1 Modeling of River Water Quality: Its Application to Forecasting and Alarm Systems HydroAsia 2014
1
Modeling of River Water Quality:
Its Application to Forecasting
and Alarm Systems
HydroAsia 2014
2
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
7
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
24
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
25
Data Based Model
2. Water Quality Modeling
26
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
27
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
29
Water Quality Forecasting (JAPAN)
Provide
data
Adminnetwork Flood control
network
River info
network
Flood forecast
3. Water Quality Forecasting System
30
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
31
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
32
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
33
Ipo Ipo
Ipo Ipo
3. Water Quality Forecasting System
34
Water Quality Prediction Using ANN
2. Water Quality Modeling
• Forecasting tomorrow WQ, Using today and yesterday WQ data
35
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
40
IV. Water Quality Alarm System
41
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
42
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
43
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
59
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
62
Thank you for your attention!