1. Settling Simulation of Muddy Lake to Identify Problematic Turbidity at the Intakes of a Reservoir
Frederick N.-F. Chou, Associate Professor
Chia-wen Wu, Research Assistant
Department of Hydraulic and Ocean Engineering,
National Cheng Kung University
Marko S.-H. Hsu, Professor
Department of Water Resources Engineering and Conservation
Feng Chia University

2. 2018/11/18
Enormous Sediments brought by Floodwaters afetr Devastating Jiji Earthquake, 2000
2. Many sediments have deposited on the river bed
YuanShan Weir
Shihmen Reservoir
4. Insufficient sluiceway capacities of reservoirs
1. The devastating Jiji earthquake had caused large scale landslides and avalanches in the watersheds
3. Aggravating and more frequent flood threaten due to climate change
Inflow of Shihmen Reservoir during Typhoon Aere with peak discharge as 8,000 cms

3. Flood-Induced Muddy Lake with High Turbidity of Stored Water
2018/11/18
Flood-Induced Muddy Lake with High Turbidity of Stored Water
Shihmen Reservoir after Typhoon Aere (8/23~8/26, 2004)

4. Turbidity Profiles in Shihmen Reservoir
2018/11/18
Turbidity Profiles in Shihmen Reservoir
causing a 19-day urban water supply outage
El.m
Treatment threshold : 3000 NTU
elevation of the original intake for public and agricultural water use
Turbidity (NTU)

5. Shihmen Reservoir and its Catchment Management Project
2018/11/18
Shihmen Reservoir and its Catchment Management Project
Water Supply Facilities Stabilization and Main Conveyance Pipeline Improvement
Catchments Conservation and Management
Emergency Water Supply and Reservoir Amelioration
Build additional facilities for drawing the reservoir water
Construct standby water storage pools and artificial lake
Revamp the functions of existing desilting facilities of reservoir

6. 2018/11/18
Purpose of this Study
Examining how settling of sediments in reservoirs affects post-typhoon public water withdrawal, by
field measurement started after 8 days
developing an experienced based 1-D model to approximately simulate the settling process of sediment flocs in a muddy lake after flood peak
calibrating model according to the field data and from laboratory experiments
difficult to efficiently, or may not be necessary to, simulate 2-D or 3-D models numerically
Simulating the settling process of muddy storage in Shihmen Reservoir after the peak of Typhoon Aere
to examine the reason of slow clarification
to inspect the consequences of different reservoir operating strategies after typhoon
for providing a reference when designing hydraulic flushing devices and backup water supply system

7. Delta, Turbidity Current and Muddy Lake
2018/11/18
Delta, Turbidity Current and Muddy Lake
Plunge Point
Inflow with immense sediments
Muddy lake by unvented turbidity current
Delta
Turbidity Current

8. Batch Settling (Diplas et al., J. Environ. Eng-ASCE, 1997)
2018/11/18
Batch Settling (Diplas et al., J. Environ. Eng-ASCE, 1997)
Mudline interface
Sediment-suspension interface
Constant rate settling
height
1st falling rate settling
2nd falling rate settling
Compression
time t

9. 1-D Settling Model of Cohesive Particles
2018/11/18
1-D Settling Model of Cohesive Particles
Governing equation
Mass balance for water
Mass balance for sediment
C：average volumetric sediment concentration
A(z)：the cross-sectional area of the vessel at height z (m2)
vf、vs：velocities of the fluid and solid components in the mixture (m/day)
qi、qo：the lateral inflow and outflow discharge within unit height (m2/day)
Ci：volumetric sediment concentration of the lateral inflow

10. Kynch’s Theory (1953) Assumptions
2018/11/18
Kynch’s Theory (1953)
Assumptions
Solid particles are all small with respect to the container, and of the same size, shape and density
Incompressible
No mass transfer between components
Settling velocity is a function of the local concentration only
One-dimensional
the concentration of particles is constant at any cross-section of the vessel

11. Drift Flux Density Function
2018/11/18
Drift Flux Density Function
Combining the mass balance of water and sediments
：drift flux density function (m/day)

12. Numerical Solving Process
2018/11/18
Numerical Solving Process
Explicit upwind difference scheme (Bürger et al., 2004)
Initial condition
initial concentration is constant throughout the volume at t = 0
Boundary condition
The surface concentration clarifies immediately as settling begins
The bottom of the vessel is a closed boundary
ZL：the height of storage in the vessel

13. 2018/11/18
Parameterization of Settling Model (Coordinates of points in the fbk function)
(C10, b10)
(C9, b9)
fbk values between the pre-defined points
Cubic spline interpolation
Constraints of parameters
fbk(C)
(C1, b1)
(C8, b8)
(C7, b7)
(C6, b6)
(C5, b5)
(C2, b2)~(C4, b4)
Concentration (106 ppm)

14. Model Calibration Analysis
2018/11/18
Model Calibration Analysis
Gauss-Newton algorithm
minimizing deviation of simulated concentrations from the corresponding experimental values (Hsu et al, 2004)
initial concentrations of 20,000 and 70,000 ppm
applying a transformation procedure to convert the original parameters into unconstrained surrogate parameters
tractable by G-N algorithm
Chou and Wu, Advances in Water Resources, 33(2), 2010
field slurry sampled from Shihmen Reservoir

15. Simulation using the Calibrated fbk Function
2018/11/18
Simulation using the Calibrated fbk Function
70,000 ppm initial concentration case
Coefficient of efficiency between the observed and simulated concentration profiles
RMSE between the observed and simulated concentration profiles

16. Case Study Shihmen Reservoir : 219 × 106 m3
2018/11/18
Case Study
To the confluence of Tamshui River
Taoyuan Irrigation Association Area
Danan Treatment Plant
Houchun Weir
Panhsin Water Treatment Plant
Shihmen Treatment Plant
Yuanshan Weir
Pinchen Treatment Plant
Shanshia Pumping Station
Shinmen Irrigation Association Area
Longtang Treatment Plant
Shanshia River
Afterbay Weir
Shihmen Reservoir : 219 × 106 m3
Upstream Tahan River

17. Intake works of the Shihmen Reservoir
2018/11/18
Intake works of the Shihmen Reservoir
Elevation (m)
Top of Dam: El m
Tunnel Spillway: El. 220 m
Shihmen Canal Intake: El 195 m
Hydro Plant Intake: El 173 m
P.RO. Intake: El m
P.RO. Outlet: El 140 m

18. 2018/11/18
Simulating the Turbidity Profile of Shihmen Reservoir after Typhoon Aere
from August 26 to September 11 in 2004
Initial concentration：25,000 ppm
from the turbidity data observed by a nearby treatment plant
The concentration of recession inflow was assumed to be 0
2 scenarios
I：using historical releasing record of each outlet
II：adding a recently constructed sluiceway (300 cms)
sluiceway sluices the bottom mud for the first 3 days following the peak
The hydropower plant releases turbid water since the storage is greater than the upper limit of the operating rule curve of Shihmen Reservoir.
The tunnel spillway vents excess inflow to assist desiltation.

19. 2018/11/18
Simulated Clear-Muddy Interface in the Storage (take threshold as 3,000 NTU)
Simulated Interface of Scenario I
Simulated Interface of scenario II
Elevations of turbidity measurements with numbers as turbidities in NTU unit
El. 236
El. 228
El. 220
Elevation of Shihmen Canal’s Intake

20. Simulated Outflow Turbidity of Reservoir
2018/11/18
Simulated Outflow Turbidity of Reservoir
Measured streamflow turbidity in YuanShan Weir
Simulated turbidity of Shihmen Reservoir’s outflow using historical operating strategy
Simulated turbidity of Shihmen Reservoir’s outflow discharge using hypothetic flushing strategy
A backup storage pond will be urgently necessary for first 4 days
for maintaining stable public water supply in this area.
3,000 NTU

21. Conclusions and Suggestions
2018/11/18
Conclusions and Suggestions
A unidimensional settling model was proposed
calibrated with laboratory experiment data
to quantify the level and impacts of high turbidity in reservoir
A historical water shortage event was investigated
confirms the effectiveness of accelerating settling by sluicing bottom storage through the sluiceway.
a backup storage tank will be urgently necessary
Simulating errors come from
invalidation of model assumptions
the unrepresentative errors of the calibrated experienced parameters
To improve the prediction accuracy
adopt recursive updating mechanisms based on the measurement feedback from real-time monitoring system
utilize any other state-of-the-art sediment routing models to acquire the concentration profile
for the initial condition of settling analysis

22. END
Comments & Suggestion