Modeling of River Water Quality: Its Application to ... · Modeling of River Water Quality: Its Application to Forecasting and Alarm Systems ... Ipo weir Han River • Large ... Watershed

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Modeling of River Water Quality:

Its Application to Forecasting

and Alarm Systems

HydroAsia 2014

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I. Introduction

II. Water Quality Modeling

III. Water Quality Forecasting System

IV. Water Quality Alarm System

V. Conclusion

Contents

31. Introduction

Global Water Resources Technology

• Major Tech in the 21th century

(US National Academy of Engineering)

• Solving the water problem will lead to Nobel Prize

(John F. Kennedy Quote)

41. Introduction

Water Resources in Korea

Total 1,277 mm × 99,460 km2 = 127 billion m3/yr

Usable resources 75.3 billion m3/yr

GW Usage 3.7 bil.

Usage 10.8 bil.

Usage 18.8 bil.

Actual usage 33.3 bil. (26%)

Loss 42.0 bil.

(32%)

51. Introduction

Four Major River Restoration Project

Ipo weirHan River

• Large-scaled river engineering works have been done in the major

rivers in Korea to provide

(1) water security

(2) flood control

(3) ecosystem vitality

(4) new public spaces for recreation

Gangjung weirNakdong River

Baekje weirGuem River

Seungchon weirYeongsan River

61. Introduction

Necessity of Water Quality Modeling

• Four Major River Restoration Project affect on the

water environment in the Korean Rivers

• Increase use of riverside areas for civil recreation

• Countermeasures for accidents from oil spills, factory

toxin, etc.

• Predict algal bloom, non-point contaminant spread for

early prevention or response

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II. Water Quality Modeling

82. Water Quality Modeling

• A method to analyze and estimate the various

physical and chemical phenomenon when

contaminants enter a water body system

• The biggest objective of water quality modeling is

to estimate the changes of ecology and water

quality changes, and find reasonable responsive

measures based on the estimates for WQ

management

WQ Modeling

92. Water Quality Modeling

WQ incident

Conceptual

model Computer

model

River data

WQ

visualization

226000 228000 230000 232000 234000 236000 238000 240000 242000

442000

444000

446000

448000

450000

•Governing eq.

•Attached eq.

WQ Modeling Process

Mathematical

model

102. Water Quality Modeling

Classification of WQ contaminants

• Point source vs. Non-point source

• Conservative vs. Non-conservative

• Soluble vs. Insoluble

• Active vs. Passive

- mineral and sand

- waste heat (hot waste water)

- organic pollutants: BOD, COD

- heavy metal: Pb, Hg, Cd, As, Cu, Cr, Zn, etc.

- chemical compound: Benzene, Phenol, DDT, etc.

- radioactive materials

- chemical warfare materials

* Contagious bacteria: dysentery, typhoid, etc.

112. Water Quality Modeling

Contaminant Input and Movement

Agricultural

Area

Urban

Area

Rainfall

Non-point

Source

Receiving

Stream

Combined

Sewer

System

Overflow

Sewage

Treatment

Plant

Separate

Sewer

System

Effluent

IndustrySTP

Accident SpillEffluent

Accident

Spill

12

Objective of WQ Modeling

Water Use Water Supply

Fisheries

Recreational

Ecological Balance

Pollutant Point

Non-point

Water Quality Standard

(Desired)

Aquatic Ecosystem Monitoring

(Actual: Measured

or Computed)

Pollutant Concentration

Desired vs. Actual

Environmental Engineering Control

Actual>Desired

Water Quality Modeling

2. Water Quality Modeling

132. Water Quality Modeling

History of WQ Modeling

• 1925: Streeter and Phelps (US Ministry of

Health) models the changes of BOD and DO in

the Ohio River by the Mississippi River

• 1950-60’s:- Changes to the Streeter-Phelps model

- Finite Segment method, the base theory for the WASP model is developed by Thomann

- The Vollenweider model for the reservoir is developed

142. Water Quality Modeling

• 1970’s:

- General purpose models using repeatable computer

programs applicable to various water bodies is

developed (DOSAG-I, QUAL-I),

- Simulates various water quality factors other than

BOD-DO

- Water Resources Engineers, Inc. develops QUAL-II, a

multi-factor river ecologic model with the support of

USEPA

- LARM model from the US Army Corps of Engineers

is developed

152. Water Quality Modeling

• 1980’s:

- After the necessity arose for the non-contaminant total quantity

regulation, the QUAL2E is developed so the QUAL-II model is

useable in PCs

- CE-QUAL-W2 is developed and the progress of computers and

numerical methods allow the water quality models to represent

complicated natural phenomenon precisely

- USEPA develops the WASP model based on Thomann eq on

Finite Segment method

- A reservoir model and river model is combined to form the

HSPF model

- Computer development leads to the creation and active

research of 2D and 3D water quality models

- US Army Corps of Engineers develops a 2D river flow and

water quality analysis model (RMA-2, RMA-4)

- DHI creates the MIKE21 model

162. Water Quality Modeling

• 1990’s:- Total Pollutant Load Management system is conducted, Environment

effects evaluation is reinforced, river information is expanded so the

necessity of water quality models is strengthened

- GUI software for multi dimensional water quality models (SMS, RAMS)

- The coordination and integration of water quality models and ecology

models

• 2000’s - present:

- Improved computer tech. embraces better GIS software, online

accessibility to data, and robust framework linking various models

- Linkage of 3D hydrodynamic models and water quality models

(EFDC, WASP) is developed by U.S. EPA

- Integration of GIS software, databases and modeling programs

implements a multipurpose environmental analysis system (BASINS)

172. Water Quality Modeling

Water Quality Model Classification

• Model spatial classification

• Model time span classification

• Model time fluctuation classification

• Model application domain classification

182. Water Quality Modeling

Spatial Classification

• 0-dimensional model- Assume that a body of water is a Continuous Stirring Tank Reactor,

and the contaminant materials are spatially equally distributed

- Description of the material movement or hydraulic mechanism is neglected

- Suitable for the assessment of the inorganic nutrients budget such as the phosphoric acid that is accumulated every year

• 1-dimensional model- Currently the most commercialized model type

- Rivers are divided longitudinally and random cross sections are assumed to have homogenous characteristics

- Lakes are divided horizontally to the water surface and assumes that each section maintains a homogenous water quality

192. Water Quality Modeling

Spatial Classification

• 2-dimensional model- Assumes that WQ change distributes in 2 directions (x-y, or y-z

direction)

- Divides sections to the x-z directions in dams or lakes with long

channels and high depths

- Rivers, estuaries and gulfs are divided into x-y directions since the

horizontal area is greater than the depth based area

• 3-dimensional model- Applied in circulation pattern of large lakes or hydrodynamics study in

large estuaries

- Drawbacks: Complicated compared to the 1,2 dimensional models and

requires high computational cost for real application

202. Water Quality Modeling

Time Span Classification

• Long-term models- Used in mainly systems with one dimensional models

- When it is better to simplify the complicated biological interactions for

determining the mass balance in natural lakes

- Time span: monthly, yearly

• Short-term models- Used in analysis of actual contaminant problems

- Able to predict the contaminant materials’ detailed affects on the system

- Time span: hourly, daily

212. Water Quality Modeling

Time Fluctuation Classification

• Dynamic model- The model prediction situations change through the course of time

- Important in modeling water quality in estuaries

- Able to simulate periodical affects

- Examples : WQRRS, CE-QUAL-RIV1, KORIV1, etc.

• Steady model- The time changes do not affect the factors in the model equation

- The external variables of the system consisting the environmental conditions are defined to be a constant regardless of the internal characteristics of the system (flow rate, water quality)

- The equation is relatively simple and easily calculated

- Suitable in establishing long term WQ management countermeasures for certain areas or finding system reactions to extreme environmental condition changes

- Examples : QUAL2E

222. Water Quality Modeling

Application Domain Classification

s = 3~25 ppt

Water

shed

Lake/

ReservoirRiver

Tidal

RiverEstuary Ocean

Lumped

Distributed

2DH

2DV

3D

1DH

2DH

3D

1DH

2DH

3D

2DH

2DV

3D

3D

River

Tidalriver

Ocean

Fresh water Brackish water Salt water

s < 3 ppt s > 25 ppt

Lake/Reservoir

Estuary/BayWatershed

232. Water Quality Modeling

Uncertainties in WQ Modeling

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Physically Based Model & Data Based Model

• Due to uncertainty of contaminant source and nonlinearity of

water quality

• Data-driven model has become a new tool as the efficient

model for prediction and forecasting the water quality factors

2. Water Quality Modeling

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Data Based Model

2. Water Quality Modeling

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Limits of Data Based Model

• Data-driven model is simple

• Can give quick and reliable results

• Cannot explain the physical processes of the systems,

typically called as ‘black-box’ model

• Can be used for supplementing the physically based model

2. Water Quality Modeling

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III. Water Quality Forecasting

System

283. Water Quality Forecasting System

Water Quality Forecasting System

• Analysis of the current WQ and estimate future WQ for

any specified area for the information provision to citizens

DATAACQUISITION

WATERQUALITY

PREDICTION

NOTIFICATIONTO

CITIZENS

1 2 3

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Water Quality Forecasting (JAPAN)

Provide

data

Adminnetwork Flood control

network

River info

network

Flood forecast

3. Water Quality Forecasting System

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WQ Forecasting System (Korea)

• Quality Control Center, National Institute of Environmental

Research

• Goal: to shift the water quality management paradigm into more

advanced level through preventive actions such as pollutant source

control and dam water flushing

• Water Quality Control Center was established in NIER in July 2010.

• A WQ forecasting system for the four major river basins was

developed for the

effective WQ management (Aug. 2011).

• Preliminary WQ forecasting (Aug. 2011 to Dec. 2011).

• Formal WQ forecast service started in the four major river basins (Jan.

2012- present).

(Materials from NIEV, 2012)

3. Water Quality Forecasting System

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The Procedure of 7-days WQ

ForecastWeather data

Hydrologic data

Water quality data

• Weather observation data

• Weather forecasting data

(UM-Regional / UM-Global)

• Flow & stage monitoring

data

• Dam water release data &

plan

• Manual WQ monitoring data

• Automatic WQ monitoring

data

• Tele-monitoring system (TMS)

data

Watershed modeling (HSPF)

River WQ modeling (EFDC)

Update ICs & BCs

PredictFlow & WQ

Update ICs & BCs

Predict WQ

HSPF model run

EFDC model run

Report forecasting results

Validatethe prediction

DataPreprocessing

DA

Validatethe prediction

3. Water Quality Forecasting System

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The Example of 7-days WQ Forecast

• Forecasting area:

the representative upstream areas of

three weirs in the Han River basin

(Kangcheon, Yeoju, and Ipo)

• Forecasting item:

water temp. and Chlorophyll-a level

- It will be extended to other WQ

variables in the future (e.g., TOC & SS)

• Forecasting model:

a HSPF-EFDC coupled model developed

in the Paldang dam watershed

(about 20,960 km2)

• Forecasting report: A 7-days WQ forecast are officially announced on every

Monday and Thursday and circulated to water management agencies in the

Han River basin via a dedicated website.

3. Water Quality Forecasting System

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Ipo Ipo

Ipo Ipo

3. Water Quality Forecasting System

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Water Quality Prediction Using ANN

2. Water Quality Modeling

• Forecasting tomorrow WQ, Using today and yesterday WQ data

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WQ Forecasting Results Using ANN

2. Water Quality Modeling

5

6

7

8

9

10

pH

0 50 100

Data set

-0.4

-0.2

0

0.2

0.4

Err

or

Observation

ANN modelR2=0.88

362. Water Quality Modeling

4

8

12

16

DO

(m

g/L

)

0 50 100

Data set

-202468

Err

or

(mg

/L)

Observation

ANN modelR2=0.90

372. Water Quality Modeling

0

40

80

120

Chl-

a (

mg/m

3)

0 50 100

Data set

-40

-20

0

20

Err

or

(mg/m

3)

Observation

ANN modelR2=0.86

382. Water Quality Modeling

1

2

3

4

5

TN

(m

g/L

)

0 50 100

Data set

-0.8

-0.4

0

0.4

0.8

Err

or

(mg

/L)

Observation

ANN modelR2=0.93

392. Water Quality Modeling

0

0.05

0.1

0.15

0.2

0.25

TP

(m

g/L

)

0 50 100

Data set

-0.06-0.04-0.02

00.020.04

Err

or

(mg

/L)

Observation

ANN modelR2=0.88

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IV. Water Quality Alarm System

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WQ forecasting system WQ alarm system

Character predictive, reliable temporal, instantaneous

Applicationbased on theoretical or physical

equations

focuses on field verification and

applicability

Situation normal situations emergency situations

Time span mid term – long term short term

Examples

Water Quality Forecasting vs Alarm System4. Water Quality Alarm System

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Water Quality Alarm Procedure

yes

no

no

수질통제소

Real-time WQ

monitoring

Data acquisition, analysis

Emergency?

Estimation using WQ

model

Controllable?

Notice users (watch or alarm)

Accident

reception

Intake or filtration Recreational facilitiesFishery or farms Normal citizens

Establish or effect

reduction measures

yes

4. Water Quality Alarm System

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Water Quality Alarm System (Germany)

4. Water Quality Alarm System

444. Water Quality Alarm System

• Water Quality Prediction for accidents

- Predict the pollutant concentration at water intake plants

in Paldang lake, Korea using the 2-D water quality

analysis model (RAMS) when there are some pollutant

spill accidents.

• With Real Stream Information

-Geomorphic Data (Coordinates, Bed Elevation)

-Hydrologic Data (Flow rate, Water Level)

-Water Quality Data (BOD/SS Concentration)

Water Quality Alarm System (Korea)

454. Water Quality Alarm System

RAMS Structure

ASCII Solution

xxxx.out

Binary Solution

xxxx.vel

HDM-2D

GUI

CTM-2D

ASCII Solution

xxxx.ot4

Binary Solution

xxxx.pol

HDM-2D Run Control

& Boundary Condition

xxxx.rc2

CTM-2D Run Control

& Boundary Condition

xxxx.rc4

ASCII Geometry

xxxx.rgo

GUIpost-process

pre-process

464. Water Quality Alarm System

Procedure

HDM-2DTwo Dimensional Flow Model

CTM-2DTwo Dimensional Advection-Dispersion Model

RAMSPre- and Post-processing

474. Water Quality Alarm System

- Two Dimensional Flow Model

HDM-2D

u, v : Depth Averaged Horizontal Velocity

x, y, t : Polar Coordinates and Time

H : Bed Elevation h : Water Depth

υT : Turbulent Viscosity Coefficient g : Gravity Acceleration

n : Manning’s Roughness Coeff. Sij : Dispersion stress

22

4/3

i j j iji i ij T

j i i j j j

u u u Su u uH hu g g gn

t x x x x x h x

( ) ( ) ( )( )

H h H h H hu v w H h

t x y

484. Water Quality Alarm System

- Two Dimensional Water Quality Model

CTM-2D

2 2

where, : Depth Averaged Concentration, , : Depth Integrated Velocity,

: Water Depth

: Elements of Dispersion Coefficient Tensor

: Magnitude of Velocity Vector

: Logitudinal Dispersi

i

ij

L

C u u v

h

D

U u v

D

( ),

on Coefficient,

: Transverse Dispersion CoefficientTD

0 0 0

1 1h z z

ij i j

z

D u u dz dz dzh

( ) i

ij

i i j

hu ChC Ch D khC Q

t x x x

2 2

2 2xx L T

u vD D D

U U 2xy yx L T

uvD D D D

U

2 2

2 2yy L T

v uD D D

U U

Fischer et al. (1979):

Alavian (1986):

494. Water Quality Alarm System

Water Intake Plant #1

Water Intake Plant #3

Water Intake Plant #2

Paldang Dam

K-Y Water Intake Plant

STP

Target Area : Paldang Dam

504. Water Quality Alarm System

HDM-2D Input - FEM mesh

Paldang Lake

Water Intake Plant #2

h=25 El. m

Q = 88 m3/s

North Han River

Q=107 m3/sSouth Han River

Q=81 m3/sGyeongancheon

514. Water Quality Alarm System

HDM-2D Result- Bed Elevation

524. Water Quality Alarm System

HDM-2D Result- Water depth

534. Water Quality Alarm System

HDM-2D Result- Velocity

544. Water Quality Alarm System

CTM-2D Case : Instant Injection at Yangsu

• Injection Point: Yangsu Bridge Downstream of North Han River

• Injection Style: Instant Mass Injection (100 ton)

100 ton

554. Water Quality Alarm System

Downstream to the Paldang Lake

North Han River

CTM-2D : Yangsu Bridge

564. Water Quality Alarm System

CTM-2D Results : Yangsu Bridge

574. Water Quality Alarm System

North Han River

CTM-2D : Yongdam Bridge

Downstream to the Paldang Lake

South Han River

584. Water Quality Alarm System

CTM-2D Results: Yongdam Bridge

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V. Conclusion

605. Conclusion

• Develop models to accurately represent the

hydraulic characteristics and water quality changes

in natural rivers

• Develop a water quality forecasting system for the

integrated WQ management and usage for everyday

life activities and recreational purposes

• Develop a alarm system for providing

countermeasures in reaction of water pollution

accidents and securing water resources

• Establish a forecast and alarm system using

information & communication technology and

satellite technology

615. Conclusion

• RAMS info

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Thank you for your attention!