meters (ADVMs), 

had the best relation with measured SSC, was less affected by 

biological fouling, and could be measured in a larger part of 

the channel than the other evaluated surrogate technologies. 

As a result, ADVMs capture more of the cross-sectional 

variability that is represented in the physical sediment 

samples collected at the study sites. Although organic matter 

concentrations were low at both sites, they most likely 

contributed to model error at low SSC. Model error at high 

SSC may have been partially due to vertical stratification of 

sediment (particularly sand), which was not always wellrepresented in the fixed-depth, horizontal sampling volume 

of the ADVMs. Improved estimates of SSC when sand 

concentrations are high may be obtained by installing an 

ADVM lower in the water column to measure backscatter in 

zones where sand is likely transported.

Overall, a single frequency ADVM was adequate to 

estimate suspended sediment in most of the streamflow 

conditions at both study sites, and optimal frequency was 

dependent on sediment characteristics. Acoustic backscatter 

provides improved estimates of suspended sediment 

concentration and load over traditional sediment-transport 

curves based on streamflow over short (monthly and storm 

event) and long (annual) time scales when sediment load 

is highly variable. In addition, acoustic backscatter better 

represents sediment contributions from a combination of 

regulated and unregulated sources, which can be difficult to 

represent with a univariate sediment-transport curve.

Sediment-surrogate technologies can be a cost-effective 

component of a long-term fluvial sediment monitoring 

program. Once an initial regression model is developed 

between surrogate data and SSC, samples can be collected 

less frequently, thus reducing long-term operation and 

maintenance costs for a sediment monitoring station. Sediment 

surrogates also allow the estimation of sediment when it is 

unsafe to sample the stream, such as during flood events. 

Inspection of the sediment record, estimated using a surrogate 

model, may reveal significant episodic sediment-transport 

events that would be difficult to detect otherwise. Traditional 

suspended-sediment estimation techniques using streamflow 

alone may provide poor results over small time scales or 

in streams with partially regulated flow, episodic sediment 

sources, and non-equilibrium sediment transport, as is the 

case for the Clearwater and Snake Rivers. Sediment-surrogate 

technologies are an effective means to obtain continuous, 

accurate estimates of suspended sediment concentrations and 

loads for general monitoring and sediment-transport modeling. 

In the Clearwater and Snake Rivers, estimates of SSC using 

surrogate models will allow water managers and scientists to 

identify the timing, magnitude, and duration of high sediment 

load and to better monitor long-term basin response to 

sediment-management strategies.

Acknowledgments

The authors wish to thank USGS employees Joseph Bunt 

(retired), Peter Elliott, Justin Schultz, and Doug Ott for their 

assistance in establishing and operating the sediment surrogate 

instruments. Special thanks go to hydrologists and hydrologic 

technicians from the USGS Idaho Water Science Center’s 

Boise and Post Falls Field Offices for assistance with sediment 

and streamflow data collection. The authors are grateful to 

fellow members of the USGS Sediment Acoustics Leadership 

Team for advancing and guiding the development of sediment 

surrogate techniques and providing valuable advice throughout 

the study.

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30 Surrogate Technologies to Estimate Suspended Sediment, Clearwater River, Idaho, and Snake River, Wash., 2008–10

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