Neuse Estuary Eutrophication Model: Predictions of Water

Quality Improvement

By

James D. Bowen

UNC Charlotte

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

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

Surf. Chl-a: Cum. Freq. Distn’s

Chl-a @ Cherry Point - Cum. Freq.

Chl-a @ New Bern - Cum. Freq.

Bottom DO Conc’s:All Segments

Cherry Pt. Bot. DO’s: Cum. Freq.

Bottom DO Conc’s: Lower Sed. Conc.

Another Special Feature of this Model Application

Emphasis on quantifying modeling uncertainties

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

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

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

Uncertainty Analysis

• Prediction uncertainty = specification uncertainty + residual error

• method similar to the “Regional Sensitivity Analysis” (Adams 1998) method used for Lake Okeechobee

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

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%

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”

Chl Conc: Prediction & Behavior

May June July Aug

New Bern Area

Cherry Pt. Area

Con

c. (

ug/

l)

20

40

60

80

NOx Conc: Prediction & Behavior

May June July Aug

New Bern Area

Cherry Pt. AreaCon

c. (

mg/

l)

0.2

0.4

0.6

0.0

DO Conc: Prediction & Behavior

May June July Aug

New Bern Area

Cherry Pt. Area

Con

c. (

mg/

l)

4

6

8

10

Determining behavior score and feasibility

• Behavior Score = avg(% within window)

• also require minimum % within window for each behavior

Required % within Behavior Window

Parameter New Bern Area Cherry Point Area

Chl-a 80% 80%Dissolved Oxygen 80% 80%

Nitrate/Nitrite 80% 70%

Specification of Variable Parameters

Parameter # Val's Values UnitsCarbon:Chl Ratio 3 70, 50,100 (g/g)Phyto N fraction 3 .08, .03, .14 (g/g dry)

Labile POM Decay Rate 3 .06, .02, .14 1/dayMax. Phyto Growth Rate 3 2 3 3, .2 .3 .3, 3 5 5 1/day

1/2 Sat'n Cst Grwth, N 3 .1, .05, .2 g/m3

Phyto Set. Vel 3 .25, .15, .30 m/d

• Key parameters and ranges taken from Adams (1998)

• Focus on parameters affecting chl-a

Search for Feasible Parameter Vectors

Preliminary Run(25 days)

Final Run(120 days)

Accept

Accept #1

Accept#2

= 31 Vectors

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

0

20

40

60

80

100med 70med 100max 70max 100freq 70freq

Con

c. (

ug/

l) o

r P

erce

nta

ge

Median Maximum % above 40 ug/l

100%

100%

100%70%

70%

70%

Chl-a Predictions - Cherry PointSegments

0

20

40

60

80

100med 70med 100max 70max 100freq 70freq

Con

c. (

ug/

l) o

r P

erce

nta

ge

Median Maximum % above 40 ug/l

100%

100%

100%

70%

70%

70%

-5

0

5

10

15

20

25

30

35

med all med CP max all max CP freq all

Per

cen

tage

Red

uct

ion

All Seg's

All Seg's

All Seg'sCP Seg's

CP Seg's

Maximum Chl-a

Median Chl-a

Fract. Above40 ug/l

WQ Improvement: Chl Conc. & Exceedence Frequency Reductions

Per

cen

tage

Red

uct

ion

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)

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%

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 %

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)

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

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

Neuse Estuary Inflows

0

200

400

600

800

0 60.83 121.7 182.5 243.3 304.2 365

Infl

ow (

m3 /s

)

MayApr AugJun Jul SepMar

1991 FlowAverageFlow

1997 Flow

Oct Nov DecFebJan

1998 Flow

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

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