Neuse Estuary Eutrophication Model: Predictions of Water Quality Improvement

1. Neuse Estuary Eutrophication Model: Predictions of Water Quality Improvement By James D. Bowen UNC Charlotte

2. Calibration Summary • Both transport and water quality model are able to simulate observed system dynamics • nutrients generally decreasing “downstream” • high nutrients may not immediately produce high chl-a

3. Predictions of Water Quality Improvement • Compared Four Cases: 1. Base Case 2. 70% N concentration 3. 70% P concentration 4. 70% N & P concentration • Water quality parameters examined: • surface water chl-a • bottom water DO

4. Surf. Chl-a: Cum. Freq. Distn’s

5. Chl-a @ Cherry Point - Cum. Freq.

6. Chl-a @ New Bern - Cum. Freq.

7. Bottom DO Conc’s:All Segments

8. Cherry Pt. Bot. DO’s: Cum. Freq.

9. Bottom DO Conc’s: Lower Sed. Conc.

10. Another Special Feature of this Model Application Emphasis on quantifying modeling uncertainties

11. Uncertainty Analysis • Objective: put “error bars” on model predictions • Error sources: model error, boundary & initial conditions, parameter error • calibration performance gives estimate of model, boundary, and inital condition error • parameter error usually estimated with sensitivity analysis

12. Uncertainty Analysis • Standard sensitivity analysis: • vary model parameters one-by-one and measure variability in model predictions • Standard sensitivity analysis may under or over predict uncertainty • Basic problem:calibration and sensitivityanalysis done as separate, independent procedures

13. Uncertainty Analysis Method • Couple uncertainty analysis w/ calibration • Determine not one but many “feasible” parameter vectors • Each feasible vector produces desired system behavior • 31 of 729 were feasible • Run model w/ each feasible vector to determine specification uncertainty

14. Uncertainty Analysis • Prediction uncertainty = specification uncertainty + residual error • method similar to the “Regional Sensitivity Analysis” (Adams 1998) method used for Lake Okeechobee

15. Establishing System Behavior • Seasonal/Spatial Trends • based upon 1991 monitoring data • nutrients decreasing downstream • early mid-estuary phytoplankton bloom • later upper-estuary bloom • several pulses of high NOx conc. @ New Bern • DO decreases through season

16. System Behavior, cont’d • Expectations of model performance • based upon Chesapeake Bay, Massachusetts Bay, & Tar-Pam studies • nutrients w/in 50% • DO w/in 20 % (.5 - 1 mg/l) • Chl-a w/in 50%

17. System Behavior Definition • Compared mid-depth spatial average concentrations to behavior max & min values • New Bern and Cherry Pt. areas • Chl, DO, and NOx conc.’s • Feasibility statistic: • % of predictions within each behavior “window”

18. May June July Aug Chl Conc: Prediction & Behavior 80 60 New Bern Area 40 Conc. (ug/l) Cherry Pt. Area 20

19. May June July Aug NOx Conc: Prediction & Behavior New Bern Area 0.6 0.4 Conc. (mg/l) 0.2 Cherry Pt. Area 0.0

20. May June July Aug DO Conc: Prediction & Behavior 10 New Bern Area 8 Cherry Pt. Area Conc. (mg/l) 6 4

21. Determining behavior score and feasibility • Behavior Score = avg(% within window) • also require minimum % within window for each behavior

22. Specification of Variable Parameters • Key parameters and ranges taken from Adams (1998) • Focus on parameters affecting chl-a

23. Search for Feasible Parameter Vectors Preliminary Run (25 days) Accept Final Run (120 days) Accept #1 Accept #2 = 31 Vectors

24. Chl-a Predictions - 31 Behavior Producing Parameter Vectors - All Seg’s

25. Chl-a Predictions - Cherry Point Segments

26. WQ Improvement: Chl Conc. & Exceedence Frequency Reductions Percentage Reduction

27. Summary • WQ improvement predicted for ‘91 conditions • Predicted WQ improvement • chl: none @ New Bern, modest @ Cherry Pt. (approx. 20%) • DO: short-term improvement minor (long-term greater)

28. Summary, Cont’d • Uncertainty Analysis • focused on effects of parameter uncertainty • small percentage (4%) of cases exhibit desired system behavior • Chl concentration reduction “error bars” • estuary median value: 10 - 16% • Cherry Pt. median: 16 - 22%

29. Summary, Cont’d • Uncertainty Analysis • Chl concentration reduction “error bars” • estuary max. chl-a value: -1 - 3% • CP max. chl-a value: 0 - 18% • Reduction in % of values exceeding water quality standard (40 ug/l) “error bars” • estuary value: 0 - 23 %

30. What’s left to do? • Repeat analysis for other years • 1997 simulations completed next month • 1998 simulations pending additional funding • Consider longer-term sediment “clean-up” • requires full calendar of monitoring data (e.g. 1998 data)

31. Looking Forward: Using MODMON monitoring for modeling • simulating different years helps to quantify uncertainty due to hydrologic variability • MODMON monitoring far superior to 1991 data set • much more frequent, many more stations, includes vertical profiles, includes more parameters, includes sed’s

32. MODMON monitoring data: 1997 vs. 1998 • 1997 features • similar hydrologically to 1991 • no downstream boundary conditions before June • dedicated downstream elevation monitor not installed • abundance of high-quality data available to aid calibration/ verification

33. Neuse Estuary Inflows

34. MODMON monitoring data: 1997 vs. 1998 • 1998 features • unusal year hydrologically with a significant fish kill • dedicated downstream elevation monitor installed • abundance of high-quality data available to aid calibration/ verification • full year of monitoring data will soon be available

35. More Things to Do • Investigate other reduction scenarios • % reduction larger in Spring, Summer • different % reductions (40%, 50%) • Conduct comprehensive error analysis • intelligent searches of parameter space • quantitative parameter filtering analysis to select variable parameters