Effects of Nonpoint Source Effects of Nonpoint Source Marsh Loading on Complex Marsh Loading on Complex

EstuariesEstuaries

Edwin A. Roehl, Jr.

John B. Cook, PEAdvanced Data Mining Intl

Greenville, SC

South Carolina coastal estuariesSouth Carolina coastal estuaries

Myrtle Beach

Charleston

Beaufort

Savannah

GeorgetownGrand Strand

A brief review of tidal dynamics A brief review of tidal dynamics

Freshwater

Saltwater

Saltwater-FreshwaterInterface

Riverine Inputs

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 Difficult to wrestle down nonpoint source effectssource 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 riverComplex 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 Consider alternative approach to NPS modelingNPS 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) modelsArtificial 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, Q3D 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 ANN model performance for hydrodynamic behaviorhydrodynamic behavior

Data mining NPS – Consider Cooper Data mining NPS – Consider Cooper River Estuary case studyRiver 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 RiverCooper RiverEstuaryEstuary

Area of no development

Little impact from all point sources

Signal decomposition of water level Signal decomposition of water level

Periodic component – Tidal range

Chaotic component – Filtered water level

Dissolved oxygen (DO) dynamicsDissolved 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 Effects of rainfall on Cooper Riveron Cooper River

Z-axis – DOD

X & Y axis – 1- and 3-day rainfalls

∆2 mg/L

2 inches

2 inches

2 inches

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 Cooper River measured and predicted DO-deficit (DOD) as result of rainfall onlyDO-deficit (DOD) as result of rainfall only

RAINAA=2-day moving window average

In addition to rainfall effects, response In addition to rainfall effects, response surfaces show effects of WLs on DODsurfaces show effects of WLs on DOD1st 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.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

Diss

olve

d ox

ygen

(mg/

L)

16

18

20

22

24

26

28

30

32

Tem

pera

ture

(d

egre

e Ce

lsiu

s)

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 RiverBeaufort RiverEstuaryEstuaryComplex 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” Decision Support Systems make “what-ifs” easy for Beaufort River TMDL easy for Beaufort River TMDL

Savannah Harbor deepening 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 modelAccuracy insights: EFDC vs. ANN model for Savannah River, GA for Savannah River, GA

EFDC R2=0.10 M2M R2=0.90

Salin

ity, P

racti

cal S

alin

ity

Uni

ts

Stre

amflo

w (c

fs)

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

Simulate reduced freshwater flows with Simulate reduced freshwater flows with EFDC and ANN model and compareEFDC and ANN model and compare

EFDC R2=0.10 M2M R2=0.90

Salin

ity, P

racti

cal S

alin

ity

Uni

ts

Stre

amflo

w (c

fs)

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

Summary for NPS Estuary ModelingSummary 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

QuestionsQuestions

Contact:

John B. Cook

Advanced Data Mining Intl; Greenville, SC

John.cook@advdmi.com

843.513.2130

www.advdmi.com