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

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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

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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

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Surf. Chl-a Cum. Freq. Distns
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Chl-a _at_ Cherry Point - Cum. Freq.
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Chl-a _at_ New Bern - Cum. Freq.
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Bottom DO ConcsAll Segments
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Cherry Pt. Bot. DOs Cum. Freq.
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Bottom DO Concs Lower Sed. Conc.
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Another Special Feature of this Model Application

• Emphasis on quantifying modeling uncertainties

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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

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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 sensitivity
  analysis done as separate, independent procedures

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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

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Uncertainty Analysis

• Prediction uncertainty specification
  uncertainty residual error
• method similar to the Regional Sensitivity
  Analysis (Adams 1998) method used for Lake
  Okeechobee

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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. _at_ New Bern
• DO decreases through season

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System Behavior, contd

• 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

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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

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Chl Conc Prediction Behavior
80
60
New Bern Area
40
Conc. (ug/l)
Cherry Pt. Area
20
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NOx Conc Prediction Behavior
New Bern Area
0.6
0.4
Conc. (mg/l)
0.2
Cherry Pt. Area
0.0
20
DO Conc Prediction Behavior
10
New Bern Area
8
Cherry Pt. Area
Conc. (mg/l)
6
4
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Determining behavior score and feasibility

• Behavior Score avg( within window)
• also require minimum within window for each
  behavior

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Specification of Variable Parameters

• Key parameters and ranges taken from Adams (1998)
• Focus on parameters affecting chl-a

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Search for Feasible Parameter Vectors
Preliminary Run (25 days)
Accept
Final Run (120 days)
Accept 1
Accept 2
31 Vectors
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Chl-a Predictions - 31 Behavior Producing
Parameter Vectors - All Segs
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Chl-a Predictions - Cherry Point Segments
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WQ Improvement Chl Conc. Exceedence Frequency
Reductions
Percentage Reduction
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Summary

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

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Summary, Contd

• 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

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Summary, Contd

• 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

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Whats 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)

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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 seds

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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

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Neuse Estuary Inflows
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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

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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