Comparing Turbidity and Streamflow Methods for Estimating Suspended Sediment in Chesapeake Bay

1. A Comparison of Turbidity-Based and Streamflow-Based Estimates of Suspended-Sediment Concentrations in Three Chesapeake Bay Tributaries John Jastram Virginia Water Science Center

2. Background • 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.

3. Study Objectives * Objectives were expanded to include nutrient estimates Evaluate the use of turbidity as a surrogate for estimating SSC in the James, Rappahannock, and N.F. Shenandoah Rivers. Compare two methods of estimating SSC: turbidity-based and streamflow-based regression models.

4. Approach Data Collection • Continuous water-quality monitoring • Water Temperature • Specific Conductance • pH • Turbidity • Sediment and Nutrient Sampling • Scheduled Monthly • Storm Events

5. Approach 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

6. Turbidity-Based Regressions • Multiple Linear Regression • Transformed Variables • Natural Logarithm • Square Root • Best Subsets Regression • Mallows CP, PRESS, Adj. R2 • Partial Residual Plots • Transformation Bias Correction

7. Streamflow-Based Regressions • 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

8. Comparison of Models • 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

9. James River n = 69 Continuous Data & Sample Data Rappahannock River n = 50 Discrete samples collected to adequately characterize the range of observed conditions NF Shenandoah River n = 27

10. Turbidity-Based Models

11. Streamflow-Based Models

12. Observed vs. Estimated SSC James River NF Shenandoah River Rappahannock River

13. Distributions of Residuals James River Rappahannock River NF Shenandoah River

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

15. Comparison of Error Statistics Error statistics for estimated concentrations and instantaneous loads

16. Effect on Summed Loads James River at Cartersville • Loads generated using LN transformed models in LOADEST • Greatly reduced width of 95% confidence intervals. • Critical improvement to enable change detection.

17. Transfer to Nutrient Estimations - TP James River Rappahannock River

18. Transfer to Nutrient Estimations - TN James River Rappahannock River

19. Further Potential Computed Suspended Sediment Concentration Computed Suspended Sediment Load Discharge, cfs http://nrtwq.usgs.gov Realtime instantaneous concentration and load estimates.

20. Challenges & Limitations • 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

21. Conclusions • 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.

22. Significance and Potential Benefits • 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

23. Additional Turbidity/Surrogate Studies by VA WSC • 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

24. John JastramDoug MoyerKen Hyer http://pubs.usgs.gov/sir/2009/5165/