1
MODELLING OF TIDAL EFFECT ON SUSPENDED SEDIMENT
DISPERSION AT SUNGAI BATU PAHAT, JOHOR
MUHAMAD AQMARR NORHAKIM BIN ISMAIL
UNIVERSITI TEKNOLOGI MALAYSIA
6
7
ACKNOWLEDGEMENTS
Praise to Allah S.W.T. the All Mighty, the Most Merciful for all the blessing
and guidance upon me throughout this study.
While the motivation and hard work in pursuing a master’s degree
must come from within, interactions with others have stimulated and sustained me
both professionally and personally during my research career. I want to acknowledge
many people whose professionally help and personal support has made it possible for
me to complete this thesis. My heart felt deepest gratitude goes to my thesis
supervisors Associate Professor Dr. Norhan Abd Rahman, Dr. Noor Baharim
Hashim, Dr. Johan Sohaili and Mr. Kamarul Azlan Mohd Nasir, for all the
assistance, knowledge and help during the thesis syllabus.
My special thanks go to Ismail, Shaarin, Shurbaini and Ridzuan from
Hydraulics and Hydrology Laboratory, Universiti Teknologi Malaysia, for their
support and cooperation during the thesis research. Extended special appreciation to
Maznah, Siti Sahlawati and Nuraini from Hydraulics and Hydrology Department,
Universiti Teknologi Malaysia, for their assistant. Besides a specific thanks to my
fellow master students Mohd Kamarul Huda Bin Samion, Mohd Aznan Shukri Bin
Mohamed Safian, Syahrulnizam @ Helmi Bin Md Sukimin, Zulkifli Mustafa, Mohd
Azreen, Abdulla Magwilla and Liew Kueh Fat for their support and great friendship.
Finally, my express is going to my family for their continuous love and
support throughout my life.
8
ABSTRACT
As part of an effort to understand and predict the nutrients dynamics in
Sungai Batu Pahat, the transport of sediment has been studied by combining
numerical model with an extensive fields program. Oversupply of sediment loads
from multiple sources will cause unstable conditions to rivers, lakes, and shallow
coastal and estuarine areas. Sediment transport also brought together sorbs chemical
that will distract ecosystem in the water and human daily activity such as recreation
and water supply. For this research, applications of Water Quality Simulation
Program 5 or WASP5 have been used to simulate suspended sediment dispersion at
Sungai Batu Pahat. TOXI5 is a WAPS5’s kinetic model that has been specialized for
toxic pollutants including sediment. TOXI5 is link to hydrodynamic model
DYNHYD5. In this study, several analyses were conducted to determine magnitude
and spatial distribution of suspended sediment in Sungai Batu Pahat due to real
observation data. Results showed that there is a significant changes to sediment
concentration due to tidal effect in this study.
9
ABSTRAK
Sebagai satu usaha untuk memahami dan meramal pergerakan nutrien di
Sungai Batu Pahat, pengangkutan sedimen telah dikaji dengan menggabungkan
bersama model numerikal dan program ekstensif lapangan. Pelepasan berlebihan
muatan sedimen dari pelbagai sumber akan menyebabkan ketidakstabilan terhadap
sungai, tasik, pesisir pantai dan kawasan muara. Pengangkutan sedimen turut
membawa bersama bahan kimia terserap yang akan mengganggu ekosistem dalam
sumber air dan aktiviti harian manusia seperti rekreasi dan pembekalan air. Untuk
kajian ini, aplikasi perisian Water Quality Simulation Program 5 atau WASP5 telah
digunakan untuk mensimulasi taburan sedimen terampai di Sungai Batu Pahat.
TOXI5 merupakan model sub kinetik dalam WASP5 yang telah dikhususkan untuk
pencemaran bertoksik termasuk sedimen. TOXI5 telah dihubungkan dengan model
hidrodinamik DYNHYD5. Dalam kajian ini, beberapa analisis telah dilakukan untuk
menentukan magnitud dan taburan sedimen dalam Sungai Batu Pahat berdasarkan
nilai cerapan data sebenar. Keputusan menunjukkan terdapat perubahan ketara
terhadap taburan kepekatan sedimen kesan daripada pasang surut dalam kajian ini.
10
TABLE OF CONTENTS
CHAPTER
CHAPTER I
CONTENTS
PAGE
TITLE
i
DECLARATION
ii
DEDICATIONS
iii
ACKNOWLEDMENTS
iv
ABSTRACT
v
ABSTRAK
vi
TABLE OF CONTENTS
vii
LIST OF TABLES
xi
LIST OF FIGURES
xii
LIST OF SYMBOLS
xiv
LIST OF APPENDICES
xvi
INTRODUCTION
1
1.0
GENERAL
1
1.1
PROBLEM STATEMENT
2
1.2
OBJECTIVES
3
1.3
SCOPE OF STUDY
4
1.4
STUDY AREA
4
11
LITERATURE REVIEW
7
2.0
GENERAL
7
2.1
TYPES OF SEDIMENT TRANSPORT
9
2.2
SEDIMENT MEASUREMENT
12
2.2.1 Particle size
12
EFFECTS OF SUSPENDED AND BEDDED
14
CHAPTER II
2.3
SEDIMENTS
2.3.1 Effects on invertebrates
16
2.3.2 Effects on Corals
18
2.3.3 Effects on Aquatic Plants
20
2.3.4 Effects on fish
21
2.3.5 Effects on Wildlife
24
2.4
CONTROLS
25
2.5
NUMERICAL MODELS
27
2.5.1 Environmental Fluid Dynamics Code (EFDC)
28
2.5.2 TELEMAC-3D
28
2.5.3 TRIVAST
29
2.5.4 RMA10 AND RAM11
30
2.5.5 TIDE-3D
30
2.5.6 MIKE3
31
2.6
2.7
INTRODUCTION TO THE WATER QUALITY
SIMULATION PROGRAM 5
31
2.6.1 The Basic Water Quality Model
33
2.6.2 Overview of the WASP5 Modelling System
34
2.6.3 The Model Network
35
2.6.4 The Model Transport Scheme
40
2.6.5 Application of the Model
41
2.6.6 Sediment Transport Model Description
43
2.6.7 Model Implementation
44
INTRODUCTION TO HYDRODYNAMIC MODEL
DYNHYD5
45
2.7.1 The Model Network
45
12
2.7.2 Application Of The Model
47
2.7.3 The DYNHYD5 Input Dataset
48
2.7.4 DYNHYD5 Output
49
LINKAGE TO WASP5
50
METHODOLOGY
54
3.0
GENERAL
54
3.1
FIELD WORK
54
3.2
LABORATORY ANALYSIS
58
3.3
MODELING PROCESS
60
3.3.1 The Hydrodynamic Equations
62
3.3.2 The Equation Of Motion
62
3.3.3 The Equation Of Continuity
63
3.4
MODEL LIMITATIONS
66
4.0
RESULTS AND DISCUSSIONS
69
4.0
GENERAL
69
4.1
MODEL CALIBRATIONS
69
4.1.1 Hydrodynamic Model Calibrations
70
4.1.2.Water Quality Model Calibrations
71
RESULTS AND DISCUSSIONS
73
4.2.1 Hydrodynamic Model Results
73
4.2.2 Water Quality Model Results
75
4.2.3 Discussions
76
2.8
4.2
13
5.0
CONCLUSIONS AND RECOMMENDATIONS
78
5.0
GENERAL
78
5.1
CONCLUSIONS
79
5.2
RECOMENDATIONS
79
5.3
REFFERENCES
APPENDICES
80
88
14
LIST OF TABLES
TITLE
TABLE NO.
2.1
Issues Associated With Sediment Transport in Rivers
2.2
Particle size classification by the Wentworth Grade
PAGE
10
Scale
13
2.3
History of WASP5 application
33
2.4
DYNHYD5 Display Variables
50
3.1
Suspended sediment concentration data
58
4.1
Stoke's Settling Velocities (in m/day) at 20ºC
74
4.2
Summary of suspended sediment concentration at
highest and lowest tide
77
15
LIST OF FIGURES
TITLE
FIGURE NO.
1.1
Location of study area
2.1
Conceptual model of biological effects of suspended
and bedded sediments in estuaries
2.2
PAGE
6
19
Idealized model of fish response to increased
suspended sediments
22
2.3
The basic WASP5 system
35
2.4
Model segmentation
37
2.5
Frequency distribution of observed and calculated
values of a quality variable
38
2.6
Representation of the model network
46
2.7
Link-node hydrodynamic linkage
52
3.1
Segment of water sampling
55
3.2
Data collection at study area
56
3.3
Current meter setting
57
3.4
Current meter location (segment seven)
57
3.5
Suspended Sediment experimental equipment
59
3.6
Suspended Sediment under laboratory process
59
3.7
Mesh construction using AutoCAD
60
3.8
Segmentation of study area
61
3.9
Simulation interface in hydrodynamic model
3.10
DYNHYD5
65
Flow chart of study methodology
67
16
4.1
Calibration process using different value of Manning’s
coefficient in hydrodynamic model DYNHYD5
4.2
The Manning’s coefficient of 0.02 fitted the
hydrodynamic model DYNHYD5
4.3
73
Calibration process using different settling velocity for
water quality model TOXI5
4.7
72
Flow profile along Sungai Batu Pahat from day five to
day seven
4.6
72
Depth profile along Sungai Batu Pahat from day five to
day seven
4.5
71
Head profile along Sungai Batu Pahat from day five to
day seven
4.4
70
74
Suspended sediment concentration along Sungai Batu
Pahat during highest tide and lowest tide at Sungai
Batu Pahat
75
17
LIST OF SYMBOLS
V
-
velocity
t
-
time
m
-
mass
M
-
momentum
F
-
force acting on the mass
A
-
area
Q
-
Flow rate
P
-
Cross Section “Wetted Perimeter”
τb
-
Average Bed Shear Stress
B
-
weir base width (m)
H
-
head above weir crest excluding velocity head (m)
Cd
-
orifice discharge coefficient (0.40 – 0.62)
A0
-
area of orifice (m2)
Do
-
orifice diameter (m)
Ho
-
effective head on the orifice measured from the centre of the
opening (m)
g
-
acceleration due to gravity (9.81 m/s2)
Z
-
vertical direction
Zb
-
bed elevation
Zw
-
zb + H = water surface elevation
q1
-
UH = unit flow rate in the x direction
q2
-
VH = unit flow rate in the y direction
qm
-
mass inflow rate (positive) or outflow rate (negative) per unit area
β
-
isotropic momentum flux correction coefficient that accounts for the
variation of velocity in the vertical direction
g
-
gravitational acceleration
18
ρ
-
water mass density
pa
-
Atmospheric pressure at the water surface
Ώ
-
Coriolis parameter
n
-
Manning’s coefficient
τbx&τby -
bed shear stresses acting in the x and y directions, respectively
τsx &τsy -
surface shear stresses acting in the x and y directions,respectively
τxx, τxy, τyx &τyy -shear stresses caused by turbulence where, for example, τxy is the
shear stress acting in the x direction on a plane that is perpendicular to
the y direction
19
LIST OF APPENDICES
APPENDIX
TITLE
PAGE
A
Topography Map for Batu Pahat
88
B
Sungai Batu Pahat Bathymetry
89
C
Sungai Batu Pahat Tidal Data
90
20
CHAPTER I
INTRODUCTION
1.0 GENERAL
Suspended sediments are usually silt and clay particles that are between 2 and
60 micrometers in diameter. Suspended sediments can be directly measured as total
suspended sediment (TSS) in milligrams per litre (mg/L) but are frequently measured
indirectly as turbidity. Turbidity is the optical property of water resulting in a loss of
light transmission caused by absorption and scattering. Turbidity is typically
measured in Nephlometric Turbidity Units (NTUs). While suspended sediments are
often the main contributors to turbidity, other non-sediment sources that affect light
transmission (that is, natural tannins and algae) can also influence turbidity.
Sediment is one of the most significant pollutants transferred by storm water.
Sediments consist largely of soil materials eroded from uplands as a result of natural
processes and human activities.
Four interactive factors that have substantially affected the suspendedsediment regime over this same period of time include increases in agriculture,
commerce and industry, transportation networks and population and urbanization.
21
Development activities dramatically alter the hydrologic cycle of a site and
ultimately of the entire watershed. The initial clearing and grading of the site
removes vegetation which intercepted and absorbed rainfall and removes natural
depressions which stored rainfall which would then infiltrate into the ground or
evaporate back into the atmosphere. The construction activity will compact the soil,
which further reduces the ability of the soil to infiltrate the rainfall and further
increases the volume and rate of storm water runoff from the site.
1.1
PROBLEM STATEMENT
The greatest sediment loads are exported during the construction phase of a
development site or any land clearing activities. Unless adequate erosion controls are
installed and maintained at the site, enormous quantities of sediment may be
delivered to the stream channel, along with attached soil nutrients and organic
matter.
High concentrations of suspended sediment in streams and lakes caused many
adverse consequences including increased turbidity, reduced light penetration,
reduced prey capture for sight-feeding predators, clogging of gills/filters of fish and
reduced angling success. Additional impacts can result after sediment is deposited in
slower moving waters including the smothering of benthic communities, alterations
in the composition of the bottom substrate, the rapid filling-in of small
impoundments which create the need for costly dredging and reductions in the
overall aesthetic value of the water resource. Sediment is also an efficient carrier of
toxins and trace metals. Once deposited, pollutants in these enriched sediments can
be remobilised under suitable environmental conditions posing a risk to benthic life.
Fine-grained suspended sediment and pollutant transport and the impact of
these processes on the local habitat are some of the main concerns in current issues
within the freshwater fluvial environment. Many anthropogenic inputs to fluvial
22
systems have pathways that are preferentially associated with suspended particulate
matter (SPM) and their deposition along rivers can create adverse environmental
conditions. None the less, only little is known of the specific physical and
biogeochemical processes that govern transport, deposition and entrainment of fine
cohesive sediment in river systems. By now, it is well recognized that much of the
suspended sediment load in rivers exists in the form of composite particles or
aggregates.
The concentration, discharge, load, and yield of suspended sediments in a
stream are important because of the relation between sediments and some water
quality constituents that have a strong association to sediments. Trace metals,
pesticides, and polychlorinated biphenyls (PCBs) have a strong affinity for and sorbs
to soils, sediments, and other particulate matter present in the environment. The
movement and distribution of these constituents in a river results from a continuous
process of sorption to fine-grained sediments and other particulate matter, movement
downstream (primarily in suspension), deposition, resuspension, movement,
redeposition, and so on, in response to variations in stream flow.
1.2
OBJECTIVE
The main objective of the study is to determine tidal effect on suspended
sediment concentration dispersion of the study area, which is near the Sungai Batu
Pahat estuary.
23
1.3
SCOPE OF STUDY
The following is the scope of work for the study:
i.
The study will be done at Sungai Batu Pahat. A part of Sungai
Simpang Kiri and Sungai Simpang Kanan are involved where
the constant inflow are taken for Sungai Batu Pahat modelling.
ii.
Currents, water levels and sediment data collection will be
collected during the study. The current tidal data is obtained
from Port of Johor and the bathymetry data is from
Geoinformation Faculty University Technology Malaysia.
iii.
Using WASP5 software as modelling tool to set up onedimensional numerical model to model the changes in flow,
water levels and sediment concentration. The simulation
period is eleven day due to the capability of computer
available. Only the high accuracy data of simulation data will
be used by neglected the data during model stabilizing period
and spoil data.
1.4
STUDY AREA
Sungai Batu Pahat is situated in the southwest of Peninsular Malaysia in the
vicinity of 1º48’00” to 1º48’54” N latitude and 102º56’00” to 102º56’30” E
longitude. Sungai Batu Pahat is located between Muar and Batu Pahat. (Appendix A)
The river opens into an estuary that joins the open sea, this being part of the Straits of
Malacca. (Figure 1.1)
The dominant flow in Sungai Batu Pahat near Batu Pahat town are driven by
the astronomical tides, with intermittent freshwater inflows causing some additional
24
flow, principally in the more shallow upstream areas and where tidal currents are low
or tidal flow does not penetrate other than on spring tides. From time-to-time there
are likely to be some very high freshwater flows in the estuary. Typical spring tide
ranges are in the order of 3m with neap tides in the range of 1m being common.
However, spring tide ranges of nearly 3.7m may occur.
Therefore, there is a
significant range of tidal regime. Sungai Batu Pahat can be described as a
sandy/muddy area, but the sediments delivered to the estuary in suspension from the
catchments will be predominantly fine silts. The movement and resuspension of
sediment particles commences when the fluid force on a particle is just larger than
the resisting force related to the submerged particle weight and friction coefficient.
In the case of fine silts, cohesive forces are also important. Thus settled mud
particles remain in a stable state on the seabed until forces that exceed those needed
to initiate sediment motion disturb them. These forces are caused by tidal and wind
driven currents, as well as by wave action. There is little wave caused water particle
motion near the seabed in the Sungai Batu Pahat area and so sediment movement is
dominated by flood flows, which may cause significant sediment re-suspension in
the upstream reaches and, subsequent transport to the entrance in Sungai Batu Pahat.
Once suspended, fine particles may be transported throughout the estuary, ultimately
settling in a more tranquil environment, in typically deeper areas. Therefore, apart
from protected areas and muddy coasts, long-term retention of silts in shallow areas
beyond the local equilibrium depth is unlikely. (Uni-Technologies Sdn. Bhd., 2006)
25
STUDY
AREA
Figure 1.1: Location of study area
26
CHAPTER II
LITERATURE REVIEW
2.0 GENERAL
Sediments play an important role in elemental cycling in the aquatic
environment. They are responsible for transporting a significant proportion of many
nutrients and contaminants. They also mediate their uptake, storage, release and
transfer between environmental compartments. Most sediment in surface waters
derives from surface erosion and comprises a mineral component, arising from the
erosion of bedrock, and an organic component arising during soil-forming processes
(including biological and microbiological production and decomposition). An
additional organic component may be added by biological activity within the water
body.
For the purposes of aquatic monitoring, sediment can be classified as
deposited or suspended. Deposited sediment is that found on the bed of a river or
lake. Suspended sediment is that found in the water column where it is being
transported by water movements. Suspended sediment is also referred to as
suspended matter, particulate matter or suspended solids. Generally, the term
suspended solids refers to mineral plus organic solids, whereas suspended sediment
should be restricted to the mineral fraction of the suspended solids load.
27
Sediment transport in rivers is associated with a wide variety of
environmental and engineering issues, which are outlined in Table 2.1. The study of
river suspended sediments is becoming more important, nationally and
internationally, as the need to assess fluxes of nutrients and contaminants to lakes
and oceans, or across international boundaries, increases. One of the most serious
environmental problems is erosion and the consequent loss of topsoil. Although
erosion is a natural phenomenon, the rate of soil loss is greatly increased by poor
agricultural practices, which result, in turn, in increased suspended sediment loads in
freshwaters. Loss of topsoil results in an economic loss to farmers, equivalent to
hundreds of millions of US dollars annually, through a reduction in soil productivity.
Good environmental practice in agriculture, which may include contour ploughing
and terracing, helps to protect against soil loss and against contamination of surface
waters.
Water users downstream of areas of heavy soil run-off may have to remove
suspended sediment from their water supplies or may suffer a reduction in the
quantity of water available because of reservoir siltation. The rapid reduction in the
storage capacity of reservoirs due to siltation is a major sediment- related problem
worldwide. Moreover, the availability of water for irrigation from the reservoir leads
to more intensive land use and increased soil erosion. These effects may also be
exacerbated by desertification (impoverishment of vegetative cover and loss of soil
structure and fertility), whether anthropogenic or climatic in origin. In addition,
gradual enrichment of reservoir waters with nutrients (some of which also arise from
agricultural practices) leads to enhanced production and increased sedimentation of
organic material originating from the water column (from decaying plankton) or
littoral zones (from decaying macrophytes).
Consequently, the rate of reservoir siltation often greatly exceeds that
predicted during design. Monitoring data for sediment transport to, and productivity
within, reservoirs are therefore required for accurate calculations of sediment
transport and deposition and for the management of major reservoirs. Further
information on monitoring and assessment approaches for reservoirs is given in the
companion guidebook Water Quality Assessments.
28
In order to protect surface water resources and optimise their use, soil loss
must be controlled and minimized. This requires changes in land use and land
management, which may also have an impact on water quality. Control of the
siltation rate in reservoirs requires that adequate data are available at the design
stage. This, in turn, demands an understanding of sediment transport and appropriate
methods for measuring sediment load and movement.
Recognition of the importance of sediments and their use in monitoring and
assessment programmes is increasing and methods are constantly being refined. For
the purposes of water quality monitoring a distinction can be made between
measuring sediment quantity and sediment quality. Some of the techniques available
for studying sediment quality (as a component of water quality studies) are not yet
widely accepted or used and have not been standardized. Although they may be
suitable for special surveys, some methods are too complex and costly for routine
monitoring programmes. A full discussion of the role of sediments and particulate
material in water quality monitoring and assessment is available in the companion
guidebook Water Quality Assessments. This present chapter concentrates on some of
the fundamental procedures required for the more common sediment measurements
necessary for water quality monitoring programmes.
2.1
TYPES OF SEDIMENT TRANSPORT
Sediment transport is a direct function of water movement. During transport
in a water body, sediment particles become separated into three categories:
suspended material that includes silt, clay and sand; the coarser, relatively inactive
bedload and the siltation load.
Suspended load comprises sand, silt and clay-sized particles that are held in
suspension because of the turbulence of the water. The suspended load is further
divided into the wash load, which is generally considered to be the silt and clay-sized
29
material (less than 62 µm in particle diameter) and is often referred to as “finegrained sediment”. The wash load is mainly controlled by the supply of this material
(usually by means of erosion) to the river. The amount of sand (less than 62 µm in
particle size) in the suspended load is directly proportional to the turbulence and
mainly originates from erosion of the bed and banks of the river. In many rivers,
suspended sediment (i.e. the mineral fraction) forms most of the transported load.
Table 2.1: Issues Associated With Sediment Transport in Rivers
Sediment size
Environmental issues
Associated engineering issues
Silts and clays
Erosion, especially loss of
topsoil in agricultural
areas; gullying
High sediment loads to
reservoirs
Chemical transport of
nutrients, metals, and
chlorinated organic
compounds
Reservoir siltation
Drinking-water supply
Accumulation of
contaminants in organisms
at the bottom of the food
chain (particulate feeders)
Silting of fish spawning
beds and disturbance of
habitats (by erosion or
siltation) for benthic
organisms
Sand
River bed and bank erosion River channel deposition:
navigation problems
Instability of river cross- sections
River bed and bank erosion Sedimentation in reservoirs
Habitat disturbance
Gravel
Channel instability when
dredged for aggregate
Habitat disturbance
Instability of river channel leads to
problems of navigation and flood
control
30
Bedload is stony material, such as gravel and cobbles that moves by rolling
along the bed of a river because it is too heavy to be lifted into suspension by the
current of the river. Bedload is especially important during periods of extremely high
discharge and in landscapes of large topographical relief, where the river gradient is
steep (such as in mountains). It is rarely important in low-lying areas.
Measurement of bedload is extremely difficult. Most bedload movement
occurs during periods of high discharge on steep gradients when the water level is
high and the flow is extremely turbulent. Such conditions also cause problems when
making field measurements. Despite many years of experimentation, sedimentmonitoring agencies have so far been unable to devise a standard sampler that can be
used without elaborate field calibration or that can be used under a wide range of
bedload conditions. Even with calibration, the measurement error can be very large
because of the inherent hydraulic characteristics of the samplers and the immense
difficulty with representative sampling of the range of sizes of particles in transit as
bedload in many rivers. Unless bedload is likely to be a major engineering concern
(as in the filling of reservoirs), agencies should not attempt to measure it as part of a
routine sediment-monitoring program. Where engineering works demand knowledge
of bedload, agencies must acquire the specialized expertise that is essential to
develop realistic field programmes and to understand the errors associated with
bedload measurement. Local universities or colleges may be able to assist in this
regard.
Siltation load is a term used by sedimentologists to describe material that is
transitional between bedload and suspended load. Siltation means “bouncing” and
refers to particles that are light enough to be picked off the river bed by turbulence
but too heavy to remain in suspension and, therefore, sink back to the river bed.
Siltation load is never measured in operational hydrology.
31
2.2
SEDIMENT MEASUREMENT
While the underlying theory is well known, the measurement of sediment
transport requires that many simplifying assumptions be made. This is largely
because sediment transport is a dynamic phenomenon and measurement techniques
cannot register the ever-changing conditions that exist in water bodies, particularly in
river systems. Some of the sources of extreme variability in sediment transport are
discussed below.
2.2.1 PARTICLE SIZE
Knowledge of the size gradient of particles that make up suspended load is a
prerequisite for understanding the source, transportation and, in some cases,
environmental impact of sediment. Although particles of sizes ranging from fine clay
to cobbles and boulders may exist in a river, suspended load will rarely contain
anything larger than coarse sand, and in many rivers 50-100 per cent of the
suspended load will be composed only of silt and clay sized particles (less than 62
µm). The size of particles is normally referred to as their diameter although, since
few particles are spherical, the term is not strictly correct. Particle size is determined
by passing a sample of sediment through a series of sieves, each successive sieve
being finer than the preceding one. The fraction remaining on each sieve is weighed
and its weight expressed as a percentage of the weight of the original sample. The
cumulative percentage of material retained on the sieves is calculated and the results
are plotted against the representative mesh sizes of the sieves. A series of eight sieves
can be used for sediment analysis, with mesh sizes from 1.25 mm to 63 µm or less.
Further details of these methods are available in the appropriate literature.
Clay particles are plate-like in shape and have a maximum dimension of
about 4 µm. Silt particles, like sand, have no characteristic shape; their size is
32
between those of clay and sand with diameters ranging from 4 µm to 62 µm. Since
the smallest mesh size of commercially available sieves is about 40 µm, the sizes of
clay and small silt particles cannot be determined by sieving, and sedimentation
techniques are used instead. The sedimentation rate of the particles is measured and
their diameter calculated from the semi empirical equation known as Stokes’ Law.
Table 2.2: Particle size classification by the Wentworth Grade Scale
Sediment size
Silts and clays
Environmental issues
Erosion, especially loss of topsoil
in agricultural areas; gullying
Associated engineering issues
High sediment loads to reservoirs
Reservoir siltation
Chemical transport of nutrients,
metals, and chlorinated organic
compounds
Drinking-water supply
Accumulation of contaminants in
organisms at the bottom of the
food chain (particulate feeders)
Silting of fish spawning beds and
disturbance of habitats (by
erosion or siltation) for benthic
organisms
Sand
River bed and bank erosion
River channel deposition:
navigation problems
Instability of river crosssections
River bed and bank erosion
Sedimentation in reservoirs
Habitat disturbance
Gravel
Channel instability when dredged Instability of river channel
for aggregate
leads to problems of navigation
and flood control
Habitat disturbance
33
There is no universally accepted scale for the classification of particles
according to their sizes. In North America, the Wentworth Grade Scale (Table 2.2) is
commonly used; elsewhere, the International Grade Scale is preferred. There are
minor differences between the two scales and it is, therefore, important to note which
scale has been selected and to use it consistently.
The boundary between sand and silt (62 µm) separates coarse-grained
sediments (sand and larger particles) from fine-grained sediments (silt and clay
particles). Coarse-grained sediments are non-cohesive, whereas fine-grained
sediments are cohesive, i.e. the particles will stick to one another as well as to other
materials. Particle cohesiveness has important chemical and physical implications for
sediment quality.
Sedimentologists and water quality programmes have adopted a convention
that considers particulate matter to be larger than 0.45 µm in diameter; anything
smaller is considered to be dissolved. This boundary is not entirely valid because
clay particles and silt can be much smaller than 0.45 µm. For practical purposes,
however, the boundary is convenient, not least because standard membrane filters
with 0.45 µm diameter pores can be used to separate suspended particles from
dissolved solids.
2.3
EFFECTS OF SUSPEND AND BEDDED SEDIMENTS
Summarizing effects data for suspend and bedded sediments is difficult for
several reasons. One reason is that there is not one agreed-upon measurement for
suspend and bedded sediments. Caux et al. (1997) provide an excellent discussion of
the various methods of measuring suspended sediments. Suspended sediments
contribute to turbidity and thus affect light transmission through the water column
(Waters, 1995). Turbidity is an optical property of water resulting in a decrease in
light transmission due to absorption and scattering. Consequently turbidity is a key
34
water quality parameter in aquatic systems in that it has a predominant influence on
the compensation point (the depth at which photosynthesis equals respiration in
plants) and is therefore a critical determinant in the distribution of submerged aquatic
vegetation (Batuik, et al., 1992). The correlation of turbidity with concentrations of
suspended solids (mg/L) is impractical because the size, shape, and refractive index
of particulate material affect turbidity but are not directly related to the concentration
of suspended solids (Caux et al., 1997), and thus the correlation is site-specific.
Various measurements are used for bedded sediments as well. These include depth of
deposition within a given time period, percent fines, geometric mean diameter, and
Fredle number (Caux et al., 1997). Fredle number is an index of permeability that has
been found to correlate well with survival-to-emergence of salmon and trout
(Lotspeich and Everest, 1981).
Another reason summarizing effects data for suspend and bedded sediments
is difficult is that there are no standard durations for suspend and bedded sediments
effects testing. Both the duration (Newcombe and MacDonald, 1991) and frequency
(Shaw and Richardson, 2001) of sediment exposures are important. For example,
some species are able to recolonize between sediment events, while some other
species may not be able to recover before the next event (Yount and Nimmi, 1990).
Newcombe and MacDonald (1991) recognized that the appropriate way to report
data for the effects of suspended sediment on aquatic organisms was to include
information on duration of exposure, as well as exposure concentration. Up until that
point, the importance of duration of exposure had been largely overlooked. They
summarized, in graphical and tabular form, much of the available data on the effects
of suspend and bedded sediments on fish and invertebrates.
Newcombe and Jensen (1996) presented an extensive data table of the effects
of suspend and bedded sediments on fish, and went a step further developing
empirical models of the effects of suspend and bedded sediments on fish. Newcombe
also developed a model for the effects of SABS on aquatic invertebrates and flora
(Newcombe, 1997) and dealing with the effects of diminished water clarity on fish
(Newcombe, 2003).
35
A recent review of the biological effects of suspended sediments on fish and
shellfish was conducted by Wilber and Clarke (2001). Their paper synthesized the
results of studies that report the dose-response relationships of estuarine aquatic
organisms to suspended sediments and then related those findings to sediment
conditions associated with dredging projects. Doseresponse graphs were modified
from Newcombe and Jensen (1996) to provide an easy reference for estimating
biological responses to suspended sediments. Wilber and Clarke (2001) also provide
tables that depict biological response as a function of suspended sediment exposure
(sediment concentration and duration). Biological response categories reported by
Wilber and Clarke (2001) include: no effect, behavioral, sub-lethal, and lethal
effects.
2.3.1 EFFECTS ON INVERTEBRATES
Elevated levels of suspend and bedded sediments have been shown to have
wide ranging effects on both pelagic and benthic invertebrates (Cordone and Kelly
1961; Maurer et al., 1986; Peddicord, 1980; Waters, 1995; Wilber and Clarke, 2001).
Effects can be classified as having a direct impact on the organism due to abrasion,
clogging of filtration mechanisms thereby interfering with ingestion and respiration,
and in extreme cases smothering and burial resulting in mortality. Indirect effects
stem primarily from light attenuation leading to changes in feeding efficiency and
behaviour (i.e., drift and avoidance) and alteration of habitat stemming from changes
in substrate composition, affecting the distribution of in faunal and epibenthic
species (Donahue and Irvine, 2003; Waters, 1995; Zweig and Rabeni, 2001).
Increased levels of suspended sediment were shown to impair ingestion rates
of freshwater mussels in laboratory studies (Aldridge et al., 1987). However, Box
and Mossa (1999) reviewed the literature on the effects of sedimentation on
freshwater mussels and concluded that the relative significance of human activities to
sediment production, and their subsequent effects on freshwater mussels, is difficult
to evaluate. Reduced feeding activity as a response to increased levels of suspended
36
sediments has also been reported for copepods (Tester and Turner, 1988; Sherk et al.,
1976) and daphnids (Arruda et al., 1983). Invertebrate drift is directly affected by
increased suspended sediment load in freshwater streams and lakes. Increases in
suspended sediments (e.g., 120 mg/L) can result in increased drift, significantly
altering the distribution of benthic invertebrates in streams (Herbert and Merkens,
1961).
Waters (1995) considers the effects of increased deposition of sediments on
benthic invertebrates as one of the most important concerns within the sediment
pollution issue, especially in regards to the dependence of freshwater fisheries on
benthic productivity. (Figure 2.1)
Waters (1995) identifies three major relationships between benthic
invertebrate communities and sediment deposition in streams:
i.
Correlation between abundance and substrate particle size.
ii.
Embeddings of substrate and loss of interstitial space.
iii.
Change in species composition with change in type of habitat
(substrate composition).
Alteration in the quality and quantity of deposited sediments can affect the
structure and function of benthic macro faunal communities by increasing substrate
embeddedness and altering substrate particle size distributions (Erman and Erman,
1984). Increased embeddedness can result in decreases in aquatic insect densities and
small increases in siltation can directly affect caddisfly pupa survival. Zweig and
Rabeni (2001) examined the response of benthic infauna to deposited fine sediments
in four Missouri streams. Five biomonitoring metrics were significantly correlated
with deposited sediments across streams. Deposited-sediment tolerance values were
developed representing responses to deposited sediments for 30 taxa. Tolerance
values where then used to develop the Deposited Sediment Biotic Index (DSBI). The
DSBI was calculated to characterize sediment impairment in the four streams. DSBI
values for each site examined were highly correlated with depth and degree of
embeddedness of deposited sediment.
37
Several studies have examined the effects of the burial of estuarine
invertebrates. Maurer et al. (1986) found that species responded differently to burial
by 36-40 cm of sediment, and that some organisms were able to migrate more easily
up through sandy sediment, while other organisms were able to migrate better
through muddy sediment. Hinchey et al. (in review) found that species-specific
response to burial by sediments varied as a function of motility, living position and
inferred physiological tolerance of anoxic conditions while buried. Their study
compared responses of five estuarine invertebrate species (three infaunal and two
epifaunal) to clean sediment burial in laboratory experiments. Hinchey et al. (in
review) suggested that effective overburden stress, which incorporates both the bulk
density of the sediment as well as the depth of burial (Richards et al., 1974), was a
better measure of the force exerted on organisms by sediment burial than depth of
sediment alone.
2.3.2 EFFECTS ON CORALS
The increased sedimentation resulting from coastal development is a major
source of coral reef degradation (Rogers, 1983, 1990; Torres, 2001). Excessive
sedimentation can adversely affect the structure and function of the coral reef
ecosystem by altering physical and biological processes (Rogers, 1990). High
sediment loads can smother tissue resulting in bleaching in the short-term and death
in the long-term (Rogers, 1983).
Cortes and Risk (1985) reported a reduced growth rate in Montastraea
annular is living in waters with average sedimentation rates between 20-1,000 mg
cm-2 d-1. Reduced growth rates and temporary bleaching in M. annularis were also
reported by Dodge et al. (1974). In a subsequent study, Torres (2001) showed that
growth rates of M. annularis were significantly lower and negatively related with
sediment deposition rates and percentages of terrigenous sediments deposited on a
coral reef on the south coast of Puerto Rico. Nemeth and Nowlis (2001) reported
bleaching of coral colonies at sediment deposition rates between 10 and 14 mg cm-2
38
d-1. Their study indicated that stress from sedimentation might lead to a decline in
living coral. An indirect effect of increased suspended sediment load was an increase
in turbidity, which caused a corresponding decrease in light penetration that limited
the photosynthetic capacity of symbiotic zooxanthellae, and furthered the decline in
coral populations.
Excessive sedimentation can affect the complex food web associated with
coral reefs, killing not only corals but other reef dwelling organisms (e.g., sponges)
which serve as food for commercially important fish and shellfish (Rogers, 1990).
Declines in tropical reef fisheries in the Carribean and the Pacific are believed to be
partially due to increased sedimentation rates (Rogers, 1985; Dahl, 1985). Increased
sedimentation is also one of several factors, which affect coral recruitment. Coral
larvae will not settle and establish themselves in shifting sediments. Consequently,
increases in sedimentation rates can alter the distribution of corals and their
associated reef constituents by influencing the ability of coral larvae to settle and
survive (Rogers, 1990).
Figure 2.1: Conceptual model of biological effects of suspended and bedded
sediments in estuaries (Waters, 1995)
39
2.3.3 EFFECTS ON AQUATIC PLANTS
Some populations of aquatic macrophytes have experienced dramatic losses
over the past two decades, a decline largely attributed to changes in underwater light
climate due to increases in suspended sediment concentrations (Best et al., 2001).
Turbidity limits the growth and distribution of aquatic plants by reducing available
light. The large-scale declines of submerged aquatic vegetation (SAV) reported in
Chesapeake Bay are believed to be directly related to increasing amounts of nutrients
and sediments entering the Bay (Batiuk et al., 1992, 2000; Dennison et al., 1993). To
address the unacceptable Bay-wide decline in SAV the U.S.EPA Chesapeake Bay
Program office established water clarity criteria. Water clarity criteria are based on
the light requirements for SAV growth and survival. The criteria take total suspended
solids (particulate matter and chlorophyll) into account, as well as epiphytic growth
and salinity regime. Water clarity criteria are used in Chesapeake Bay because it is
assumed that they will result in achievement of clarity/solids levels that would not
impair other habitats/organisms (with the exception that the water clarity criteria may
not fully protect "smothering" of bottom soft or hard bottom habitats with larger
sized sediment particles from sources that "bypass"/ don't influence shallow water
habitats), since the SAV represent one of the components of the Chesapeake Bay
ecosystem that is most sensitive to increases in SABS. A detailed explanation of the
derivation of Chesapeake Bay water clarity criteria can be found in U.S. EPA
(2003b).
SAV are also subject to burial, although different species have different
tolerances for sediment accretion, and different sediment entrainment qualities
(Fonseca and Fisher, 1986). These different tolerances can result in changes in
species composition in addition to overall loss of SAV as a result of increased
siltation (Terrados et al., 1998). It is not always possible to separate out the effects of
burial from the other effects of increased sediment input, e.g. reduced light
penetration (Terrados et al., 1998).
40
2.3.4 EFFECTS ON FISH
Of all of the taxonomic groups, fishes, particularly salmonids, have received
the most attention from suspend and bedded sediments researchers. This is because
of the commercial and recreational importance of salmonids, and the obvious impact
that logging and other land use activities have had on salmonid fisheries, particularly
in the Pacific Northwest (Waters, 1995). There are three major effects of suspended
sediment on fishes:
i.
Direct physiological effects of suspended sediment, such as
suffocation.
ii.
Effects due to decreases in water clarity.
iii.
Effects
due
to
sediment
deposition,
leading
to
increased
embeddedness or burial of eggs and larvae (Waters, 1995; Wilber and
Clarke, 2001).
The conventional wisdom (at least since the publication of Newcombe and
MacDonald, 1991) is that both the degree of exposure (measured as TSS or turbidity,
or decreased water clarity) and the duration of the exposure are important. It follows
that the longer the duration and the greater the exposure, the more severe the effects.
Therefore, it is expected that the first, mild, primarily behavioral effects would be
seen with low intensity, short-term exposures. As the duration of exposure and
intensity of exposure increase, sublethal effects are manifested, and lethal effects
begin to be expressed at more intense exposures of longer duration (Figure 2.2). The
timing of exposure to suspended sediment is also very important, as it may affect
different life history stages in different ways. Different life-history stages of the same
species may also have differing abilities to avoid exposure.
41
Figure 2.2: Idealized model of fish response to increased suspended sediments
(Rogers, 1985)
Newcombe and Jensen (1996) summarized much of the available data on the
effects of suspended sediment on fishes, and fit the data into empirical models in the
form of data “triplets”, with matched biological effect, concentration and duration
information. The effects were scored on a qualitative “severity of ill effect” (SEV)
scale, that included responses ranging from no behavioural effects (lowest on the
scale) to behavioural effects (low on the scale), to sub lethal effects (higher on the
scale), to lethal effects (highest on the scale).
Different models were developed for different age groups of fishes: juvenile
and adult salmonids together, adult salmonids, juvenile salmonids, eggs and larvae of
salmonids and non-salmonids, adult estuarine non-salmonids, and adult freshwater
non-salmonids. The models were presented both in visual form (as three-dimensional
response surfaces) and as linear regression equations, and were also used to
interpolate and extrapolate from the empirical data. The tabular forms of the models
are presented in Appendices A and D. They are taken from Newcombe (1997) and
Newcombe (personal communication). Appendix A also includes an empirical model
for the effects of suspended sediments on invertebrates as well as an empirical model
for plants. Appendix D corrects the error in the estuarine adult fish model from
42
Newcombe and Jensen (1996) identified by Wilber and Clarke (2001). Although the
visual presentations in Newcombe and Jensen (1996) of the models look complete, it
is evident from the figures of the “empirical data” that there are not enough data for
the various groups of organisms (with the possible exception of the salmonids) to fill
in the idealized model of fish response to increased suspended sediments. This is
because there are not enough data, and because of the great variability in the data.
Wilber and Clarke (2001) also summarized the effects of increased turbidity
and reduced water clarity on the feeding of fishes, but did not include the data in
their tables or figures, because most of them are reported in turbidity units which are
difficult to convert to suspended solids concentrations (Caux et al., 1997). It is very
difficult to make generalizations about these data. Some fishes are able to hunt better
as suspended solids increase, at least up to a point, because of increased contrast
between the prey and the surrounding water. Some larval fish, like striped bass, seem
to be able to feed under extremely turbid conditions, or even complete darkness. This
ability could be very important for a fish that follows the turbidity maximum for its
abundant food (Chesney, 1993).
Centrarchids (e.g., smallmouth and largemouth bass), on the other hand, may
be severely impacted in their ability to feed by even small increases in turbidity (J.
Sweeten, personal communication). Suspended sediment has little if any effect on the
nests of centrarchids due to their nesting behaviour of "fanning" eggs (J. Sweeten,
personal communication). However, low concentrations of suspended sediment
caused reduced growth in smallmouth bass (Micropterus dolomieui). The inhibition
concentration (IC) 25 value for a one day exposure was only 11.4 mg/L suspended
bentonite (Sweeten and McCreedy, 2002). The authors concluded that even low
concentrations of suspended sediment at this early life-stage may strongly affect year
class strength. Other fish may be excluded from desirable habitat because of
increased turbidity (Ponton and Fortier, 1992).
Despite the difficulties in putting together the data on the effects of turbidity
on fishes, Newcombe (2003) has developed an impact model for clear water fishes
exposed to excessively cloudy water. This is discussed in the modeling section
below.
43
The effects of increased suspend and bedded sediments resulting in increased
embeddedness, on salmonids in particular, have been well documented (e.g., Waters,
1995). An increased supply of fine sediment to a stream can cause the gravel
interstices of a streambed to be filled in. This process can cause reduced hatching due
to the reduction in flow through the streambed and the resulting decrease in dissolved
oxygen. It can also cause reduced larval survival because of armoring of the
sediment surface, which traps the larvae. Increased sedimentation in other habitats
(e.g., estuaries) can cause burial of eggs (Wilber and Clarke, 2001). Even a small
amount of deposited sediment can cause a problem. Winter flounder eggs, for
example, will suffer reduced hatching success if buried to only one half an egg
diameters (D. Nelson, NMFS, unpublished data).
2.3.5 EFFECTS ON WILDLIFE
There are very few published reports on the effects of suspend and bedded
sediments on aquatic-dependent wildlife (i.e., birds and mammals). For the most
part, aquatic-dependent wildlife are more mobile than the fish, invertebrates and
plants discussed above, and therefore aquatic-dependent wildlife can avoid most of
the direct effects of increased suspend and bedded sediments. A heron or an osprey,
for example, can avoid more turbid areas, and choose areas of clearer water. If and
when the water clears in the area, the bird can return. If increases in suspend and
bedded sediments are wide-spread and long-term, however, they might cause a
problem for aquatic-dependent wildlife that consume aquatic prey. A bear, for
example, may have to abandon part of its range if there is failure of a salmon run.
Loons are thought to require clear water for fishing, and may avoid nesting areas
with inadequate water clarity (McIntyre, 1988).
Most of the studies of the relationship between turbidity and aquaticdependent wildlife involve field studies with birds. Van Eeerden and Voslamber
(1995) describe a mass (group) fishing behaviour of cormorants, which was
apparently developed as a response to an increase in the turbidity of a lake in the
44
Netherlands. Stevens et al. (1997) found that water birds were most abundant on the
clear and variably turbid segments of the Colorado River and least abundant on the
more turbid lower segment, providing evidence that turbidity makes it difficult for
birds to forage effectively.
2.4
CONTROLS
Many sediment control techniques have been used to reduce erosion and limit
sediment input to streams and rivers. Some of the more prevalent methods include
the implementation of fabric barriers, sediment traps and basins, water diversions,
plantings, and proper road construction and maintenance (Waters, 1995). Although
forestry, mining, roading and construction activities are important sources of
sediment to lotic environments, they are overshadowed by sediment input from
agricultural sources (Waters, 1995). Because of this, we emphasize the importance of
riparian buffer strips and livestock fencing to reduce sediment input.
Simply defined, riparian areas are vegetated corridors along rivers and
streams. They may be considered important ecosystems. Lowrance et al. (1985)
stated, “Riparian ecosystems are the complex assemblage of organisms and their
environment existing adjacent to and near flowing water. Riparian ecosystems are
also a special class of wetlands.” Riparian zones are often viewed as prime
agricultural areas, both for crop and live stock production, because of seasonal
nutrient enrichment by flooding (Lowrance et al., 1935). A major effect of riparian
vegetation is the retardation of erosion by decreased surface water velocity that
allows deposition of eroded material in the riparian zone before it enters the lotic
environment (Lowrance et al., 1985; Schwab et al., 1993). In addition to sediment
entrapment, riparian zones also filter nutrients from run- off for storage in plant
material. They also provide bank stabilization and in-stream temperature regulation
through shading. Because of their soil characteristics, riparian zones store large
volumes of water. This water is released in a more even manner than in cleared
45
riparian areas. Thus, lush riparian areas can facilitate consistency in annual flow
patterns (Lowrance et al., 1985). Levels of suspended sediment increase quickly
during storm events when riparian vegetation is absent (Schlosser and Karr, 1981).
Whitworth and Martin (1990) compared streams with and without riparian filter
strips and found that most stream sites with filter strips had a higher total number and
taxa richness of macro invertebrates. They also stated that sites with riparian strips
also had higher species richness, diversity, total density, and index of biotic integrity
(IBI) of fish (Whitworth and Martin, 1990).
Recommendations for optimal widths of streamside riparian zones vary in the
literature. Published requisite widths for buffer strips are dependent on watershed use
and hillside slope. Erman and Mahoney (1983) found that riparian buffer strips of
~30 m were inadequate to protect streams from the effects of logging in Northern
California mountain watersheds. To ensure proper function, Waters (1995)
recommends a guideline width for riparian zones of 50 to 300 m, depending on local
conditions. Although there are no hard and fast rules for the determination of
requisite riparian strip widths, it is prudent to take a conservative management
approach to ensure the link between riparian quality and stream biota.
This is particularly true for near stream flood plains. Wilkin and Hebel (1982)
found that the majority of eroded material in a watershed came from cropped flood
plains, more than from cropped uplands within watersheds. From this, it is prudent to
conclude that cropped land should not extent to the water’s edge, and that riparian
zones not be made available for grazing. We do emphasize that there is a trade-off
between in-stream benefits and economic loss concerning the determination of
riparian widths. Riparian widths that are larger than needed to inhibit the
transmission of eroded material to aquatic environments remove valuable land from
production. The area of research concerning the optimal width of riparian zones
obviously needs further development. Where possible, we recommend that riparian
zones, whatever width, be removed from crop and cattle production through fencing
and the development of alternative water sources.
In the American Fisheries Society position statement on the effects of
livestock grazing on riparian and stream ecosystems, overgrazing was listed as a
46
significant source of degradation to riparian areas (Armour et al., 1991). Degradation
of riparian areas by livestock reduces the sediment filtering function of these areas.
Further impacts are often stream bank collapse and erosion due to bank trampling.
Elimination of livestock grazing in riparian areas has been shown to have a
restorative effect on stream biota. These benefits include increases in allochthonous
input, increases of the standing stock and biomass of fish, increases in food for fish,
and increases in cover for fish (Armour et al., 1991). Also, decreases in stream
temperatures, reductions of sediment on substrata, increases in vegetative cover,
decreases in average stream width, increases in average depth, and increases in bank
stability have been shown to occur (Armour et al., 1991). In other words, the
restoration of riparian zones results in significant positive effects on biotic and
abiotic conditions in lotic environments.
2.5
NUMERICAL MODELS
As human activities in estuarine and coastal area increase, water quality
management in estuarine and coastal water has received increased. Since many
processes affect the water quality in water column, it is difficult to assess the relative
importance of each process. To end this, numerical model and computer simulation
based on physical and biogeochemical principles is useful to aiming understanding
the system and to provide consistent, rational predictions of dynamic responses of the
system to changes in specified factors. Most numerical models of water quality
consist of a hydrodynamic model and the water quality model linked either internally
or externally.
47
2.5.1 ENVIRONMENTAL FLUID DYNAMICS CODE (EFDC)
EFDC is a hydrodynamic model that incorporates hydrodynamics, salinity,
riperature, dye, cohesive and non-cohesive sediments, toxicants, and water quality
state variable transport. It is a three-dimensional model that uses a Cartesian
curvilinear-orthogonal grid in the horizontal, and a sigma transformation in vertical
and it uses a finite volume-finite difference formulation to ensure conversion mass.
Dr. Hamrick at the Virginia Institute of Marine Science originally developed the
model, with funding from the Commonwealth of Virginia.
The hydrodynamic module includes a variety of forcing, including tides,
wind, inflow and outflow, high frequency of surface wave radiation stresses and
supports much type of boundary conditions. EFDC can simulate both cohesive and
non-cohesive sediment transport including settling, resuspension, and bed process.
Beside that, the model also includes a eutrophication water quality based on the 1CM
model developed by the USACE waterways experiment station and a toxicant
transport and fate model based on the EPA model TOXI5 (part of the WASP5
system).
2.5.2
TELEMAC-3D
TELEMAC-3D is a three-dimensional, finite-element model developed by
Patrick Sauvaget of the Laboritoire d’ Hydraulique de France. The modelling system
consists of a number of modules that are assembled for individual simulation. The
codes are proprietary. The system has been applied to more than 100 sites throughout
the world to date.
The model uses sigma transportation in the vertical. The hydrodynamic
model can be simulate forcing due to tides, wind, rivers and thermal exchange
through the free surface, and supports a wide variety of boundary condition. Salinity
48
and temperature are simulated and used to calculated local density. A variety of
turbulence-closure formulations are included. The sediment can transport model can
simulate single non-cohesive sediment or a range of cohesive sediments and
processes include settling, resuspension, and bed compaction. The water quality
model includes eutrophication and an “open architecture” for user-specified toxicant
fate relationships but partitioning to sediment and bacterial fates processes are not
included.
2.5.3 TRIVAST
TRIVAST is a three-dimensional, finite-difference hydrodynamic and water
quality model. It can be configured as a two-dimensional, depth-averaged model, or
as a three-dimensional model. There are no transformations in the horizontal or
vertical directions. The two-dimensional version of code, DIVAST, has been used
extensively. TRIVAST is still somewhat developmental, and they have been a few
applications to date. The models are proprietary.
The hydrodynamic module simulates hydraulic variables, salinity and
temperature. It can be forced by the tides, rivers, inflows, wind and density currents
support slip, no-slip and radiation boundary conditions. The horizontal eddy
viscosity c linked to local shear velocity and depth. A two-layer mixing length
turbulence model is used to calculate vertical eddy viscosity. Time stepping is
handled using an ADI scheme. The sediment module simulates both cohesive and
non-cohesive sediments. Processes simulated include settling and resuspension, but
no multiple sediment layers, compaction or bed armouring. The water quality module
includes comprehensive eutrophication kinetics, bacterial transport and fate and
toxicant fate processes.
49
2.5.4 RMA10 AND RAM11
RMA10 and RMA11 developed by US Resource Management Associated
three dimensional, finite-element models. In the vertical, a modified sigma
transformation is used in which the surface is mapped to a horizontal surface but the
- remains fixed in world coordinates. The three-dimensional codes are more recent
developments and have been used less often to date.
RMA10 is a three-dimensional, finite element and hydrodynamic model. It is
simu1ates hydraulic variables, salinity and temperature. It can be forced by tides, rain
inflows, wind, Coriolis acceleration, evaporation and precipitation and include slip
and non-slip boundary conditions.
RMA11 is a three-dimensional, finite-element sediment transport and water
quality model. The sediment module can simulate both cohesive and non-cohesive
sediments. The model does not handle variable sediment sizes in the same run.
Processes include settling, resuspension, as a function of critical shear stress and bed
compaction. The water quality module can simulate comprehensive eutrophication
kinetics and bacteria fate.
2.5.5
TIDE-3D
TIDE-3D is a three-dimensional model developed by U.S. Geological
Survey. It is a finite-element in horizontal, and continuous in the vertical, using a
sigma transformation. The program is non-proprietary and has been used mainly for
each purpose. A parallel-process version is available.
The hydrodynamic solves for hydraulic variables and salinity. It can be
forced by tides, rivers, wind, inflows and density and includes a variety of boundary
conditions. Eddy viscosity is calculated using an analytic mixing length formulation
50
algebraic stability function. There is a harmonic version, which does not require
marching and variably weighted explicit or implicit version.
2.5.6
MIKE3
MIKE3 is generalized mathematical modelling system for a wide range of
application. The model is propriety and developed by Danish Hydraulic Institute. It
is a three-dimensional, finite-difference model that uses an orthogonal grid in the
horizontal and a z-plane structure in the vertical. The hydrodynamic sub model of
MIKE3 simulates hydraulic variables, salinity and temperature. Various types of
forcing and boundary conditions are included. Besides that, various turbulence
closure models are also included and bottom stress are handle through a specified
roughness coefficient. Currently, there is no sediment processes include in MIKE3.
The water quality sub model includes toxicant transport and fate, comprehensive
bacterial transport and fate routine and a medium sized eutrophication module.
2.6
INTRODUCTION TO THE WASP5 MODEL
The Water Quality Analysis Simulation Program 5 (WASP5), an
enhancement of the original WASP (Di Toro et al., 1983; Connolly and Winfield,
1984; Ambrose, R.B. et al., 1988). This model helps users interpret and predict water
quality responses to natural phenomena and man-made pollution for various
pollution management decisions. WASP5 is a dynamic compartment-modeling
program for aquatic systems, including both the water column and the underlying
benthos. The time-varying processes of advection, dispersion, point and diffuse mass
loading and boundary exchange are represented in the basic program.
51
Water quality processes are represented in special kinetic subroutines that are
either chosen from a library or written by the user. WASP5 is structured to permit
easy substitution of kinetic subroutines into the overall package to form problemspecific models. WASP5 comes with two such models, which is TOXI5 for toxicants
and EUTRO5 for conventional water quality. Earlier versions of WASP have been
used to examine eutrophication and PCB pollution of the Great Lakes (Thomann,
1975; Thomann et al., 1976; Thomann et al, 1979; Di Toro and Connolly, 1980),
eutrophication of the Potomac Estuary (Thomann and Fitzpatrick, 1982), kepone
pollution of the James River Estuary (O'Connor et al., 1983), volatile organic
pollution of the Delaware Estuary (Ambrose, 1987), and heavy metal pollution of the
Deep River, North Carolina (JRB, 1984). In addition to these, numerous applications
are listed in Di Toro et al., 1983.
The flexibility afforded by the Water Quality Analysis Simulation Program is
unique. WASP5 permits the modeler to structure one, two, and three dimensional
models; allows the specification of time-variable exchange coefficients, advective
flows, waste loads and water quality boundary conditions; and permits tailored
structuring of the kinetic processes, all within the larger modeling framework
without having to write or rewrite large sections of computer code. The two
operational WASP5 models, TOXI5 and EUTRO5, are reasonably general. In
addition, users may develop new kinetic or reactive structures. This however requires
an additional measure of judgment, insight, and programming experience on the part
of the modeler. The kinetic subroutine in WASP (denoted "WASPB"), is kept as a
separate section of code, with its own subroutines if desired.
The model had been applied in Mississippi since 1996. Modelling for Back
Bay of Biloxi and Saint Louis Bay is done successfully. The modelling involves
hydrodynamic, water quality and eutrophication modelling. In Malaysia it have been
applied in Langat River, Semenyih River and Skudai River. Malaysia. Table 2.3
summarize the application of WASP5.
52
Table 2.3: History of WASP5 applications
APPLICATION SITE
Back Bay Of Biloxi, Mississippi, (1998)
St. Louis Bay, Mississippi, (1996)
Skudai River, (2002)
Langat River, Selangor, (2003)
Semenyih River, Selangor, (2005)
Skudai River, Johor Bahru, (2002)
REMARKS
Water Quality And Hydrodynamic
Models For Back Bay Of Biloxi.
Technical Report. (Hydrodynamic And
Water Quality Parameter)
Fecal Coliform Water Quality Model Of
The St. Louis Bay Estuary. Report.
(Hydrodynamic And Eutrophication)
Application Of WASP5 For Skudai
River Estuarine System. Thesis M.Sc.
(Hydrodynamic)
Tide Phenomena And Impact To The
Estuarine. Thesis B.Sc. (Hydrodynamic)
Sediment Modelling In Semenyih River
Using Wasp5 Software. Thesis B.Sc.
(Sediment)
Hydrodynamic Modelling For Langat
River Estuarine Using Wasp5 Software.
Thesis B.Sc. (Hydrodynamic And Water
Quality Parameter)
2.6.1 THE BASIC WATER QUALITY MODEL
WASP5 is a dynamic compartment model that can be used to analyze a
variety of water quality problems in such diverse water bodies as ponds, streams,
lakes, reservoirs, rivers, estuaries, and coastal waters. This section presents an
overview of the basic water quality model. Subsequent chapters detail the transport
and transformation processes in WASP5 for various water quality constituents.
The equations solved by WASP5 are based on the key principle of the
conservation of mass. This principle requires that the mass of each water quality
constituent being investigated must be accounted for in one way or another. WASP5
traces each water quality constituent from the point of spatial and temporal input to
its final point of export, conserving mass in space and time. To perform these mass
balance computations, the user must supply WASP5 with input data defining seven
important characteristics:
53
i.
Simulation and output control.
ii.
Model segmentation.
iii.
Advective and dispersive transport.
iv.
Boundary concentrations.
v.
Point and diffuse source waste loads.
vi.
Kinetic parameters, constants, and time functions.
vii.
Initial concentrations.
These input data, together with the general WASP5 mass balance equations
and the specific chemical kinetics equations, uniquely define a special set of water
quality equations. These are numerically integrated by WASP5 as the simulation
proceeds in time. At user-specified print intervals, WASP5 saves the values of all
display variables for subsequent retrieval by the post-processor programs W4DSPLY
and W4PLOT. These programs allow the user to interactively produce graphs and
tables of variables of all display variables.
2.6.2 OVERVIEW OF THE WASP5 MODELLING SYSTEM
The WASP5 system consists of two stand-alone computer programs,
DYNHYD5 and WASP5 that can be run in conjunction or separately (Figure 2.3).
The hydrodynamics program, DYNHYD5, simulates the movement of water while
the water quality program, WASP5, simulates the movement and interaction of
pollutants within the water. While DYNHYD5 is delivered with WASP5, other
hydrodynamic programs have also been linked with WASP. RIVMOD handles
unsteady flow in one-dimensional rivers, while SED3D handles unsteady; threedimensional flow in lakes and estuaries.
WASP5 is supplied with two kinetic sub-models to simulate two of the major
classes of water quality problems: conventional pollution (involving dissolved
oxygen, biochemical oxygen demand, nutrients and eutrophication) and toxic
pollution (involving organic chemicals, metals, and sediment). The linkage of either
54
sub-model with the WASP5 program gives the models EUTRO5 and TOXI5,
respectively. This is illustrated in Figure 2.3 with blocks to be substituted into the
incomplete WASP5 model. The tracer block can be a dummy sub-model for
substances with no kinetic interactions. In most instances, TOXI5 is used for tracers
by specifying no decay.
The basic principle of both the hydrodynamics and water-quality program is
the conservation of mass. The water volume and water-quality constituent masses
being studied are tracked and accounted for over time and space using a series of
mass balancing equations. The hydrodynamics program also conserves momentum,
or energy, throughout time and space.
Figure 2.3: The basic WASP5 system
2.6.3 THE MODEL NETWORK
The model network is a set of expanded control volumes, or “segments,” that
together represents the physical configuration of the water body. As Figure 2.4
illustrates, the network may subdivide the water body laterally and vertically as well
as longitudinally. Benthic segments can be included along with water column
55
segments. If the water quality model is being linked to the hydrodynamic model, then
water column segments must correspond to the hydrodynamic junctions.
Concentrations of water quality constituents are calculated within each segment.
Transport rates of water quality constituents are calculated across the interface of
adjoining segments.
Segments in WASP5 may be one of four types, as specified by the input
variable ITYPE. A value of 1 indicates the epilimnion (surface water), 2 indicate
hypolimnion layers (subsurface), 3 indicate an upper benthic layer, and 4 indicate
lower benthic layers. The segment type plays an important role in bed sedimentation
and in certain transformation processes. The user should be careful to align segments
properly. The segment immediately below each segment is specified by the input
variable IBOTSG. This alignment is important when light needs to be passed from
one segment to the next in the water column, or when material is buried or eroded in
the bed.
Segment volumes and the simulation time step are directly related. As one
increase or decreases, the other must do the same to insure stability and numerical
accuracy. Segment size can vary dramatically, as illustrated in Figure 2.4. The spatial
and temporal scale of the problem being analysed than by the characteristics of the
water body or the pollutant per se dictates characteristic sizes more. For example,
analysing a problem involving the upstream tidal migration of a pollutant into a
water supply might require a time step of minutes to an hour.
56
Figure 2.4: Model segmentation
By contrast, analysing a problem involving the total residence time of that
pollutant in the same water body could allow a time step of hours to a day. In Figure
2.5, the first network was used to study the general eutrophic status of Lake Ontario.
The second network was used to investigate the lake-wide spatial and seasonal
variations in eutrophication. The third network was used to predict changes in nearshore eutrophication of Rochester Embayment resulting from specific pollution
control plans.
As part of the problem definition, the user must determine how much of the
water quality frequency distribution must be predicted. For example, a daily-average
dissolved oxygen concentration of 5 mg/L would not sufficiently protect fish if
fluctuations result in concentrations less than 2 mg/L for 10% of the time. Predicting
extreme concentration values is generally more difficult than predicting average
values. Figure 2.5 illustrates typical frequency distributions predicted by three model
time scales and a typical distribution observed by rather thorough sampling, as they
would be plotted on probability paper. The straight lines imply normal distributions.
Reducing the model time step (and consequently segment size) allows better
simulation of the frequency distribution. This increase in predictive ability, however,
also entails an increase in the resolution of the input data.
57
Once the nature of the problem has been determined, and then the temporal
variability of the water body and input loadings must be considered. Generally, the
model time step must be somewhat less than the period of variation of the important
driving variables. In some cases, this restriction can be relaxed by averaging the
input over its period of variation. For example, phytoplankton growth is driven by
sunlight, which varies diurnally. Most eutrophication models, however, average the
light input over a day, allowing time steps on the order of a day.
Figure 2.5: Frequency distribution of observed and calculated values of a quality
variable
Care must be taken so that important non-linear interactions do not get
averaged out. When two or more important driving variables have a similar period of
variation, then averaging may not be possible. One example is the seasonal
variability of light, temperature, nutrient input, and transport in lakes subject to
eutrophication. Another example involves discontinuous batch discharges. Such an
input into a large lake might safely be averaged over a day or week, because largescale transport variations are relatively infrequent. The same batch input into a tidal
estuary cannot safely be averaged, however, because of the semi-diurnal or diurnal
tidal variations. A third example is salinity intrusion in estuaries. Tidal variations in
58
flow, volume, and dispersion can interact so that accurate long-term predictions
require explicit simulation at time steps on the order of hours.
Once the temporal variability has been determined, then the spatial variability
of the water body must be considered. Generally, the important spatial characteristics
must be homogeneous within a segment. In some cases, this restriction can be
relaxed by judicious averaging over width, depth or length. For example, depth
governs the impact of reaeration and sediment oxygen demand in a column of water.
Nevertheless, averaging the depth across a river would generally be acceptable in a
conventional waste load allocation, whereas averaging the depth across a lake would
not generally be acceptable. Other important spatial characteristics to consider
(depending upon the problem being analysed) include temperature, light penetration,
velocity, pH, benthic characteristics or fluxes, and sediment concentrations.
The expected spatial variability of the water quality concentrations also
affects the segment sizes. The user must determine how much averaging of the
concentration gradients is acceptable. Because water quality conditions change
rapidly near a loading point and stabilize downstream, studying the effects on a
beach a quarter-mile downstream of a discharge requires smaller segments than
studying the effects on a beach several miles away.
A final, general guideline may be helpful in obtaining accurate simulations
water column volumes should be roughly the same. If flows vary significantly
downstream, then segment volumes should increase proportionately. The user should
first choose the proper segment volume and time step in the critical reaches of the
water body then scale upstream and downstream segments accordingly.
Of course, actual volumes specified must be adjusted to best represent the
actual spatial variability, as discussed above. This guideline will allow larger time
steps and result in greater numerical accuracy over the entire model network, as
explained in the section on "Simulation Parameters".
59
2.6.4 THE MODEL TRANSPORT SCHEME
Transport includes advection and dispersion of water quality constituents.
Advection and dispersion in WASP are each divided into six distinct types, or
"fields." The first transport field involves advective flow and dispersive mixing in the
water column. Advective flow carries water quality constituents "downstream" with
the water and accounts for in stream dilution. Dispersion causes further mixing and
dilution between regions of high concentrations and regions of low concentrations.
The second transport field specifies the movement of pore water in the
sediment bed. Dissolved water quality constituents are carried through the bed by
pore water flow and are exchanged between the bed and the water column by pore
water diffusion.
The third, fourth, and fifth transport fields specify the transport of particulate
pollutants by the settling, resuspension, and sedimentation of solids. Water quality
constituents sorbed onto solid particles are transported between the water column and
the sediment bed. The user can define the three solids fields as size fractions, such as
sand, silt, and clay, or as inorganic, phytoplankton, and organic solids. The sixth
transport field represents evaporation or precipitation from or to surface water
segments.
Most transport data, such as flows or settling velocities, must be specified by
the user in a WASP5 input dataset. For water column flow, however, the user may
"link" WASP5 with a hydrodynamics model. If this option is specified, during the
simulation WASP5 will read the contents of a hydrodynamic file for unsteady flows,
volumes, depths, and velocities.
60
2.6.5 APPLICATION OF THE MODEL
The first step in applying the model is analysing the problem to be solved.
What questions are being asked? How can a simulation model be used to address
these questions? A water quality model can do three basic tasks describe present
water quality conditions, provide generic predictions, and provide site-specific
predictions. The first, descriptive task is to extend in some way a limited site-specific
database. Because monitoring is expensive, data seldom give the spatial and temporal
resolution needed to fully characterize a water body. A simulation model can be used
to interpolate between observed data, locating, for example, the dissolved oxygen sag
point in a river or the maximum salinity intrusion in an estuary. Of course such a
model can be used to guide future monitoring efforts. Descriptive models also can
be used to infer the important processes controlling present water quality. This
information can be used to guide not only monitoring efforts, but also model
development efforts.
Providing generic predictions is a second type of modelling task. Site-specific
data may not be needed if the goal is to predict the types of water bodies at risk from
a new chemical. A crude set of data may be adequate to screen a list of chemicals for
potential risk to a particular water body. Generic predictions may sufficiently address
the management problem to be solved, or they may be a preliminary step in detailed
site-specific analyses.
Providing site-specific predictions is the most stringent modelling task.
Calibration to a good set of monitoring data is definitely needed to provide credible
predictions. Because predictions often attempt to extrapolate beyond the present
database, however, the model also must have sufficient process integrity. Examples
of this type of application include waste load allocation to protect water quality
standards and feasibility analysis for remedial actions, such as tertiary treatment,
phosphate bans, or agricultural best-management practices.
Analysis of the problem should dictate the spatial and temporal scales for the
modelling analysis. Division of the water body into appropriately sized segments was
61
discussed in Section "Model Network." The user must try to extend the network
upstream and downstream beyond the influence of the waste loads being studied. If
this is not possible, the user should extend the network far enough so that errors in
specifying future boundary concentrations do not propagate into the reaches being
studied.
The user also should consider aligning the network so that sampling stations
and points of interest (such as water withdrawals) fall near the centre of a segment.
Point source waste loads in streams and rivers with unidirectional flow should be
located near the upper end of a segment. In estuaries and other water bodies with
oscillating flow, waste loads are best centered within segments. If flows are to be
input from DYNHYD5, then a WASP4 segment must coincide with each
hydrodynamic junction. Benthic segments, which are not present in the
hydrodynamic network, may nevertheless be included in the WASP5 network.
WASP5 segment numbering does not have to be the same as DYNHYD5 junction
numbering. Segments stacked vertically do not have to be numbered consecutively
from surface water segments down.
Once the network is set up, the model study will proceed through four general
steps involving, in some manner, hydrodynamics, mass transport, water quality
transformations, and environmental toxicology. The first step addresses the question
of where the water goes. This can be answered by a combination of gagging, special
studies, and hydrodynamic modelling. Flow data can be interpolated or extrapolated
using the principle of continuity. Very simple flow routing models can be used; very
complicated multi-dimensional hydrodynamic models can also be used with proper
averaging over time and space. At present, the most compatible hydrodynamic model
is DYNHYD5.
The second step answers the question of where the material in the water is
transported. This can be answered by a combination of tracer studies and model
calibration. Dye and salinity are often used as tracers. The third step answers the
question of how the material in the water and sediment is transformed and what its
fate is. This is the main focus of many studies. Answers depend on a combination of
laboratory studies, field monitoring, parameter estimation, calibration, and testing.
62
The net result is sometimes called model validation or verification, which are elusive
concepts. The success of this step depends on the skill of the user, who must combine
specialized knowledge with common sense and scepticism into a methodical process.
The final step answers the question of how this material is likely to affect
anything of interest, such as people, fish, or the ecological balance. Often, predicted
concentrations are simply compared with water quality criteria adopted to protect the
general aquatic community. Care must be taken to insure that the temporal and
spatial scales assumed in developing the criteria are compatible with those predicted
by the model. Sometimes principles of physical chemistry or pharmacokinetics are
used to predict chemical body burdens and resulting biological effects. The
bioaccumulations model FGETS (Barber, et al., 1991) and the WASTOX food chain
model (Connolly and Thomann, 1985) is good examples of this study.
2.6.6 SEDIMENT TRANSPORT MODEL DESCRIPTION
Sediment transport is potentially a very important process in aquatic systems.
Excess sediment can affect water quality directly. Water clarity and benthic habitats
can be degraded. Sediment transport also influences chemical transport and fate.
Many chemicals sorbs strongly to sediment and thus undergo settling, scour, and
sedimentation. Sorption also affects a chemical's transfer and transformation rates.
Volatilisations and base-catalysed hydrolysis, for example, are slowed by sorption.
Both sediment transport rates and concentrations must be estimated in most toxic
chemical studies.
In general, the stream transport capacity for suspended sediment is in excess
of its actual load, and the problem is one of estimating sediment source loading
namely, watershed erosion. In areas of backwater behind dams or in sluggish
reaches, the stream transport capacity may drop enough to allow net deposition.
Strongly sorbed pollutants may build up significantly. Because sediment transport
63
can be complex, site-specific calibration of the settling, scour, and sedimentation
rates is usually necessary.
Sediment size fractions, or solids types, are simulated using the TOXI5
program. Simulations may incorporate total solids as a single variable, or, alternately,
represent from one to three solids types or fractions. The character of the three solids
types is user-defined. They may represent sand, silt, and clay, or organic solids and
inorganic solids. The user defines each solid type by specifying its settling and
erosion rates, and its organic content.
WASP5 performs a simple mass balance on each solid variable in each
compartment based upon specified water column advection and dispersion rates,
along with special settling, deposition, erosion, burial, and bed load rates. Mass
balance computations are performed in benthic compartments as well as water
column compartments. Bulk densities or benthic volumes are adjusted throughout the
simulation.
The user can vary all solids transport rates in space and time. There are,
however, no special process descriptions for solids transport. Erosion rates, for
example, are not programmed as a function of sediment shear strength and water
column shear stress. Consequently, the TOXI5 sediment model should be considered
descriptive, and must be calibrated to site data.
2.6.7 MODEL IMPLEMENTATION
To simulate sediment transport with WASP5, use the pre-processor or a text
editor to create a TOXI5 input file. Simple datasets are provided for use as templates
to edit and adapt. The model input dataset and the input parameters will be similar to
those for the conservative tracer model. To those basic parameters, the user will add
benthic segments and solids transport rates. During the simulation, solids variables
64
will be transported both by the water column advection and dispersion rates and by
these solids transport rates.
In WASP5, solids transport rates in the water column and the bed are input
via up to three solids transport fields. These fields describe the settling, deposition,
scour, and sedimentation flows of three kinds of solids. The transport of particulate
chemicals or the particulate fraction of simulated chemicals follows the solids flows.
The user must specify the dissolved fraction (i.e. 0.0) and the solids transport field
for each simulated solid under initial conditions. To simulate total solids, solids 1
must be used.
2.7
INTRODUCTION TO DYNHYD5
The WASP5 hydrodynamics model DYNHYD5 is an update of DYNHYD4
(Ambrose, et al., 1988), which was an enhancement of the Potomac Estuary
hydrodynamic model DYNHYD2 (Roesch et al., 1979) derived from the original
Dynamic Estuary Model (Feigner and Harris, 1970). DYNHYD5 solves the onedimensional equations of continuity and momentum for a branching or channeljunction (link-node), computational network. Driven by variable upstream flows and
downstream heads, simulations typically proceed at 1 to 5-minute intervals. The
resulting unsteady hydrodynamics are averaged over larger time intervals and stored
for later use by the water-quality program.
2.7.1 THE MODEL NETWORK
A physical interpretation of this computational network can be developed by
picturing the links as channels conveying water and the nodes as junctions storing
65
water (Figure 2.6). Each junction is a volumetric unit that acts as a receptacle for the
water transported through its connecting channels. Taken together, the junctions
account for all the water volume in the river or estuary. Parameters influencing the
storage of water are defined within this junction network. Each channel is an
idealized rectangular conveyor that transports water between two junctions, whose
midpoints are at each end. Taken together, the channels account for all the water
movement in the river or estuary. Parameters influencing the motion of water are
defined within this channel network. The link-node computational network, then, can
be viewed as the overlapping of two closely related physical networks of channels
and junctions.
Figure 2.6: Representation of the model networks
66
Junctions are equivalent to segments in the water quality model, whereas
channels correspond to segment interfaces. Channel flows are used to calculate mass
transport between segments in the water quality model. Junction volumes are used to
calculate pollutant concentrations within water quality segments.
Link-node networks can treat fairly complex branching flow patterns and
irregular shorelines with acceptable accuracy for many studies. They cannot handle
stratified water bodies, small streams, or rivers with a large bottom slope. Link-node
networks can be set up for wide, shallow water bodies if primary flow Directions is
well defined. Results of these simulations should be considered descriptive only.
2.7.2 APPLICATION OF THE MODEL
A great deal of flexibility is allowed in laying out the network of
interconnected channels and junctions that represent a system, but there are several
guidelines for making the best representation. First, both hydraulic and quality
factors should be considered when selecting boundary conditions. Ideally, the
downstream boundary should extend to a flow gage, a dam, or the ocean. The
upstream boundary should extend to or beyond the limits of any backwater or tidal
effects on the inflowing streams. Such a network eliminates problems associated
with dynamic boundary conditions, such as changing salinity or other quality
conditions, which could be present if an inland point were chosen for the seaward
boundary. Other considerations influencing boundary locations and the size of
network elements include the location of specific points where quality predictions are
required, the location of existing or planned sampling stations (and the availability of
data for verification), the degree of network detail desired, and the computer time
available for solution.
In most applications of DYNHYD5, Manning's roughness coefficient (n) will
be the primary calibration parameter. The value of n can be highly variable,
67
depending on such factors as bed roughness, vegetation, channel irregularities in
cross-section or shape, obstructions, and depth. Values of n can potentially vary from
less than 0.01 to greater than 0.08. For the larger rivers, reservoirs, and estuaries to
which DYNHYD5 can be applied, however, values will usually fall between 0.01
and 0.04. Deeper, straighter reaches have lower roughness coefficients. In general,
the value of n increases upstream as channels become more constricted and shallow.
When calibrating DYNHYD5, changing the value of n in one channel affects
both upstream and downstream channels. Increasing n causes more energy to be
dissipated in that channel. As a result, the height of a tidal or flood wave will
decrease and the time of travel through the channel will increase. Lowering n
decreases the resistance to flow, resulting in a higher tidal or flood wave and a
shorter time of travel.
2.7.3 THE DYNHYD5 INPUT DATASET
This section describes the input required to run the DYNHYD5
hydrodynamics program. This information is provided to the user who elects not to
use the preprocessor program PREDYN. PREDYN allows you to create or modify
datasets with relative ease and has complete online help. The user should be
cautioned about potential changes to the dataset or manual that may accompany
version updates of the software. The printed manual may become dated as
enhancements are made or errors are identified and corrected. To arrange the input
into a logical format, DYNHYD5 data are divided into 12 groups, A through L:
i.
A - Simulation Control
ii.
B - Printout Control
iii.
C - Hydraulic Summary
iv.
D - Junction Data
v.
E - Channel Data
vi.
F - Inflow Data
68
vii.
G - Seaward Boundary Data
viii.
H - Wind Data
ix.
I - Precipitation/Evaporation Data
x.
J - Variable Junction Geometry Data
xi.
K - Variable Channel Geometry Data
xii.
L - WASP5 Junction to Segment Map
2.7.4 DYNHYD5 OUTPUT
DYNHYD5 simulations produce several files that may be examined by the
user. These files use the file name of the input data set with a unique extension *.DDF, *.OUT, *.HYD, and *.RST (where * is the name of the input data set). The
DDF file contains 17 display variables for each channel at each print interval
throughout the simulation. These variables are defined in Table 2.4. To examine
these variables in graphical or tabular form, the user may run the WASP5
postprocessor.
The OUT file contains a record of the input data along with any simulation
error messages that may have been generated. A printed record of user-selected
junction and channel volumes and flows at print intervals throughout the simulation
is provided. The HYD file contains averaged hydrodynamic variables for use in
future WASP5 simulations. These include basic network and inflow information;
junction volumes (m3), flows (m3/sec), depths (m), and velocities (m/sec); and
channel flows (m3/sec). This file is in ASCII format. The RST file contains a
snapshot of junction volumes and channel flows at the conclusion of the simulation.
This file may be read by DYNHYD5 to continue a series of simulations.
69
Table 2.4: DYNHYD5 Display Variables
2.8
Number
Variable
Definition
1
Q
Channel flow, cms
2
V
Channel velocities, m/sec
3
Y(1)
Upstream junction, m
4
Y(2)
Downstream junction, m
5
CN
Manning’s coefficient
6
DG1
Upstream depth, m
7
DG2
Downstream depth, m
8
FLOWG(1)
Upstream flow, cms
9
FLOWG(2)
Downstream flow, cms
10
QDIR(1)
Upstream direction
11
QDIR(2)
Downstream direction
12
VELOG(1)
Upstream velocity, m/sec
13
VELOG(2)
Downstreamvelocity, m/sec
14
MOM
Channel momentum
15
FRIC
Channel friction
16
GRAV
Channel gravity
17
WIN
Wind on channel, m/sec
LINKAGE TO WASP5
The hydrodynamic results generated by a DYNHYD5 simulation can be
stored for use by WASP5 water quality simulations using an external formatted file
containing segment volumes at the beginning of each time step and average segment
interfacial flows during each time step. WASP5 uses the interfacial flows to calculate
mass transport and the volumes to calculate constituent concentrations. Segment
depths and velocities may also be contained in the hydrodynamic file for use in
calculating reaeration and volatilisations rates.
70
When linking DYNHYD5 to WASP5, both the networks and the time steps
must be compatible (though not identical). This linkage is accomplished through an
external file chosen by the user at simulation time. The first step in the hydrodynamic
linkage is to develop a hydrodynamic calculation network that is compatible with the
WASP5 network. Note that each WASP5 segment corresponds exactly to a
hydrodynamic volume element, or node. Each WASP5 segment interface
corresponds exactly to a hydrodynamic link, denoted in the figure with a connecting
line.
To link with WASP5 the user must specify which DYNHYD5 junctions will
be linked to WASP5 segments. (Figure 2.7) It is no longer necessary to link junctions
and segments one to one; the user has the capability of linking a section of the
hydrodynamic simulation. It is important to insure the "windowed" section is
contiguous. When linking boundary junctions to WASP5 they are designated as 0
segment (the WASP convention for boundaries).
WASP5 may have additional segments not represented by junctions. For
example, WASP5 benthic segments will have no corresponding junctions. Junction
numbering need not correspond to segment numbering. Junction to segment mapping
is specified in the DYNHYD5 input data set.
The WASP5 time step must be an even multiple of the DYNHYD5 time step.
The ratio of time steps must be specified in the DYNHYD5 input data set as
parameter NODYN. Typical ratios are between 6 and 30. DYNHYD5 averages each
channel flow over NODYN hydrodynamic time steps, and stores this average value
for use at the corresponding WASP5 segment interface. DYNHYD5 stores each
junction volume at the end of NODYN time steps for use at the corresponding
WASP5 segment.
71
Figure 2.7: Link-node hydrodynamic linkage
This averaging and storage process continues for the entire hydrodynamic
simulation. WASP5 will use these flows and volumes, repeating the sequence if the
water quality simulation is longer than the hydrodynamic simulation. If the volumes
of the segments differ by more then 5% from the beginning to the end of the
hydrodynamic summary file, the WASP5 simulation will not continue beyond the
hydrodynamic simulation.
It is important to note that the hydrodynamic model has additional nodes
outside of the WASP5 network. These additional nodes correspond to WASP5
boundaries, denoted by nominal segment number "0." These extra hydrodynamic
nodes are necessary because while flows are calculated only within the
hydrodynamic network, WASP5 requires boundary flows from outside its network.
72
To implement the hydrodynamic linkage, the user must specify flow option 3
in the input dataset. If IQOPT is set to 3, a menu of previously prepared
hydrodynamic files (*.HYD) is presented. Following the choice of a proper file, the
hydrodynamic file will reset the simulation time step. The time steps read in Data
Group A will be ignored. Similarly, water column segment volumes will be read
from the hydrodynamic file. The user must nevertheless enter a time step and
volumes for each segment in the usual location. During the simulation, flows and
volumes are read every time step.
73
CHAPTER III
METHODOLOGY
3.0 GENERAL
The methodology involves three important stages. The first stage is
fieldwork. Secondly, laboratory work and the finally modelling process.
3.1
FIELD WORK
The first stage of the methodology is fieldwork at Sungai Batu Pahat, Batu
Pahat Johor. During the fieldwork, varieties of data are collected 10 days starting
from 27/8/2006 to 7/9/2006. This includes water sample, flow and water level.
The water samples are collected for two days, 28/8/2006 and 29/8/2006. The
first data collected is use as initial data for the quality model. Meanwhile the second
data is for validation process for the quality model.
74
Segment 140
S u n g a i S im p a n g K iri
1 40
Ka
139
13 8
13 7
13 6
1 35
13 4
13 3
132
Su
13 1
130
12 9
ng
ai
m
Si
pa
ng
na
n
Segment 120
124
1 23
12 2
1 21
1 20
1 19
1 18
1 17
11 6
11 5
11 4
12 8
12 7
1 2 1 12 0 11 9 11 8
12 6 12 5 12 4 1 23 1 22
1 17
11 3
Segment 112
11 6 1 1 5 1 14 11 3 11 2
1 11
11 0
10 9
1 08
1 07
10 6
1 05
1 04
10 3
102
1 01
1 00
99
98
97
96
95
94
93
92
91
90
89
88
87
86
85
84
83
82
81
80
79
78
Segment 77
77
76
75
74
73
72
71
70
69
68
67
66
65
64
63
62
61
S ung
a i Ba
tu P a
hat
60
59
58
57
56
55
54
53
52
51
50
49
48
47
46
45
44
43
42
41
39
40
38
37
36
35
34
16
14
13
12
15
17
33
18
32
19
20
31
21
22
23
24
25
29
26
27
30
28
11
10
9
8
7
6
5
4
3
2
1
S e la t M e la k a
Segment 1
Figure 3.1: Segment of water sampling
75
The water samples are collected at the downstream, which is at the estuaries
and at the upstream, which is Sungai Simpang Kiri (segment 120) and Sungai
Simpang Kanan (segment 140), river junction (segment 112) and under the bridge
(segment 77). (Figure 3.1 and Figure 3.2)
The current meter recorded flow every fifteen minute for ten days from
27/8/2006 to 7/9/2006 (Figure 3.3). The data recorded will be use in validation
process for the hydrodynamic model. The current meter is located near the
downstream, which is segment seven. (Figure 3.4)
Figure 3.2: Data collection at study area
76
Current meter setting
Figure 3.3: Current meter setting
Current meter location
Figure 3.4: Current meter location (segment seven)
77
3.2
LABORATORY ANALYSIS
The second stage is the laboratory analysis. During this stage the water
sample collected is process to obtain the concentration of sediment. Figure 3.5 show
the equipment used to determine the suspended sediment.
The laboratory analysis is done at Environmental Laboratory Universiti
Teknologi Malaysia. Figures 3.6 show the suspended sediment under the laboratory
process. Table 3.1 shows the segment number and the location of segment at the
study area. Two sets of data are used. The first data set is initial data for water quality
model and the second set of data is use in validation process.
Table 3.1: Suspended sediment concentration data
Segment Number
1
77
112
120
140
Location
Estuaries
Under the
bridge
River junction
Sungai
Simpang Kanan
Sungai
Simpang Kiri
Initial Data
Concentration On
28/8/2006 (mg/L)
Validation Data
Concentration On
29/8/2006 (mg/L)
188
200
97
100
77
90
113
99
10
7
78
Suspended sediment experimental equipment
Figure 3.5: Suspended Sediment experimental equipment
Suspended sediment under laboratory process
Figure 3.6: Suspended Sediment under laboratory process
79
3.3
MODELLING PROCESS
Segmentation of Sungai Batu Pahat is done during the desk study. The mesh
construction is done using AutoCAD. Figure 3.7 shows the mesh construction.
Sungai Batu Pahat is divided into 140 segments. Segment one is at the estuaries and
segment 142 is at Sungai Simpang Kiri. Figure 3.8 show the entire segment of
Sungai Batu Pahat, part of Sungai Simpang Kiri and part of Sungai Simpang Kanan.
Segmentation in AutoCAD.
Figure 3.7: Mesh construction using AutoCAD
80
S ung ai S im pa ng K iri
1 40
139
138
13 7
pa
136
1 35
134
133
132
n
Su
131
130
129
i
ga
m
Si
ng
Ka
na
n
12 4
1 23
1 22
1 21
1 20
119
118
1 17
1 16
115
1 14
128
12 7
12 1 12 0 1 19 11 8
113
12 6 1 25 1 2 4 1 2 3 1 2 2
1 1 7 1 16 1 1 5 1 14 1 1 3 1 1 2
1 11
110
1 09
10 8
1 07
10 6
105
104
103
10 2
1 01
10 0
99
98
97
96
95
94
93
92
91
90
89
88
87
86
85
84
83
82
81
80
79
78
77
76
75
74
73
72
71
70
69
68
67
66
65
64
63
62
61
60
Paha
t
59
58
57
56
a i Ba
tu
55
54
53
52
Sung
51
50
49
48
47
46
45
44
43
42
41
39
40
38
37
36
35
34
16
14
13
12
15
17
33
18
32
19
20
31
21
22
23
24
25
29
26
27
30
28
11
10
9
8
7
6
5
4
3
2
1
S ela t M elaka
Figure 3.8: Segmentation of study area at Sungai Batu Pahat
81
3.3.1 THE HYDRODYNAMIC EQUATIONS
The hydrodynamic model solves one-dimensional equations describing the
propagation of a long wave through a shallow water system while conserving both
momentum (energy) and volume (mass). The equation of motion, based on the
conservation of momentum, predicts water velocities and flows. The equation of
continuity, based on the conservation of volume, predicts water heights (heads) and
volumes. This approach assumes that flow is predominantly one-dimensional, that
Coriolis and other accelerations normal to the direction of flow are negligible, that
channels can be adequately represented by a constant top width with a variable
hydraulic depth (i.e., "rectangular"), that the wavelength is significantly greater than
the depth, and that bottom slopes are moderate. Although no strict criteria are
available for the latter two assumptions, most natural flow conditions in large rivers
and estuaries would be acceptable. Dam-break situations could not be simulated with
DYNHYD5, nor could small mountain streams.
3.3.2 THE EQUATION OF MOTION
The equation of motion is given by:
∂u
∂u
= −u
+ ag , λ + af + aw , λ
∂t
∂x
(3.1)
where:
∂U
∂t
U∂ U
∂t
= the local inertia term, or the velocity rate of
change with respect to time, m/sec2
= the Bernoulli acceleration, or the rate of
momentum change by mass transfer; also defined as the convective
inertia term from Newton's second law, m/sec2
a g, λ
= gravitational acceleration along the λ axis of the
channel, m/sec2
82
af
= frictional acceleration, m/sec2
a w, λ
= wind stress acceleration along axis of channel, m/sec2
x
= distance along axis of channel, m
t
= time, sec
U
= velocity along the axis of channel, m/sec
λ
= longitudinal
3.3.3 THE EQUATION OF CONTINUITY
The equation of continuity is given by:
∂A
∂Q
= −
∂t
∂x
(3.2)
where:
A
= cross-sectional area, m2
Q
= flow, m3/sec
For rectangular channels of constant width B:
∂H
1 ∂Q
=−
∂t
B ∂x
(3.3)
where:
B
= width, m
H
= water surface elevation (head), m
∂H
= rate of water surface elevation change with respect to
∂t
∂Q
B∂x
time, m/sec
= rate of water volume change with respect to distance
per unit width, m/sec
83
Modelling process also done during this stage. Two models are involve, the
Hydrodynamic model, which is DYNHYD5 and water quality model TOXI5.
For the hydrodynamic model the input parameter are:
i.
Junction parameter
a. Surface elevation or head, m
b. Surface area, m2
c. Bottom elevation, m
d. Volume, m3
ii.
Channel Parameters
a. Length, m
b. Length, m
c. Width, m
d. Cross-sectional area, m2
e. Roughness coefficients, sec/m 1/3
f. Initial velocity, m/sec
g. Hydraulic radius, m
h. Channel orientation, degrees
iii.
Inflow or Outflow Parameter
iv.
Downstream Boundary Parameters
v.
Wind Parameters
The junction parameter and some of the channel parameter are taken
according to the bathymetric map. Appendix B. The roughness coefficient, which is
the Manning’s coefficient, is the calibration parameter. Three value of Manning’s
coefficient are use that is 0.02, 0.03 and 0.04. Initial velocity is set 0.01m/sec. The
value of inflow is 10 m3/sec at Sungai Simpang Kiri and 30 m3/sec at Sungai
Simpang Kanan. The downstream boundary is the tidal data for Sungai Batu Pahat
Year 2006 achieve from Johor Bahru Port Office. Appendix C. the wind data is
neglected since the wind data is not available. The hydrodynamic model is simulated
for eleven day. Figure 3.9 shows the simulation interface of hydrodynamic model.
84
Figure 3.9: Simulation interface in hydrodynamic model DYNHYD5
For the water quality model the input parameter are:
i.
Environment parameter
a. System
b. Bed Volume Option
c. Bed Time Step
ii.
iii.
Transport Parameters
a.
Number of Flow Fields
b.
Sediment Transport Velocities, m/sec
c.
Cross-Sectional Areas, m2
Boundary Parameter
a.
Boundary Concentrations, mg/L
b.
Waste Loads, kg/day
c.
Solids Transport Field
d.
Solid Density, g/cm3
e.
Initial Concentrations, mg/L
f.
Dissolved Fraction
The environment parameter is set default in the model. The sediment
transport velocities which is the calibration parameter is the Stoke’s settling velocity.
Three values are use in the calibration process, which are 0.19 m/day, 1.21 m/day
85
and 4.7 m/day. The cross sectional’s area is calculated internally by the model. The
boundary condition is based on the data collected during fieldwork (Table3.1). The
other boundary data are set defaults in the model. Figure 3.10 illustrates the study
methodology. The water quality model also simulated for eleven day.
3.4
MODEL LIMITATIONS
Hydrodynamic (DYNHYD5) model can link to the water quality (TOXI5)
with the same grid. However, there are some problems in this approach. The
numerical computations in the finite segment model, WASP/TOXI5 has two
constraints: integration time step and numerical dispersion, which are closely related
to each other (Lung, 2001).
The magnitude of numerical dispersion is a function of the Courant number
(Lung, 2001). Given the same spatial and temporal resolution as in the hydrodynamic
model, the mass transport computations in water quality model cannot be carried out
without generating excessive numerical dispersion (Lung et al., 1989). While longer
time steps
will increase the Courant number and thereby reduce numerical
dispersion to some extent, there is an upper bound for the time-step to maintain
numerical stability (Lung, 2001).
As reminder, the TOXI5 modelling framework has a hydrodynamic module,
the one-dimensional DYNHYD5 module, whose output is fed into the TOXI5
module. However, the one-dimensional hydrodynamic calculation limits its use in
two- or three-dimensional sediment modelling work (Lung, 2001). DYNHYD5 is
limited only to one-dimensional calculations and does not support two- or threedimensional hydrodynamic calculations. The mass transport calculation can therefore
be used to calibrate mass transport coefficient using conservative tracers, a much
more practical approach toward quantifying two- or three-dimensional mass
transport.
86
Study Area:
The study will focus on Sungai Batu Pahat.
•
•
Collection of data:
Water level, current meter, water sample
Bathymetry plan, land use map, tidal record.
Segmentation process:
142 mesh are constructed through the Sungai Batu Pahat, including parts of Sungai Simpang
Kiri and Sungai Simpang Kanan
Segment number one located at Sungai Batu Pahat estuary that will be the downstream
boundary. Segment number 120 located at Sungai Simpang Kanan and segment number 143
located at Sungai Simpang Kiri. Both are upstream boundary. Figure 1.3 illustrates the whole
segment involved.
DYNHYD5 Simulation Modeling
(1D-Finite Deferent Method)
Determine water movement.
The equation of motion
∂u
∂u
+ ag , λ + af + aw , λ
= −u
∂x
∂t
The equation of continuity
∂A
∂Q
=−
∂x
∂t
For rectangular channels of
constant width B
∂H
1 ∂Q
=−
∂t
B ∂x
TOXI5 Simulation Modeling
(1D-Finite Deferent Method)
Determine suspended sediment concentration.
Stoke’s velocity equation
Vs =
8.64 g
( ρp + ρw) dp
18µ
Mass balance equation
∂
∂ 
∂C 
( AC ) =  ∆UxAC + ExA
 + ∆A( SL + SB )
∂
∂x 
∂x 
TOXI5 calibration
Adjustment of Stoke’s settling
velocity
o V = 0.19 m/day
o V = 1.21 m/day
o V = 4.70 m/day
DYNHYD5 calibration
Adjustment of Manning’s
coefficient
n = 0.02
n = 0.03
n = 0.04
Linkage DYNHYD5 to TOXI5
Simulate the entire system
DYNHYD5 results
Achieve
Day 5 to Day 7 Flow profile
Day 5 to Day 7 Depth profile
Day 5 to Day 7 Head profile
Determine day of highest tide
and lowest tide.
WASP5 results
Achieve suspended sediment concentration
during highest tide and lowest tide
DISCUSSION AND CONCLUSION
Figure 3.10: Flow chart of the study methodology
87
Generally, unknowns solved for in hydrodynamic model include velocities
and water surface elevations. The accurate prediction of water surface elevations or
velocities is not sufficient to test the model application for sediment purposes, but the
models must also accurately transport as well (Ambrose et al., 1990). Therefore, data
requirement, can be used to evaluate hydrodynamic prediction. An intensive datasampling program that includes concurrent water surface elevation, velocity and dye
dispersion provides the best assessment of the hydrodynamic model application.
88
CHAPTER IV
RESULTS AND DISCUSSIONS
4.0 GENERAL
As stated previously, this study involves observation site data and model
simulation. For every test case, results from both sources are presented together for
comparison purpose. Input parameters for each simulation are provided and results
from both sources were analysed.
4.1
MODEL CALIBRATION
An eleven-day period was chosen as the calibration period because of the
ability of the computer available to simulate input file are limited so thus the data
from the field-monitoring program conducted. Model-data comparisons included
suspended sediment, and current velocities. The fieldwork observation started from
27/08/2006 until 7/09/2006.
89
4.1.1 HIDRODYNAMIC MODEL CALIBRATIONS
Formally, model calibration involves the adjustment of certain model input
quantities in an attempt to achieve a specified level of model performance.
Calibration of the hydrodynamic model DYNHYD5 involved adjustment of the
Manning’s coefficient. The Manning’s values used are 0.02, 0.03 and 0.04. (Figure
4.1.)
CALIBRATING PROCESS
0.6
0.5
VEELOCITY (m/s)
0.4
0.3
0.2
0.1
0
0
50
100
150
200
250
-0.1
TIME (hour)
manning 0.02
manning 0.03
manning 0.04
OBSERVED
Figure 4.1: Calibration process using different value of Manning’s coefficient in
hydrodynamic model DYNHYD5
From the calibration process Manning’s coefficient of 0.02 suited to the
hydrodynamic model. The different between simulate and observed within minimum
of 1.45% up to 26.78%. Figure 4.2 illustrates the result for hydrodynamic model
DYNHYD5 calibration.
90
BEST FITTED
0.6
0.5
VELOCITY (m/s)
0.4
0.3
0.2
0.1
0
0
50
100
150
200
250
-0.1
TIME (hour)
manning0.02
observed
Figure 4.2: The Manning’s coefficient of 0.02 fitted the hydrodynamic model
4.1.2 WATER QUALITY MODEL CALIBRATION
The total solid collected from the study area is assumed to be suspended
sediment. In the calibration of suspended sediment concentration, several parameters
were inputted. The parameters inputted were the sediment specific gravity, sediment
settling velocity, boundary stress below which deposition occurs, suspension rate,
and boundary stress above which resuspension occurs. The parameters adjusted were
the sediment settling velocity. The settling velocity is according to Stoke’s settling
91
velocities that are provided in the WASP5 manual (Table 4.1). Three settling
velocity chosen are 0.19 m/day, 1.21 m/day and 4.7 m/day. Figure 4.6 illustrates the
calibration process for water quality model TOXI5.
Table 4.1: Stoke's Settling Velocities (in m/day) at 20ºC
Particle diameter, mm
1.8
Particle Density, g/cm3
2
2.5
2.7
Fine Sand
0.3
0.05
300
94
400
120
710
180
800
200
0.05
0.02
0.01
0.005
0.002
94
15
3.8
0.94
0.15
120
19
4.7
1.2
0.19
180
28
7.1
1.8
0.28
200
32
8
2
0.31
0.002
0.001
0.15
0.04
0.19
0.05
0.28
0.07
0.32
0.08
Silt
Clay
CONCENTRATION (mg/L)
CALIBRATION PROCESS
200
150
100
50
0
0
50
100
150
SEGMENT NUM BER
set.vel. 0.19 m/day
set.vel. 1.21 m/day
set.vel. 4.7 m/day
OBSERVED
Figure 4.6: Calibration process using different settling velocity for water quality
model (downstream to upstream)
92
4.2
RESULTS AND DISCUSSIONS
4.2.1 HIDRODYNAMIC MODEL RESULTS
After the calibration process, the model is simulated to obtain head profile,
flow profile and depth profile for Sungai Batu Pahat. From the model output file the
result were transfer to Microsoft Excel Format. Figure 4.3, Figure 4.4 and Figure 4.5
illustrate the result. Only day five, six and seven are taking account in the modelling
to obtain water profiles. Day one to four is neglected because the model in stabilizing
process.
HEAD PROFILES
3
2.5
HEAD(m)
2
1.5
1
0.5
0
0
20
40
60
80
100
120
140
160
SEGMENT NUMBER
DAY 5
DAY 6
DAY 7
Figure 4.3: Head profile along Sungai Batu Pahat from day five to day seven
(downstream to upstream)
93
DEPTH PROFILES
12
10
DEPTH (m)
8
6
4
2
0
0
20
40
60
80
100
120
140
160
SEGMENT NUMBER
DAY 5
DAY 6
DAY 7
Figure 4.4: Depth profile along Sungai Batu Pahat from day five to day seven
(downstream to upstream)
FLOW PROFILES
300
FLOW(m3/s)
250
200
150
100
50
0
0
20
40
60
80
100
120
140
160
SEGMENT NUMBER
DAY 5
DAY 6
DAY 7
Figure 4.5: Flow profile along Sungai Batu Pahat from day five to day seven
(downstream to upstream)
94
Day five (1/9/2006) water profile is assumed to be the day of highest tide
occur according to the highest head level achieve in the simulation process. Day six
(2/9/2006) are assume to be where the lowest tide occurs due to lowest head recorded
in the simulation period. After determine the day of the highest and lowest tide, the
hydrodynamic model DYNHYD5 is link to TOXI5 model to simulate the suspended
sediment concentration of the study area.
4.2.2 WATER QUALITY MODEL RESULTS
From the calibration process Stoke’s settling velocity of 4.7m/day suited the
water quality model. The different between simulate and observed within minimum
of 1.20% up to 3.84%. After the calibrating process, the water quality model is used
to predict the suspended sediment concentration on the day five where the highest
tide occur and day six where the lowest tide occur. The concentration result are
illustrate in Figure 4.7.
CONCENTRATION (mg/L)
SUSPENDED SEDIMENT CONCENTRATION
180
160
140
120
100
80
60
40
20
0
0
50
100
150
SEGMENT NUMBER
LOWEST TIDE
HIGHEST TIDE
Figure 4.7: Suspended sediment concentration along Sungai Batu Pahat during
highest tide and lowest tide at Sungai Batu Pahat (downstream to upstream)
95
4.2.3 DISCUSSIONS
From all the result obtains in the simulation process, the data are summarized
in Table 5.1. The maximum head recorded at segment number 73 to segment 142
during the highest tide. The rest of the segment is achieving the minimum head of 2.6
meter. Meanwhile during the lowest tide event, segment one received the maximum
head of 1.87 meter and segment 142 receiving the minimum head of 0.94 meter. The
head profile during the lowest tide is not consistent through out the segment. During
the highest tide event the flow are heading to the upstream of the river with
maximum flow rate of 12.4m3/s recorded at segment 112 and minimum flow of
0.986m3/s at segment number 113. During the lowest tide the flow are heading to the
estuary with highest flow of 263m3/s recorded at segment number two and lowest
flow is 2.42m3/s at segment number 141. Segment 76 to 142 are the deepest segment
during highest tide that is 10.8 meter and the shallowest segment during the highest
tide is segment number three and four with the depth of 5.6 meter. Meanwhile during
the lowest tide segment number 76 achieving depth of 9.75 meter and segment
number three recorded the shallowest depth that is 4.87 meter.
The maximum suspended sediment concentration occur in the same segment
during the highest tide and lowest tide which is segment number one. During the
highest tide segment number one achieving suspended sediment concentration of 161
mg/L and 136mg/L during the lowest tide. Segment number 140 sharing the
minimum concentration during highest and lowest tide. The minimum concentration
during the highest tide is 22.3 mg/L and 10.3 mg/L of concentration during lowest
tide.
In reality, chemical and biological laws in addition to these physical
processes control cohesive sediment transport. The transport is also dependent on
the type of sediment and therefore analytical expressions that describe these
processes are semi-empirical. Therefore much of this information has been obtained
from laboratory and field experiments. Another important issue is salinity. Fine clay
particles have electrostatic properties and flocculate in saline water. The extent of
96
flocculation depends upon the salinity and concentration of suspended particles.
(Uni-Technologies Sdn Bhd, 2006)
Table 5.1: Summary of suspended sediment concentration at highest and lowest tide.
HIGHEST
TIDE
SEGMENT
NUMBER
LOWEST
TIDE
SEGMENT
NUMBER
MAX.
MIN
MAX.
MIN
MAX
MIN
MAX
MIN
HEAD
HEAD
FLOW
FLOW
DEPTH
DEPTH
CONC.
CONC.
(m)
(m)
(m3/s)
(m3/s)
(m)
(m)
(mg/L)
(mg/L)
2.61
2.60
12.40
0.986
10.80
5.60
161.00
6.98
73-142
1-73
112
113
76-142
3-4
1
140
1.87
0.94
263.00
2.420
9.75
4.87
136.00
7.00
1
142
2
141
76
3
1
140
This is a preliminaries modelling for water quality in Sungai Batu Pahat.
Calibration using water level data will resulted a better accuracy of calibration. An
eleven-day period was chosen as the simulation period because of the ability of the
computer to simulate input file are limited so thus the data from the field-monitoring
program conducted.
The DYNHYD5 is a box model where the true cross section of the river has
been neglected. An intensive database coupled with a high-resolution, physically
comprehensive hydrodynamic model can be used to determine hydrodynamic
circulation in future study. The time-dependent, three-dimensional software such as
Environmental Fluid Dynamics (EFDC) model developed by Hamrick (1992)
provided the modelling framework. EFDC solved prognostic equations for surfaceelevation, velocity components, temperature, salinity, and turbulence energy. All
equations were written in curvilinear, coastline-fitted coordinate systems combined
with a free surface and bottom following sigma-coordinate. An imbedded turbulence
sub-model was employed to provide vertical mixing coefficients for momentum,
temperature, and salinity.
97
CHAPTER V
CONCLUSIONS AND RECOMMENDATIONS
5.0 GENERAL
Sungai Batu Pahat is a vital water body in the South of Johor Coast region
with designated uses for shellfish, fresh water supply and primary contact recreation.
The study area is located in one of the most rapidly growing regions of the state of
Johor. Water quality in the estuary is controlled by several municipal and industrial
discharges as well as agricultural and urban runoff and seepage from septic tank
effluents. Current data indicate the water quality concentrations in the estuarine
system impair the designated uses of the water body (IEWRM, 1999).
After the degree of pollution is identified, public awareness on the important
of Sungai Batu Pahat to the life use must be increased in order to improve the current
status of the environment of Sungai Batu Pahat. Cooperation of the public is the most
important to achieve the goals that is to protect our environment. Public awareness
can be done by several ways such as newspapers, seminars, campaign and so on.
Furthermore, the authority must always upgrade the protection and water quality
management of Sungai Batu Pahat in order to maintain the cleanliness.
98
5.1
CONCLUSIONS
Studies in Sungai Batu Pahat, with tides ranging from 0.94 meter to 2.6
meter, have shown that tidal cycles play a significant role in sediment logical
processes. From this analysis the suspended sediment concentration for entire of
Sungai Batu Pahat are dominant by the tidal. Only segment 140, which is about 14
kilometres from the downstream, which the upstream boundary at Sungai Simpang
Kiri is not influent by the tidal. The highest different of suspended sediment
concentration from the observation percentage from the study is 30.53% and the
lowest different of suspended sediment concentration in percentage are to be 2.98%.
From the Total Suspended Solid aspect, Sungai Batu Pahat can be classified
as Class III according to Interim National Water Quality Standard for Malaysia.
Class III define the river as Water Supply III that means extensive treatment is
required and Fishery III where it is common of economic value and suitable for
tolerant species in aquatic life.
5.2
RECOMMENDATIONS
This study is the preliminary effort for development of coupled
hydrodynamic and water quality models for Sungai Batu Pahat. The water quality
model is capable of simulating sediment concentration. From the modelling result, it
is prudent that the input conditions of the model diverge greatly from reality for
some part of the segments. It is anticipated and recommended that the development
of this model be continued to synthesize additional field data into modelling process
as that data become available.
99
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APPENDIX A
108
APPENDIX B