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Selley 1988, defines sedimentation as the process of deposition of a solid material from a state of suspension or solution in a fluid usually air or water. Sedimentation occurs when ground cover is removed allowing increased rates of erosion through physical forces acting upon the ground surface and removing loose weathered material (Carver, 1998). It can reduce soil productivity, and it can also increase sediment and other pollution loads in receiving waters (Rudra et al., 1998). Soil erosion has increased throughout the 20th century and is becoming an extremely serious environmental problem (Morgan, 2005). The scale of soil erosion is difficult to grasp, but at least a billion hectares of earth's soil has been seriously degraded because of water erosion alone (Oldeman and Lynden, 1998). In some areas current rates of erosion were 25 to 50 mm per year (one hundred tons per acre) (Marsh, 1998).
Types of Erosion
On sloping land, soil moves downhill owing to gravity. Displacement will occur too by the splash action of raindrops. Forest gives the most effective barrier to natural erosion. Loss of forest cover resulting from land development always increases soil erosion. Erosion spans a range of phenomena from surface erosion (sheetwash and rills) through gullies (advanced surface erosion) to mass soil movement (landslips, slumps, debris avalanches and landslides) (FAO, 2008). The main types (forms/features) of soil erosion by water are interill, rill, and gully eroion (FAO, 1990; Stocking and Murnaghan, 2001; Troeh et al., 2004). Interill erosion is best described as the process of detachment and transport of soil by raindrops and very shallow flow (Troeh et al., 1994). Gullies usually form channels that cannot completely be smoothed by normal agricultural tilling operations (Laflen and Roose, 1998)
Another point of view from (SCWW, 2009), there four types of soil erosion on exposed terrain, i) splash erosion results when the force of raindrops falling on bare vegetated soil detaches soil particles; ii) sheet erosion occurs when these soil particles are easily transported in a thin layer, or sheet, by water flowing and; iii) if this sheet runoff is allowed to concentrate and gain velocity, it cuts rills and gullies as it detaches more soil particle. These types of erosion illustrates in Figure 2.1. Because soil erosion is highly variable and its consequences are costly, it is useful to obtain knowledge on where and when erosion is occurring (Poesen et al., 2003).
Figure 2.1: Types of Erosion (SCWW, 2009)
Soil erosion created a huge number of problems. For example in India where the climate shifts from periods of minimal rain followed by the monsoon season can cause catastrophic flooding (Ives, 1989). Areas can be affected by erosion in several ways, two of which are: reduction of viable soil and increased levels of soils degrading the watershed and drainage basin, which settle and smother plants and bottom dwelling species (Sharma et al., 2001). Erosion decreases soil's water storage capacity, and usually causes reduced infiltration on eroded sites, thus speeding both subsurface and overland flow (FAO, 1979). The transported and deposited erosion is called sediment. When, transported and deposited process is called sedimentation.
Soil from land surfaces are eroded through detachment of material by either water (raindrop impact and runoff) or wind (eolian) (USGS, 2003). Soil erosion by water is the dominant transport mechanism from upland sources and commonly is expressed quantitatively and incorporated into a sediment budget (Leopold et al., 1966; Dietrich and Dunne, 1978; Swanson et al., 1982; Gellis et al., 2001). Sediment removed and transported from upland sources typically is reported as a yield over time (km2 per year).
From Allen et al. (2003), upland sediment sources refer to material eroded from hillslope surface areas adjacent to stream corridors. Upland region include forests, rangeland, agriculture (cropland and pasture), rural and urban area. Land surface characteristics strongly influence the sediment flux in certain watershed region. And it is important to discuss about sediment removal from upland surfaces.
In a classic paper on sediment derived from land surfaces, Wolman and Shick (1967) discussed post-colonial land-use change in the northeastern United States and its effect on sediment yield (Figure 2.2). Wolman and Shick proposed that in the late 1800s, when forestland was converted to agriculture, sediment yields increased from 35 metric tons per square kilometer to 210 metric tons per square kilometer. During 1960s, many rural areas near cities became urbanized resulting in another increase in sediment from construction activity when sediment yields exceeded 35,000 metric tons per square kilometer).
Figure 2.2: Land use history and sediment yield from the Potomac River Basin in the northeastern United States, from the late 1700s to the 1960s, projected to approximately 2000 (Wolman and Shick, 1967)
Several others studies provide estimates of sediment yield from around the world. Holeman (1968) reported annual world sediment yield of 20 billion tons, suggested that Africa, Europe and Australia have very low sediment yield (<120 tons per square miles per year). Asia as the most rapid developing region contributes the highest with the degree of yielding up to 80% of the sediments reaching the ocean annually. Patuxent River in United State doubled its sediment yield after urbanization (344 metric tons per square kilometer) compared to pre-urbanization vaues of (143 metric tons per square kilometer) (Robert and Pierce, 1976). While for Western Run Basin in north of Baltimore estimated that land clearing for agriculture caused 34% of eroded sediment to be transported through the basin and 66% of sediment in storage (Costa, 1975).
For Peninsular Malaysia phenomena had been explained by Lai et al., (1996) where a positive relationship between mean annual specific suspended sediment yield and mean annual runoff was evident for catchments having less than 50% forest. Figure 2.3 shows the sediment yield from selected catchments in Peninsular Malaysia. Under agriculture, soil loss depends on type of crops and type of land preparation. For example, high soil erosion during initial and preparation stage then further reduced when the trees matured. It was estimated that between 1905 and 1939, 33.5 million tonnes of sediment entered the river systems of the peninsula from rubber plantation (Leigh, 1973). Other sources of sediment include areas affected by mining and urbanization. The high sediment loads (about 5900 tonnes per kilometer per year) were reported for the 27 km2 Jinjang River catchment where large abandoned mines had been left to eroded (Balamurugan, 1991). Balamurugan (1991) also reported that urbanization is another sources of high annual sediment loads. For example, Kelang River basin (380 km2) within which much of Kuala Lumpur is located, has a sediment yield of about 800 tonnes per kilometer per year.
Figure 2.3: The Influence Of Land Use Upon Suspended Sediment Yield In Peninsular Malaysia: 1) Batangsi River, Logging Ongoing; 2) Chongkak River, First Year After Logging; 3) Chongkak River, Second Year After Logging; 4) Lawing River, Undisturbed Forest.
220.127.116.11 Sediment Transport
Basically, transport of sediment particles is classified in various modes as described in Figure 2.4 and Figure 2.5. From (Graf, 1998) described that total sediment transport is a combination of bed material and wash load transport. The bed material load is derived from the river bed and is typically sand-sized or gravel size. Bed load transport load occurs when sediment particles are sliding, rolling and undergoing saltation (jumping) under the tractive force exerted by the water flow (Campos, 2001). The frequency and magnitude of the transport of coarse sand and gravels has potential implications for stream and river channel stability, the development of stable channel engineering protocols and assessments of aquatic habitat (Biedenharn et al., 2000). Bed load transport characteristics of the gravel bed rivers flowing through the watershed influence the total sediment load, including fine sediment transported in suspension (Smith et a., 2000).
Figure 2.4: Modes of Sediment Transport (Graf, 1998)
The wash load consists of sediment that has been flushed into the river from upland sources and is sufficiently fine grained that the river is always able to carry it in suspension while suspended load transport dispersed in the flow by turbulence and is carried for considerable distances without touching the bed (Ackers and White, 1973).
Figure 2.5: Sediment Transport Modes (Northwest Hydraulic Consultants, 2003)
Main factor influencing reservoir life is the loss of effective water loss storage. This is a matter of significant concern around the world. Sedimentation itself results in an annual loss of 0.5% to 2.0% in the water storage capacity of reservoirs (yang, 2003). A sample survey of 23 large Indian reservoirs suggests the average storage loss due to sedimentation is about 0.92% per year. Among the most sedimented reservoirs, the Nizam Sagar reservoir has lost 60.74% of its storage capacity over a period of 62 years, while the corresponding losses in the Maithon and the Hirakud reservoir are 55% over 50 years and 24% over 38 years respectively (Rathore et al., 2006). Reservoir sedimentation also was measured in Loch Raven and Pretty Boy Reservoir, average of 1.4 to 1.5 cm per year resulting in total accumulation of 10,100 and 8, 740 m3 per year (Ortt et al., 2000). A subtraction of Triadelphia 2004 bathymetry from the original topography surveys yields a positive volume (sediment gain) of 1919 acre feet (2.37 million per cubic meter), while a subtraction of Rocky Gorge 2005 bathymetry also yields a positive volume of 1970 acre feet (2.43 million per cubic meter) (Ortt et al., 2008). The result shows in Table 2.1 below.
Table 2.1: Calculated storage capacity loss rates for Triadelphia Reservoir (Ortt et al., 2008)
Table 2.2: Calculated storage capacity loss rates for Rocky Gorge Reservoir (Ortt et al., 2008)
2.2.1 Deposition Patterns
The coarser particles are the first ones to be deposited and generally they are laid in the backwater formed before the reservoir, in the reservoir headwater and tail and last deposited (called delta deposit) is composed principally of gravel and sand (Campos, 2001). The amount of suspended fine material available strongly influences deposition potential. Variations in local elevation across a bottomland and correlated length of hydroperiod (length of time flow) also have been cited as important factors affecting deposition rate (Kleiss, 1996). The bed load and coarse fraction of suspended load are deposited immediately to form the delta deposits, while fine sediments with lower settling velocities are transported deeper into the reservoir by either stratified or non stratified flow (Julien, 1995). Generalized depositional zones in reservoirs discussed by Morris and Fan (1997) shows in Figure 2.6 below.
Figure 2.6: Generalized Depositional Zones in Reservoir (Morris and fan, 1997)
There are also several depositional pattern identified by Morris and Fan (1997). The pattern depends on the inflowing sediment characteristics and reservoir operation. Figure 2.7 shows multiple patterns can exist simultaneously in different areas of the same reservoir. The four basic types of deposition pattern are discussed below:
1. Delta deposits contain the coarsest fraction of the sediment load which is rapidly deposited at the zone of inflow. It may consist entirely of coarse sediment (d>0.062) or may also contain a large fraction of finer sediment such as silt.
2. Wedge-shaped deposits are thickest at the dam and become thinner moving upstream. This pattern is typically caused by the transport of fine sediment to the dam by turbidity currents. Wedge-shaped deposits are also found in small reservoirs with a large inflow of fine sediment, and in large reservoir operated at low water level during flood events, which causes most sediment to be carried into the vicinity of the dam.
3. Tapering deposits occur when deposit become progressively deposition of fines from the water moving towards the dam.
4. Uniform deposits are unusual but do occur. Narrow reservoir with frequent water level fluctuation and a small load of fine sediment can produce nearly uniform deposition depths.
Figure 2.7: Depositional Pattern (Morris and Fan, 1997)
2.2.2 Reservoir Releasing and Trap Efficiency
The sediment release efficiency in a period of time is a function of the size of incoming sediment load, the duration of the sediment particles in the reservoir, the characteristics of the reservoir and the ratio of the incoming water discharge to the outgoing water discharge (Mamede, 2008). In a simple equation, sediment release efficiency of a reservoir is a ratio of released sediment to the total sediment inflow over specified time period, while trap efficiency is the ratio of sediment retention to the total incoming. Trap efficiency depends primarily upon the fall velocity of the various sediment particles, flow rate and velocity through the reservoir (Yang, 1996), as well as the size, depth, shape, and operation rules of the reservoir.
Churchill (1948) presented a method to estimate the trap efficiency of a reservoir using the sedimentation index of the reservoir (Si), which is defined as the ratio of the retention period (tr) to the mean flow velocity through the reservoir (v). The minimum data required to use this method are storage volume, annual inflow and reservoir length. The sedimentation index of the reservoir is computed as follow:
C is the reservoir storage capacity at the mean pool level for the analysis period (m3); I is the average daily inflow rate during study period (m3s-1); and L is the reservoir length at mean operating pool level (m).
Probably, the most widely used method for sediment trap efficiency was developed by Brune (1953). Brune determined a empirical relationship for estimating long-term trap efficiency in normally impounded reservoirs based on the correlation between the capacity to inflow ratio (C:I) and trap efficiency (te) observed in Tennessee Valley Authority reservoirs in the southeastern United States (see Figure 2.8).
Figure 2.8: Brune curve for estimating sediment trapping or release efficiency in conventional impounded reservoir (Morris and Fan, 1997)
According to Borland (1971), the Churchill method is more applicable for estimating sediment retention in desilting and semi-dry reservoirs. Both methods are based on the ratio of volume to inflow, disregarding features such as the grain size of the inflowing sediment load and the outlet configuration (Morris and Fan, 1997). In 1966, Karaushev proposed a method to predict sediment trapping efficiency, taking into account a sediment property and the fall velocity. None of the other methods mentioned previously consider any sediment characteristics (Campos, 2001).
2.2.3 GIS Methods to Predict Reservoir Sedimentation
Current trend towards a more efficient management of reservoir is using the application of Geographical Information System (GIS). GIS is not just a graphical and mapping tool, but it is a type of tool similar to a database but the data has a spatial reference and it can be mapped on screen or media. GIS is used for importing, analyzing, modeling, visualizing, and reporting information for the reservoir and gives functions of spatial data management, mapping and analysis to assist decision making (Maguire, 1992; Chang, 2004).
Maps are useful for varied purposes, but it is difficult and time consuming to make overlays and to understand these overlays without GIS. Mapping the reservoir bathymetry has been used to define reservoir bed characteristics. Repetitive bathymetric mapping can help in determining the sedimentation rates, scour and deposition of bed material (Fattah et al., 2004). Using GIS, maps not only are viewed simultaneously as layers, but also can be analyzed in algebraic equations as spatial variables. Environmental quality values, evaporation and transpiration values (Chang et al., 1997) and surface water quality analysis (Hoover, 1997) are some of the examples.
UNESCO-IHE Institute for Water Education (2010) comparing the traditional approach and GIS approach for reservoir sedimentation in Aswan High Dam Reservoir and concludes that a key difference between the traditional and GIS analysis approaches is that the GIS approach calculates sediment volumes over the entire reservoir area by comparing digital surfaces, whereas the traditional approach applies an average area method to calculate volumes based on a limited number of cross sections. Rooney and Smith (1999) used GIS methods to model bathymetric data and used this model and rainfall data to determine sedimentation rates in the Tomales Bay Estuariy, California. Evans and Seamon (1997) determined that the use of GIS was important in the determination of the sediment yields of a watershed, more specifically, using a digital elevation model to calculate the soil erosion potential.
Although maps can be manipulated with a GIS to view answer to specific questions quickly and easily than numerical models and required less measured data. Therefore, numerical modelling will also be considered in this study.
18.104.22.168 Surface Interpolators
Regionalization, or spatial interpolation, is a method in which a complete representation of a surface, physical or abstract, can be modeled within a region (Hoover, 1998). An interpolator is an algorithm of mathematical equations that can produce surface. Interpolation is most commonly used to display estimated values for all locations on a map in GIS from original survey points. The attributes of sample points can range from physical properties, such as elevation or temperature, to abstract properties (Bayes, 2000). The sample points are most generally taken from the real situation. It is unrealistic and impossible to take samples at every location. It was due to the practical constraints. A larger data set resulting in a more accurate representation of the real world. However, due to the sampling expenses and time constraints, a minimum numbers of sample are generally taken. Therefore, the best interpolation methods need to be used.
Many interpolators exist and each one uses a different method to generate a surface. Each interpolator has different and variable purposes. Interpolators are classified by the way in which data are input, the way data are analyzed, the output structure, and transitions of the output. Interpolators are classified by the way in which data are input, the way data are analyzed, the output structure, and transition of the output (Star, 1990). Depends on the condition that is being interpolated and the desire results, an interpolator can be chosen. Spatial interpolators categories are determine by the ways each and every interpolator uses the input data.
2.2.4 Numerical Modelling to Predict Reservoir Sedimentation
Due to the improvement of computing systems, the use of numerical model has increased very fast in the past few decades. According to Simoes and Yang (2006), the advances have occurred particularly in the fields of sediment transport, water quality, and multidimensional fluid flow. Additionally, many numerical models can be obtained free of charge from public domain. The implementation of automatic grid generators, geographical information systems and improved data collection techniques can accelerate the use of numerical models as a popular tool for solving sedimentation problem.
Numerical models can be one, two or three-dimensional. Among these, one-dimensional models are more applicable because they do not require extensive amounts of computer time and calibration data. Furthermore, the assumption of one-dimensional flow is appropriate for the analysis of many types of sediment problems in rivers and reservoirs that normally have elongated geometry (Morris and Fan, 1997).
22.214.171.124 One Dimensional Model
One dimensional model is the first computational model to be developed for reservoir sedimentation. This means, only one dimension was considered for modeling purposes which is downstream direction. Campos (2001) discussed that these models have several advantages and disadvantages; one of them is the simplicity of the formulations and few requirements of computer resources making possible the simulation of large reaches of rivers or reservoirs. The main disadvantage of 1D model is the impossibility of simulating curved flows and recirculating zones or secondary flows.
U.S Army Corps of Engineers had developed a general 1D sediment transport model called HEC-6 in the beginning of the 1970s. it is a well-known model for sediment transport due to its pioneering approach. The model is able to compute scour and deposition by simulating the interaction between the hydraulics of the flow and the rate of sediment transport and also be used for the analysis of long-term river and reservoir behavior (U.S. Army, 1972). While according to Rice (1981), lopes I the one developed the first 1D model exclusively for reservoir sedimentation in his PhD thesis in 1978. It takes into account the flow and sediment characteristics, reservoir geometry, reservoir operation and pattern of deposition in the reservoir.
Several others 1D numerical model has been developed in recent decades for simulation of sediment behavior in river and reservoirs such as:
FLUVIAL-12 model is an erodible-boundary model that was formulated and developed for water and sediment routing in natural and man-made channels. It simulates inter-related changes in channel bed profile, channel width and bed topography induced by the channel curvature (Chang, 1998).
GSTARS model (General Stream Tube Model for Alluvial River Simulation) is a steady, non-uniform flow model which simulates certain aspects of two dimensional flows by using the stream tube concept for hydraulic computation (Yang et al., 1989). GSTARS 2.0 significantly revised and expanded the capabilities of GSTARS for PC applications (Yang et al., 1998). With a new graphical interface, GSTARS 2.1 replaced GSTARS 2.0 for cohesive and non-cohesive sediment transport in rivers (Yang and Simoes, 2000). GSTARS 3.0 further expanded the capabilities of GSTARS 2.1 for cohesive and non-cohesive sediment transport in rivers and reservoirs (Yang and Simoes, 2002). New version of GSTARS series called GSTARS-1D model which emphasizes unsteady cohesive sediment transport (yang et al., 2004).
CCHE1D Model (One-Dimensional Channel Network Model) is able to calculate unsteady flow in channel network using either the diffusive wave model or the dynamic wave model, taking into account the difference between the flows in the main channel and flood plains of a compound channel, and the influence of hydraulic structures such as culverts, measuring flumes, bridge crossing and drop structures (Wu and Vieira, 2002).
126.96.36.199 Two-Dimensional Model
Two dimensional numerical models for flow and sediment transport are becoming widely used due to the advent of fast personal computers and to the existence of a significant numbers of commercially available models (Simoes and Yang, 2006). Vertically averaged approach was applied to two-dimensional model when vertical variations of velocity and temperature are not significant, which is occasionally the case for shallow water bodies (Sebnem, 2004). The density variations are not considered, and the vertically average continuity and Navier-Stokes equations are solved. Several 2D hydrodynamic models have been developed since 1980s such as by McAnnaly (1989), Evans et al. (1990), Hoogan and Twiss (1993), Sloff (1997), Greco and Molino (1997), Tarela and Menendez (1999), Letter et al. (2000), Choi and Garcia (2002), Shojaeefard et al. (2007).
This research will used a free model developed by National Center for Computational Hydro science and Engineering (NCCHE), The University of Mississippi as one as a group of many models in hydro science area. The 2D hydrodynamic model (CCHE-2D) used for simulating the flow field is based on the solution of Navier-Stokes equations for turbulent flow (Jia et al., 2002). The governing equations of CCHE-2D used for simulating the flow field are the momentum equations in x and y directions, in addition to the continuity equation and sediment transport equation (Jia and Wang, 1999).
CCHE2D was applied to two flume studies to determine the capabilities of the model to simulate flow and sediment transport around vegetation bars with various wavelengths (Wu and Wang, 2006). A CCHE2D model was constructed based on the data supplied by Bennett et al. (2002) and also simulated a fixed bed with alternating vegetative bars along both banks. The authors concluded that CCHE2D successfully simulated flow pattrns influenced by the vegetated bars. Bennett et al. (2003) simulated two vegetation bars on opposite banks with a movable bed.
CCHE2D was also implemented on a curvy reach of the Mississippi river to test the model's ability to simulate complex hydraulics and in-stream structures in a natural channel (Jia et al., 2002). The Victoria bendway, located between Arkansas and Mississippi is a highly curved area with a complex bend. The river changes heading in this location and cuts back 108 degrees with a radius of curvature of approximately 1280m. Three dams and six submerged weirs have been placed in the main channel to realign the flow and improved channel navigation. CCHE2D model results were compared with measured three-dimensional velocity data. The authors agree that the model give a better result compared to the complex geometry, uncertainties of bed roughness, and highly three-dimensional flow patterns.
Another study was implementation of CCHE2D model on 9.5 miles of the Salt River or Rio Salado to simulate the sediment transport and hydraulic processes responsible for the change in bed elevation (Chen et al., 2007). The bed material gradation was determined to be 90% very coarse sand and gravels with a median particle diameter range of 20mm to 40mm, 2% fine sand, and 0.6% wash load containing clays and silts. The computational mesh was constructed based on channel topography data extracted from a digital contour map. When compared to a one-dimensional hydraulic model (HEC-RAS), results showed that CCHE2D more accurately predicted water surface elevation based on a comparison with an actual five years recurrence interval (R.I) flood event. The HEC-RAS model did not produce floodplain inundation during the five years R.I event, while actual changes of the floodplain were observed during the event. No field measurements of suspended or bed load were used for comparison in this study. Thus, this paper provided no validation of the sediment transport module within CCHE2D.
Jia et al. (2004) were implementing CCHE2D on the lower Yellow River in China to calculate the flood routing and sediment transport between the Huayuankow and Jiahetan gauging stations. The lower Yellow River has a mean annual runoff of 58 billion m3 and a mean annua sediment amount of 4.24 billion tons. The sediment is non-uniform, with sizes ranging from 0.002mm to 0.5mm. Two floods (in 1982 and 1986) were simulated with the model. The simulated flow fields, discharge, water levels, and sediment concentrations were compared to the measured values. The simulated matched well with the measured data. The authors concluded that the model is capable of handling the complex topography and abrupt wetting and drying processes that occur in the lower Yellow River.
2.3 WATER QUALITY INDEX
A water quality index for rivers in Malaysia is common with many other indices systems, relates a group of water quality parameters to a common scale and combines them into a single number in accordance with a chosen method or model computation (DOE, 2004).
Water Quality Index is summarized amounts of water quality data into simple terms of example slightly polluted or clean, for reporting to management and the public in a consistent manner, and it can tell whether the overall quality of water bodies poses a potential threat to various uses of water, such as habitat for aquatic life, irrigation water for agriculture and livestock, recreation and aesthetics, and drinking water supplies.
The main objective of the Water Quality Index system in Malaysia is to use as preliminary means of assessment of a water body for compliance with the standards adopted for five designated classes of beneficial uses as in Table 2.1:
Table 2.1: Classes of the River and Uses
Conservation of natural environment
Water Supply I - practically no treatment necessary
Fishery - very sensitive aquatic species
Water Supply II - conventional treatment required
Fishery II - sensitive aquatic species
Recreational use with body contact
Water Supply III - extensive treatment required
Fishery III - common of economic value and tolerance species; livestock drinking
None of the above
(Source : DOE, 2004)
The parameter chosen for Water Quality Index based on the Department of Environmental formula are DO, BOD, COD, SS, NH3N, and pH. The ranges of the overall Water Quality Index and Sub Water Quality Index for BOD, COD, SS, and NH3N set for the classification of water quality into "clean", "slighted polluted" and "polluted" are shown in Table 2.2 below where next table shows the sub-indices used to get Water Quality Index.
Table 2.2: The Ranges Of Water Quality Index And Sub Water Quality Index
Water Quality Index (WQI)
Biochemical Oxygen Demand (BOD)
Ammoniacal Nitrogen (NH3N)
Suspended Solid (SS)
(Source : DOE, 2005)
The formula used in the calculation of Water Quality Index (WQI) :
WQI = 0.22*SIDO + 0.16*SICOD + 0.15*SIAN + 0.16*SISS + 0.12*SIpH
Where : SI - sub index of each parameter
DO - Dissolve Oxygen
BOD - Biological Oxygen Demand
COD - Chemical Oxygen Demand
(Source : DOE, 2004)
Once the number between 1 and 100 has been determined, the result can be further simplified by assigning it to a descriptive category. The index results reports or scores mean as:
Clean : ( WQI Value 81-100) - water quality is protected with a virtual absence of threat or impairment ; condition very close to natural or pristine levels. These index values can only be obtained if all measurements are within objectives virtually all of the time.
Slightly Polluted : ( WQI Value 60-80) - water quality is usually protected but occasionally threatened or impaired; conditions sometimes depart from natural or desirable levels.
Polluted : ( WQI Value 0-59) - water quality is almost always threatened or impaired; conditions usually depart from natural or desirable levels.
Table 2.3: Sub-Indices of Water Quality Index
Subindex for Dissolved Oxygen, SIDO (in % saturation)
SIDO = 0 for x < 8
= 100 for x > 92
= -0.395 + 0.030 x2 - 0.00020 x3 for 8 < x < 92
Subindex for Biochemical Oxygen Demand, SIBOD
SIBOD = 100.4 - 4.23x for x < 5
= 108e-0.055x - 0.10x for x > 5
Subindex for Chemical Oxygen Demand, SICOD
SICOD = -1.33x + 99.1 for x < 20
= 103e-0.0157x - 0.04x for x > 20
Subindex for Ammoniacal Nitrogen, SIAN
SIAN = 100.5 - 105x for x < 0.3
= 94e-0.573x - 5 Ix-2I for 0.3 < x < 4
= 0 for x > 4
Subindex for Ammoniacal Nitrogen, SIAN
SIAN = 100.5 - 105x for x < 0.3
= 94e-0.573x - 5 Ix-2I for 0.3 < x < 4
= 0 for x > 4
Subindex for pH, SIpH
SIpH = 17.2 - 17.2x + 5.02x2 for x < 5.5
= -242 + 95.5x - 6.67x2 for 5.5 < x < 7
= -181 + 82.4x - 6.05x2 for 7 < x < 8.75
= 536 - 77.0x + 2.76x2 for x > 8.75
Note: Concentration in mg/L for all parameters except pH
(Source: DOE, 2004)
Traditional report on water quality typically consist of complex variable-by variable, and water body-by-water body statistical summaries. This type of information is of value to water quality experts, but may not be meaningful to publics who want to know about their local water bodies and for managers and policy makers who require concise information about those water bodies. The index also allows water quality data to be compiled and reported in a consistent manner. WQI also used to identify the suitability for the beneficial users of the river. Index ranges used are shown in Table 1.4 below.
Table 1.5 show the general rating scale for the Water Quality Index and uses and Table 1.6 is a interim Interim National Water Quality Standards Classes which is a limitation recommended for protection of the beneficial uses in each of water quality classes. Table 1.7 shows the two different standards A and b limitation to sewage and effluent discharge. Standard A applies for discharges into inland waters within the catchment area where standard B applies for discharges into other inland waters.
Table 1.4: DOE Water Quality Index Classes
Biochemical Oxygen Demand
Chemical Oxygen Demand
Total Suspended Solids
Water Quality Index
(Source: DOE, 2005)
Public water supply
Necessary treatment become more expensive
Minor purification require
Purification not necessary
Obvious pollution appear
Only for boating
Doubtful for water contact
Becoming polluted still acceptable need bacteria count
Acceptable for all sports
Marginal for trout
Acceptable for all fish
Coarse fish only
Handy fish only
Doubtful for sensitive fish
Treated water transportation
100Table 1.5: General Rating Scale for The Water Quality Index And Uses
Table 1.6: Interim National River Water Quality Standards for Malaysia
Ammoniacal Nitrogen (mg/l)
Electric. Conductivity* (mmhos/cm)
Total Dissolved Solid* (mg/l)
Total Suspended Solid (mg/l)
Normal + 2
Normal + 2
Fecal Coliform# (counts/100ml)
Total Coliform (counts/100ml)
No visible floatable materials/debris, or no objectionable odour, or no objectional taste
* Related parameters, only one recommended for use
# Geometric mean
+ Maximum not to be exceeded
(Source: DOE, 2005)
Table 1.7: Standard A and B Limitation to Sewage and Effluent Dischharge
Parameter (mg/l unless otherwise stated)
Maximum Permitted Value
BOD5 at 200C
Oil and Grease
The legislation does not specify any tolerance percentiles for the maximum permitted values and as such they are absolute values.
# Where two or more of these metals are present in the effluent, the concentration of these metals shall not be greater than 0.50 mg/l in total
*Where two or more of these metals are present in the effluent, the concentration of these metals shall not be greater than 3.0 mg/l or 1.0 mg/l in total for solution forms.
+ When both phenol and free chlorine are present, the concentration of phenol shall not be greater than 0.2 mg/l nor the concentration of free chlorine greater than 1.0 mg/l.
(Source : DOE, 2005)