United States 

Department of 

Agriculture

Forest Service

Pacific Southwest 

Research Station

General Technical Report

PSW-GTR-212

March 2009

Implementation Guide 

for Turbidity Threshold 

Sampling: Principles, 

Procedures, and Analysis

Jack Lewis and Rand Eads DEPAR TMENT OF AGRICULT URE
The Forest Service of the U.S. Department of Agriculture is dedicated to the principle of 

multiple use management of the Nation’s forest resources for sustained yields of wood, 

water, forage, wildlife, and recreation. Through forestry research, cooperation with the 

States and private forest owners, and management of the National Forests and National 

Grasslands, it strives—as directed by Congress—to provide increasingly greater service 

to a growing Nation.

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opportunity provider and employer.

Authors 

Jack Lewis (retired) was a mathematical statistician, U.S. Department of 

Agriculture, Forest Service, Pacific Southwest Research Station, Redwood 

Sciences Laboratory, 1700 Bayview Drive, Arcata, CA 95521; Rand Eads is 

a surface water specialist, RiverMetrics LLC, P.O. Box 507, Lafayette, OR 

97127-0507.
Abstract

Lewis, Jack; Eads, Rand. 2009. Implementation guide for turbidity threshold 

sampling: principles, procedures, and analysis. Gen. Tech. Rep. PSW-GTR-212. 

Albany, CA: U.S. Department of Agriculture, Forest Service, Pacific Southwest 

Research Station. 87 p.

Turbidity threshold sampling uses real-time turbidity and river stage information 

to automatically collect water quality samples for estimating suspended sediment 

loads. The system uses a programmable data logger in conjunction with a stage 

measurement device, a turbidity sensor, and a pumping sampler. Specialized 

software enables the user to control the sampling process, plot and correct the 

data, and estimate suspended loads. This implementation guide describes the 

entire process, including instrumentation, installation, field procedures, software 

usage, data collection, laboratory methods, data interpretation, data reduction, 

and analysis.

Keywords: Turbidity, suspended sediment, sediment loads, pumping sampler, 

stream gaging, water quality.
Contents

1 Overview

1 Background

3 Purpose of Turbidity Threshold Sampling

4 What Is Turbidity Threshold Sampling and How Does it Work?

5 Station Visits

6 Equipment Needed

6 Estimation of the Suspended Sediment Load

6 Limitations to Using Turbidity Threshold Sampling

8 Site Selection and Equipment Installation

8 Selection Criteria for Gaging Sites

17 Shelter Design

21 Selection of Instrumentation

35 Site Operations

35 Routine Procedures

39 Depth-Integrated Sampling

39 Developing a Stage-Discharge Relationship

40 Equipment Troubleshooting

41 Turbidity Threshold Sampling Program

41 Logic

43 Guidelines for Setting Turbidity Thresholds

46 Entering Thresholds in the Campbell TTS Program

47 Laboratory Procedures

47 Standard Methods

47 Additional Recommendations

50 Evaporation Method

51 Laboratory Turbidity

52 Data Processing

52 Raw Data Plots

55 Laboratory Data

56 Processing the Stage Data

60 Documentation
61 Converting Stage to Discharge

62 Processing the Turbidity Data

66 Adjusting Point Suspended Sediment Concentration to the 

Cross-Section Mean

73 Estimating Sediment Concentration and Load

80 Documentation

82 Quality Evaluation

85 Acknowledgments

85 Acronyms

86 English Equivalents

86 References

Implementation Guide for Turbidity Threshold Sampling: Principles, Procedures, and Analysis

1

Overview

Background

How traditional sampling methods for suspended sediment are inadequate—

Suspended sediment levels in streams and rivers can fluctuate rapidly, often 

independent of changes in water discharge. Estimation of suspended sediment 

loads (SSL) has usually been based on infrequent sampling (typically biweekly to 

monthly) that is inadequate to characterize the temporal variability in suspended 

sediment concentration (SSC), and usually misses the periods of greatest concentration. Even daily sampling is inadequate for all but the largest river systems. For 

example, daily SSC data from the South Fork Eel River poorly represents the actual 

pattern of SSC, as estimated by turbidity at 10-minute intervals (fig. 1).

In small rain-dominated watersheds, a dozen or more samples might be 

required in one day to describe the shape of the plot of SSC versus time. Sampling 

at fixed time intervals is usually very inefficient. Sampling frequency should be 

greatest when variability in sediment concentration is greatest. Water discharge 

and turbidity contain information about variability in sediment concentration and, 

because they are measurable in real time, they can be incorporated in sampling 

algorithms. An automated system is required to implement such a sampling protocol, and the sampling algorithms may need to be rather sophisticated.

Figure 1—Suspended sediment concentration (SSC) as estimated from turbidity at 10-minute intervals, 

compared with the subset that would have been observed by sampling once daily at 8 am.

Sampling at fixed time 

intervals is usually 

very inefficient.

SSC mg/L

26 27 28 29 30 31 10 11 12

South Fork Eel R 

(114 km2) at Branscomb

10-minute

8am daily

1 2 3 4 5 6 7 8 9

Dec 2004 Jan 2005

o

o o o

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o o o o o o o o

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0
2

GENERAL TECHNICAL REPORT PSW-GTR-212 

Discharge sediment rating curves and their shortcomings—

Because measurement of SSC requires processing of physical samples in a 

laboratory, accurate estimation of SSL requires a continuously measurable 

surrogate variable that is related to SSC. Before the advent of continuous turbidity 

monitoring, water discharge was the primary surrogate for SSC. Relationships 

between SSC and discharge, called discharge sediment rating curves, are 

commonly used to estimate SSC and SSL. These rating curves usually result 

in very poor estimates of SSL for many reasons (Rieger and Olive 1984, 

Walling and Webb 1981):

• Suspended sediment concentration is often out of phase with discharge, 

leading to systematic biases in load estimation.

• Rising and falling limb data may not be appropriately represented.

• High concentrations and discharges are inadequately represented. 

• The rating curves change with time.

• The rating curves cannot account for episodic sediment delivery from 

erosion events, nor differences between rainfall and snowmelt runoff.

Discharge sediment rating curves account only for the energy provided by 

water discharge and ignore other processes and sources of variability related to 

sediment supply. However, surrogate measurements other than discharge can be 

used to derive sediment rating curves.

Turbidity sediment rating curves: a relatively new alternative—

With the advent of commercially available submersible sensors in the 1990s, in 

situ turbidity can be routinely recorded. A rating curve employing turbidity as 

the surrogate variable (turbidity sediment rating curve) can be used to estimate 

SSC much more accurately than a discharge sediment rating curve (Lewis 1996). 

Turbidity is an optical property of a suspension that is directly related to its particle 

concentration and composition, as well as the properties of the instrument used 

to measure it. Although much better than a discharge-based rating, a turbidity 

rating is only valid for a particular instrument type and range of suspended particle 

composition. Just as discharge-based ratings can change with time, so can turbidity 

ratings (although shifts are less pronounced, see fig. 2), because suspended particle 

composition (e.g., the proportion of fine organic material) can change with time.

Therefore, the best results are obtained by determining a turbidity sediment 

rating curve for each seasonal or episodic period being estimated. This requires 

that sampling be undertaken in each estimation period. If the range of sample data 

spans the range of turbidity recorded, and estimation periods are short (e.g., storm 

events), there should be little variability in the scatter of the data.
Implementation Guide for Turbidity Threshold Sampling: Principles, Procedures, and Analysis

3

Purpose of Turbidity Threshold Sampling

The Turbidity Threshold Sampling (TTS) method was developed by the U.S. 

Department of Agriculture, Forest Service to efficiently utilize continuous turbidity 

measurements in triggering suspended sediment samples from pumping samplers 

(Lewis 1996, Lewis and Eads 2001). This system provides a practical means to 

estimate SSC and SSL more accurately than other methods in general use today. A 

continuous electronic record of turbidity provides details about sediment transport 

that cannot be obtained manually and can greatly enhance our understanding of 

sediment dynamics (Kirchner et al. 2004). Short-duration, high-amplitude sediment pulses would not be reliably identified by fixed-interval or discharge-driven 

sampling schemes (even with automatic sampling) unless deployed at very high 

sampling frequencies.

The TTS method uses real-time information about stream turbidity to activate 

a pumping sampler to verify that sediment pulses are real and to provide a set of 

turbidity and SSC pairs from which to compute a turbidity-sediment rating curve. If 

a pumping sampler is activated whenever the turbidity reaches one of a specified set 

of thresholds distributed over the expected turbidity range, then one can be certain 

that the entire range of conditions will be represented in the sample data (except 

when the sensor’s range is exceeded).

Pumping samplers are best deployed in streams whose sediments are finetextured and well mixed in the flow. These conditions should be verified at the 

Figure 2—North Fork Caspar Creek, station Arfstein (3.83 km2), 1995. All data were recorded at 10-minute intervals. Colors 

represent individual storm hydrographs. NTU = nephelometric turbidity units.

Flow (L/s)

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(mg/L)

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50 100 500 1000
4

GENERAL TECHNICAL REPORT PSW-GTR-212 

outset of a sampling program by manually collecting depth-integrated samples at 

several locations in the cross section where a pumping sampler is to be deployed. 

If a reliable representation of the mean SSC cannot be obtained with a pumping 

sampler, the pumped sample SSC can often be adjusted by using a relationship with 

SSC from simultaneous depth-integrated samples.

A turbidity monitoring program could be established without concomitant 

pumped samples, but that approach is not recommended unless water clarity is 

truly the only issue of concern. Even in that case, there is no standard instrument 

for measuring turbidity, and relationships among instruments vary according to the 

sediment being measured (Lewis et al. 2007). If suspended sediments or solids are 

the parameter of concern, then the SSC should be measured.

What Is Turbidity Threshold Sampling and How Does It Work?

Turbidity Threshold Sampling is an automated method for water quality sampling 

using real-time surrogate data. Turbidity and water depth are measured at short 

intervals (from 5 to 15 minutes) by using in situ sensors, and a programmable data 

logger uses these measurements to make real-time determinations of whether a 

pumping sampler should be activated. Turbidity Threshold Sampling is not limited 

to sampling suspended sediment. Mercury, phosphorus, and fecal coliform bacteria 

are often adsorbed on sediment particles; therefore, sampling for these and other 

constituents could be improved by using TTS. Total dissolved solids or specific 

chemical salts could also be sampled and estimated by modifying TTS to employ 

electrical conductivity as the surrogate variable.

Data collection and sampling criteria—

A programmable data logger records water depth and turbidity at frequent, fixed 

time intervals so that conditions between data points may be considered nearly 

constant. At each recording interval, the turbidity and the water depth are used to 

make a decision whether to activate a pumping sampler. The decision criteria are:

• Water depth is adequate to submerge the turbidity sensor.

• Water depth is adequate to submerge the pumping sampler intake.

• A turbidity threshold has been met.

Thresholds are scaled so that their spacing decreases with increasing turbidity. 

The objective is to optimize sampling efficiency in both small and large events: only 

enough samples are collected to reliably estimate sediment loads. There are two sets 

of turbidity thresholds: one for rising turbidity conditions and another for falling 

turbidity conditions. Because the period of falling turbidity is usually longer than 

the period of rising turbidity, and it represents the majority of the sediment load, 

more turbidity thresholds are needed under falling conditions. The criteria for setting 

Turbidity Threshold 

Sampling is an 

automated method 

for water quality 

sampling using realtime surrogate data.
Implementation Guide for Turbidity Threshold Sampling: Principles, Procedures, and Analysis

5

thresholds, meeting thresholds, and distinguishing rising from falling conditions are 

discussed in detail later in the “Turbidity Threshold Sampling Program” section.

Station Visits

When all conditions have been met to activate the pumping sampler, the data 

logger triggers the pumping sampler to collect a sample from the stream. During 

a service visit, the full sample bottles are collected from the pumping sampler and 

are replaced with empty bottles. The latest electronic data are downloaded from the 

data logger, and the recorded water depth is compared with a manual depth reading 

from a staff plate in the stream or stilling well. All equipment is checked for proper 

functioning, and any debris is cleared from the flume or measuring section of the 

stream as well as from the turbidity sensor and pumping sampler intake. Detailed 

field notes are recorded, and these are an essential part of the record.

At sites where a flume or weir has not been installed, it is necessary to collect 

manual discharge measurements over the full range of discharges. This allows a 

relationship between water depth and 

discharge to be established.

It will also be necessary to collect 

simultaneous pumped and depthintegrated suspended sediment pairs 

to establish a relationship between the 

pumping sampler SSC and the depthintegrated cross-sectionally averaged 

SSC. Ideally, this relationship will be 

one-to-one (y = x), but a correction 

might be needed if the location of the 

pumping sampler intake is not representative of the average SSC in the cross 

section.

The flow of information collected 

in the field can be visualized in figure 

3. Field measurements, samples, 

electronic data, and field notes are 

processed into various types of 

information that will ultimately 

result in estimates of continuous 

SSC, sediment loads, graphs, and 

documentation. Figure 3—Information types collected in the field and data processing steps 

required to finalize sediment records. DI = depth integrated.

Discharge

measurements

Electronic

data Sediment samples

(pumped and DI)

Field

notes

Define storms Enter data and validate

Estimate

sediment 

loads

Determine relation 

between pumped 

and DI samples

Compute continuous SSC, sediment loads;

Produce plots and documentation

Correct and 

finalize flow and 

turbidity data

Determine 

SSC (and 

lab turbidity)

Field

Develop 

discharge

rating equation
6

GENERAL TECHNICAL REPORT PSW-GTR-212 

Equipment Needed

Essential equipment includes:

• Flow measurement device (flume or weir)

• Turbidity sensor

• Stage (water depth) sensor

• Data logger

• Pumping sampler

• Power source (power line or battery)

• Current meter (if rated flume or weir not installed).

• DH-81, D-74, or other manually operated depth-integrating samplers

• Shelter to house equipment

Estimation of the Suspended Sediment Load

Ideally, a linear regression between SSC and turbidity is computed for each period 

of interest. Turbidity, the predictor variable, is used to estimate the response variable, point SSC (the SSC at the location of the pumping sampler intake), at each 

measurement interval. The estimated point SSC is corrected, if necessary, to obtain 

a cross-sectional mean value (applying the regression between the paired point 

and depth-integrated samples). The SSC and water discharge are then multiplied 

for each measurement interval and summed over the period of record to obtain an 

estimate of SSL. It is possible to estimate the variance of the estimated SSL, but 

the variance estimate can be unreliable for small samples or for data that do not 

conform well to the regression model (Lewis 1996).

In many cases, an examination of the data reveals periods when the turbidity 

values are incorrect owing to contamination. During such periods, either (1) discharge can be used in place of turbidity, or (2) if the frequency of pumped samples 

is adequate, segments can be interpolated between pairs of points on the graph 

of SSC versus time. The choice between these methods can only be made after 

examining one or more of the following graphs for each period of estimation:

• Turbidity, SSC, and discharge versus time

• Suspended sediment concentration versus turbidity

• Suspended sediment concentration versus discharge

Limitations to Using Turbidity Threshold Sampling

To date, TTS has been applied most successfully in temperate, rain-dominated 

watersheds with predominantly fine sediment transport. There are many conditions 

that can present difficulties for sediment monitoring and automated stream gaging 

in general.
Implementation Guide for Turbidity Threshold Sampling: Principles, Procedures, and Analysis

7

Freezing temperatures—

In watersheds where freezing conditions persist, several problems are likely 

to make sediment monitoring more difficult.

• Buildup of ice and snow in the discharge measurement section can 

invalidate the stage-discharge relationship.

• Ice at the stage sensing location can invalidate stage measurements.

• Ice in the pumping sampler intake tubing can prevent sample collection.

• Low battery voltage caused by temperatures below freezing can 

cause equipment failure.

• In remote areas where snow is common, gaging sites may be 

inaccessible for extended periods.

Snowmelt—

Watersheds where runoff is primarily from snowmelt do not necessarily preclude 

TTS if gaging sites are located at lower elevations where temperatures are 

moderate. The lengthy hydrographs can be sampled when turbidity threshold 

conditions are met, just as storm hydrographs would be sampled in rain-dominated 

watersheds. If turbidity pulses occur daily, it is possible to curtail sampling by 

modifying parameters in the program.

Fouling—

Algal growth on the turbidity sensor optical surface may corrupt the turbidity 

record and trigger samples at inappropriate times. Therefore, it is essential that 

sensors be equipped with an automatic cleaning mechanism, such as a wiper, in 

watersheds where fouling is expected. It is important to provide access to sensors 

during high flows (via a pivoting or articulating boom) and to schedule routine site 

visits to ensure proper equipment functioning.

Particle size and velocity—

In streams whose suspended load is predominantly sand, it is difficult to obtain 

representative pumped samples because the pumping samplers we have tested are 

inefficient at sampling coarse particles. Unless the sampler intake nozzle is oriented 

directly into the flow (not advisable because it invites plugging of the nozzle), 

large particles are difficult to sample owing to their relatively high momentum and 

mass. This is particularly true where stream velocity is high or where the pump is 

elevated significantly above the sampler intake.

Coarse particles also tend to be nonuniformly distributed in the cross section. 

In particular, greater concentrations are expected closer to the bed and in areas 

where velocity or turbulence is greater. Under these conditions, it may be difficult 

It is important to 

provide access to 

sensors during high 

flows and to schedule 

routine site visits 

to ensure proper 

equipment functioning.
8

GENERAL TECHNICAL REPORT PSW-GTR-212 

to establish a consistent relationship between the point SSC of pumped samples and 

the cross-sectionally averaged SSC of depth-integrated samples.

Channel size—

Sediment in large channels with low gradients is less likely to be well-mixed than 

in small channels with steep gradients. If the sediment is not uniformly distributed 

in the cross section, it may be difficult to find a sampling location at which the 

SSC is representative, and it may be difficult to establish a consistent relationship 

between the SSC of pumped and depth-integrated samples.

Although pumping sampler line velocity varies by manufacturer and model, 

and depends somewhat on head type, pumping sampler efficiency tends to fall off 

as the sampler is raised in elevation above the sampling point. It may be impossible 

to mount the sampler above flood level in larger channels, while maintaining the 

ability to obtain samples at all flows.

Turbidity range—

All sensors have a maximum turbidity limit. For most sensors, the maximum is 

under 2,000 turbidity units. Turbidity Threshold Sampling enters a fixed-interval 

sampling mode when turbidity levels are above the specified sensor limit. The 

advantages of TTS are lost, however, when conditions require fixed-interval 

sampling. In streams where turbidity routinely exceeds the sensor limit, TTS will 

not function optimally. When turbidity exceeds the sensor limit for more than a 

few hours, it is likely that either all of the bottles in the pumping sampler will be 

exhausted, or too few samples will be collected to reliably estimate the load.

Site Selection and Equipment Installation

Selection Criteria for Gaging Sites

The general location of a TTS gaging site should be based on the study objectives. 

Selection criteria often include geographic location, watershed area, proximity of 

tributaries, soil type, geology, aspect, elevation, forest type, and disturbance history. A discharge measurement section should be located at or near the TTS gaging 

site. The presence of large unstable trees should be assessed for windfall hazards, 

and they should be removed before installing the station if they are determined to 

be unsafe (Butcher and Gregory 2006).

Site accessibility—

Field crews must have reasonable site access to service equipment and to collect 

manual measurements during storm events. To acquire storm-based manual 

measurements at a gaging site, the access time would ideally be less than 1 hour 
Implementation Guide for Turbidity Threshold Sampling: Principles, Procedures, and Analysis

9

and no longer than about 3 hours. Timely access to the site is particularly important 

for watersheds that have a rapid hydrograph response. Sites that are difficult to 

access because of snow accumulation, road failures, or stream crossings should be 

avoided because data quality is likely to be compromised by infrequent site visits 

that rarely coincide with storm events. It is difficult to predict when the sampling 

equipment will require servicing because of unanticipated local conditions. In 

general, sites are visited just before expected storms, during and after storms, and 

at a frequency of every 2 or 3 weeks during interstorm periods. The frequency of 

interstorm visits will largely depend on the likelihood of fouling and the available 

amount of data logger memory. Because there are only 40 standard work hours out 

of 168 hours in a week, important storm events more often than not occur at times 

when it is difficult to mobilize field personnel. Remote telecommunications from 

the gaging sites (via satellite, cell phone, radio, or land line) can alert personnel of 

maintenance needs and data collection failures by providing frequent information 

on the status of the equipment, thereby allowing field personnel to schedule their 

site visits based on the equipment status.

On large rivers it may be possible to service TTS stations by boat. A boat, fitted 

with the appropriate sampling equipment, does provide a means to collect manual 

measurements on rivers when neither a bridge nor a cable way is present. However, 

this approach presents additional logistic and safety concerns. Boats can also delay 

the response to storm events because of the additional tasks of fueling, transportation, and launching.

Channel geometry and sediment mixing—

If a tributary enters the main channel close to the selected gaging site (fig. 4), and it is 

desirable to measure the contribution of the tributary, sediment samples should be collected at an adequate distance downstream to allow complete mixing of the sediment 

(Guy and Norman 1970). The distance downstream necessary to achieve adequate 

mixing is determined by the stream velocity and turbulence, sediment particle sizes, 

and the relative contribution of sediment by the main channel and the tributary. For 

instance, complete sediment mixing will require a greater distance downstream if the 

tributary contributes only a small percentage of the total flow and a large percentage 

of the suspended load. If possible, determine when adequate mixing is achieved by 

collecting depth-integrated samples at a series of cross sections downstream of the 

confluence. The samples should be individually analyzed in the laboratory (not combined) to produce SSC for each vertical sample at each cross section. The distribution 

of SSC values in any downstream cross section may change during storm events 

because of local hydraulic conditions, but it should still be possible to determine the 

mixing trend. This sampling process, besides providing information about mixing, 
10

GENERAL TECHNICAL REPORT PSW-GTR-212 

will also determine the most representative sampling location for the turbidity sensor 

and pumping sampler intake at the selected gaging site.

Under highly turbulent conditions, or in narrow channels, the suspended silt 

and clay may be fully mixed after only a short distance below a confluence. Once 

mixed, the fine sediment will remain suspended under moderate flow velocities, 

whereas sand requires local turbulence or upwelling and high flow velocities to 

remain suspended for even short distances, owing to its greater ratio of mass to 

surface area. With stream velocity greater than 3 m/sec and a pumping head of 5 

to 7 m, we found that particles larger than 0.5 mm were severely undersampled, 

and particles larger than 1.0 mm were not represented at all (fig. 5).

Specific site requirements for a TTS station are similar to those used to select a 

desirable cross section for discharge measurements (Wagner et al. 2006). Channel 

conditions would include a stable and well-defined control, a straight uniform 

channel above and below the cross section, and stable channel banks high enough 

to contain flood flows (Rantz 1982). Careful inspection of the streambanks for 

deposited woody debris at the proposed gaging site will provide clues about recent 

high flow events and whether overtopping flows may compromise the sample data. 

Unfortunately, once the flow spreads across a terrace or flood plain, estimating 

discharge and sediment loads becomes very problematic.

Adequate mixing and suspension of sediment in steep-gradient channels 

dominated by fine-grained particles normally reduces concerns about locating a 

representative sampling position in the cross section. Large, low-gradient channels, however, may require more careful site selection that could include sampling 

the cross section at different flows to determine whether a representative turbidity 

Once the flow 

spreads across a 

terrace or flood plain, 

estimating discharge 

and sediment loads 

becomes very 

problematic.

Figure 4—Incomplete sediment mixing during dam removal, McKenzie River, Oregon.

USDA Forest Service
Implementation Guide for Turbidity Threshold Sampling: Principles, Procedures, and Analysis

11

sampling point can be located. Manual suspended sediment samples should always 

be collected at the same cross section near the gaging site (unless you are determining sediment mixing as explained above) because the ratio of suspended load to 

total load will vary based on local hydraulic conditions even though total load may 

be equivalent at different cross sections (Edwards and Glysson 1999). Ideal gage 

locations are rarely found for this type of channel, and sites are usually selected 

because of ready access and proximity to a bridge or road.

Depositional reaches are undesirable gage locations because adequate sampling 

depth is difficult to maintain during low discharge periods, particularly at sites with 

planar bed surfaces lacking a well-defined and stable thalweg. An aggrading bed 

can bury the stage sensor and position the turbidity sensor too close to the bed (fig. 

6). Depositional reaches may also have highly variable suspended sediment particle 

sizes, making it difficult to develop relationships between turbidity and SSC. The 

nearby formation or reworking of gravel bars can alter flow depths across the channel, or cause the local bed elevation to change, making it difficult to develop a stable 

stage-discharge rating (fig. 7).

Figure 5—Sand fractions are underrepresented in pumped samples from Grass Valley Creek near Weaverville, 

California. Comparison of suspended sediment concentration (SSC) from depth-integrated D-49 samples and 

simultaneous pumped samples for (a) fines < 0.062 mm, (b) fine sand < 0.5 mm, (c) coarse sand > 0.5 mm, and 

(d) all suspended particles. DIS = depth-integrated samples. DIS fines < 0.062 mm (mg/L) DIS sand > 0.5 mm (mg/L)

DIS sand < 0.5 mm (mg/L) DIS SSC total (mg/L)

y=x y=x

y=x y=x

500

50

5

1

500

50

5

1

500

50

5

1

500

50

5

1

a

c

b

d

Pumped fines < 0.062 mm (mg/L) Pumped sand < 0.5 mm (mg/L)

Pumped sand > 0.5 mm (mg/L) Pumped SSC total (mg/L)

1 5 10 50 500 1 5 10 50 500

1 5 10 50 500 1 5 10 50 500
12

GENERAL TECHNICAL REPORT PSW-GTR-212 

Figure 6—Deposition at Freshwater Creek, California, resulted in inadequate sampling depth at 

sensor location. 

Figure 7—A stream 

reach with easily 

mobilized alluvial 

deposits makes a poor 

gaging site, Decker 

Creek, Humboldt 

Redwoods State Park, 

California.
Implementation Guide for Turbidity Threshold Sampling: Principles, Procedures, and Analysis

13

Figure 8—South Fork Cuneo Creek, Humboldt Redwoods State Park, California. Measuring discharge is difficult in this type of 

channel because of the turbulence created by the large substrate and unstable channel geometry.

Small and medium-sized channels with alternating pool-riffle-run sequences 

often maintain adequate flow depth and mixing of sediment in the reaches between 

pools (Wagner et al. 2006). Careful onsite inspection and evaluation during storms 

are desirable for each potential gaging site to eliminate unforeseen sampling 

complications (such as excess turbulence). New installations may require adjustments, modifications, or relocation of the equipment when site visits or poor-quality 

data indicate that undesirable sampling conditions are present. Pools or channel 

depressions may be the only appropriate locations for equipment in small channels 

to avoid loss of data due to inadequate sampling depth during periods of low flow.

Steep channels that transport mostly bed load during storm events should be 

avoided. Shallow or wide channels with a coarse substrate and high stream velocities 

typically have an unstable geometry and are difficult to monitor with TTS (fig. 8).

Discharge measurement considerations—

If the gaging site is to be located above a confluence, a site should be selected 

upstream of stable bedrock or other form of bed elevation control. Below the 

control, an adequate drop in channel slope is needed to remove potential effects of 

downstream conditions (backwater) on local velocities (Kennedy 1984). A rated 

hydraulic structure, such as a flume (fig. 9) or weir, eliminates the need to develop a 

stage-discharge rating and reduces errors inherent with the velocity-area discharge 

method (Ackers et al. 1978). Although there is a large capital investment for the 

purchase and installation of hydraulic structures, they greatly improve long-term 

data quality and consistency.
14

GENERAL TECHNICAL REPORT PSW-GTR-212 

For channels that have unstable beds and lack a well-defined natural control, 

a permanent hydraulic structure, such as a metal or concrete sill, keyed into the 

bed and channel banks, can provide a stable section for establishing a discharge 

rating (figs. 10 and 11). Discharge measurement inaccuracies may, in some 

circumstances, be the largest source of error when computing sediment loads. 

Gaging at an unstable cross section requires frequent and repeated current meter 

measurements, resulting in multiple rating equations that are only valid for brief 

periods. This greatly increases the cost of fieldwork and data processing, and 

results in lower quality sediment-load estimates.

For all but the smallest watersheds, either a bridge or a cableway will be 

required to collect discharge and depth-integrated measurements during high 

flows (fig. 12). Where the flow is too deep to wade, manual measurements require 

sampling equipment that is heavy and difficult to transport over steep and uneven 

terrain, requiring most gaging sites to be located within a reasonable distance to a 

road. Prefabricated sampling platforms can be used for channel widths up to about 

70 ft (21.4 m) in ideal settings, but long spans are normally accommodated by 

existing bridges or cableways (Martinez and Ryan 2000).

Figure 9—Montana Flume with wing walls and sampling bridge (the creek flows from right to 

left), Xray tributary, Caspar Creek Experimental Watershed, California.
Implementation Guide for Turbidity Threshold Sampling: Principles, Procedures, and Analysis

15

Figure 10— Rated section with steel sill and side walls in North Fork Caspar Creek, California.

Figure 11—Alamitos Creek, near San Jose, California. Gage has a concrete sill, hardened walls, and 

a traditional stilling well.
16

GENERAL TECHNICAL REPORT PSW-GTR-212 

Organic loading—

Organic materials, such as logs, branches, and root wads, are normally present 

in forested watersheds and play an important role in controlling the geometry of 

alluvial channels (fig. 13). Large woody material is transported during storm events 

and is often responsible for damaging equipment by direct impact, or by forming 

debris jams. Debris jams change the local hydraulic conditions and may result in 

streambank collapse or changes to the channel control feature (from scour and 

deposition). Selecting a suitable gaging site and the proper sensor mounting will 

reduce the likelihood of damaged equipment or lost data. Large woody material can 

exert excessive force on structures and equipment. Therefore the best practice is 

to choose sites and equipment that do not become debris traps. This would include 

avoiding channel constrictions, protruding root wads, channel bends, bedrock 

outcrops, and spanner trees just above the water surface. It is prudent to survey the 

channel above the proposed gaging site for debris jams. If the debris jams are large 

and occur with some frequency, expect that mobilized debris rafts will periodically 

pass through the gaging site. 

Figure 12—Discharge measurement from bridge using a 

crane, reel, and sounding weight.

U.S. Bureau of Reclamation
Implementation Guide for Turbidity Threshold Sampling: Principles, Procedures, and Analysis

17

Figure 13—Debris jam breaking up, North Fork Caspar Creek, California.

Fine organic materials, including ash, can represent a significant portion of the 

suspended material transported in forested and burned watersheds (Madej 2005). 

Fine particulate matter (and sediment) can foul turbidity sensors when it is deposited on the sensor’s optical surface.

Security—

Gaging site security is a concern that must be addressed during the planning stages 

and reevaluated during the study if problems occur. Generally avoid locations that 

are considered public places like swimming or fishing areas. When the gaging site 

is near homes, try to involve local residents. Explain to them the value of the data 

and ask them if they will help to keep the site secure. Locate the gaging site some 

distance behind a locked gate and place an information sign near the site, but not on 

the equipment, as signs are often used for target practice. Use signage appropriate 

for groups that might find the site, explaining the purpose of the site and the repercussions of vandalism.

Shelter Design

A shelter provides a dry environment for field personnel to service the equipment 

and protects the instrumentation from the weather and vandalism (fig. 14). The most 

useful design is a walk-in painted plywood shelter with adequate work surfaces, 

several clear fiberglass panels in the roof to allow for natural lighting, and ventilation to prevent condensation. Depending on the distance from the road, sections of 

A shelter provides a 

dry environment for 

field personnel to 

service the equipment 

and protects the 

instrumentation from 

the weather and 

vandalism.
18

GENERAL TECHNICAL REPORT PSW-GTR-212 

the shelter can be prefabricated and transported to the site. Onsite shelter construction is readily accomplishable, however, with the wide array of battery-operated 

hand tools that are now available.

A shelter with a footprint of 2 by 3 m is adequate for a two-person field crew 

(fig. 15). Use standard construction and framing techniques for stud walls, floor 

joists, and rafters, and paint the shelter to match the background colors. For security, 

omit windows from the design and install a prehung steel entry door with a highquality deadbolt lock. A simple shed roof is standard except when snow loads are 

expected. Roofs are a common point of entry for vandals, however, and, if the roof 

is composed of fiberglass panels, expanded wire can be stapled to the ceiling joists 

to prevent unwanted access. Expanded wire can also be stapled to the outside of a 

wooden shelter to prevent entry with a chainsaw. Eliminating all gaps in structural 

joints and screening entry holes for conduit prevents mice, rats, and wasps from 

infesting the shelter. Locate the floor of the shelter above the highest expected flow, 

but place the shelter as close as possible to the stream to reduce cable and tubing 

runs. Lanterns, headlamps, and fiberglass light panels in the ceiling will provide the 

background and task lighting required for site visits. Place shelves at a comfortable 

work height, and plan enough shelf area to accommodate a laptop, pumping sampler, 

notebook, two batteries, and a minimum of 24 to 48 sample bottles. Place the 

Figure 14—A walk-in shelter provides a dry environment for retrieving data and pumped 

samples and recording field notes.
Implementation Guide for Turbidity Threshold Sampling: Principles, Procedures, and Analysis

19

Figure 15—Walk-in shelter, 

Caspar Creek Experimental 

Watershed, California.

pumping sampler on a lower shelf to facilitate lifting the mid-section of the sampler 

during bottle replacement. Providing an area for rain gear storage prevents water 

from dripping on the laptop and field notes during station visits.

In areas prone to vandalism, consider the entry methods and tools that vandals 

would be likely to use. During shelter construction use secure bolt-nut-screw 

combinations and over-sized hardware (hasps, hinges, and bolts), and protect hinges 

with carriage bolts. Use combination locks or padlocks that have unique keys 

and use multiple locks or a locking bar on doors. Adding internal lock boxes and 

locking straps can deter theft if the shelter is entered. The U.S. Geological Survey 

(USGS) uses a walk-in metal shelter, but its weight requires a road to the site, a 

forklift, and a concrete foundation pad.

An alternative shelter design uses a standard construction lock box of the type 

often used to secure tools at construction sites (fig. 16). Several brands are available, 

and they use similar techniques to prevent entry. The boxes are practical because no 

construction is necessary, but they lack protection from rain while the box is open 

during servicing.
20

GENERAL TECHNICAL REPORT PSW-GTR-212 

It is essential that equipment be serviceable while it is raining. A temporary 

tarp system may provide adequate cover during site visits when it is raining as long 

as high winds are not present. A carport-style shelter (four posts and roof) with 

sides (fig. 17) is a better solution than a tarp. This type of shelter lacks the shelf 

space of a conventional shelter, but an overhead cover could allow for shelving 

attached to the posts. The shelters can be secured by attaching them to a poured 

concrete block or to bedrock, or by chaining them to a nearby object. The securing 

method should prevent the entire box from being stolen, for example by cutting a 

chain with bolt cutters. The shelters are somewhat heavy and awkward, but four 

persons can carry one over moderate terrain for a reasonable distance. Although 

they are not airtight, they tend to condense moisture on the inside. Adding several 

louvered-screened vents to the sides (low and high) will increase the airflow and 

reduce condensation. Rigid insulation foam can be glued to the inside to further 

eliminate condensation and moderate interior temperatures. The shelter can be 

camouflaged by painting it in several colors that blend with the natural background.

Figure 16—Lock box equipment shelter, Beaver Creek, Arizona.

Arizona Department of Environmental Quality
Implementation Guide for Turbidity Threshold Sampling: Principles, Procedures, and Analysis

21

Selection of Instrumentation

Data logger—

There are currently three Campbell Scientific1 data loggers (the CR800, CR850, and 

CR1000) capable of using the CRBasic version of TTS software that was developed 

at Redwood Sciences Laboratory (RSL). Two Campbell Scientific data loggers that 

are no longer in production (the CR10X and the CR510) run the older mixed-array 

TTS software. Campbell data loggers were selected because the programming 

language permitted the coding of complex algorithms and control loops that were 

required by the TTS software logic. The design philosophy for a public version of 

TTS uses commercially available data loggers and sensors. This allows the end 

user to receive technical advice and repairs from the various manufacturers. The 

difference between the CR800 series and CR1000 is the number of available sensor 

connections. The CR800, when all the TTS sensors are connected, has a more 

limited capacity to connect additional sensors.

Figure 17—

Carport-style shelter, 

Little Jones Creek, 

California.

1 The use of trade or firm names in this publicaiton is for reader information and does not 

imply endorsement by the U.S. Department of Agriculture of any product or service.
22

GENERAL TECHNICAL REPORT PSW-GTR-212 

Running TTS at 10-minute intervals, the CR800, CR850, or CR1000 can record 

data for about 400 days before memory is exhausted. Under most circumstances, 

the amount of memory that comes standard in the CR10X and CR510 data loggers is also adequate; without additional sensors connected to the data logger, the 

memory in these models will last about 3 weeks (recording at 10-minute intervals). 

When memory has been filled, new data will be written over the existing data, 

starting at the beginning of the file. Because gaging sites should be visited at regular intervals, the limitation of about 3 weeks is reasonable. If more than the 3 weeks 

between site visits is anticipated or many additional sensors will be connected, the 

user may wish to order additional flash memory when purchasing the data logger.

Pumping sampler—

Method of operation—Automatic pumping samplers, or wastewater samplers, are 

battery-powered devices that pump a predetermined volume to a sample bottle from 

a water source. The samplers use one of two pump types: peristaltic and vacuum 

compressor. Both types of pumps draw the sample up the intake tube by removing 

air from the line, creating a partial vacuum in the tube. The sample is then delivered 

to a bottle in the base of the sampler via a distributor arm.

Peristaltic devices are more common and operate by rapidly moving a set of 

rollers over flexible tubing inside of the pump housing, thus squeezing the tube to 

remove air and move the liquid towards the bottle. Peristaltic pumps have more 

moving parts and the pump tubing must be replaced at regular intervals (about 

500,000 revolutions). The silicon pump tubing loses its elasticity over time and may 

become abraded or cracked from the rollers and sand particles. Peristaltic samplers 

generally have lower line velocities than vacuum pump samplers.

Vacuum pump samplers pull the sample into a measuring chamber, and the controller pushes the sample, via the distributor arm, into the sample bottle. By closing 

and opening valves, and reversing the direction of the compressor, the sampler can 

move fluid towards the sample bottle or towards the stream to purge the line.

Sample bottles—

Sample bottles are available in several shapes and volumes and are made of 

several types of materials. It is important to choose the sampler brand and bottle 

type carefully so that bottles are interchangeable across samplers. Sample bottles 

can be conveniently stored and transported in plastic milk crates.

To match the average volume of a depth-integrated sample, pumped sample 

volume is usually set to 350 ml. Larger volumes reduce variability but add to the 

laboratory processing cost. Select a sample bottle volume that is larger than the 
Implementation Guide for Turbidity Threshold Sampling: Principles, Procedures, and Analysis

23

sample size you expect to collect. A 350-ml sample in a 500-ml bottle leaves spare 

capacity if the programmed volume is exceeded.

Plastic bottles are lighter than glass and are more resilient, although cracks may 

occur along the seams, resulting in leakage. If other constituents besides sediment 

are to be measured, it may be necessary to use a Teflon intake line and glass sample 

bottles with Teflon caps. Disposable sample bags are appealing because they do 

not require washing (they are discarded after use) and they take up little room, but 

our experience has shown that it is difficult to remove all of the sediment from a 

container that is not rigid.

Head height and line length—The length of the intake tube and height of the 

pumping sampler above the stream limit the placement of the pumping sampler. The 

maximum head height (from the water surface to the pump) depends on the sampler 

manufacturer and model. Frictional and gravitational reductions in the transport 

velocity of water in the intake line may cause starvation of sediment in the sample, 

particularly when sediment particles are coarse. To maintain the highest line velocity, place the sampler so that it is just above the highest expected flow. The intake 

line must be free of dips, and it should have a continuous downward slope to the 

stream. Samplers with peristaltic pumps do not develop sufficient pressure on the 

air purge cycle to remove plugs of water left in dips in the line. Placing the intake 

line inside of polyvinyl chloride (PVC) conduit, or a similar design, prevents dips 

from forming in the intake line. Route the intake line as directly as possible from 

the pumping sampler to the stream. Excessive head heights and long line lengths require longer pump cycles and place higher power demands on the station’s battery.

Intake mounting—At most sites, it is best to place both the automatic pumping 

sampler intake and the turbidity sensor on an articulating boom that positions the 

equipment close to the thalweg (see “Sampling boom” below), at about mid-depth 

in the flow during non-storm periods, and above the bed-load transport zone during 

storms (Lewis and Eads 2001). Interstorm periods or extended low-flow periods 

may require positioning the sensor and intake closer to the bed, or in a channel 

depression, to maintain adequate sampling depth. Although the sensor and intake 

rise in the water column with increasing velocity and move closer to the bed with 

decreasing velocity, care is required when setting the depth to achieve the best 

position for all measurable flow conditions. Position the opening of the intake 

facing downstream and parallel to the flow lines. The ideal orientation would be 

facing upstream, but debris fouling and the inability of the sampler to purge the 

intake line against the force of the current eliminates this as a possible orientation 

(Winterstein 1986). A rigid intake nozzle is not required, but if it is installed, ensure 

At most sites, it is 

best to place both the 

automatic pumping 

sampler intake and 

the turbidity sensor 

on an articulating 

boom that positions 

the equipment close to 

the thalweg at about 

mid-depth in the flow 

during non-storm 

periods, and above 

the bed-load transport 

zone during storms.
24

GENERAL TECHNICAL REPORT PSW-GTR-212 

that the inside diameter is the same as that of the intake line and that it is not drilled 

with radial holes.

In small streams with Montana or Parshall flumes, the pumping sampler intake 

can be mounted to the floor in the throat section of the flume. Convergence of 

flow in the flume ensures that the intake will be submerged at nearly all flows and 

will rarely be buried by sediment. Although pumping samplers are inefficient at 

sampling bed-load material, flume-mounted intakes may, at times, inadvertently 

collect a portion of the bed load. This should be avoided if the objective is to sample 

suspended sediment exclusively. The pumping sampler intake can be mounted in a 

fixed or adjustable position. We recommend, however, that the intake be mounted 

on an articulating boom in proximity to the turbidity sensor at about mid-depth in 

the water column.

Cross-contamination and sampling sequence—Cross-contamination occurs 

when material left in the intake tubing from a prior sample or drawn by pressure 

gradients into the intake nozzle between samples is deposited into subsequent 

sample bottles (fig. 18). Cross-contamination is likely to occur when the wetted 

surface of the delivery system is not adequately flushed between samples. Higher 

Figure 18—High-frequency (10-minute) record of suspended sediment concentration (SSC) from 

three pumping samplers operated in series at Grass Valley Creek near Weaverville, California. One 

rotation consisted of 24 bottles collected sequentially by each sampler. The ISCO 2700 sampler at 

the site had no water purge cycle; as a result, bottle 1 of each sampler in each rotation (shown as a 

circled point) was usually contaminated with sediment that had been drawn by pressure gradients 

into the intake nozzle during the 490 minutes elapsed since the last sample collected by the same 

sampler. Turbidity was measured by an in situ OBS-3 sensor. FBU = formazin backscatter unit.

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Feb. 3, 1998 Feb. 4, 1998

0 500 1000 1500

0 50 100 150

Turbidity (FBU)

Turbidity

SSC

Suspended sediment concentration (mg/L)

6 12 18 0 6 12 18
Implementation Guide for Turbidity Threshold Sampling: Principles, Procedures, and Analysis

25

line velocities, reduced wetted surface area, and the absence of dips in the intake 

line reduce the possibility of cross-contamination.

Modern samplers have several options to purge (flush) the intake line. Before a 

sample is collected, the line is purged with air. In theory this removes debris from 

the intake orifice and flushes the remaining water and sediment from the line. In 

practice, peristaltic pumps have inefficient air purges because they cannot develop 

adequate back pressure. More importantly, after the air purge, stream water is 

drawn into the intake line until it reaches a sensor located on the stream side of the 

pump, then reverses the flow direction and expels the water from the intake line 

back into the stream. Some samplers allow up to three water purges, but we find 

that a single water purge is a good compromise between energy use and intake line 

normalization. Following the purges the sample is drawn into the intake line and 

deposited into the bottle. To ensure accurate sample volumes as head height varies, 

modern samplers electronically measure the volume. After the sample is collected, 

the sampler attempts to purge all of the water from the intake line back into the 

stream, completing the sampling cycle.

Sediment particle size considerations—Silt and clay particles (< 63 μ) dominate 

the suspended load in many river systems. Local hydraulic conditions can suspend 

sand-size material for brief periods under favorable conditions, but sand is mostly 

transported as bed load. Placement of the intake nozzle should normally be located 

above the bed-load transport zone. In rivers that transport a high proportion 

of sand, where the pumping sampler has a lift greater than 4.5 m, where flow 

velocities are very high, or where the length of the intake is long, it is likely that 

the sand fraction will be underrepresented in the sample. If the momentum of large 

particles is greater than the suction force at the intake orifice, the particles will not 

be drawn into the intake. Once inside the intake line, gravity will act on the mass 

of larger particles to overcome low transport velocities, allowing the large particles 

to drop out of suspension and fall towards the stream. Comparison between 

simultaneous depth-integrated and point samples will determine if larger particles 

are underrepresented (fig. 5). We recommend routinely performing sand fraction 

analysis in the laboratory on the sample pairs.

Point and isokinetic samples—Samples collected with a pumping sampler are 

point samples. Regardless of the intake mounting method, the sample is collected 

from a single vertical point in the cross section and is not integrated across the 

channel. As discussed previously, a lack of adequate sediment mixing may produce 

a bias in the sample (the sample may not be representative of the flow-weighted 

average cross-sectional concentration). Depth-integrated sampling (fig. 19) is the 
26

GENERAL TECHNICAL REPORT PSW-GTR-212 

standard method to collect a representative sediment sample that is integrated both 

vertically and horizontally within the cross section (Federal Interagency River 

Basin Committee 1952).

The design of depth-integrated samplers allows sediment and water to enter 

the sample bottle isokinetically, i.e., at the same velocity as the stream. Isokinetic, 

depth-integrated samples represent each sampled point of the cross section in 

proportion to its flow velocity, hence they are flow-weighted. These samplers 

prevent enrichment or starvation of the sampled sediment by evacuating air from 

the sample bottle at the same rate as the water enters the nozzle. In contrast, pumping samplers maintain nearly a constant transport velocity in the intake line that 

is rarely the same velocity as the stream. This concern has limited the widespread 

use of pumping samplers for suspended sediment sampling because of the uncertainty of collecting representative samples. Although the Environmental Protection 

Agency (EPA) recommends a line transport velocity of 2 to 10 ft/s (0.6 to 3.0 m/s), 

we suspect that many sampler installations are unable to achieve even the minimum 

recommended velocity. Sampler transport velocities rarely exceed 6 ft/s (1.8 m/s) 

under ideal settings. Collecting simultaneous 

pumped samples and depth-integrated samples 

over a range of flow conditions is recommended to 

develop a relationship for correcting biases in the 

point samples. In our experience with well-mixed 

streams, there is normally little or no bias in the 

point sample concentrations; but a bias model 

should be developed, the point samples should be 

corrected if a difference exists, and the correction 

should be stated in reports or publications.

Turbidity sensor—

Turbidity concepts and instrument design—There 

is a wide variety of optical instruments available 

today that are suitable for measuring turbidity. 

Section 6.7 of the National Field Manual for the 

Collection of Water Quality Data (Anderson 2004) 

describes many of these instruments, discusses 

comparability of measured values, and sets forth 

standard reporting units. The optical design has a 

strong influence on the reported turbidity value. 

Different turbidity values can therefore be reported 

when the same water sample is measured by 

Figure 19—Collecting a suspended sediment sample using a 

US DH-48 wading type sampler.

Isokinetic, depthintegrated samples 

represent each 

sampled point of 

the cross section in 

proportion to its flow 

velocity, hence they 

are flow-weighted.
Implementation Guide for Turbidity Threshold Sampling: Principles, Procedures, and Analysis

27

different instruments (Downing 2005, Lewis et al. 2007). The polymer formazin 

has been recognized as a turbidity standard for many years, and instruments of 

different designs will report approximately equivalent units when measuring a 

standard in which both instruments were calibrated. Variability in surface water 

measurements is caused by particle size, color and shape, fine organic material, 

incident light, and air bubbles. Sensor characteristics that influence readings include 

optical geometry (e.g., forward or backward scatter), wavelength of the emitted 

light, sampled volume, and internal signal filtering.

For surface water applications with moderate to high levels of turbidity, we recommend nephelometric or backscatter sensors. They have a wide linear range, with 

some sensors reading up to 4,000 turbidity units (in formazin). Most applications 

will require a self-cleaning sensor with a mechanism such as a wiper to remove 

sediment and organics that collect on the optical surface. Sensors that use the SDI12 protocol retain their calibration constants internally, so moving the sensor from 

one location to another does not require modifying the data logger program. Digital 

output is more robust at rejecting electronic noise than low-level analog signals, and 

it permits long cable lengths without signal degradation. It is very important to use 

the same brand and model of sensor for comparisons both within and across watersheds to eliminate differences due to sensor design (Lewis et al. 2007).

Sampling recommendation—To avoid stray readings, record the median of a rapid 

series of turbidity measurements rather than the result of a single instantaneous 

measurement. The median is preferred to a mean average because it is less sensitive 

to outlier values. Some digital sensors permit the recording of multiple statistics, 

including mean, median, range, and variance.

Sensor connections—The TTS software, at the time of this writing, will accept 

three types of wiring connections from a turbidity sensor: single-ended analog (0 to 

2.5 V), differential analog (0 to 2.5 V), or SDI-12 digital. For analog sensors, differential wiring is preferred. Although other types of wiring connections are possible, 

the software would first require modifications.

Sensor deployment—Along with the pumping sampler intake, we recommend 

mounting the turbidity sensor on an articulated boom that positions the equipment close to the thalweg at about mid-depth in the flow during non-storm periods 

and above the bed load transport zone during storm flows (Lewis and Eads 2001). 

Direct sunlight on water above the sensor may produce erratic readings during low 

turbidity conditions. When possible, place the sensor in a location with permanent 

overhead shade.
28

GENERAL TECHNICAL REPORT PSW-GTR-212 

Avoid locating the turbidity sensor and sampler intake in high-velocity or 

turbulent sections of the channel. At high flows, obstructions or large scour holes 

in the channel can cause local entrainment of air bubbles and coarse sediment that 

may result in a poor relationship between turbidity and SSC. Clay and silt materials 

scatter light more efficiently than sand because scattering is primarily a function 

of number of particles and their shape; clay particles return a turbidity signal that 

is several orders of magnitude higher than sand particles at an equivalent concentration. Locally induced energy conditions may cause large fluctuations in the 

suspended load particle makeup. For instance, if silt and clay normally dominate the 

suspended load composition, but sand becomes periodically suspended during high 

flows, the result would likely be a relatively small change in turbidity associated 

with a large increase in SSC.

Sampling boom—

Sampling booms position the turbidity sensor and sampler intake within the sampling cross section at the desired depth, while effectively deflecting large organic 

debris during storm events (see “Intake mounting” above). The design should allow 

the field personnel to retrieve the boom at any flow without entering the stream. We 

have developed four sampling boom applications: bank-mounted, bridge-mounted, 

cable-mounted, and bed-mounted. Besides the photographs below (fig. 20), detailed 

drawings and photographs for three of these applications can be viewed on the TTS 

Web page (http://www.fs.fed.us/psw/topics/water/tts/). A protective and streamlined 

housing for the turbidity sensor can reduce the impact of organic debris collisions 

and reduce bubble generation near the optics. The housing may include sun protection and should be custom designed for each sensor model (fig. 21).

The placement of the sampling boom must be carefully considered. The outside bend of a stream receives the brunt of the impacts from large woody debris 

transported during high flows. Articulating sampling booms are designed to shed a 

moderate amount of large debris, but positioning the boom close to the bank, where 

the thalweg is likely to be located, can cause problems when debris is retained on 

nearby objects such as protruding roots, trapped logs, or overhanging tree limbs. 

The flow direction in a channel bend may shift at different flows, imposing lateral 

stresses on the boom, resulting in misalignment of the turbidity sensor housing, 

thereby raising the likelihood of increased local turbulence and fouling from debris.

The design should 

allow the field 

personnel to retrieve 

the boom at any 

flow without entering 

the stream. 
Implementation Guide for Turbidity Threshold Sampling: Principles, Procedures, and Analysis

29

Figure 20—(A) Bank-mounted articulating boom holds pumping sampler intake and turbidity sensor at Upper Jacoby Creek in 

Humboldt County, California. (B) Bridge-mounted boom supports turbidity sensor at Prairie Creek, Humboldt County, California. 

(C) Cable-mounted boom supports turbidity sensor at Arfstein station on North Fork Caspar Creek, Mendocino County, California. 

Sensor housing is fitted with sun shield. (D) Bottom-mounted boom with float in Nissouri Creek, Ontario, Canada. Boom positions 

turbidity sensor and sampler intake at a fixed proportion of the depth of flow.

A B

C D
30

GENERAL TECHNICAL REPORT PSW-GTR-212 

Stage measurement—

Stage and gage height definition—Stage is the height of the water above a datum 

elevation such as the channel bed (Langbein and Iseri 1972). Gage height is generally synonymous with stage, although gage height is more appropriate when used 

with a reading on a gage or staff plate.

Measurement accuracy—

The USGS stage measurement accuracy guideline states that: 

Stage data are to be collected with sufficient accuracy to support computation of discharge from a stage-discharge relation and that procedures and 

equipment used are to be capable of sensing and recording stage with an 

uncertainty of no more than 0.01 ft (0.3 cm) or 0.20 percent of indicated 

reading, whichever is larger [Yorke 1996].

The ISO standard 4373-1979(E), clause 7, states that:

For the measurement of stage, in certain installations an uncertainty of ±10 

mm may be satisfactory; in others [depending on site instrumentation] 

an uncertainty of ±3 mm or better may be required; however, in no case 

should the uncertainty be worse than ±10 mm or 0.1 percent of the range, 

whichever is greater (Maidment 1993).

Figure 21—This housing for a DTS-12 sensor was constructed from square aluminum tubing, 

cut off diagonally at the optical end, which is oriented downstream.
Implementation Guide for Turbidity Threshold Sampling: Principles, Procedures, and Analysis

31

Stilling wells—Stilling wells were once the primary device used to reduce the 

effects of surface waves on stage sensing devices. Stage can be measured inside 

the stilling well by means of a float and counterweight that is mechanically linked 

to a stage-sensing device. The stilling well gained wide acceptance because it 

effectively dampened water-surface fluctuations by allowing the water to enter the 

large-diameter well through several small-diameter pipes. Stilling wells located 

in rivers that transport large sediment loads require a flushing device to remove 

accumulated sediment from the intake pipes. Clogged and buried intake pipes result 

in the loss of data. Large-diameter stilling wells are now considered hazardous 

because personnel must climb down a ladder on the inside of the stilling well to 

read the staff plate and service equipment. These are no longer recommended 

because of the risk from falls (requiring the use of fall protection equipment) and 

from the possible accumulation of toxic fumes or the lack of oxygen (requiring an 

oxygen source). Small-diameter stilling wells are still commonly attached outside 

flume walls to improve stage readings from pressure transducers.

Staff plates—Staff plates are installed upstream of, but close to, the natural or 

engineered channel control (flumes and weirs have required mounting locations). 

In open channel conditions, the staff plate is mounted with its face parallel to the 

streamflow lines, facing the bank from which it will be read. It should be mounted 

in an area of low turbulence that is protected from debris accumulation. The zero 

point of the staff plate is mounted below the lowest stage of interest. Staff plates are 

available in 3.3-ft, or 1.0-m lengths. Bridge abutments allow for vertical stacking of 

staff plates while reducing the chance of debris accumulation. For accurate water 

height measurement, the best staff plate location is often in the middle of the side 

face of the abutment. When more than one staff plate is required to cover the range 

of expected flows, each staff section is separately mounted and surveyed so the 

top of the lower staff is at the same elevation as the bottom of the upper staff. The 

staff plates are surveyed to a stable and permanent benchmark to allow for their 

reinstallation at the original elevations if they are lost during a flood. Steep viewing 

angles between the observer and staff plate should be avoided to reduce the reading 

error. Wide channels will require the use of binoculars to achieve the necessary 

magnification for an accurate reading. Two styles of staff plates are available: those 

with numeric markings every 0.02 ft (0.6 cm), and those marked like a survey 

rod, with a pattern of black and white lines and numeric values spaced some 

distance apart. Our experience indicates that fewer reading errors are made with 

staff plates that have frequent numeric markings. The stage sensor’s electronic 

offset is adjusted to agree with the staff plate at the time of installation, and 
32

GENERAL TECHNICAL REPORT PSW-GTR-212 

periodically thereafter. Under most conditions, the staff plate reading is considered 

to be the most accurate stage; however, because it is less precise than stage 

sensor measurements, particularly during turbulent flow conditions, sensor data 

should not be automatically adjusted to match all staff plate readings (see “Stage 

adjustment philosophy” under “Data Processing”).

Stage sensing devices—

There are many modern stage-sensing devices available today. There are four 

general categories: noncontact, float and counterweight, submersible pressure 

transducer, and nonsubmersible pressure transducer.

Noncontact stage sensor—Ultrasonic noncontact sensors work by broadcasting a 

cone-shaped sound wave perpendicular to the water’s surface and converting the 

bidirectional travel time (at the speed of sound) to a distance after compensating 

for the ambient air temperature. The sensor is mounted in a fixed location over the 

stream channel, often on a bridge, and above the highest expected maximum stage. 

Increasing the height of the sensor and rough water surface conditions reduce the 

stage precision. Noncontact sensors are used in channels with highly mobile beds 

that undergo frequent scour and fill, and where other methods are not reliable. The 

accuracy of the sensor may not be adequate for some applications.

Shaft encoder with a float and counterweight—A shaft encoder connected to a 

float and counterweight mounted inside a stilling well can produce accurate stage 

readings, provided the intakes remain free from sediment and the float tape or 

beaded line does not slip on the shaft’s pulley. The encoder produces a digital pulse 

proportional to the shaft rotation with a precision of about 0.01 ft (0.3 cm).

Submersible pressure transducer—Submersible pressure transducers can be 

mounted in a length of pipe and secured at right angles to the flow below the lowest 

stage of interest. They operate by sensing changes in the water pressure and return 

a voltage proportional to the stage. The sensor depth rating should be slightly 

larger than that for the maximum expected stage at the gage location. A calibration 

coefficient (slope or multiplier) is usually supplied by the manufacturer or it can 

be determined empirically by calibration. Differential pressure transducers have a 

tube inside the cable that is vented to the atmosphere to compensate for changes in 

barometric pressure. The vent tube must terminate in a dry atmosphere to prevent 

failure due to condensation forming in the vent tube. The pressure transducer is 

connected to a data logger, and its output can be adjusted by changing the offset 

(y-intercept) in the data logger program to agree with the current staff plate reading. 

Under most conditions, 

the staff plate reading 

is considered to be the 

most accurate stage; 

however, because it is 

less precise than stage 

sensor measurements, 

particularly during 

turbulent flow conditions, sensor data 

should not be automatically adjusted to 

match all staff plate 

readings.
Implementation Guide for Turbidity Threshold Sampling: Principles, Procedures, and Analysis

33

A stage averaging routine in the data logger program can electronically dampen 

wave oscillations from the stream, thereby eliminating the need for a stilling well.

Nonsubmersible pressure transducer (bubbler)—Bubblers are pressure-sensing 

devices that detect pressure changes at the orifice of a small tube mounted in the 

stream. The pressure sensor and other hardware are located in the gage shelter. 

More specifically, modern bubblers use a small battery-powered compressor to 

supply the necessary air pressure to maintain a constant bubble rate at the terminus 

of the orifice tube. As the stage in the river rises, more air pressure is required from 

the compressor to maintain a constant bubble rate to overcome the increased water 

pressure. The pressure changes are measured by a transducer that is connected to 

the orifice line. Unlike the submersible pressure transducer that can be lost in high 

flows or damaged by debris, the orifice tube and protective conduit are inexpensive 

and easy to replace. Bubblers are normally used on larger rivers because they can 

measure water depths of 30 m or more.

Sensor connections—

There are four common types of output signals available from stage sensors. An 

analog signal can be either a differential signal output (two data wires, high and low) 

or an amplified single-ended connection (one data wire). In the case of a differential 

signal, the data logger is responsible for signal conditioning and processing. Because 

the output voltage has a low magnitude, it is susceptible to interference from stray 

electrical signals and from changes in temperature. Single-ended sensors are more 

expensive because of the additional circuitry, but they provide for on-board signal 

conditioning and amplification with output voltages between zero and 5 V. Sensors 

can also operate in a current loop, normally between 4 and 20 mA. The current 

changes in proportion to the stage, and it is immune to external conditions (this 

type of sensor is a good choice for use in electrically noisy environments). There 

are two types of digital sensors. Some sensors output a digital data stream that must 

be captured by the data logger and controlled with handshaking. These devices 

are losing favor because the data logger programming is more demanding. Finally, 

a digital sensor can use the SDI-12 protocol. This allows multiple sensors to be 

connected to a single digital port because each sensor has a digital address. The 

SDI-12 sensors have an additional benefit of retaining their calibration information 

internally and can therefore be moved between data loggers without altering the 

sensors’ parameters in the data logger program. Programming statements are 

straightforward, and handshaking between the data logger and sensor is handled 

automatically.

A stage averaging 

routine in the data 

logger program can 

electronically dampen 

wave oscillations from 

the stream, thereby 

eliminating the need 

for a stilling well.
34

GENERAL TECHNICAL REPORT PSW-GTR-212 

TTS stage sensor—The TTS program currently accepts wiring from a differential 

pressure transducer, such as a Druck model 1230. Connecting a sensor that has 

different specifications (such as an SDI-12 interface) will require modifications 

to the data logger program. In larger river systems where the turbidity sensor 

and sampler intake are always submerged, water depth is not needed by the TTS 

program, so stage can be collected by independent equipment and later appended 

to the TTS file. This situation could arise where an agency such as the USGS is 

collecting stage and discharge data at the site.

Security—

When installing equipment, it is prudent to take into consideration possible 

vandalism and to plan accordingly. Paint the equipment to match the background 

color and place it in a location away from public access, preferably behind 

vegetation. Engrave all the instrumentation in multiple locations with an electric 

etching pencil, listing the owner’s name and phone number where it would be 

difficult to deface if the equipment is stolen. Lock all the equipment to prevent 

tampering and, when possible, store it inside a secure structure. If possible, lock 

equipment down inside the shelter to further prevent theft if the shelter is broken 

into. Remove equipment from the gaging site to a secure location when not in use.

Redundancy—

Equipment redundancy permits immediate replacement of lost or damaged 

equipment with minimal loss of data. Some manufacturers will loan replacement 

equipment while your device is being repaired, but many will not. If equipment is 

lost or stolen, it may take between 3 and 6 weeks to replace. Unfortunately these 

circumstances often occur when data loss is unacceptable. Plan to acquire replacement parts and spare equipment before commencing the monitoring project. Add 

redundant equipment and parts when affordable.

Calibration—

Follow the manufacturer’s recommendation for recalibration of their device. In 

some settings, more frequent calibration may be necessary to satisfy the requirements of a regulatory agency, or if the sensor is unstable and has excessive drift. 

Always have an instrument recalibrated if it has been damaged. It is good practice 

to calibrate instruments at least annually. Pressure transducers are readily calibrated with basic supplies, but other devices, such as turbidity sensors, require the 

accurate formulation of calibration solutions and, sometimes, a custom calibration 

container. Turbidity sensors require adjustments to the hardware or to the firmware 

during the calibration procedure, and this is best left to the manufacturer. The 
Implementation Guide for Turbidity Threshold Sampling: Principles, Procedures, and Analysis

35

manufacturer will supply a certification of calibration, and this document may be 

required by a regulatory agency.

Site Operations

Routine Procedures

Visual inspection of the gage site and maintenance—

Physical changes to the site may alter the stage-discharge relation or the sedimentturbidity relation, or both. Upon arriving at the gaging station, make a visual 

inspection of the channel, sensors, and sampler intake. Check the channel conditions at the gaging site and in the immediate vicinity above and below the gage. 

Note changes to the streambed composition (e.g., fines covering the armor layer, 

or the formation of a new scour hole), failure of a streambank, or the accumulation 

of debris. If the channel has a natural bed control, make a close inspection of the 

control to determine if any changes have occurred. This may be difficult to detect 

in gravel or sand bed channels that lack control structures such as bedrock or 

boulders. If any unusual conditions are identified, make detailed notes on the field 

form and include a sketch to clarify complex changes. The most common human 

failure is to write incomplete notes in the field that will later be of marginal use 

when anomalies in the data are discovered. A good assessment of the note taking 

detail is to ask if someone unfamiliar with the site could fully understand the notes 

without a verbal explanation.

The following maintenance steps are not necessarily in the correct sequence 

for a site visit because some are not required at every site visit.

Sites that have engineered structures, such as a flume, may require sediment 

removal from the floor of the structure or from the approach to prevent errors in 

discharge. Read and note all staff plate and wire weight stage readings before removing sediment or debris from the channel or flow structure. Read and note the stage 

again after the material is removed from these devices and the flow has normalized 

(this will determine whether the stage has been artificially altered). If a stilling well 

is in use, check the intakes and flush if necessary after large storm events. Always 

note each time that personnel enter and exit the channel because both the stage 

and the turbidity may be altered by disturbance to the bed or flow pattern. Perform 

in-channel work immediately after a data logger wake-up interval and attempt to 

complete the work several minutes before the next data logger wake-up.

Both the turbidity sensor and the pumping sampler intake can be adversely 

affected by debris or sediment accumulation. Depending on the orientation of the 

pumping sampler nozzle, fine sediment can accumulate inside the intake line near 

The most common 

human failure is to 

write incomplete notes 

in the field that will 

later be of marginal use 

when anomalies in the 

data are discovered.

Always note each time 

that personnel enter 

and exit the channel 

because both the 

stage and the turbidity 

may be altered by 

disturbance to the 

bed or flow pattern.
36

GENERAL TECHNICAL REPORT PSW-GTR-212 

the orifice, particularly if the pumping sampler’s purge cycle is inadequate. Debris 

lodged on a sampling boom that is in proximity to the turbidity sensor or pumping 

sampler intake can disrupt the flow and generate air bubbles, or it can directly affect 

the turbidity sensor’s optical field. Before removing debris, attempt to determine 

whether it could have been causing sampling errors. Recording this information 

in the field notes will help correct data errors and identify samples that can be 

discarded. For reconstruction of erroneous turbidity data, it may be useful to record 

laboratory turbidity on those samples, even if they are not processed for SSC.

Again, turbidity sensors with a mechanical means to remove fine sediment 

and organic matter from the optical surface are preferred, unless prior experience 

indicates that the turbidity sensor is not prone to fouling. At sites that have high 

nutrient loads, warm temperatures, and abundant sunlight, fouling can take place 

rapidly (fig. 22). If filamentous algae grows on the housing near the sensor’s optical 

field, the contamination may be outside the reach of the wiper; but it can cause 

erroneous readings by waving in front of the sensor and must be manually removed. 

It is a good practice to remove accumulated material from the end of the sensor and 

the wiper arm with a toothbrush and a nonabrasive liquid soap. Do not manually 

move the wiper arm. Always check the optical surface to ensure the wiper has been 

functioning properly.

Figure 22—Optical contamination resulting from the wiper not being activated, Guadalupe River 

near San Jose, California.

If the sampler intake has been in place for an extended amount of time, or if it 

shows signs of biologic growth, rinse the inside of the intake tube with a solution of 

bleach and water. This can be accomplished without removing the intake tubing if it is 
Implementation Guide for Turbidity Threshold Sampling: Principles, Procedures, and Analysis

37

mounted on a boom that can be raised above the water surface. Place the intake in the 

bleach solution and manually run the pumping sampler’s pump forward to draw the 

solution into the tube, but stop the pump before the solution is deposited in a sample 

bottle. Leave the solution in the tube for 10 minutes. Purge the solution back into the 

container by reversing the pump, and then rinse the tube with clear water several 

times in the same manner, being careful to not allow bleach into the stream.

For sites that transport high sediment loads, check the stage-sensing device 

after each significant storm event. Check bubbler orifices for damage or plugging 

and examine submersible pressure transducers for sediment accumulation around 

and, depending on sensor design, inside of the protective cap. Submersible stage 

sensors will usually continue to function when buried, but changes in stage may be 

attenuated if the sediment is fine and the sediment layer is deep. Bubblers are more 

susceptible to burial malfunctions.

Reading the stage—

If the stage is low enough to safely enter the stream, approach the staff plate but 

maintain enough distance to prevent the wave influence from your legs from affecting the stage. Read the stage while keeping your eyes at a low angle to the water 

surface. If the water is too deep for wading, use binoculars. Avoid reading the stage 

from a high location such as a bridge. If the stream velocity is causing the water 

to build up on the leading edge of the staff plate, take the average reading between 

the leading and trailing edge of the staff plate. Do this several times and take the 

average of all the readings.

Field notes—

Field notes are best taken immediately after each specific activity, such as reading 

the staff plate, rather than waiting until the completion of the site visit. Use a 

standard form rather than a notebook to prompt field personnel to complete and 

document all required tasks. Besides specific line item tasks, adequate space 

should be provided for detailed descriptions and sketches. Care should be taken 

to provide information about any activity or condition that might affect the data 

being collected, such as the submergence of the turbidity sensor or flume cleanings. 

If the original field form is brought back to the office, bring a copy to the field 

at the next site visit. Maintaining copies of all the field forms for a site from the 

current hydrologic year in chronological order can provide valuable clues when 

troubleshooting equipment. Both the date and the field form number can serve as 

identification. A typical numbering scheme starts at one at the beginning of each 

hydrologic year.
38

GENERAL TECHNICAL REPORT PSW-GTR-212 

Checking the current sample status—

Read the “Next Sample” counter on the pumping sampler. This number is one more 

than the number of samples that have been collected. Between wakeup intervals 

remove the midsection from the sampler and visually check the bottles containing 

samples. Ensure that the correct number of samples has been collected and that 

their volumes are acceptable. We set the pumping sampler controller to collect a 

sample volume between 330 and 350 ml for 1000-ml bottles, and at 300 ml for 

500-ml sample bottles. Samples below 150 ml are considered suspect and overfilled 

bottles are discarded. A large sample volume reduces the sample variance but 

increases the laboratory processing cost.

Check the data logger sample counter to make certain that it agrees with the 

pumping sampler. The data from the data logger are downloaded to a laptop, and sample bottles are replaced when there is a chance that all bottles will be filled before the 

next visit. This “change out” threshold can differ from site to site, but it is generally 

when about half the bottles contain samples. Several variables control this number: 

site access, expected storms, available field personnel, and sampling parameters.

Downloading the data and changing the sample bottles—

The sample bottles and accompanying data are always processed together in 

the implementation of TTS that runs on Campbell data loggers. The data are 

downloaded (“dumped”) to a laptop computer, any bottles containing samples are 

replaced with empty bottles, and the data dump number is incremented. If sample 

bottles do not need to be replaced on a particular site visit, a partial data file is 

downloaded for plotting but the data dump number is not incremented, and it would 

not be considered a “data dump.” After a data dump, the data should immediately 

be backed up on a secondary memory device. The data file name contains the threeletter station identification, the start date, and the data dump number. The sample 

bottles are labeled with the station ID, the data dump number, and the sample 

number (bottle position).

Plotting the data—

The TTS Data Plotting software, available on the TTS Web page, allows field personnel to view the data file in the field and to check for errors. The plot displays the 

date and time on the x-axis and stage and turbidity on the y-axis. Pumped samples are 

shown on the turbidity trace. The plot can assist in diagnosing equipment problems 

and provide the field crew with an assessment of whether or not the sampling parameters, such as the turbidity thresholds or the minimum stage, are set correctly.
Implementation Guide for Turbidity Threshold Sampling: Principles, Procedures, and Analysis

39

Depth-Integrated Sampling

Collection of isokinetic, depth-integrated samples involves using either an equalwidth-increment (EWI) or equal-discharge-increment (EDI) sampling method. 

The EWI or EDI methods usually result in a composite sample that represents 

the discharge-weighted concentrations of the stream cross section being sampled 

(Wilde et al. 1999). Manually operated isokinetic samplers, such as the DH-81, 

D-74, or other depth-integrating samplers, are designed to collect composite samples that are automatically weighted by discharge if the sampler is passed through 

the water column at a constant rate.

The importance of depth-integrated and pumped sample pairs—

Because pumped samples are collected at a single point in the stream, they may 

not represent the cross-sectional average SSC. Analyses of simultaneous point 

and depth-integrated samples will determine if a correction to the point sample 

data is required.

When to collect depth-integrated samples—

The sample pairs should be distributed over the measured range of turbidities 

recorded at the station. Twenty or more pairs may be needed to detect small biases 

in pumped samples. After the range of data has been covered, sampling should be 

periodically repeated to determine if the relationship has changed.

How to collect depth-integrated samples—

Refer to the “Field methods for measurement of fluvial sediment” for a complete 

description of the procedure (Edwards and Glysson 1999). 

Developing a Stage-Discharge Relationship

A rating equation must be developed for gaging sites that don’t have a discharge 

measurement structure such as a weir or flume. Discharge measurements should be 

collected over the range of possible flows at the site. High flow measurements are 

very important and the most difficult to collect. If the channel control is stable, it may 

be possible to develop a good relationship with as few as 20 or 30 stage-discharge 

pairs. If the channel control is not stable, ongoing measurements will be required 

to develop a new rating each time the channel control changes. For channels with 

highly mobile beds, it may not be possible to develop a rating.

How to collect discharge measurements—

Refer to “Discharge Measurements at Gaging Stations” for a complete description 

of the procedure (Buchanan and Somers 1969).
40

GENERAL TECHNICAL REPORT PSW-GTR-212 

Equipment Troubleshooting

How to evaluate equipment problems—

The most important concept for troubleshooting instrumentation is to approach 

the problem systematically. Because the components of a monitoring system are 

interconnected, a problem with a sensor may be presented as an error message 

from the data logger. Randomly testing various parts of a system often leads to 

confusion and rarely identifies the problem. Although a problem can be analyzed 

from the data logger towards an external component, it is usually more efficient to 

work in the opposite direction. Recording detailed field notes assists by structuring 

the troubleshooting and provides a valuable record should the problem arise in the 

future. Beyond the philosophy of troubleshooting, each component of a monitoring 

system may have unique attributes that are specific to that particular model. The 

“TTS Field Manual,” available on the TTS Web page (http://www.fs.fed.us/psw/

topics/water/tts/), has specific component-level diagnostics to aid field personnel.

Diagnostic tools—

Problem diagnosis requires an understanding of each part of the monitoring system 

and its correct functioning, diagnostic data logger programs to test specific components, and a multimeter to test voltage levels and circuit continuity.

Field personnel must first understand how a device normally functions 

before they can correctly identify failures. For instance, is an erratic trace from a 

turbidity sensor due to suspended sediment, a faulty connection, a failing sensor, 

or optical fouling?

Component-specific diagnostic programs can test a sensor or pumping sampler 

by reporting measured values or by triggering a sample. The diagnostic program 

must account for wiring connections that are specific to data logger and sensor 

models. For instance, the control port designation that triggers the pumping sampler 

depends on the data logger model. Use of diagnostic programs is more efficient 

than troubleshooting during the normal operation of the TTS program because they 

repeatedly and quickly test a component without waiting for a normal wake-up 

interval every 10 or 15 minutes.

A multimeter is an electronic instrument that can detect both voltage levels 

and circuit continuity. In the voltage mode, the multimeter can report supply 

voltages, for example from the battery to the data logger, and it can measure voltage 

levels from sensors. The continuity mode is useful for detecting broken or poorly 

connected wires and for checking relay closures.

The most important 

concept for troubleshooting instrumentation is to 

approach the problem 

systematically.
Implementation Guide for Turbidity Threshold Sampling: Principles, Procedures, and Analysis

41

Most field personnel can be trained to diagnose failures at the component level 

and to replace the sensor, data logger, or pumping sampler. If adequate replacement 

equipment is available, it is more efficient to simply change out a component (e.g., 

a sensor or sampler controller), than to attempt repairs, unless the field crew is 

experienced in diagnosing problems. Follow the “TTS Field Manual” or the equipment manual supplied by the manufacturer when replacing components.

Turbidity Threshold Sampling Program

The TTS program has been developed for three different brands of data logger at 

the time of this writing: Campbell, Forest Technology Systems (FTS), and Onset 

Tattletale (see footnote 1). The Onset version, written in TXBASIC for Tattletale 

data loggers, is no longer supported. The Campbell program is freely available 

from the TTS Web page for model CR510 and CR10X data loggers. The CR510 and 

CR10X are no longer manufactured. A new TTS version is available for the replacement Campbell data loggers (CR800 and CR1000) that are programmed in Campbell’s CRBasic language. The FTS version of TTS can only be obtained from FTS.

Logic

The TTS algorithm attempts to collect physical samples at specific turbidity 

thresholds. For details, programmers may consult the pseudocode (available from 

the TTS Web page). Thresholds are usually chosen so that the square roots are 

evenly spaced to adequately define loads for small storms without oversampling 

large storms. A programmable data logger, typically recording at 10- or 15-minute 

intervals, instructs an automatic pumping sampler to collect a sample whenever 

a threshold is crossed. To avoid recording ephemeral turbidity spikes caused by 

passing debris, the median of a rapid succession of turbidity measurements is 

recorded at each interval. To limit sampling of false turbidity fluctuations caused 

by snagged debris or biofouling, a threshold must be met for two intervals before 

signaling the sampler. Because most sediment is discharged while turbidity is 

in recession, a separate, larger set of thresholds is used when turbidity is falling. 

Reversals are detected when the turbidity drops 10 percent below the preceding 

peak, or rises 20 percent above the preceding trough. In addition, the change must 

be at least five turbidity units, and the new course must continue for at least two 

intervals before declaring a reversal. At the time a reversal is detected, a sample is 

collected if a threshold has been crossed since the preceding peak or trough, unless 

that threshold has already been used in a specified number of preceding intervals. 
42

GENERAL TECHNICAL REPORT PSW-GTR-212 

The user can modify the specific thresholds and numerical parameters mentioned 

here. Specific conditions recognized by the program are described below, with 

default values of user-specified parameters followed parenthetically by the name

 of the parameter that controls it in the CRBasic implementation:

1. Base flow. This condition occurs when the stage is less than the “minimum 

stage” parameter (minimumStage). Minimum stage is usually set to the 

lowest stage at which both the turbidity sensor and pumping sampler 

intake are adequately submerged and functional. No sampling takes 

place in this mode.

2. Rising turbidity. The turbidity condition is defined as rising at the first 

interval emerging from base flow. For sampling to occur in a rising 

turbidity condition, the turbidity must be equal to, or greater than, the next 

rising threshold for two (persistence) intervals that are not necessarily 

consecutive. When emerging from base flow, a startup sample is 

immediately collected if the turbidity is above the lowest rising threshold, 

and no rising thresholds have been sampled within the past 72 (startWait) 

intervals.

3. Turbidity reversal. A turbidity reversal occurs when the turbidity has 

changed direction for two (persistence) intervals, and has dropped 10 

(peakChange) percent from the prior peak or risen 20 (troughChange) 

percent from the prior trough, and changed at least five (reversalMinimum) 

turbidity units in both cases. Upon detection of a reversal, a sample is 

collected if a threshold has been crossed since the previous peak or trough, 

unless that threshold was already sampled in the past 18 (waitCount) 

intervals.

4. Falling turbidity. Sampling occurs during decreasing turbidity when 

the turbidity is less than or equal to the next falling threshold for two 

(persistence) intervals.

5. Overflow mode. The overflow condition occurs when the turbidity sensor 

output exceeds the data logger’s millivolt limit setting (mvLimitOBS, OBS-3 

sensors) or turbidity limit setting (turbLimit, DTS-12 and NEP395 sensors). In 

this mode, the sampler begins sampling at fixed time intervals. The number of 

intervals skipped between samples is specified by the fixedTimeWait variable.
Implementation Guide for Turbidity Threshold Sampling: Principles, Procedures, and Analysis

43

The complete list of parameters that the user can modify is as follows:

CR800/1000 CR510/10X Description

minimumStage min_stg Minimum stage for sampling

turbC1 turb_mult Calibration slope for turbidity sensor (OBS3 only)

turbC0 turb_off Calibration offset or intercept for turbidity sensor 

stgMultiplier stg_mult Calibration slope for pressure transducer

stageOffset stg_off Calibration offset for pressure transducer 

<eliminated> logger Data logger model (CR510 or CR10X)

qCalc qcalc Discharge calculation: 0 = not active, 1 = active

qMultiplier q_mult Multiplier for discharge calculation (multiply by this value)

qExponent q_exp Exponent for discharge calculation (raise stage to this power)

turbDevice turb_dev Turbidity device (OBS3, OBS3+, DTS-12, or Analite NEP395)

delayTime delay Delay that selected port remains high to trigger a sample

reversalMinimum rev_val Minimum absolute change in turbidity to detect a reversal

peakChange revpct1 Minimum reversal percentage from rising to falling 

troughChange revpct2 Minimum reversal percentage from falling to rising

persistence stay Minimum number of intervals before a threshold or new reversal

waitCount rep_wait Minimum number of intervals between utilizations of the same threshold

mvLimitOBS mv_limit Above this limit, samples are collected at fixed time intervals 

(for OBS-3, OBS-3+ sensors)

turbLimit ntu_limit Above this limit, samples are collected at fixed time intervals

(for DTS-12, NEP395 sensors)

fixedTimeWait lim_skip Number of intervals skipped between samples when in overflow mode

<eliminated> obs3poly Calibration type for OBS3 or OBS3+ sensors (linear or polynomial)

obs3Range obs3range Range setting for OBS3+ sensors (1X or 4X)

samplers samplers The number of pumping samplers connected (1 or 2)

startWait startwait Minimum number of intervals between most recent rising sample and a 

subsequent startup sample (taken when emerging from base flow)

wipeStage wipe_stg Minimum stage for activation of turbidity sensor wiper

waterTempDevice wtemp_dev Water temperature device (Campbell T107, DTS-12, or Analite NEP395)

Guidelines for Setting Turbidity Thresholds

General considerations—

The following guidelines are designed to collect a few samples in small storms 

and more, but not too many, in large storms. The terms “few” and “too many” are 

subjective, and it is up to each investigator to define the desired range of sample 

abundance by setting the number of thresholds. The guidelines are based on 

simulations made with data from the Caspar Creek Experimental Watersheds, and 

are designed to accurately and economically estimate suspended sediment loads 

for, on average, the six largest storm events each year (Lewis 1996). At some of 

It is up to each 

investigator to define 

the desired range of 

sample abundance 

by setting the number 

of thresholds.
44

GENERAL TECHNICAL REPORT PSW-GTR-212 

the gaging sites with relatively high or low sediment loads, we have had to alter 

thresholds and other sampling parameters. When an extreme erosion event occurs 

in a watershed, the sediment regime may be altered drastically for some period 

of time, and sampling parameters should be adjusted accordingly. In different 

environments or where objectives differ, parameters will need customizing. For 

example, if one had a special interest in sampling the first few small events of the 

wet season, then an extra threshold or two might be temporarily added near the low 

end of each threshold scale. If relatively more emphasis is to be placed on low flows, 

then the square root scale might be replaced with a logarithmic scale. However, any 

alteration that places more emphasis on low turbidity conditions will result in more 

samples (and higher costs) unless the number of thresholds is reduced at the same 

time.

Using the threshold calculator applet—

The turbidity threshold calculator on the TTS Web page can be used with any Web 

browser that has a Java 2 or more recent Java plug-in installed.

• Using the Sensor Maximum slider, set the maximum reading (turbidity 

units) that your sensor can record. This value can be determined by calibration and may not be the same as the nominal range given by the manufacturer. The manufacturer should be able to provide the necessary calibration 

information.

• Set N, L, and U on the Rising Threshold sliders, based on the criteria 

described below under “Rising thresholds.”

• Set N, L, and U on the Falling Threshold sliders, based on the criteria 

described below under “Falling thresholds.”

• If desired, test the thresholds as described below under “Simulating TTS.”

• Install the thresholds in the TTS Campbell program as described below 

under “Entering thresholds in the Campbell TTS program.”

Rising thresholds—

• Determine the lowest non-zero threshold, L. This should be a value that 

is above typical interstorm turbidity values. In small streams it should 

also be a value that is expected to occur only after the stage rises enough 

to submerge both the turbidity sensor and the pumping sampler intake. 

• Determine the highest threshold, U, within the range of your turbidity 

sensor.
Implementation Guide for Turbidity Threshold Sampling: Principles, Procedures, and Analysis

45

• Determine the number of thresholds, N, between L and U (including both 

L and U).

• Use the threshold calculator applet, or manually calculate thresholds 

as follows: 

• Compute d = (U0.5 – L0.5)/(N – 1)

• The thresholds between L and U are (L0.5 + d)2, (L0.5 + 2d)2, ... , 

(L0.5 + (N – 2)d)2

• Because of the way the algorithm was written, before TTS Revision 

5.0, additional thresholds were needed at zero and above the sensor 

measurement range, for example, 9999. Starting with Revision 5.0 the 

extra thresholds are no longer needed.

• The complete set of rising thresholds to be assigned is therefore: 

L, (L0.5 + d)2, (L0.5 + 2d)2, ... , (L0.5 + (N – 2)d)2, U

Falling thresholds—

The procedure is similar to that for rising thresholds, except guidelines for determining L, U, and N are slightly different, and thresholds are assigned in descending 

order in the Campbell TTS program.

• L should be a value that is at or above typical interstorm turbidity values. In 

small streams it should be a value that is expected to occur before the stage 

falls enough to expose either the turbidity sensor or the pumping sampler 

intake. It is best to choose a different L for falling turbidity than for rising 

turbidity. Otherwise it is likely that, in a small storm event where only the 

lowest rising and falling thresholds are exceeded, only two samples would 

be collected, both at nearly the same turbidity.

• N should be higher than that chosen for rising thresholds. 

• N and U can be altered in a trial-and-error fashion to minimize redundancy 

between rising and falling thresholds. However, because samples are not 

taken precisely at the threshold turbidity, but occur only when the threshold 

has been passed for two intervals, the risk of resampling the same turbidity 

is not all that great of a concern. Samples are least likely to occur precisely 

at thresholds in rising turbidity conditions and at high turbidity levels in 

general, that is, when turbidity tends to change most rapidly.

• The complete set of falling thresholds to be assigned is: 

 U, (L0.5 + (N – 2)d)2

, ..., (L0.5 + 2d)2, (L0.5 + d)2, L
46

GENERAL TECHNICAL REPORT PSW-GTR-212 

Examples—

• Sensor range 0 to 500

Rising (L = 10, U = 450, N = 10): 

10 27 51 84 125 174 231 296 369 450

Falling (L = 15, U = 475, N = 17): 

475 427 382 340 300 262 227 195 165 137 112 90 70 52 37 25 15

• Sensor range 0 to 1000

Rising (L = 15, U = 900, N = 10): 

15 46 94 158 240 338 453 585 734 900

Falling (L = 20, U = 950, N = 17):

950 851 758 670 587 510 439 372 311 256 206 161 122 89 60 37 20

• Sensor range 0 to 2000

Rising (L = 20, U = 1850, N = 10): 

20 77 170 300 467 670 910 1187 1500 1850

Falling (L = 30, U = 1900, N = 17): 

1900 1698 1507 1328 1160 1004 858 724 602 491 391 302 225 159 105 62 30

Simulating Turbidity Threshold Sampling—

The TTS algorithm can be simulated with any existing Campbell TTS data file 

and a few functions written in the R programming language. If you have already 

installed R for making data plots, then it is simple to add the simulation functions. 

These functions and instructions for installation and usage can be obtained from the 

TTS Web page. The package provides the capability to read a raw TTS data file and 

determine when sampling would have occurred for a hypothetical set of thresholds. 

A plot can be produced that shows the record of stage, turbidity, and simulated 

pumped samples. Many of the program parameters specified in the TTS program 

can be altered for the simulation to determine optimal settings.

Entering Thresholds in the Campbell TTS Program

Copy the thresholds into Sub 7 of the CR510/10X program or Sub Initialize of the 

CRBasic program. The rising thresholds are entered from lowest to highest. In older 

versions before TTS Rev. 5.0, the values 0 and 9999 must be included. The falling 

thresholds are entered from highest to lowest. In the CR510/10X program, eight 

thresholds are entered per Bulk Load statement; the ninth parameter is the starting 

address in memory where the previous eight values will be stored.

The number of thresholds must also be entered in Sub 7 or Sub Initialize, 

where nfalling (or nFalling) and nrising (or nRising) are assigned. In all of the 

above examples, the values entered are 10 and 17, respectively. (Before TTS 

Revision 5.0, the value 9999 would have been included in the counts but 0 

would have been excluded).

The package provides 

the capability to read 

a raw TTS data file 

and determine when 

sampling would 

have occurred for a 

hypothetical set of 

thresholds. 
Implementation Guide for Turbidity Threshold Sampling: Principles, Procedures, and Analysis

47

Laboratory Procedures

Standard Methods

Two standard methods are widely cited in the United States for determining the 

total amount of suspended material in a water sample. They are:

• Method D 3977-97 (SSC), “Standard Test Methods for Determining 

Sediment Concentration in Water Samples” of the American Society for 

Testing and Materials (2000).

• Method 2540 D (TSS), “Total Suspended Solids Dried at 103° to 105°C” 

(APHA 1998).

The TSS method involves subsampling, whereas the SSC analytical method 

measures all sediment (above a minimum filter pore size) and the mass of the 

entire water-sediment mixture. The terms SSC and TSS are often used interchangeably in the literature to describe the concentration of solid-phase material 

suspended in a water-sediment mixture. However, the analytical procedures for 

SSC and TSS differ and may produce considerably different results for samples 

that have a high sand fraction. Gray et al. (2000) compared the SSC and TSS 

analytical methods and derivative data, and concluded that the SSC method is the 

more accurate and reliable.

Additional Recommendations

Standard procedures are not generally detailed enough for reliable laboratory implementation, and a detailed laboratory manual is usually required. The manual used 

at RSL is available upon request. This section discusses procedural details, some of 

which are specific to the RSL manual. Some of the procedures are nonstandard and, 

if the methods are adopted, should be noted as such in reports.

Marking water levels on sample bottles—

The water level in each sample bottle is marked in the field to monitor whether any 

of the sample is lost during transport to the lab. If any water is lost in transport, the 

error is noted and the bottle is refilled to the field mark with lab-grade or distilled 

water before processing (see “Lab-grade and distilled water” below).

Storing bottles—

Sample bottles should be stored at a cool temperature, and light should be excluded 

to inhibit algal growth.
48

GENERAL TECHNICAL REPORT PSW-GTR-212 

Acidification—

Acidification is a nonstandard procedure applied to most samples processed at 

RSL. Three drops of 15 percent hydrochloric acid (equal parts muriatic acid and 

water) are added to samples in the field to reduce algal growth during storage. 

Acid should not be added to samples in which colloids may be naturally dispersed 

owing to electrostatic repulsion. Acidification will likely cause flocculation of 

such samples, altering effective particle sizes and turbidity. In naturally acidic 

samples from forest streams, however, there is little danger of physical alteration 

of the sample by acidification.

Gravimetric determination of sample volumes—

Determination of the sample volumes by gravimetric measurements is more 

precise than volumetric methods because analytical balances can measure to the 

nearest 0.1 g (0.1 ml H2O at room temperature) or better, whereas volumes of 300 

to 500 ml are measurable with resolution no better than 1 ml using volumetric 

flasks or cylinders.

Lab-grade and distilled water—

Lab-grade water is tap water that has been filtered for sediment particles. Optimally 

rinse water will be filtered to the pore size of the filters being used for SSC determination. If samples will be processed using the evaporation method, then distilled or 

deionized water may be required; otherwise dissolved solids in the tap water could 

contribute to the final sediment weight.

Filter pore sizes—

Various standards are used at different labs. The EPA typically uses 1.5-μ filters, 

whereas the USGS often uses 0.45- or 0.70-μ filters. Redwood Sciences Laboratory uses 1.0-μ filters. Sample processing time is directly related to the amount of 

fine sediment in the sample and the filter pore size. The filter pore size should be 

chosen with care, taking into account the watershed geology, organic transport, 

and study objectives. It is important to use the same filter pore size for the duration of the study and report filter pore size in published analyses because results 

of different studies, even in the same watershed, may not be comparable when 

methods are different.

Use of multiple filters—

The ASTM Method D 3977-97 (SSC) states “Even though a high-concentration 

sample may filter slowly, users should not divide the sample and use two or more 

filters.” Justification is not provided, but one might surmise that the motive has 

to do with consistency. Very fine particles will not be trapped on filters, but the 
Implementation Guide for Turbidity Threshold Sampling: Principles, Procedures, and Analysis

49

effective filter pore size decreases during the filtration process. If a single filter is 

always used, then trap efficiency is only a function of concentration, filter type, 

and particle size distribution of the sample. Suspended sediment concentration 

determined with a single filter should therefore be more repeatable than with 

multiple filters, unless the precise amount of sediment on each filter is specified 

and controlled. However, complete filtration through a single filter in many cases 

is impractical or impossible as filter pores become plugged. Therefore, RSL 

procedures allow for the use of multiple filters.

Handling of prefilters—

Standard Method 2540 D (TSS) requires prerinsing filters with three successive 

20-mL aliquots of reagent-grade water with vacuum assistance, then drying, desiccating, and weighing the filter to the nearest 0.0001 g. After recording the initial 

weight, additional rinsing, drying, and weighing is required until a constant weight 

of ± 0.0005 g is reached. Washing the filter before use removes small fibers that 

would otherwise be washed from the filter when the sample is vacuumed through 

the filter. This step precludes negative filter weights that would result from the loss 

of loose fibers. Also, glass fiber filters in most environments hold a small amount 

of ambient water. This water weight can only be removed by drying. In practice, 

multiple prefiltering steps are not generally needed to achieve a constant weight of 

glass-fiber filters, and routinely repeating prerinse cycles until a constant weight can 

be verified is burdensome relative to the benefit. The RSL procedures follow ASTM 

Method D 3977-97 (SSC) in requiring just one prerinse with vacuum assistance, followed by drying, cooling in a desiccator cabinet, and weighing.

Handling of organic matter and debris—

At RSL, organic matter is considered part of the suspended load and is not dissolved or burned off. Debris is not removed unless some dimension is greater than 

the diameter of the pumping sampler intake (1/4-in or 3/8-in [0.4-cm or 1.0-cm]). 

Any debris of a size that potentially could have been excluded by the physical size 

of the intake is removed during processing.

Settling and filtering the supernate—

For many samples, processing time can be reduced by settling the sample so that 

the supernate can be decanted and filtered ahead of the settled portion of the 

sample. Complete settling followed by suctioning of the supernate is required for 

efficient filtering of those samples that contain a large proportion of very fine sediment. Complete settling may take a week without disturbing the sample to allow the 

smallest particles to settle on the bottom of the bottle. Suctioning will speed up the 

filtering process tremendously, sometimes by hours.

Any debris of a size 

that potentially could 

have been excluded 

by the physical size of 

the intake is removed 

during processing.
50

GENERAL TECHNICAL REPORT PSW-GTR-212 

Handling of manual, depth-integrated samples—

At RSL, sand fraction analysis is performed on all depth-integrated and pumped 

sample pairs to help identify depth-integrated samples that may erroneously 

include bed material, and to determine if a particle size bias is present in the 

pumped sample.

Quality coding—

Besides notes that may be recorded on lab sheets, RSL employs two codes that are 

saved with the data to provide information about the disposition and processing of 

each sample. The first code is a tracking code that indicates problems that may have 

occurred before a sample was processed, for example, lost or discarded samples or 

labeling problems. The second code identifies various sample attributes and processing problems, such as the presence of organics, spillage, and weighing errors. 

Assigning an appropriate code can be a subjective task. Understanding how the 

codes will ultimately be used can help lab technicians make better judgments about 

code assignments.

Evaporation Method

The ASTM Method D 3977-97 (SSC) includes procedures for evaporation as well 

as filtration. For a given sample, evaporation is the preferred method when filtration 

would require multiple filters. Evaporation produces higher concentrations than 

filtration (for the same sample) because results can include very fine particles that 

pass through filters. If a group of samples is to be processed (the usual situation), it 

may be preferable for consistency and simplicity to process high-SSC samples with 

multiple filters. However, when concentrations are very high, it becomes impractical to use the filtration method. 

Decanting—

Decanting of samples that are to be processed with the evaporation method is not 

done by pouring because it will disturb the sediment that has settled on the bottom 

of the bottle. Using a J-tube on the end of a vacuum hose will remove most of the 

supernate with very little disturbance of the sediment layer. Removal of supernate 

is important so that all of the sediment and rinse water will fit into a single dish for 

evaporating. Using pressurized rinse water reduces rinse water volumes.

Filtering and subsampling the supernate—

Because evaporation leaves dissolved as well as suspended solids, the ASTM procedure requires subsampling the supernate after the sediment has settled to compute a 

dissolved-solids correction factor. We also filter the supernate before subsampling, 
Implementation Guide for Turbidity Threshold Sampling: Principles, Procedures, and Analysis

51

in case the supernate contains suspended clays, and include this filtered sediment in 

the total SSC.

Laboratory Turbidity

Understanding your lab turbidimeter and applying a consistent procedure is 

important when reading the turbidity of samples in the laboratory. Samples from 

different locations may behave differently owing to differences in soil composition 

(especially particle size and shape) and organic matter content. The basic procedure 

for measuring turbidity in the laboratory at RSL is to:

• Agitate the sample. 

• Record the first value displayed (to limit settling time).

• Repeat the above steps two times.

• Compute and record the average of the three individual readings.

Laboratory turbidity can be useful for validating or reconstructing field 

turbidity measurements (e.g., when turbidity exceeded the field sensor’s range). 

A relation between lab turbidity and SSC can be established from a subset of 

samples collected at fixed time intervals, then used to estimate SSC for the 

remaining samples. 

Subsampling—

To measure laboratory turbidity, a small volume of the sample must generally be 

extracted from field samples for placement in the turbidity meter. This is best done 

before analyzing samples for SSC, but the turbidity subsample must be rinsed 

back into its original container before SSC is determined. Therefore, it is important to determine the original sample volume before subsampling. Subsampling 

is an imprecise process, but measures can be taken to maximize the probability 

of achieving a representative sample. Samples should be swirled, not shaken, to 

prevent introduction of air bubbles into the subsample. Special pour spouts can aid 

in transferring samples quickly with minimal spillage, but pouring should always 

be done over a clean pan, so any spillage can be recovered.

Dilution—

When turbidity exceeds the range of the lab meter, it may be necessary to dilute 

samples to obtain an estimate of turbidity. A convenient procedure is to split the 

turbidity subsample into two subsamples of about equal volume, and add water until 

each diluted subsample is the same volume as the original subsample. The final 

turbidity will be the average of the turbidity of each diluted subsample, multiplied 

by the dilution factor: [(a + b) ÷ 2] × 2 = a + b, which is simply the sum of the two 

turbidity values.
52

GENERAL TECHNICAL REPORT PSW-GTR-212 

Dilution should be avoided if an alternate method is available, because it does 

not always reduce turbidity proportionally to the dilution factor. Uncorrected results 

from dilution cannot be expected to be accurate without experimental validation. 

Experimentation at RSL has shown that results of dilutions by the above procedure 

can result in a value 30 percent too low. Section 6.7 of the “National Field Manual 

for the Collection of Water Quality Data” recommends that at least three dilutions 

be made, at about 80, 50, and 20 percent of the original concentration (Anderson 

2004). If the turbidity is linearly related and positively correlated to the percentage 

diluted, then the relation can be used to estimate the concentration at 100 percent. 

If the response is nonlinear, alternative instrument designs that compensate for 

interferences should be considered. Turbidity values that are obtained by dilution 

should be appropriately documented and reported with qualification.

Data Processing

When the data have been collected in the field and uploaded to your computer, the 

work has only just begun. Stage data are subject to errors because of transducer 

drift and other calibration problems, debris and obstacles in the channel, and 

changes in channel form such as deposition, scour, and bank erosion. Turbidity data 

are subject to errors because of drift and other calibration problems, interference 

from debris, algae, bugs, bed load, the water surface, nearby objects, bubbles, and 

sunlight. Problems with batteries, connections, and electrical cables can result in 

periods of missing data due to loss of power. Occasional failures of sensors are to 

be expected. Finally, user errors when interacting with the program and hardware 

often result in lost data. It is essential to carefully document actions and observations taken in the field to understand and cope with data problems later.

Raw Data Plots

It is a very good practice to assemble a binder containing raw data plots, annotated 

with notes from the field. Annotations should include information such as:

• Turbidity sensor submerged or not submerged

• Turbidity sensor cleaned

• Debris removed from boom or housing

• Work performed in the stream channel

• Sediment removed from flume, stilling well, or cross section

• Sensor offset changed

• Sensor moved

• Equipment changes or problems

• Any data spikes caused by field crew
Implementation Guide for Turbidity Threshold Sampling: Principles, Procedures, and Analysis

53

• Staff plate readings, if the software does not automatically show them

• Data corrections that will be needed, with explanations

Each plot should contain no more than 1 to 2 weeks’ data to clearly view 

details. It is very useful to include both stage and turbidity as well as sample 

bottles and observer staff plate readings. Additional plots of storm events are also 

very helpful. Choose a scale that is reasonable for the data. Inappropriate scales 

can hide important information. Perceptions of the slope of line segments are 

optimized when absolute values of the orientations are centered on 45 degrees 

(Cleveland 1993).

Software for plotting and correcting raw data—

Because of the large number of data plots needed, it is inefficient to create graphs 

one at a time using a program like Excel or SigmaPlot. A customized script, function, or program can quickly produce plots with a single command or user action. 

The RSL has developed two methods for creating customized plots, and, at the time 

of this writing, a commercial system is under development by FTS.

TTS Data Plotting—Developed in R, TTS Data Plotting was designed to quickly 

read and plot raw data in the field. The program displays raw stage, turbidity, and 

sample bottles directly from a raw or corrected data file as illustrated in the following example (fig. 23). It can also display corrected stage and turbidity, discharge, 

rainfall, and water and air temperature from appended and corrected data files. 

Installation instructions and software can be found on the TTS Web page.

TTS Adjuster—The second program, TTS Adjuster, written in Java, reads and 

displays raw Campbell data, corrected data, lab sediment concentrations, observer 

staff plate readings, and electronic field notes. Figure 24 is an example plot showing 

raw and corrected stage and turbidity data, staff plate readings, and the timing of 

pumped samples. The TTS Adjuster also appends multiple raw data files and allows 

the user to adjust and correct errors. The result is one file, in a specific format, for 

each station and year containing both raw and corrected data, and codes identifying 

the types of corrections applied. Installation instructions, software, and other documentation can be found on the TTS Web page.

StreamTrac—There is also a processing program called StreamTrac for sale by 

FTS. The FTS program is a full-featured system, designed to work with TTS data 

collected by either the Campbell or FTS versions of TTS. The software is designed 

for maximum flexibility, includes most of the features of TTS Adjuster, and is 

capable of computing sediment loads.

Choose a scale that 

is reasonable for the 

data. Inappropriate 

scales can hide 

important information.
54

GENERAL TECHNICAL REPORT PSW-GTR-212 

Figure 24—Example of a graph produced by TTS Adjuster for data correction and adjustment.

Figure 23—Example of a graph produced by TTS Data Plotting for displaying raw data.

WIL Station

Stage

min

stg

0

0.2

0.4

0.6

0.8

1.0

50

0

100

150

200

250

Turbidity

3

4

5

6

7

8

9

10

11

12

13

14

12/28/02

Stage

Turbidity

Bottle

0 6 12 18 0 6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

2.2

6

7

5

4

3 1 2

-0.027

-0.014

-0.006 +

Stage (ft)

Turbidity (NTU)

Station NFC HY04

90

80

70

60

50

40

30

20

10

0

corr turb

cor stage

raw turb

raw stage

bottle

right staff

02Feb04

00:00

03Feb04

00:00

04Feb04

00:00

05Feb04

00:00

06Feb04

00:00

07Feb04

00:00
Implementation Guide for Turbidity Threshold Sampling: Principles, Procedures, and Analysis

55

Laboratory Data

Because lab data entry errors are propagated through all sediment analyses, it is 

important that all data entry be double-checked. It is a good practice to enter the 

data twice, each time by different individuals, and then resolve all differences 

between the two data sets by using the lab sheets to eliminate errors. The following 

information pertaining to pumped samples should be recorded:

1. Station

2. Water year

3. Dump number (incremented each time bottles are removed from pumping 

sampler)

4. Bottle number (starting at one within each dump)

5. Initial bottle weight (containing sample)

6. Final bottle weight (tare)

7. Total number of filters used in processing sample

8. Filter number

9. Initial filter weight (tare)

10. Final filter weight (including oven-dried sample)

11. Particle size class (if sample has been sieved)

12. Quality code (denotes conditions such as the presence of organic particles, 

low volume, spillage, and other lab errors)

The date and time of pumped samples need not be entered, because that information is recorded for each dump and bottle number in the electronic file from the 

data logger. For depth-integrated samples, all of the above information is needed, 

as well as the following:

13. Date and time, or matching pumped sample dump and bottle number

14. Location(s) in the cross section (if multiple verticals are sampled)

Several filters are used if a sample contains large quantities of sediment or if 

information about grain size distribution is needed. In the latter case, the sample 

would be sieved one or more times to separate it into different size classes, each 

of which would be separately filtered. Items 7 through 12 in the above list would 

be entered for each filter that originated from the same bottle. The quality code 

could include many levels for various types and severity of conditions, or it might 

be broken into several codes, each denoting the severity of a particular type of 

condition. The suspended sediment concentration is computed according to the 

following formula:
56

GENERAL TECHNICAL REPORT PSW-GTR-212 

where

ms = mass of sediment

mw = mass of water

mw+s = mass of whole sample (water and sediment)

Vw = volume of water

Vs = volume of sediment

ρw = density of water

ρs = particle density of sediment

If masses are measured in grams and densities in grams per cubic centimeter, 

multiplying by 106

 will convert results from grams per cubic centimeter to milligrams 

per liter. The mass of sediment, ms, is obtained from the difference between initial 

and final filter weights, summing over all pertinent filters. The mass of the whole 

sample, mw+s , is obtained from the difference between final and initial bottle weights. 

The density of water can be assumed to be 1.00 g/cm3 and the particle density of the 

sediment is often assumed to be 2.65 g/cm3, which is an approximate average for 

the dominant soil minerals: quartz, feldspars, micas, and clay minerals (Donahue et 

al. 1977). However, an accurate value of particle density can be determined from a 

sample’s mass and volume by using a pycnometer (Black 1965).

The mean cross-sectional SSC from multivertical depth-integrated samples 

collected by using the equal transit-rate method can be computed by compositing 

ms and mw+s for all the bottles collected before applying equation (1) above (Guy 

and Norman 1970).

Processing the Stage Data

Before computing the sediment load, it is desirable to have as accurate a record of 

water discharge as possible. Any errors in water discharge will translate directly 

into errors of equal magnitude in sediment load. Discharge is computed from the 

electronic stage record by using the discharge rating equation. The electronic stage 

record is computed by applying a calibration equation to the output from the stage 

sensing device. Various sources of error in stage are possible:

• The slope or intercept of the sensor calibration can be incorrect.

• The stage can be artificially elevated by obstructions such as woody 

debris, sediment, or sampling devices temporarily placed in the discharge 

measurement section.

• The stage may be missing (defective sensor or electronic or human failure).

Any errors in water 

discharge will translate 

directly into errors of 

equal magnitude in 

sediment load.

SSC =

ms

= ms= ms(1) Vw + Vs mw +

 ms mw+s – ms 

+

 ms 

ρw ρs ρw ρs
Implementation Guide for Turbidity Threshold Sampling: Principles, Procedures, and Analysis

57

In case 1, the stage record can generally be adjusted to give correct values. In 

cases 2 and 3, a reconstruction is needed by interpolation or with reference to a 

nearby gaging site.

Adjustments for stage sensor calibration problems—

Time-variable shifts—Usually, it is the intercept rather than the slope of the calibration that causes incorrect stage readings. The most common causes of intercept 

errors are incorrect entry of the intercept in the data logger program and change in 

the vertical placement of the sensor. These errors result in a constant shift in the 

stage data. If the problem is that the intercept is drifting gradually, a time-variable 

shift can be applied by prorating the differences between observer and electronic 

stages over time. In either case, the stage usually can be adjusted to match staff 

plate readings (observer stages) recorded by field personnel. If the drift is assumed 

to be linear with time, the corrected stage, E't

 , can be computed by using:

Et = Et + 

 (O1 – E1)(t2 – t) + (O2 – E2)(t – t

1) (2) ' 

t

2 – t1

where

Et = electronic stage at time t, where t1 ≤ t ≤ t2

E1 = electronic stage at time t1

E2= electronic stage at time t2

O1 = observer stage at time t1

O2 = observer stage at time t2

If O1 ≠ E1 and O2 ≠ E2, then, in TTS Adjuster, the correction must be done in 

two steps, by shifting first at one boundary and then the other. Equation 2 is then 

applied twice. In the first step, we have O2 = E2, and in the second step, we have 

E1 = O1, and Et

 is replaced by E't from the first step. The effect is equivalent to a 

single application of the equation as written.

Y-proportional shifts—If the slope of the calibration is incorrect, the differences 

between the observer and electronic stages will be a function of stage height. In 

this case, a y-proportional shift is required. The corrected stage, Et

', is computed 

by using:

Et = Et + 

 (O1 – E1)(E2 – Et

) + (O2 – E2)(Et – E1) 

(3) '

E2 – E1

As with time-variable shifts, if O1 E1 and O2 E2, then, in TTS Adjuster, the 

correction must be done in two steps, by shifting first at one boundary and then the 

other. Equation 3 is then applied twice. In the first step, we have O2 = E2 , and in the 
58

GENERAL TECHNICAL REPORT PSW-GTR-212 

second step, we have E1 = O1, and Et is replaced by Et

' from the first step. The effect 

is equivalent to a single application of the equation as written.

Stage adjustment philosophy—

Precision considerations—The electronic stage data are more precise than manual 

readings of stage. Therefore, it is to be expected that discrepancies will routinely be 

found between electronic and manual stages. Staff plate readings are rarely more 

precise than 0.5 cm, and precision declines with increasing stage owing to wave 

action and turbulence. Precision can be improved by reading the stage in a stilling 

well, but precision is still significantly less than that of an electronic device such 

as a pressure transducer. Therefore, we do not recommend routinely adjusting all 

electronic data to agree with observer records.

Accuracy considerations—Adjustments should only be made when there is a 

systematic bias in one direction. If the differences between electronic and manual 

stage data are as often positive as negative, without a systematic temporal pattern, 

and if they fall within the precision of the manual stage readings, then it is best not 

to adjust the data. If the differences are consistent in sign, then an adjustment may 

be warranted.

Application of constant and time-variable shifts—If there is no trend in the 

magnitude of the differences, it is probably best to estimate an average difference, 

and apply that average as a constant shift. If there is a consistent time trend in 

the magnitude of the differences, then it is appropriate to apply a time-variable 

shift over the duration of the trend. If there is a nonlinear time trend, then it is 

appropriate to apply time-variable shifts between consecutive pairs of observer 

records.

Application of y-proportional shifts—Y-proportional shifts are only appropriate 

if it is known that an erroneous calibration slope was applied. If there is no evidence 

that the slope error changed over time, then it may be best to apply a single 

y-proportional shift over the duration of the erroneous data, rather than applying 

separate shifts between each consecutive pair of observer records.

Interpolation of missing data—Short periods of missing data can often be 

adequately filled by linear interpolation. Whether or not linear interpolation 

will be satisfactory depends on a consideration of hydrologic conditions at the 

time. If there is a change of direction in the hydrograph, or if the period of 

missing data is lengthy, then a linear interpolation is not appropriate.
Implementation Guide for Turbidity Threshold Sampling: Principles, Procedures, and Analysis

59

Reconstruction of missing data—Reconstruction of missing data from another 

hydrograph is usually the best way to repair a record when linear interpolation is 

inappropriate. The hydrograph may be from another station or, during recession 

periods, from another time period at the station being repaired. Reconstructions 

can be reliable and realistic if the missing stage data are strictly increasing or 

decreasing. The basic method is to rescale another hydrograph segment so that it 

is continuous with the endpoints of the segment being repaired. Reconstructions 

can be constrained to agree with intervening observer readings by proceeding in a 

piecewise fashion between consecutive pairs of observer readings.

Reconstruction methods employed by TTS Adjuster—These methods copy 

a section of the record from the reference station into the segment being 

reconstructed. The curve is rescaled to be continuous with the existing data at 

both segment boundaries. The scaling is based only on the stage or turbidity at 

each segment boundary and the corresponding values at the reference station. 

The segment being copied should have approximately the shape that you want to 

transfer. It need not start at the same stage, turbidity, or time as the start of the 

segment being reconstructed.

1. Linear relation: This method assumes a linear relationship between the 

responses at the two stations. 

2. Quadratic relation: This method treats the ratio of responses as a linear 

function of the response at the reference station. It is equivalent to a 

quadratic relationship (with no intercept) between the responses at the 

two stations. 

3. Time-linear ratio: This method treats the ratio of the responses as a 

linear function of time. 

4. Time-linear difference: This method treats the difference in turbidity 

or unit-area discharge as a linear function of time.

The choice between these methods is ultimately determined by comparing the 

results of each to records from similar hydrologic periods, but some generalities can 

be made. If all the missing discharges are bounded between the starting and ending 

values, the first method is usually satisfactory. If not, the second method usually 

produces more reasonable results for lower discharges. The third method sometimes 

works when the first two fail, because it restricts the discharge ratio to values 

between the starting and ending ratios. The fourth method is an alternative that 

will often behave similarly to the third method.

If all four methods fail to produce reasonable results, it is probably because the 

missing discharges are not between the starting and ending values. In that case, it 
60

GENERAL TECHNICAL REPORT PSW-GTR-212 

may be necessary to fit a relationship that includes additional discharge pairs from 

other hydrographs: pairs that encompass the range of discharges being predicted. 

However, unless the relationship is forced to pass through the data pairs at either 

end of the gap, there is no guarantee that the resulting hydrograph will be continuous at the boundaries of the reconstructed period.

To obtain the stage for reconstructed periods, reconstructed discharges must 

be converted back to stage height by inverting the discharge rating equation. 

For equations that cannot be algebraically inverted, this might require the use of 

numerical methods. The Newton-Raphson method (Chapra and Canale 1988) finds 

the roots of a univariate function and converges very rapidly for smooth discharge 

rating equations when a starting guess is taken from the nearest known stage on 

the hydrograph.

Reconstruction of peaks and other nonmonotonic segments—

When the missing data are not strictly increasing or decreasing, reconstruction by 

simply rescaling another hydrograph is much less reliable. Independent estimates of 

the missing peaks and other local minima and maxima may be needed to ensure a 

trustworthy result. Observer readings can be invaluable in this regard. Peaks should 

be reconstructed from a nearby gaging site by using a method that accounts for the 

difference in watershed areas (e.g., method 3 or 4 in TTS Adjuster).

Documentation

It is important to document changes to the raw data. Users of the data must be 

able to distinguish raw data from corrected and adjusted data. The TTS Adjuster 

produces a data file with both raw and corrected data, so any user can readily 

observe differences, and it includes a data column identifying the type of correction 

performed, if any:

• Unedited, good data

• Unedited, questionable data

• Bad data, replaced with missing value

• Constant shift

• Variable shift

• y-proportional shift

• Linear interpolation

• Reconstruction from another site

• Freehand reconstruction

It is also a good practice to document corrections and their rationale via handwritten annotations on the raw or corrected data plots, or both. The FTS processing 

When the missing 

data are not 

strictly increasing 

or decreasing, 

reconstruction by 

simply rescaling 

another hydrograph is 

much less reliable. 
Implementation Guide for Turbidity Threshold Sampling: Principles, Procedures, and Analysis

61

software, StreamTrac, will allow users to place text boxes on plots and either hide 

or display them for printing.

Converting Stage to Discharge

Development of a rating curve—

The accuracy of computed sediment loads is limited by the accuracy in discharge, 

so it is important to establish a high-quality stage-discharge rating curve if stage 

is not measured in an engineered device such as a weir or flume. Development of 

stage-discharge relations in open channels requires that a range of discharges be 

measured by using a current meter (Buchanan and Somers 1969). The number of 

discharge measurements required to achieve any given level of accuracy depends 

on many factors, including the equipment used, the skill of the hydrographer, flow 

depth, channel shape, rate of change of flow, and the stability of the cross section. 

An ongoing program of regular measurements should be undertaken to detect shifts 

in rating curves in response to changes in channel geometry near the measurement 

section (Kennedy 1983).

Fitting a stage-discharge rating curve—

Curve fitting requires a great deal of judgment and consideration of hydraulic 

factors that can cause bends and breaks in rating curves (Kennedy 1984). The 

availability of regression software on every desktop becomes a disadvantage if it 

promotes the use of oversimplified rating curves. The USGS employs rating tables, 

not simple equations such as power functions, for calculating discharge (Kennedy 

1983). Given the computing power available today, more consideration should be 

given to nonparametric curve fitting methods, such as locally weighted regression, 

also known as loess (Cleveland 1979, Cleveland and Devlin 1988, Cleveland et al. 

1988). Loess is flexible enough to fit smooth functions of arbitrary shape, and it 

can be implemented as a table if predictions need to be made in an environment 

outside that of the curve-fitting software. Loess is available as part of the “modreg” 

package with R, a free software package for exploratory data analysis, statistical 

computations, and graphics.

Complex ratings—A simple relation between stage and discharge may not exist 

at sites where the water-surface slope is variable owing to variable backwater or 

variable storage caused by a shifting downstream control (Kennedy 1984). In such 

situations, a plot of stage versus discharge may plot as a loop, and complex ratings 

are required that involve an additional variable such as change in rate of stage, 

water surface slope, or index velocity.

It is important to 

establish a high-quality 

stage-discharge 

rating curve if stage 

is not measured in 

an engineered device 

such as a weir or flume.

An ongoing program of 

regular measurements 

should be undertaken 

to detect shifts in 

rating curves in 

response to changes 

in channel geometry 

near the measurement 

section.
62

GENERAL TECHNICAL REPORT PSW-GTR-212 

Quality assurance of streamflow records—The following standards were adopted 

by the New Zealand Water Resources, Department of Scientific & Industrial 

Research, for measurement of discharge and rating curve construction (Maidment 

1993):

• Stage-discharge rating curves shall invariably express the stage-discharge 

relation objectively and shall therefore be tested for absence from bias and 

goodness-of-fit in periods between shifts of control, and for shifts in control. (ISO standard 1100/2-1982, clause 7.1)

• 95 percent of all flows estimated from a stage record with a rating applied 

shall be within 8 percent of the actual values.

Standard procedures—Maidment (1993) listed the following sources of 

standard procedures for streamflow data:

• ISO Handbook 16 (ISO 1983)

• WMO Technical Regulations, vol III (WMO 1988)

• WMO Guide to Hydrological Practices (WMO 1981)

• USGS Water Supply Paper 2175 (Rantz 1982)

• Relevant chapters of Techniques of Water Resources Investigations of 

the U.S. Geological Survey

Processing the Turbidity Data

Adjustments for turbidity sensor calibration problems—

Procedures for solving turbidity sensor calibration problems are exactly analogous 

to those for stage sensor calibration problems. The only difference is that manual 

readings for turbidity are not generally available unless a portable turbidimeter is 

employed at field visits, or unless lab turbidities are recorded on some or all pumped 

samples. In either case, there are two problems that must be dealt with:

1. It is difficult to extract representative subsamples from pumped samples 

with high turbidity.

2. Portable and lab turbidimeters cannot be expected to produce equivalent 

results to field turbidimeters except under low-turbidity conditions (<10 

turbidity units).

It may be possible to deal with the second problem by establishing a relationship between turbidimeters. Unfortunately, it is difficult to establish a relationship 

that can be regarded as unique and unchanging for any given pair of turbidimeters 

(Lewis et al. 2007). Such relationships depend on the type of suspended material 

(mineralogy, organic content, particle size, etc.).
Implementation Guide for Turbidity Threshold Sampling: Principles, Procedures, and Analysis

63

Turbidity adjustment philosophy—The difficulty of representative subsampling is 

probably serious enough that it is not fruitful to attempt minor shifts in the turbidity record based on manual readings of high turbidity in pumped samples. Our 

recommendation is to apply shifts based only on manual readings of low turbidity 

samples below about 10 turbidity units, unless it is known that there was a serious 

calibration error. Keep in mind that calibration errors are unimportant if the only 

use of the turbidity data is to determine sediment loads. For sediment load calculations, it is only important to know the relationship between measured turbidity, 

with or without calibration errors, and SSC. Calibration errors are important only if 

the monitoring objectives require that a consistent and uniform turbidity record be 

collected, for example, for comparison with other streams.

Validating the turbidity record—

There are two primary tools needed to validate the turbidity record: first, a 

knowledge of typical patterns of turbidity in relation to hydrographs, and second, 

corroborating SSC or turbidity data from lab measurements, redundant field measurements, or data from upstream and downstream gaging sites. Examining scatterplots of pumped sample SSC versus field turbidity is probably the most typical 

means of validating questionable turbidity peaks.

Typical turbidigraphs—Recognition of typical and atypical turbidigraphs comes 

from experience in examining such records from a particular gaging site, but some 

generalities can be stated (fig. 25).

• Turbidity generally rises more rapidly than it falls (all examples in fig. 25)

• Falling turbidity usually exhibits a smooth exponential decay similar to flow 

(fig. 25a–c and 25e). 

• In most but not all watersheds, turbidity peaks at or before the streamflow 

peak (all examples).

• When stage and turbidity data are overlaid on similar scales, it is common 

for turbidity to fall from its peak more steeply than stage (fig. 25a–b and 25e).

• Turbidity often starts to decline during rising hydrographs when the rate of 

increase in stage declines (fig. 25a–c, 25e, and 25f).

• Corresponding turbidity peaks typically decline for a series of similar 

discharge peaks (fig. 25c).

• Short-duration turbidity pulses are commonly superimposed on the recession 

of a turbidigraph, indicating sediment delivery from local streambanks or 

hillslopes (fig. 25d–e).

• Turbidity can be somewhat erratic, especially during rising and peak conditions 

(fig. 25e–f). These are the most difficult data to validate. Corroborating data 

are invaluable during such periods.

Calibration errors are 

unimportant if the only 

use of the turbidity 

data is to determine 

sediment loads. 

For sediment load 

calculations, it is only 

important to know the 

relationship between 

measured turbidity, 

with or without 

calibration errors, 

and SSC. 
64

GENERAL TECHNICAL REPORT PSW-GTR-212 

Figure 25—Examples of storms illustrating good-quality turbidity data.

0

100

200

300

400

500

600

0

100

200

300

400

500

Dec 14

12 18 0

A

C D

B

Stage (feet) Stage (feet)

Turbidity (NTU)

Stage (feet)

Turbidity (NTU)

Stage (feet)

Turbidity (NTU)

Turbidity (NTU)

Turbidity (NTU)

0.8

0.6

0.4

0.2

0

0.25

0.20

0.15

0.10

0.05

0

Stage (feet)

Dec 6

6 12 18 0

E

0

0.1

0.2

0.3

0.4

0.5

0.6

0

100

200

300

400

Dec 12 Dec 13 Dec 14

Stage

Turbidity

0.05

0.10

0.15

0.20

0.25

0.30

20

30

40

50

Jan 11 Jan 12 Jan 13

F

Stage (feet)

Turbidity (NTU)

0

0.1

0.2

0.3

0.4

0.5

0.6

05

0

100

150

200

250

300

Feb 19 Feb 21 Feb 23

0

0.1

0.2

0.3

0.4

0.5

Feb 25 Feb 26 Feb 27

0

20

40

60

80

100
Implementation Guide for Turbidity Threshold Sampling: Principles, Procedures, and Analysis

65

Problem identification—

Progressive fouling—This condition is caused by the gradual buildup of 

sediment, algae, or fine debris on the optical window of the sensor, or a 

surface visible to the sensor. The turbidity gradually flattens or increases 

while streamflow is receding or low, then it suddenly drops when the sensor is 

cleaned (fig. 26a). Progressive fouling can generally be avoided by deploying 

sensors that use a mechanical method to remove the contamination.

Debris fouling—Debris fouling from twigs, leaves, and other objects usually causes 

upward spikes and erratic fluctuations in turbidity until the debris is dislodged. Spikes 

are often very brief, after which turbidity may drop suddenly to levels present before 

the problem occurred (fig. 26b). Samples collected during spikes due to debris fouling 

are located to the right of the general trend on plots of SSC versus turbidity (fig. 26c).

Direct sunlight—Some sensors are affected by direct sunlight. The altered 

signal is site specific, but a characteristic wave of similar duration and shape is 

superimposed on successive sunny days. The waveform may include both a spike 

and a dip (fig. 26d).

Air bubbles—Air bubbles caused by turbulent flow may produce high-frequency 

noise on the turbidity record. The amplitude of the fluctuations generally increases 

with increasing flow (fig. 26e).

Nonsubmergence—Nonsubmergence of the sensor often results in a decrease 

in turbidity. This can be caused when high flow raises an inadequately weighted 

boom to the water surface. Nonsubmergence can also occur when there is inadequate flow to cover a sensor. This condition creates a temporary rise in turbidity 

while the sensor is partially submerged, followed by depressed turbidity while the 

sensor is suspended in the air (fig. 26f–g). The characteristic pattern that appears 

when a sensor is being exposed may often be seen in reverse when the sensor is 

again submerged. The rising pattern will often be compressed in time because 

the stage generally rises faster than it falls.

Bed load and burial—When a sensor is partly buried by bed load or is in the 

saltation zone, it usually results in a very noisy pattern similar to debris fouling 

(fig. 26h). Spikes can be equally steep on the rising and falling side. To identify bed 

load influence, it is helpful to examine scatterplots of SSC versus turbidity (fig. 26i) 

and particle size distributions of pumped samples. Assuming the pumping sampler 

intake is also in the bed load zone, scatterplots will show outliers both above and 

below the general trend, and pumped samples are likely to be enriched in sand. 

These situations can be challenging to resolve. In the example (fig. 26h), there 
66

GENERAL TECHNICAL REPORT PSW-GTR-212 

were no samples collected during the large spike after sample 24 because pumping 

sampler capacity had been reached. The shape of that spike is plausible, but the 

weight of evidence points to continued fouling:

• The previous 9 hours’ data was apparently disturbed by bed load.

• It is a very large spike for a relatively calm period in the hydrograph.

• After the spike, sample 1 is again an outlier with high SSC.

Interference from channel—

Interference from the channel boundaries or objects such as wood in the channel 

is not always obvious because the general pattern of turbidity may be quite 

reasonable. An indication of this problem is when there is a poor relation between 

turbidity and SSC despite a reasonable turbidigraph that looks valid in other 

respects (fig. 26j–k).

Interference from water surface—

When the water surface is visible to the sensor, reflected light can cause an elevation in apparent turbidity. This may be partly responsible for characteristic patterns 

associated with exposure and resubmergence of sensors at low flows, as illustrated 

in the paragraph on nonsubmergence.

Adjusting Point Suspended Sediment Concentration to the 

Cross Section Mean

Before estimating sediment loads, one must decide whether the point SSC obtained 

from pumped samples should be adjusted to be more representative of the entire 

stream cross section. When multiplying SSC by flow, it is assumed that SSC is a 

discharge-weighted mean concentration. That is, each SSC in the cross section is 

weighted by the local discharge when computing the mean. A well-designed study 

plan will require that paired simultaneous pumped and depth-integrated samples 

are collected over the full range of flows and turbidities. Therefore, when the time 

comes to compute sediment loads, one can examine the relation between the two 

types of samples to determine whether an adjustment to the point SSC is needed.

An adjustment to the point SSC is warranted when:

• There is a statistically significant difference between point and 

depth-integrated concentrations, and 

• The prediction error is smaller than the error being corrected.

A well-designed study 

plan will require that 

paired simultaneous 

pumped and depthintegrated samples 

are collected over 

the full range of flows 

and turbidities.
Implementation Guide for Turbidity Threshold Sampling: Principles, Procedures, and Analysis

67

Figure 26—Examples of storms illustrating poor-quality turbidity data.

A

C

D

B

Stage (feet) Stage (feet)

Stage (feet)

Stage (feet)

Turbidity (NTU)

Turbidity (NTU) Turbidity (NTU) Turbidity (NTU)

2.0

1.5

1.0

0.5

E

7

8

Progressive fouling

Air bubbles

Debris fouling

Direct sunlight

Feb 27 Feb 29 Mar 2 Mar 4

60

50

40

30

20

10

0

6

7

8

0

0.2

0.4

0.6

10

20

30

40

50

60

70

Dec 13 Dec 14

12 18 0 6 12 18 0

30 40 50 60 70 80

20

40

60

80

100

120

1

2

3

4

5 6 7

8

Turbidity (NTU)

Suspended sedimentconcentration (mg/L)

1.6

1.8

2.0

2.2

2.4

2.6

0

50

100

150

Feb 1 Feb 5 Feb 9

0.2

0.4

0.6

0.8

1.0

10

20

30

40

50

60

70

Dec 28 Dec 29

18 0 6 12 18 0 6 12 18 0

Stage

Turbidity

Stage

Turbidity

Stage

Turbidity

Stage

Turbidity
68

GENERAL TECHNICAL REPORT PSW-GTR-212 

Figure 26—Examples of storms illustrating poor-quality turbidity data (continued).

J

Stage (feet)

Turbidity (NTU)

Interference?

Stage

Turbidity

H

Stage (feet)

Turbidity (NTU)

Bed load/burial

Stage

Turbidity

4

5 6 7

89

10

11

12

1415

16

17

18

1920

21

22

23

24

1 2 3 4

5

6

7

0

0.2

0.4

0.6

0.8

1.0

0

200

400

600

800

Start

End

Dec 31 Jan 1 100 200 300 400

0

500

1000

1500

4

5

76

9 8

10

11

12

14

15

16

17

18

19

20

21

22

23

24

1

2

3

4

5 6 7

Turbidity (NTU)

Suspended sediment 

concentration (mg/L)

Suspended sediment 

concentration (mg/L)

I

Turbidity (NTU)

K

0

0.1

0.2

0.3

0.4

0.5

0.6

0

200

400

600

1

2

3

4

5

6

7

8

9

10 11 12

Dec 12 Dec 13

G

Stage (feet)

Turbidity (NTU)

Nonsubmergence

0

0.2

0.4

0.6

0.8

1.0

1.2

0

50

100

150 Skipping on

water surface?

Disturbed

at time of

correction

Suspended in air

Jan 1 Jan 2

Stage

Turbidity

100 200 300 400 500 600

0

200

400

600

800

1

2 3

4

6 5

7

9 8 10

1211

F

Stage (feet)

Turbidity (NTU)

0

0.2

0.4

0.6

0

100

150

200

250

300

50

Zone of partial

submergence

Feb 6 Feb 9 Feb 12 Feb 15

Nonsubmergence

Stage

Turbidity
Implementation Guide for Turbidity Threshold Sampling: Principles, Procedures, and Analysis

69

F-test—

Before testing for a significant difference, egregious outliers should be removed, 

as they are likely to be the result of operator error in collecting manual samples. A 

common error, for example, is to enrich the sample by stirring up sediment when 

the sampler contacts the streambed. Cook’s distance is a standard statistic that 

can help identify outliers with undue influence on the regression coefficients. An 

F-test can be used to determine whether there is a statistically significant difference 

between a simple linear regression line and a line of perfect agreement ( y = x). The 

appropriate F-statistic is:

F =

 (SSW – SSE)/2

 SSE /(n – 2)

where SSW = ∑( yi – xi)

2

, SSE = ∑(yi – ŷi)

2

 , and ŷi denotes the fitted 

(predicted) values. The F-statistic is compared to the F(2, n – 2) distribution.

Transformations—

The F-test requires that the regression have normally distributed residuals with 

independent errors unrelated to x. If residuals are greater for larger values of x, 

it may be appropriate to transform both x and y before computing the regression. 

The most commonly useful transformation is the logarithm. The major drawback 

of the logarithmic transformation is that it cannot be applied to zero. If a logarithmic transformation is applied to the data, then a correction must be made after 

making predictions, when retransforming back to the original scale. The simplest 

method is the Baskerville (1972) correction, also known as the quasi maximum 

likelihood estimate, which requires multiplying the back-transformed prediction 

by the factor exp(0.5s

2

), where exp denotes exponentiation to the base e of the 

natural logarithm, and s = (SSE/(n – 2))0.5 is the residual standard error from the 

transformed regression.

Prediction error—

If the prediction error is greater than the error of using an unadjusted point concentration, then more error may be introduced by making the adjustment than not. 

Prediction error, in this context, is that error due to uncertainty in the regression 

coefficients, and can be assessed by looking at confidence bands around the regression line. If the confidence bands enclose the line y = x, then it is better not to 

introduce further error by adjusting the data. Generally, the confidence bands will 

not enclose the line if the F-test was significant at the same level of confidence.

(4)
70

GENERAL TECHNICAL REPORT PSW-GTR-212 

Shifting relationships—

It is important to be aware that the relationship between pumped and isokinetic 

sample concentrations might shift over time. This would likely occur with a change 

in channel morphology or other factors affecting turbulence at the pumping site. 

Therefore, it is a good idea to examine the relationship for different periods. If the 

relationship is shifting, then it may be necessary to use different relationships for 

different periods. Doing so reliably, however, requires a large number of paired 

samples. The application of multiple relationships should be done with great caution 

because, in environmental impact studies, they can confound real changes in SSC. 

Using multiple relationships within a particular analysis should be avoided unless 

there is unequivocal evidence that it is necessary.

In an ongoing study, the data set of paired pumped and depth-integrated 

samples will grow each year. Regressions done on the growing data set will 

vary slightly from year to year even if there is no real change in the relationship. 

Therefore it is best not to apply any adjustments until after all the data for a 

particular analysis have been collected. At that time, a decision can be made 

whether an adjustment is needed, and if so, then the adjustments can be based upon 

all the data. In long-term studies, the decision to make an adjustment will constrain 

and perhaps complicate subsequent analyses of the same data.

Estimating Sediment Concentration and Load

After the complete data set has been corrected, the primary task required for 

computing sediment flux is to estimate SSC at the same frequency as that of the 

recorded discharge and turbidity. Once the SSC has been estimated, the sediment 

load (load, flux, and yield are all synonymous terms) can be simply calculated as

L = Σktqici (5) i

where t is the time between measurements, and qi and ci are the instantaneous water 

discharge and estimated SSC for interval i. Generally, the conversion factor k will 

also be required to express the result in the desired measurement units. Before 

the advent of computers, it was common to sum average fluxes, weighted by flow 

duration, over flow classes (i.e., the flow-duration sediment-rating-curve method). 

However, with today’s computing power, there is no longer any reason to use such 

an approximate method.
Implementation Guide for Turbidity Threshold Sampling: Principles, Procedures, and Analysis

71

Software—

Most of the methods described in this section are available in a set of procedures 

developed by RSL for use within the R software package. The procedures and 

instructions for their use and installation can be found on the TTS Web page. 

The procedures are designed to be used for estimating sediment loads for input 

files created specifically by TTS Adjuster. The FTS processing system, still under 

development at this writing, is planned to include many of these methods as well.

Surrogate variables—

Instantaneous SSC is normally estimated by using one of three surrogate variables: 

turbidity, discharge, or time. Turbidity is the preferred surrogate. If the turbidity 

data are of high quality and if the range of turbidity is wide enough, the relationship between turbidity and SSC is usually very good and certainly better than the 

relationship between discharge and SSC. However, when turbidity data are of poor 

quality or missing, it may be necessary to fall back on discharge as the surrogate. 

If enough pumped samples were collected during the period of bad turbidity data, 

then instead of using a relationship between discharge and SSC, a better estimate 

of SSC can be obtained simply by interpolating SSC over time between samples. 

In the interpolation scenario, time is effectively the surrogate variable.

Defining the relationship between surrogate and SSC—

The selection of the data and determination of the surrogate relationship(s) are 

subjective, but must be guided by general principles. The data used to determine a 

surrogate relationship should ideally

• Be representative of the period whose flux is being estimated.

• Span the range of both variables in the period whose flux is 

being estimated.

• Include enough samples to reasonably define the relationship.

Annual loads—

For calculating an annual sediment flux, assuming the turbidity data are complete, 

it may be reasonable to fit a single relationship between turbidity and SSC based on 

a whole year’s data. Such a relationship is likely to overestimate some storm events 

and underestimate others, but these errors tend to balance one another and the 

annual flux may often be estimated accurately enough (often to within 10 percent) 

by the one relationship.

Replacing the estimated SSC during storm events by using storm-based 

relationships can result in an even more accurate estimate of annual load, and 

this can be used to validate or reject the first procedure.
72

GENERAL TECHNICAL REPORT PSW-GTR-212 

Storm event loads—

For calculating a storm event load, it is best to use only data from that storm event, 

assuming enough samples were collected. Using a relationship based on samples 

from the entire year or, worse, from a prior year is likely to severely miscalculate a 

storm event load. If an inadequate number of samples were collected to reasonably 

define the relationship, or if the samples collected do not span the range of turbidity or SSC or both, for the given event, then additional samples should be included 

from neighboring periods.

Dividing the data—

Just as with annual loads, it may be more accurate to divide a storm event into 

multiple periods and multiple relationships. This decision should be based on an 

examination of scatterplots between turbidity or discharge and SSC. If the samples 

in the scatterplot are numbered, then it is easy to tell if a relationship has shifted 

during the event. If the relationship clearly shifted, and if enough samples were 

collected to define both relationships, it usually is best to divide the event into two 

periods. The drawback to dividing the data is that the individual relationships will 

be less precise because they are defined by fewer points.

The form of the relationship—

Linear regression—Linear regression is usually the simplest relation considered. 

If several different models seem to fit the scatterplot adequately, the simplest 

relationship is usually preferred. However, there are other considerations. When 

dissolved substances are present that cause turbidity, or when SSC is determined by 

filtration and the filtrate contains fines that cause turbidity, concentrations near zero 

will have significantly positive turbidity, and linear relationships between turbidity 

and SSC will have a negative intercept. The result is usually that predictions of SSC 

are negative for some range of low turbidity values that have been recorded. The 

solution is to either set negative predictions to zero, or to adopt a model that never 

predicts negative SSC, for example, a log-log regression or a power function.

Transformed linear regression and bias corrections—Transformations can be 

used to produce models that cannot make negative predictions, to develop linear 

relationships, to normalize residuals, to equalize variance, or a combination of 

the above. Sometimes a transformation can accomplish more than one of these 

objectives, but there are no guarantees. Retransformed predictions from loglog regressions are always positive, but they have the drawback that they cannot 

be evaluated when the predictor is zero. The square root transformation may 

accomplish the same thing without eliminating predictions at zero.

Using a relationship 

based on samples 

from the entire year 

or, worse, from a 

prior year is likely to 

severely miscalculate 

a storm event load.
Implementation Guide for Turbidity Threshold Sampling: Principles, Procedures, and Analysis

73

Transformation of the response (the SSC) has a drawback. The prediction must 

be retransformed back to the original units, which introduces a bias (Koch and 

Smillie 1986, Miller 1984). The bias arises because regression predicts the mean 

of a normal distribution of responses for a given x, and the transformed mean of a 

normal distribution is not equivalent to the mean of the transformed distribution. 

To correct for retransformation bias, Duan (1983) introduced the nonparametric 

“smearing” estimator:

where ŷsm is the corrected prediction in original units, h is the inverse transformation, ŷ0 is the prediction before retransformation, and ei

 are the residuals from the 

regression. For square root transformations, the smearing estimator takes the form 

ŷsm = ŷ0

2

 + 2 ŷ0 ē + ei

2

/n where ē is the mean residual.

For transformations by ln (logarithm to the base e), the bias is always 

negative, and increases with regression variance. The smearing estimator 

becomes ŷsm = exp (ŷ0) exp (ei)/n. If the ln transformed regression has 

normally distributed residuals, there is an exact minimum variance unbiased 

estimator, but it is quite complex and computationally demanding (Cohn et al. 

1989). A more widely known, approximate correction for ln transformations 

when residuals are normally distributed is

ŷ

sm = 

1 ∑h( ŷ0

 + ei) n

n

i =1

ŷ

qmle = exp(0.5s2

 + ŷ0)

where s is the residual standard error of the transformed regression. The QMLE 

subscript stands for quasi-maximum likelihood estimator (Cohn et al. 1989). This 

estimator has also been called the naive correction, or the Baskerville (1972) correction. More generally, for logarithms to the base b, the QMLE correction factor is 

exp[0.5ln(b)

2

s

2

], which, for base 10 logarithms, yields ŷqmle = exp (2.65s

2

 + ŷ0 ).

Power function—The power function y = cxk can be obtained by retransforming logb ( y) = a0 + a1 logb (x) to obtain c = ba0

 and k = a1 , but it can also be fitted 

directly by using nonlinear least squares (NLS) estimation (Bates and Watts 1988). 

The estimated coefficients will be different from the retransformed linear regression, with or without correction. The log-log regression gives more weight to data 

with small x. Algorithms for NLS are iterative calculations that are provided in 

many statistical packages such as S-PLUS and R. Fitting a power function by NLS 

instead of log-transformed regression has the advantages that no bias-correction is 

necessary and data with large x are not deemphasized. In Microsoft Excel, power 

function regressions are computed by retransforming the log-log regression, not by 

(6)

(7)
74

GENERAL TECHNICAL REPORT PSW-GTR-212 

The advantage of loess 

is its flexibility. It nearly 

always can produce 

an acceptable fit to the 

data, but care must be 

taken not to over-fit 

the data.

NLS. Excel ignores the bias correction, so predictions based on Excel power models 

favor data with small x and are negatively biased.

Loess—Locally weighted polynomial regression (loess) is a nonparametric curvefitting technique that can estimate a wide class of regression functions without 

distortion (Cleveland 1979, Cleveland and Devlin 1988, Cleveland et al. 1988). It is 

similar to a moving average. The fitted value at each x is the value of a regression 

fitted to data in a neighborhood near x using weighted least squares, with the closest points being weighted most heavily. The degree of smoothing, which affects the 

number of points in each neighborhood, is determined by the user. The advantage 

of loess is its flexibility. It nearly always can produce an acceptable fit to the data, 

but care must be taken not to over-fit the data. With SSC and turbidity, we recommend smoothing until no more than one inflection point (where curvature switches 

between convex and concave) is present. A statistic that characterizes the amount of 

smoothing is the equivalent number of parameters, which can be compared to the 

number of parameters estimated by a polynomial fit. The loess computation seems 

to require at least two more data points than the equivalent number of parameters, 

so it cannot generally be applied with less than about five samples.

At many sites, organic sediment becomes dominant at low values of turbidity, 

causing a reduction in the slope of the rating curve near zero. The slope change is 

most apparent in annual plots of turbidity and SSC. Loess is often the most satisfactory modeling method to capture this slope change (fig. 27).

Figure 27—Loess curve fitted to yearly data from South Fork Caspar Creek, California.

•••

• • • • •

• • • •

• •

•

• • ••

• • •

•

• •

•

•

•

•

•

•

•

•

•

•

•

•

•

•

•

• •• • •

•

•

•

•

• • •

•

•

•

••

• • •

•

•

•

•

• • • •

Turbidity (NTU)

Suspended sediment concentration (mg/L)

Loess fit to annual data

600

400

200

0

0 100 200 300
Implementation Guide for Turbidity Threshold Sampling: Principles, Procedures, and Analysis

75

Because there is no simple algebraic expression for a loess curve, prediction is 

usually done within the statistical package that computed the curve. S-PLUS and 

R have prediction capabilities, but the built-in prediction methods do not permit 

extrapolation beyond the range of the data used to fit the model. Extrapolation can 

be done manually by computing regression lines for the last few points of the curve 

at either end of the range. The RSL procedures for computing sediment loads in R 

automatically extrapolate linearly when necessary.

Hysteresis and pairwise fitting—It is common for the relationship between 

surrogate and target variable to shift during an event. The shift is usually near 

the turbidity or discharge peak and, on a scatterplot, the shift often appears as a 

loop pattern known as hysteresis. Hysteresis loops for SSC and turbidity are less 

common and narrower than loops for SSC and discharge. It might be possible to 

approximate a loop as two curves, in which case the data could be divided into 

two periods, as discussed above, applying two of the previous methods to estimate 

the load.

Another approach to hysteresis loops, which is occasionally useful, is to connect each consecutive pair of samples with a line segment, using each segment 

to predict the period between that pair’s sample collection times. The reason this 

method is only occasionally useful is that, if turbidity or discharge is the surrogate 

variable, all the line segments must have positive slopes to produce a reasonable 

result (fig. 28). If any of the segments have negative slope, and some usually do 

have negative slope near the peak, the surrogate time series for that segment will be 

inverted, that is, surrogate peaks will be converted to SSC troughs and vice versa.

Linear time-interpolation—The one situation in which pairwise fitting is often 

useful is when time is the surrogate variable. In that case, pairwise fitting becomes 

linear interpolation between consecutive sample times (fig. 29). But SSC cannot 

always move in the same direction as time, so the positive slope restriction, 

which makes sense when turbidity or discharge is the surrogate, does not apply. 

As mentioned when surrogate variables were first discussed, if enough pumped 

samples were collected during a period of bad turbidity data, then instead of 

using a relationship between discharge and SSC, a better estimate of SSC can 

often be obtained simply by linear time-interpolation. A smoother curve can be 

obtained by using cubic spline interpolation, but a spline is not constrained to the 

y interval defined by each data pair, so the result is often less realistic than linear 

interpolation.
76

GENERAL TECHNICAL REPORT PSW-GTR-212 

Figure 28—Pairwise fit captures hysteresis in this data from the Xray tributary of Caspar Creek, 

California.

40 60 80 100 120 140 160 180 200

Pairwise fit

Turbidity (NTU)

Suspended sediment concentration (mg/L)

300

250

200

150

100

50

0

1

2

3

4

5

6

7

8

9

Figure 29—Linear time interpolation of suspended sediment concentration (SSC) for a storm 

at the Xray tributary of Caspar Creek, California.

0

200

400

600

800

1000

1200

Linear time interpolation

Suspended sediment concentration (mg/L)

Jan 1 Jan 2 Jan 3
Implementation Guide for Turbidity Threshold Sampling: Principles, Procedures, and Analysis

77

Variance of the estimated sediment load—

The variance of the estimated sediment load is the variance of the sum of the 

estimated fluxes for all the intervals in the period being estimated. The variance of 

a sum is the sum of the variances and covariances among the items being summed. 

The covariances cannot be ignored because flux estimates for similar values of 

turbidity are highly correlated. The covariance matrix of estimated fluxes is derived 

from the covariance matrix of the regression coefficients, assuming that the surrogate variable for intervals to be predicted is just a set of constants (not random).

Case of simple linear regression—The derivation is not difficult for simple 

linear regression, but the computation is demanding because the dimension of 

the covariance matrix of estimated fluxes is N × N, where N is the number of 

intervals being predicted. The elements of this covariance matrix must be 

summed to obtain the estimated variance of the load:

 V = s

2

Z'(X'X)

-1

Z (8)

where s is the residual standard error of the regression; X is the n × 2 design 

matrix whose rows are (1, xi

), xi is the ith sampled turbidity value; and Z is the 

N × 2 matrix whose rows are (ktqj, ktqj xj), qj and xj are the discharge and turbidity 

for the jth interval to be predicted, t is the length of the interval between instantaneous discharge readings, and k is a units conversion factor that expresses the load 

in the desired units.

Log-log regression—Variance computation is much more complicated for 

retransformed predictions from log-log regression (Gilroy et al. 1990).

Power functions—An approximate covariance matrix can be obtained for power 

functions by using the delta method for vector-valued functions (Rice 1994). If the 

power function is b0X b1

 , where X is an N × 1 vector of surrogate variables, the 

N × N covariance matrix of estimated concentrations is

where B = (b0, b1)' and V(B) is the coefficient covariance matrix estimated by using 

NLS. The dimension of the first bracketed matrix is N × 2, V(B) is 2 × 2, and the 

last matrix is 2 × N. The covariance matrix of estimated fluxes also includes the 

discharge, time interval, and units conversion factor:

 V = (kt)

2

QCQ (10)

where Q is an N × N diagonal matrix containing the discharges for each interval, 

and k and t are scalars defined as before.

C ≅ ∙Xb1, b0 ln(b1)X b1

∙V(B)∙Xb1, b0 ln(b1)Xb1∙

′ (9)
78

GENERAL TECHNICAL REPORT PSW-GTR-212 

Loess—The author is not aware of any methods that have been developed for 

estimating the covariance matrix for loess predictions or derived fluxes.

Coefficient of variation—A more intuitive way of expressing the variance of the 

estimated sediment load is the coefficient of variation, which is the standard error 

as a proportion or percentage of the estimated load. The standard error is just the 

square root of the variance, so the coefficient of variation is given by

A more intuitive 

way of expressing 

the variance of the 

estimated sediment 

load is the coefficient 

of variation, which 

is the standard error 

as a proportion or 

percentage of the 

estimated load. 

where V is the sum of the elements of the matrix V defined by equation 8 or equations 

9 and 10 above and L is the estimated sediment load defined by equation 5 above.

Statistical criteria for selecting among models—

There are several statistical criteria available for selecting between competing models. 

No statistic should be used as a yardstick, however, without considering the other 

factors mentioned previously in the sections “Storm event loads” and “The form of 

the relationship.” Models that are based on different data sets (e.g., with or without 

certain data pairs) should not be ranked based on a statistic. For small sample sizes, 

statistics are not very reliable. All other things being equal, the model that expresses 

the relationship in simplest terms should be the model chosen. In particular, models 

with too many parameters will tend to have greater prediction error.

Coefficient of determination (r

2

)—The coefficient of determination, r

2

, is an expression of the shape of a scatterplot. It is the squared correlation of predicted and 

observed values and can be interpreted as the proportion of total variance of y that 

is explained by x, a value always between zero and 1. Therefore, r

2

 depends strongly on the range of the data sampled. If the range of x is very small, on the order of 

the residual standard error, then r

2

 will be close to zero. If the range of x is much 

larger than the residual standard error, even as a result of one extreme value, then r

2

will be close to 1. In addition, r

2

 can be heavily influenced by a few points, so it is 

best not to use r

2

 if the values of x are not evenly distributed. Because r

2

 is unitless, 

it is possible to compare r

2

 between transformed and untransformed regressions. It 

is available from loess as well as linear and nonlinear regression models.

CV = 100 ∙

L

V (11)
Implementation Guide for Turbidity Threshold Sampling: Principles, Procedures, and Analysis

79

Residual standard error (s)—Residual standard error is an expression of the 

average deviation of points from the regression line. It is expressed in the same 

units as the y measurement; therefore, it cannot be used to compare regressions on 

transformed and untransformed data. It is available from loess as well as linear and 

nonlinear regression models.

Coefficient of variation of the estimated load (CV)—Coefficient of variation (CV) 

is a good yardstick for comparing loads estimated by transformed or untransformed 

linear regression, or both, but may not be available for models other than linear 

regression. For small sample sizes, CV may be a poor error estimate and is likely 

to be the least reliable of the criteria mentioned here.

Other statistics—There are other statistical criteria for selecting among competing 

regression models that are beyond the scope of this report. These include prediction 

residual sum of squares (PRESS), Mallows’ Cp , and Akaike’s information criterion 

(AIC). Unless the models being compared have different numbers of parameters, 

these criteria will generally give the same ordering of models as the residual standard error.

Quality Evaluation

Many considerations determine the quality of a sediment load estimate derived by 

using the methods described in this report. The following factors can be used to 

qualitatively rate a sediment load estimate.

• Sediment sample coverage

■ Were an adequate number of samples collected?

 • For a simple turbidigraph with one peak and no spikes, four or 

 more samples are needed.

 • Extra samples are desirable for complex turbidigraphs with multiple

peaks or spikes. We like to have at least two or three samples for 

each additional extended peak and one sample for each spike. 

■ Are there samples covering the full range of turbidity for the storm? If 

 the range of samples is too small, the regression slope will be unreliable, 

and extrapolation errors are likely. If the range or number of samples 

 is too small, samples from a neighboring storm event should be considered 

 for inclusion.

■ Are samples well-distributed temporally?

 • Were samples collected near the start, peak, and end of the storm?

 • Are there any extended periods with no samples?
80

GENERAL TECHNICAL REPORT PSW-GTR-212 

■ If there were periods of turbidity data with poor or questionable quality, 

were samples collected during those times? These are needed to validate 

 the turbidity and to permit time-interpolation if they show that the turbidity 

 measurement was bad.

• Relationship of the SSC to turbidity

■ What is the variance about the regression line? Low variance is meaning-

 less if there are only two data points or if one end of the regression line

 is based on a single point.

■ What is the CV of the estimated load? This expresses the standard error 

 of the estimate as a percentage. As with regression variance, if the sample 

 coverage is poor, this measure is meaningless.

■ Can the relation be expressed by a single regression? For a given data set,

 using more relationships divides the data into smaller groups, so there is

 greater uncertainty about each relationship.

■ Can the relation be expressed as a simple linear regression? Curvilinear 

fits are more susceptible to extrapolation error and require more data 

 for support.

• Quality of recorded turbidity

■ Refer to the above section “Validating the turbidity record” and figures

 25 and 26 for validation guidelines.

• Quality of pumped samples

■ Were the sample volumes too low or too high? 

■ Are there known problems in the transport or processing of the samples?

■ Were there any conditions (e.g., particle sizes, stream velocity, freezing 

 conditions, pumping sampler problems) that might have compromised the 

quality or representativeness of the pumped samples?

Documentation

The database containing annual results should include all of the following:

A. Raw data

B. Field forms

C. Raw data plots, annotated with notes from field forms

D. Corrected plots (optional)

E. A table or file containing at least the following information at the data logger’s recording interval (usually 10 or 15 minutes):

1. Date

2. Time
Implementation Guide for Turbidity Threshold Sampling: Principles, Procedures, and Analysis

81

3. Dump number (if applicable)

4. Bottle number (for intervals with pumped samples)

5. Raw stage

6. Corrected stage

7. Code for stage quality or type of correction

8. Discharge

9. Raw turbidity

10. Corrected turbidity

11. Turbidity code for quality or type of correction

12. Estimated concentration for periods in which flux was calculated

F. A table or file for each pumped sample that was collected, containing at least

1. Date

2. Time

3. Dump number (if applicable)

4. Bottle number

5. Lab concentration

6. Lab quality code (if available)

7. Items 5 to 11 merged from section E (optional)

G. Documentation of the procedures used for estimating sediment loads. 

This should be very detailed so that the whole process could be reproduced 

from scratch. For each period for which flux was estimated (there may be 

multiple periods per storm event), at least the following should be recorded:

1. Start and end times

2. Surrogate variable used (turbidity, flow, or time)

3. Type of model used (linear, loess, power, etc.)

4. Which sediment samples were included in the model for estimating SSC

5. Scatterplot showing the samples and model that were employed

6. Whether or not the concentrations were adjusted based on a relation 

between point and depth-integrated SSC

7. Explanations of the reasoning behind the above choices

8. Estimated sediment flux and related statistics

9. Summary plot showing discharge, turbidity, and estimated concentration

against time (one plot per storm event)

10. If sediment loads were estimated by using RSL’s load estimator package 

for R, some of the above documentation for estimating sediment loads 

could be recorded simply by saving a list of the R commands that were 

executed to compute the loads.
82

GENERAL TECHNICAL REPORT PSW-GTR-212 

Acknowledgments

The authors thank Mark Uhrich, Bob Ziemer, and Mike Furniss for reviewing 

the report. We also appreciate Elizabeth Lemon’s thorough copyediting and 

Tom Lisle’s helpful comments.

Acronyms

ASTM American Society for Testing and Materials

CV Coefficient of Variation

EPA Environmental Protection Agency

FBU2 Formazin backscatter units

FTS Forest Technology Systems, Ltd.

ISO International Standards Organization

NLS Nonlinear least squares 

NTU2 Nephelometric turbidity units

R Free software for statistical computing and graphics

PSW Pacific Southwest Research Station, U.S. Forest Service

RSL Redwood Sciences Laboratory (PSW field office, Arcata, 

 California)

SDI-12 Serial Data Interface at 1200 baud (digital data 

 communications protocol)

S-PLUS A commercial statistical software package

SSC Suspended sediment concentration

SSL Suspended sediment load

TTS Turbidity Threshold Sampling

TTS Web page http://www.fs.fed.us/psw/topics/water/tts/

USGS U.S. Geological Survey

2 Turbidity data reporting units were established by the U.S. Geological Survey in the 

National Field Manual for the Collection of Water Quality Data (Anderson 2004).
Implementation Guide for Turbidity Threshold Sampling: Principles, Procedures, and Analysis

83

English Equivalents

When you know: Multiply by: To find:

Meters (m) 3.2808 Feet (ft)

Centimeters (cm) .3937 Inches (in)

Millimeters (mm) .03937 Inches (in)

Microns (μ) .00003937 Inches (in)

Square kilometers (km2

) .3861 Square miles (mi2)

Meters per second (m/s) 2.2369 Miles per hour (mph)

Milliliters (mL) .06102 Cubic inches (in3

)

Grams (g) .03527 Ounces (oz)

Grams per cm3

 (g/cm3) 62.428 Pounds per ft 3 (lb/ft3)

Degrees Celsius (C) 1.8C + 32 Degrees Fahrenheit (F)

References

Ackers, P.; White, W.R.; Perkins, J.A.; Harrison, A.J.M. 1978. Weirs and flumes 

for flow measurement. Chichester, United Kingdom: Wiley & Sons. 327 p.

American Public Health Association [APHA]. 1998. Total suspended solids 

dried at 103-105 degrees Celsius Method 2540D. In: Standard methods for the 

examination of water and waste water. 20th ed. Washington, DC: 2-57 to 2-58.

American Society for Testing and Materials. 2000. Standard test methods for 

determining sediment concentration in water samples: D 3977–3997, Vol. 11.02, 

Water (II): 395–400.

Anderson, C.W. 2004. Turbidity (version 2.0) In: National field manual for the 

collection of water-quality data. Techniques of Water-Resources Investigations. 

Book 9. Chap. A6. Section 6.7. U.S. Geological Survey. http://water.usgs.gov/

owq/FieldManual/Chapter6/6.7_contents.html. (7 September 2007).

Baskerville, G.L. 1972. Use of logarithmic regression in the estimation of plant 

biomass. Canadian Journal Forestry. 2: 49–53.

Bates, D.M.; Watts, D.G. 1988. Nonlinear regression analysis and its applications. 

New York: Wiley & Sons. 365 p.

Black, C.A., ed. in chief. 1965. Methods of soil analysis, Part 1. Madison, WI: 

American Society of Agronomy, Inc.: 371–373.

Buchanan, T.J.; Somers, W.P. 1969. Discharge measurements at gaging stations. 

Techniques of Water-Resources Investigations. [Place of publication unknown]: 

U.S. Geological Survey. 65 p. Book 3. Chap. A8.
84

GENERAL TECHNICAL REPORT PSW-GTR-212 

Butcher, G.; Gregory, L. 2006. Continuous water-quality sampling programs: 

operating procedures. The Province of British Columbia, Resource Information 

Standards Committee. 115 p.

Chapra, S.C.; Canale, R.P. 1988. Numerical methods for engineers, 2nd ed. 

New York: McGraw-Hill: 152–153.

Cleveland, W.S. 1979. Robust locally weighted regression and smoothing 

scatterplots. Journal of the American Statistical Association. 74: 829–836.

Cleveland, W.S. 1993. Visualizing data. Summit, NJ: Hobart Press: 66–79.

Cleveland, W.S.; Devlin, S.J. 1988. Locally-weighted regression: an approach 

to regression analysis by local fitting. Journal of the American Statistical 

Association. 83: 596–610.

Cleveland, W.S.; Devlin, S.J.; Grosse, E. 1988. Regression by local fitting: 

methods, properties, and computational algorithms. Journal of Econometrics. 

37: 87–114.

Cohn, T.A.; DeLong, L.L.; Gilroy, E.J. [et al.] 1989. Estimating constituent 

loads. Water Resources Research. 25(5): 937–942.

Donahue, R.L.; Miller, R.W.; Shickluna, J.C. 1977. Soils: An introduction to 

soils and plant growth. 4th ed. Englewood Cliffs, NJ: Prentice-Hall. 283 p.

Downing, J. 2005. Turbidity monitoring. In: Down, R.D.; Lehr, J.H., eds. 

Environmental instrumentation and analysis handbook. Hoboken, NJ: 

Wiley & Sons: 511–546. Chapter 24. 

Duan, N. 1983. Smearing estimate: a nonparametric retransformation method. 

Journal of the American Statistical Association. 383(78): 605–610.

Eads, R.; Lewis, J. 2002. Continuous turbidity monitoring in streams of 

northwestern California. In: Glysson, G.D.; Gray, J.R., eds. Proceedings of the 

federal interagency workshop on turbidity and other sediment surrogates, April 

30–May 2, 2002, Reno, NV: U.S. Geological Survey Circular 1250. 3 p.

Edwards, T.K.; Glysson, G.D. 1999. Field methods for measurement of fluvial 

sediment. Techniques of Water-Resources Investigations. Book 3, Chap. C2. 

[Place of publication unknown]: U.S. Geological Survey. 87 p.
Implementation Guide for Turbidity Threshold Sampling: Principles, Procedures, and Analysis

85

Federal Interagency River Basin Committee. 1952. The design of improved 

types of suspended sediment samplers. Report No. 6. A study of methods used 

in measurement and analysis of sediment loads in streams. Project Offices of 

Cooperating Agencies at St. Anthony Falls Hydraulic Laboratory. Minneapolis, 

MN. 103 p.

Gilroy, E.J.; Hirsch, R.M.; Cohn, T.A. 1990. Mean square error of 

regression-based constituent transport estimates. Water Resources Research. 

26(9): 2069–2077.

Gray, J.R.; Glysson, G.D.; Turcios, L.M.; Schwarz, G.E. 2000. Comparability of 

suspended-sediment concentration and total suspended solids data. Water Resources 

Investigations Report 00-4191. Reston, VA. U.S. Geological Survey. 14 p.

Guy, H.P.; Norman, V.W. 1970. Fluvial sediment concepts: Techniques of WaterResources Investigations. [Place of publication unknown]: U.S. Geological 

Survey. 55 p. Book 3, Chap. C1. 

International Standards Organization [ISO]. 1983. Liquid flow measurement in 

open channels. Handbook 16. [Place of publication unknown.]

Kennedy, E.J. 1983. Computations of continuous records of streamflow. 

Techniques of Water-Resources Investigations. [Place of publication unknown]: 

U.S. Geological Survey. 53 p. Book 3, Chap. A13.

Kennedy, E.J. 1984. Discharge ratings at gaging stations. Techniques of WaterResources Investigations. [Place of publication unknown]: U.S. Geological 

Survey. 59 p. Book 3, Chap. A10. 

Kirchner, J.W.; Feng, X.; Neal, C.; Robson, A.J. 2004. The fine structure of 

water-quality dynamics: the (high-frequency) wave of the future. Hydrological 

Processes. 18. 1353–1359. 

Koch, R.W.; Smillie, G.M. 1986. Bias in hydrologic prediction using logtransformed regression models. Water Resources Research. 22(5): 717–723.

Langbein, W.B.; Iseri, K.T. 1972. General introduction and hydrologic definitions. 

In: Manual of hydrology: Part 1. General surface-water techniques. Water Supply 

Paper 1541-A. U.S. Geological Survey. http://water.usgs.gov/wsc/glossary.html. 

(17 February 2009). 

Lewis, J. 1996. Turbidity-controlled suspended sediment sampling for runoff-event 

load estimation. Water Resources Research. 32(7): 2299–2310.
86

GENERAL TECHNICAL REPORT PSW-GTR-212 

Lewis, J.; Eads, R. 2001. Turbidity threshold sampling for suspended sediment 

load estimation. In: Proceedings, 7th Federal Interagency Sedimentation 

Conference. 1(111): 110–117.

Lewis, J.; Eads, R.; Klein, R. 2007. Comparisons of turbidity data collected 

with different instruments. Arcata, CA. Report on a cooperative agreement 

between the California Department of Forestry and Fire Protection and 

USDA Forest Service—Pacific Southwest Research Station PSW agreement 

06-CO-11272133-041. 27 p.

Madej, M.A. 2005. The role of organic matter in sediment budgets in forested 

terrain. In: Sediment Budgets II. Proceedings of the 7th IAHS scientific 

assembly at Foz do Iguacu, Brazil. IAHS Publ. 292. International Association 

of Hydrological Sciences: 9–15.

Maidment, D.R., ed. in chief. 1993. Handbook of hydrology. New York: 

McGraw-Hill: 1400 p. 

Martinez, M.H.; Ryan, S.E. 2000. Constructing temporary sampling platforms for 

hydrologic studies. Gen. Tech. Rep. RMRS-GTR-64. Ogden, UT: U.S. Department 

of Agriculture, Forest Service, Rocky Mountain Research Station. 10 p.

Miller, D.M. 1984. Reducing transformation bias in curve fitting. American 

Statistician. 38: 124–126.

Rantz, S.E. 1982. Measurement and computation of streamflow: Volume 1. 

Measurement of stage and discharge. Water Supply Paper 2175. Washington, 

DC: U.S. Geological Survey. 284 p.

Rice, J. 1994. Mathematical statistics and data analysis. 2nd ed. Belmont, CA: 

Duxbury Press. 672 p.

Rieger, W.A.; Olive, L.J. 1984. The behavior of suspended sediment concentrations during storm events. In: Loughran, R., ed. Drainage basin erosion and 

sedimentation. Newcastle, New South Wales, Australia: 121–126.

Wagner, R.J.; Boulger, R.W., Jr.; Oblinger, C.J.; Smith, B.A. 2006. Guidelines 

and standard procedures for continuous water-quality monitors: station 

operation, record computation, and data reporting. Techniques and methods 

1-D3. U.S. Geological Survey. 51 p. http://pubs.usgs.gov/tm/2006/tm1D3/. 

(17 February 2009).
Implementation Guide for Turbidity Threshold Sampling: Principles, Procedures, and Analysis

87

Walling, D.E.; Webb, B.W. 1981. The reliability of suspended sediment load data. 

In: Erosion and sediment transport measurement. Proceedings of the Florence 

symposium. IAHS Publ. No. 133. International Association of Hydrological 

Sciences: 177–194.

Wilde, F.D.; Radtke, D.B.; Gibs, J.; Iwatsubo, R.T., eds. 1999. Collection of 

water samples: Techniques of Water-Resources Investigations. Book 9. Chap. A4. 

U.S. Geological Survey. http://pubs.water.usgs.gov/twri9A4/. (17 February 2009). 

Winterstein, T.A. 1986. Effects of nozzle orientation on sediment sampling. In: 

Proceedings of the 4th Federal Interagency Sedimentation Conference. Las 

Vegas, NV: Subcommittee on Sedimentation of the Interagency Committee 

on Water Data. 1: 20–28.

World Meteorological Organization (WMO). 1981. Guide to hydrological 

practices. Vol. I, Data acquisition and processing. Geneva.

World Meteorological Organization (WMO). 1988. Technical Regulations. 

Vol III. Hydrology. Geneva.

Yorke, T.H. 1996. Technical memorandum 96.05. Office of Surface Water. 

U.S. Geological Survey. http://water.usgs.gov/admin/memo/SW/sw96.05.html 

(17 February 2009). 

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