Development of the Neuse Estuary Eutrophication Model: Background and Calibration By James D. Bowen UNC Charlotte

Neuse River Estuary Model Neuse Estuary Pamlico Sound Applied Water Quality Modeling Research

Neuse River Estuary

Facts About the Neuse River 3rd Largest River Basin in NC (6,234 mi 2 ) 200 miles long, 3000 stream miles Estuary in lower 50 miles 1.5 million people in basin, mostly near headwaters Nutrient loading has doubled since 70’s

Neuse River Problems: Algal Blooms Blue-GreenAlgae Bloom near New Bern

1997 Bottom Water DO Conc. Neuse River Problems: Low DO

Low DO and Fish Kills: Cherry Point Streets Ferry

Water Quality Research Project MODMON = MOD eling and MON itoring Interdisciplinary Applied Research –Water Quality and Biological Monitoring –Water Quality Modeling to predict w.q. improvement (30% nutr. red.)

Neuse Estuary Eutrophication Model Physical Processes

Neuse Estuary Eutrophication Model Water Column Biological Processes

Neuse Estuary Eutrophication Model Benthic/Water- Column Interactions

Neuse Estuary Eutrophication Model

Special Features of Modeling Unusually challenging system to model intermittent, weak stratification (wind driven) no strong tidal forcing sediments have important effects on nutrient and DO dynamics blooms of several different phytoplankton different times and places

Neuse Estuary Eutrophication Model based upon 2-d laterally averaged model CE-Qual-W2 Nutrient, phytoplankton, organic matter, DO model 3 phytoplankton groups (V.3) –summer assemblage, diatoms, dinoflagellates

W2 Phytoplankton Growth Model 0 1  /  max Light, Nutrients  =  max * min  /  max ) * T.R.M. Temperature 0 1 T.R.M. T opt

W2 X-section Representation trapezoidal cross-sections for each segment Layer 1 Layer 4 S1S2S3 S4 S1S2S3 Sediment Compartments quasi-3d sediment/water-column interaction model

W2 Sediment Submodel simple sediment diagenesis model –1 constituent: Sediment organic carbon (SOC) –SOC fate processes: redistribution, decomposition –SOD decomposition rate determines fluxes: O 2 demand, PO 4 release, NH 3 release –N, P, S, Fe redox reactions not considered e.g. NH 3 /NO 3, NO 3 /N 2, SO 4 /H 2 S –can simulate sediment “clean-up”

1991 Simulation Description Time Period: –March 1 - September 27, 1991 Boundary Data Frequency –Daily Flow and NO 3, monthly WQ Hydrodynamic Calibration Data –hrly. water elevations, salinities, 3 estuary stations WQ Calibration Data –monthly mid-water nutrients, DO, 4 estuary stations

H 2 O & N Inflows

Inflow N/P molar ratio Redfield Ratio

Other Model Characteristics 62 horizontal segments, 18 layers execution time step = 10 min. 2 branches: Neuse & Trent Rivers 12 tributaries: 9 creeks, 3 WWTP’s 16 state variables Boundary Conditions: Streets Ferry, Oriental

Neuse Estuary Model Results Transport Model Water elevations –time histories –spectral analysis Salinity distributions –time one segment –animations

Cherry Point MarchAprilMay Observed Model

Water New Bern Julian Day MAE = 0.1 m

Elev. Fluctuations - Power Spectrum Observed Cherry Point n = Frequency (Cycles/day) Amplitude (m)

Cherry Point Model: Surface Model: Bottom Observed: Top Bottom Mar May July Sep Salinity (ppth)

Modeled Salinities - September 1991

1991 Predicted Salinities: May - Sept. animation

Neuse Estuary Nitrogen

Neuse Estuary Chl-a Conc.’s

WQ Conditions: Summary Seasonal/Spatial Trends nutrients decreasing downstream April mid-estuary phytoplankton bloom June upper-estuary phytoplankton bloom several pulses of high NOx New Bern August high-flow event –high nutrients, low New Bern –high Sept. New Bern

1991 WQ Simulations Single parameter displays –Nitrate –Phytoplankton –Cumulative chl-a Multi-parameter display –New Bern time history

Modeled Nitrate - September 1991

1991 Predicted Nitrates: May - Sept. animation

Modeled DO - September 1991

1991 Predicted DO: May - Sept. animation

Modeled chl-a - September 1991

1991 Predicted chl-a: May - Sept. animation

Water Quality Prediction - New Bern Surface Middle Sal. NOx DO Chl Mar May July Sep Middle

Calibration Summary Transport Model –elevation variations predicted within 0.1 m –salinity variations within 2 ppth –dynamics nicely represented Water Quality Model –blooms of phytoplankton well represented –seasonal variations also represented –New Bern chl-a shows influence of physical processes

Summary, continued Water Quality Model –DO dynamics fit expectations based on 1997 monitoring Overall model performance –consistent with previous modeling efforts –sufficient for water quality improvement predictions