Neuse Estuary Eutrophication

ModelP a m lico S o u n d

E x ch an ge

A tm o sp h ere

L oa ding s E x ch an ge

R ivers, C reek s ,G ro u n d w a ter

O rg a n ic M a tter

D isso lvedO x yg en

A lg a eN euse R iver E stu ary

N u tr ien tsIn o rg a n icS ed im en ts

B e n th ic O r g an ic M a tte r

In o rg a n icC a rb o n

Sedim entE x ch an ge

V erticalM ixing

E stu ar in eC irculatio n

G row th/M orta lity /R ecyc ling

Sedim ents

NitrogenInputs Cause and Effect

RelationshipsCause and Effect

Relationships

Frequency of Hypoxia

Duration of Stratification

HarmfulAlgal Blooms

Carbon Production

SedimentOxygenDemand

RiverFlow

AlgalDensity

ChlorophyllViolations

Number ofFishkills

FishHealth

ShellfishAbundance

NeuBERNBayes Net

Estuary Model

“Given (conditional on) a 30%reduction in nitrogen loading, what is

the probability of an algal bloom?”

p(ALG|30% N-load reduction) = ?

Model proposed by Chen and Orlob (1972)

1,22211111111 )( FVCVCMsRQC

dt

dCEAQC

dt

dVCin

Phytoplankton (Algae) Mass Balance

(equation from the model proposed by Chen and Orlob (1972))

where: V = segment volume (m3) C1 = phytoplankton concentration (g/m3) Q = flow volume (m3/t) E = diffusion coefficient (m2/t) A = segment surface/bottom area (m2) µ1 = phytoplankton growth rate (t-1) R1 = phytoplankton respiration rate (t-1) s1 = phytoplankton settling rate (t-1) M1 = phytoplankton mortality rate (t-1) µ2 = zooplankton growth rate (t-1) C2 = zooplankton concentration (g/m3) F2,1 = fractional feeding preference

For phytoplankton settling, another common approach is to treat

phytoplankton settling as a velocity term with an areal loss:

(Chapra and Reckhow 1983, Chapter 14)

phytoplankton settling (mass/time) = v1AC1

Parameter Selection Example: Phytoplankton Settling

Table 1. Phytoplankton settling velocities (Bowie et al. 1985)