Effects of Nonpoint Source

Marsh Loading on Complex

Estuaries

Edwin A. Roehl, Jr.

John B. Cook, PE

Advanced Data Mining Intl

Greenville, SC

South Carolina coastal estuaries

Myrtle Beach

Charleston

Beaufort

Savannah

Georgetown

Grand Strand

A brief review of tidal dynamics

Freshwater

Saltwater

Saltwater-FreshwaterInterface

RiverineInputs

Coastal Inputs

“…estuaries may never really be steady-state

systems; they may be trying to reach a balance

they never achieve.”

Keith Dyer, from Estuaries – A Physical Introduction (1997)

Difficult to wrestle down nonpoint

source effects

Difficult to measure and predict NPS impacts on

upland areas

Data sets sparse as compared to point source data

Equations and models to estimate loads can have large

prediction errors (50-100%)

NPS problem compounded on the coast

Low-gradient system with little or no slope

Tidal complexities of receiving stream

Poorly defined drainage areas

Limited understanding of runoff process along the coast

Complex forces on a tidal river

Overland flow

from watershed

Tidal forcing

from ocean

connection

•Small contributing watershed

•Little freshwater inflow

•Tidally dominated

Consider alternative approach to

NPS modeling

Data mining Transforming data into information

Amalgamation of techniques from various

disciplines: information theory, signal processing,

statistics, machine learning, chaos theory,

advanced visualization

The physics is manifested in the data

Need to extract the information from large

data sets of continuous monitoring w/in

estuary

Artificial Neural Networks (ANN) models

Mathematical representation of the brain

provides complicated behaviors from “simple” components

- neurons and synapses

models created by training the ANN to learn relationships

between variables in example data

form of machine learning from Artificial Intelligence (AI)

x1

x2

x3

x4

x5

y1

y2

inputs outputs

3D response surfaces for SC, WL, Q

Surface created by ANN model

“Unseen” variables set to constant value

Manifestation of historical behavior of system

Provides insight into the process dynamics or physics

ANN model performance for

hydrodynamic behavior

Data mining NPS – Consider Cooper

River Estuary case study

Sensitivity of DO to rainfall, water

tidal-level flushing action and tidal

range determined

Model able to simulate rainfall

effects/amounts

System had long-term data bases

>3 years of 15-minute WL, DO, SC,

WT

Cooper River

Estuary

Area of no

development

Little impact

from all point

sources

Signal decomposition of water level

Periodic component

– Tidal range

Chaotic component –

Filtered water level

Dissolved oxygen (DO) dynamics

Measured DO time

series

Dissolved-oxygen deficit

= difference b/w saturation

and measured

Or, in equation form:

DO deficit (DOD) =

DO [saturated f(T and salinity)]

- DO (measured)

Effects of rainfall

on Cooper RiverZ-axis – DOD

X & Y axis – 1- and 3-day

rainfalls

∆2 mg/L

The sensitivity of DOD

to rainfall :

DOD/inch ≈ 2 mg/L/ 8 in.

of rainfall over 2 days

= 0.25 mg/L per inch of

rainfall.

Cooper River measured and predicted

DO-deficit (DOD) as result of rainfall only

RAINAA=2-day moving window average

In addition to rainfall effects, response

surfaces show effects of WLs on DOD

1st response surface shows “Low WL” = higher DOD

(range of 3.0 to 4.5 mg/L)

2nd response surface shows “High WL” = lower DOD

(range of 1.5 to 2.8 mg/L)

Data-Driven model’s accuracy, Cooper R.

3

4

5

6

7

8

9

10

8/21/93 0:30 8/22/93 0:30 8/23/93 0:30 8/24/93 0:30

Date and time

Dis

so

lve

d o

xy

ge

n (

mg

/L)

16

18

20

22

24

26

28

30

32

Te

mp

era

ture

(de

gre

e C

els

ius

)

Measured Neural Network BRANCH/BLTM

Water temperature

Dissolved oxygen

• Mixing - Tides, Flows from 3 Rivers

• Weather (T, P Dew Point)

• Point Discharge Wastewater

Treatment Plants

• Non-Point Discharges - rainfall,

50% overbank storage

Beaufort River

Estuary

Complex tidal system

>9 foot tide range

Net flow to the north

Model developed for

TMDL and NPDES permits

Model simulates 3.5 years

of historical conditions

Decision Support Systems make “what-ifs”

easy for Beaufort River TMDL

Savannah Harbor deepening

Model hydrodynamics

How far does salinity intrude when Harbor is deepened?

What happens when fresh water flows are low?

Accuracy insights: EFDC vs. ANN model

for Savannah River, GA

EFDC R2=0.10M2M R2=0.90

Salin

ity,

Pra

ctic

al S

alin

ity

Un

its

Stre

amfl

ow

(cf

s)

EFDC unable to

predict peaksSource: Conrads, P., and Greenfield, J., (2008)

Simulate reduced freshwater flows with

EFDC and ANN model and compare

EFDC R2=0.10M2M R2=0.90

Salin

ity,

Pra

ctic

al S

alin

ity

Un

its

Stre

amfl

ow

(cf

s)

Source: Conrads, P., and Greenfield, J., (2008)

Summary for NPS Estuary Modeling

Stormwater and tidal effects (as well as point

source impacts) can be quantified using Data

Mining techniques

3D visualization gives valuable insight into

process physics of the system

Data Mining can be used with traditional

approaches to minimize errors in load

estimates from NPS

Questions

Contact:

John B. Cook

Advanced Data Mining

Intl; Greenville, SC

John.cook@advdmi.com

843.513.2130

www.advdmi.com