Development of the Neuse Estuary Eutrophication Model Background

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

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

Neuse River Estuary

Facts About the Neuse River • • 3 rd 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-Green. Algae Bloom near New Bern

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

Low DO and Fish Kills: 94 -96 Cherry Point Streets Ferry

Water Quality Research Project MODMON = MODeling and MONitoring • 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 groups @ different times and places

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

W 2 Phytoplankton Growth Model 1 m /mmax 1 0 T. R. M. Light, Nutrients 0 Topt Temperature m = mmax * min(m / mmax) * T. R. M.

W 2 X-section Representation • trapezoidal cross-sections for each segment Layer 1 S 2 S 3 S 4 S 3 S 2 S 1 Layer 4 Sediment Compartments • quasi-3 d sediment/water-column interaction model

W 2 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, velocities @ 3 estuary stations • WQ Calibration Data – monthly mid-water nutrients, DO, chl-a @ 4 estuary stations

H 2 O & N Inflows - 1991

Inflow N/P molar ratio - 1991 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: Flow @ Streets Ferry, Elevation @ Oriental

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

Elevations @ Cherry Point Observed Model March April May

Water Level @ New Bern MAE = 0. 1 m Julian Day

Elev. Fluctuations - Power Spectrum Amplitude (m) Observed @ Cherry Point n = 0. 020 Model Frequency (Cycles/day)

Salinities @ Cherry Point 0 Salinity (ppth) 4 Model: Surface Observed: Top Bottom 8 12 Model: Bottom 16 Mar May July Sep

Modeled Salinities - September 1991

1991 Predicted Salinities: May - Sept. animation

Neuse Estuary - 1991 Nitrogen

Neuse Estuary - 1991 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 conc. @ New Bern • August high-flow event – high nutrients, low chl-a @ New Bern – high Sept. chl-a @ 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 0 Surface Sal. Middle 6 NOx . 5 0 DO Chl Surface 10 Middle 4 50 0 Mar May July Sep

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