A Comparison of Turbidity-Based and Streamflow-Based Estimates of Suspended-

Sediment Concentrations in Three Chesapeake Bay Tributaries

John JastramVirginia Water Science

Center

Streamflow has been used as a surrogate to estimate fluvial sediment transport for over a half century (Campbell and Bauder, 1940).

Improved streamflow-based models (ESTIMATOR) have traditionally been used to estimate sediment and nutrient loadings to the Bay.

• Variability in relation between streamflow and constituent concentrations leads to large uncertainty terms.

Turbidity has been recognized as an effective sediment surrogate for decades (Walling, 1977).

Recent technological advances have enabled the in-situ measurement of turbidity at high temporal resolution.

CBP funded a study of the effectiveness of turbidity-based SSC estimation in Bay tributaries.

1. Evaluate the use of turbidity as a surrogate for estimating SSC in the James, Rappahannock, and N.F. Shenandoah Rivers.

2. Compare two methods of estimating SSC: turbidity-based and streamflow-based regression models.

* Objectives were expanded to include nutrient estimates

Data Collection

Continuous water-quality monitoring

• Water Temperature• Specific Conductance• pH• Turbidity

Sediment and Nutrient Sampling

• Scheduled Monthly• Storm Events

Data Analysis

Generate site-specific turbidity-based multiple regression models.

Generate site-specific streamflow-based multiple regression models (ESTIMATOR).

Compare quality of estimates from each method• Accuracy and precision of estimates

ε])f(xβ)...f(xβy)f(turbiditββ[fSSC kkjj ˆˆˆˆ10

1

) 2cos(ˆ) 2sin(ˆ)(ˆ)(ˆ)/ln(ˆ)/ln(ˆˆ)ln( 652

432

210 ttttttqqqqc cccc

ε])f(xβ)...f(xβy)f(turbiditββ[fSSC kkjj ˆˆˆˆ10

1

• Multiple Linear Regression Transformed Variables Natural Logarithm Square Root

Best Subsets Regression Mallows CP, PRESS, Adj. R2

Partial Residual Plots Transformation Bias Correction

Multiple Regression Model (ESTIMATOR)• Explanatory variables:

Streamflow Time Seasonality

Calibration Datasets• Two models generated per site using:

9-year window Typically used for Bay tributaries To allow overall comparison of approaches

Study period Same data window used for turbidity-based models To allow direct comparison to turbidity-based models

) 2cos(ˆ) 2sin(ˆ)(ˆ)(ˆ)/ln(ˆ)/ln(ˆˆ)ln( 652

432

210 ttttttqqqqc cccc

Comparison of accuracy and precision of concentration estimates from each method.• Hypothesis tests

Squared-ranks Tests for homogeneity of variance Are the variances of the streamflow-based estimates greater than

those of the turbidity-based estimates? Estimated Concentrations Residuals

• Comparison of error statistics for concentration and instantaneous load estimates MSE SSE MAE

• Graphical evaluation of observed and estimated concentrations

James Rivern = 69

Rappahannock Rivern = 50

NF Shenandoah Rivern = 27

ContinuouContinuous Data s Data

&& Sample Sample

DataData

Discrete samples

collected to adequately characterize the range of

observed conditions

Observed Observed vs. vs.

Estimated Estimated SSCSSC

Rappahannock River

NF Shenandoah River

James River

James River

Rappahannock River

NF Shenandoah

River

Distributions Distributions of Residualsof Residuals

•Tests for homogeneity of variance•Estimated Concentrations•Residual• H0 = Variance Streamflow-based > Variance Turbidity-based

Error statistics for estimated concentrations and instantaneous loads

Loads generated using LN transformed models in LOADEST

Greatly reduced width of 95% confidence intervals.

Critical improvement to enable change detection.

James River at Cartersville

James River

Rappahannock

River

James River

Rappahannock

River

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Realtime instantaneous concentration and load estimates.

http://nrtwq.usgs.gov

Data Collection• Sensor Fouling

Missing data• Sensor Deployment

Data Analysis• Missing Data• Tools for load estimation

High temporal resolution Data Transformations

Uncertainty of summed loads

Use of continuous water-quality data as a surrogate for sediment and nutrients is a viable approach in Bay tributaries.

Turbidity-based estimation models can provide estimates of concentration and load with less uncertainty than the typically applied streamflow-based methods.

Limitations of data analysis procedures need to be resolved to support temporally dense datasets and alternate transformations.

Methodology has been developed to generate load data with increased accuracy and precision• Facilitates change detection

Adoption of this approach could result in • Immediate improvements in data quality

Improved ability to detect change• Long term reductions in sample collection needs

Indian Creek Pipeline Monitoring SIR 2009-5085 (Hyer and Moyer)

South River Mercury SIR 2009-5076 (Eggleston)

Roanoke River Flood Reduction Project Masters Thesis (Jastram, 2007) JEQ Article (Jastram, Hyer, and others, 2010) SIR (≈2012)

Fairfax County Watershed Study Difficult Run Executive Order

Smith Creek Executive Order

http://pubs.usgs.gov/sir/2009/5165/http://pubs.usgs.gov/sir/2009/5165/