13

Chapter 2

WATER QUALITY MODELING MANAGEMENT

PERSPECTIVES AND INITIATIVES

2.1 INTRODUCTION

Generally, this chapter discusses types of model used as an efficient decision support

systems and river basin management. Section 2.2 and 2.3 discusses the types of

deterministic and statistical model and their application in environmental management

especially in river water quality. Section 2.4 focuses on general river management in

Malaysian, especially in the Langat River Basin as well as the use of modeling

techniques in river basin management.

Water quality modeling is one of the important elements in water resource

management. The development of water quality models depend on the various

objectives and purposes, and based on a number of different modeling techniques.

The uses for which river models have been developed include, environmental impact

assessment (climate, river use or land use change (long-term) and combined sewer

overflows or accidental spills (short-term)); flood forecasting; planning and consent

setting; and operational (on-line) management. Modeling techniques may be loosely

categorized as being either deterministic or statistically based.

14

2.2 DETERMINISTIC MODELS

Deterministic models attempt to represent all the physical and chemical processes

involved in the mathematical terms, with parameters obtained either empirically or

from available data, or estimated by observation or experience. The differential

equations are usually simplified in order to find solutions that are suitable for the

purpose of the model. Deterministic modeling can generally be divided into 5 types,

(i) hydrological modeling (i.e. the unit hydrograph and mechanistic flow modeling),

(ii) velocity determination, (iii) transport mixing (i.e. the advective dispersion

equation, aggregated dead zone analysis and reactor theory), (iv) kinetic processes

and, (v) pollution from diffuse sources.

Deterministic modeling techniques vary in complexity from simple mass balance

calculations, as used in the model TOMCAT (Bowden and Brown, 1984), to

extravagant catchment-scale hydrological models such as the SHE (Abbott et al.,

1986), which attempt to incorporate a full description of all the processes involved.

Such models may be expensive in terms of data collection, or suffer inaccuracies from

parameter estimation. Solution of the equations involved may require simplifications

and assumptions which detract from the model performance, and experience is

generally required from the user before optimum results are achieved.

Although the movement of water through the hydrological cycle is well understood,

the application of an entirely deterministic modeling technique is limited by the

degree of randomness, particularly in the timing and intensity of precipitation. Thus,

in order to develop models for water resources planning and design, and flood and

15

drought risks assessments, a stochastic element must be incorporated. A prerequisite

for stochastic hydrological analysis involves a long, consistent outline of statistical

techniques that are employed in river flow and quality modeling. Generally, most

statistical modeling of the environment involves use of time series analysis and Monte

Carlo analysis. Errors may be encountered in statistical modeling due to linearization,

curve-fitting procedures and parameter estimation, or the lack of a suitable length of a

time-series data. Evolution of deterministic programs can be seen based on

development of several programs. Some of the programs and methods are discussed

below.

2.2.1 QUAL2E and QUAL2E UNCAS

The enhanced stream water quality model QUAL2E is widely used in the United

States as a water quality planning tool for developing total maximum daily loads

(Brown and Barnwell, 1987). Built upon QUAL2E, the model QUAL2E-UNCAS

enables uncertainty analysis, first order error analysis and Monte Carlo simulations to

be performed. The models simulate the major reactions of nutrient cycles, algal

production, benthic and carbonaceous demand, reaeration and their effects on DO,

assuming that the stream is well mixed. They can predict up to 15 water quality

determinand concentrations namely DO, BOD, Temp., algae as chlorophyll a, organic

nitrogen as N, Ammonia as N, Nitrate as N, Nitrite as N, Organic phosphorous as P,

Dissolved phosphorous as P, Coliform bacteria, a non-conservative pollutant,

conservative constituent I, conservative constituent II, conservative constituent III

(Chapra, 1997).

16

QUAL2E treats the river as a series of reaches and describes transport by means of

the MCSTR concept, using finite difference methods for equation solution. The model

may be used in conjunction with field sampling for identifying the magnitude and

geochemistry of non-point sources. It can be operated in steady state or time-variable

modes, although at present it can only implement diurnal simulations (Chapra, 1997).

The effects of point source impulse loads cannot be modeled (USEPA, 1995).

QUAL2E has been employed to create a water quality model of the Yarra River

catchment in Victoria, Australia (Ng et al., 1999). It has also been used to compare

various remediation strategies for the Mapocho river in Chile (Dussailant et al. 1997).

QUAL2E also applied to simulate the in-stream processes at Bonello watershed Italy

(Galbiati et al., 2006).

QUAL2E is unsuitable for modeling river systems that vary temporally in terms of

both river flow rate and the discharges that they receive (Shanahan et al., 1998).

Furthermore, non-point source pollution loads are not represented. When these

limiting factors are combined, the assumptions made by QUAL2E may differ

significantly from reality. The successful application of QUAL2E demands

considerable experience from the user (US EPA, 1996). The accompanying user

manual (Brown and Barnwell, 1987) assumes that the prospective modeler possesses

a firm grasp of hydrology and modeling principles. The fact that QUAL2E is the most

established river water quality model available worldwide suggests that in many cases

it may have been applied to situations for which it has not been implicitly developed

(Shanahan et al., 1998). Furthermore, the need to calibrate the model to fit the river

17

system under consideration requires the availability of data that describes a pollution

event, due to the overall improvement in water quality in recent years.

2.2.2 MIKE II

In general, the formulation of the Danish model MIKE II (DHI, 1992) is similar to

that of QUAL2E (Rauch et al., 1998). MIKE II, however, discriminates between the

dissolved, suspended and sedimented fractions of organic matter, thereby allowing the

mechanistic representation of sediment oxygen demand and the accumulation, decay

and resuspension of organic matter in the sediment. This feature can improve the

representation of the nitrogen and phosphorous cycles, although MIKE II does not

consider nitrite as an intermediate product. MIKE II was employed in a recent

application involving the assessment of the pollution of the river Buriganga in

Bangladesh (Kamal et al., 1999).

2.2.3 QUASAR

A model that can be used as an operational or planning tool for river management is

QUASAR (Whitehead et al., 1997), which was developed at the Institute of

Hydrology in the UK. The model simulates flow and eight water quality variables as

listed in Table 2.2. Inputs to the model may include tributaries, groundwater inflow,

rainfall runoff, effluent discharges and storm water sewer overflows. The continuous

stirred tank reactors (MCSTR) approach is used to present the advection and

18

dispersion of pollutants, and first order reaction equations account for decay and

sedimentation.

2.2.4 Other Programs

Besides the described programs, there are also other models and approaches

developed for water quality modeling. These are summarized in Table 2.1.

19

Table 2.1: Others deterministic models widely used in river modeling and management

Model Descriptions

1. HERMES Simulates pollutant concentration profiles along the river system

2. Receiving water

impact nodels

(RIMS)

Assesses the polluting effects of transient discharges

3. Real-time Assessment of Transient Spills

(RATS)

Assesses the polluting effects of transient discharges (Norreys and Clucjie, 1996)

4. Storm Water

Management Model

(SWMM)

Simulates the movement of precipitation and pollutants from the ground surface through pipe and channel networks,

storage and treatment units and concentrations (USEPA, 1993;1998)

5. Systeme Hydrologique

Europeen (SHE)

Physically-based, distributed modeling system for constructing and running models of all or any part of the land

phase of the hydrological cycle for any geographical area (Abbot et al., 1986).

6. HSPF Simulates all the water quantity and quality processes that occur in the watershed (Hydrocomp Inc., 1996).

7. TOPography-based

MODEL

(TOPMODEL)

Represents the runoff resulting from rainfall incident upon ungauged catchments (Beven et al., 1984; Shaw, 1994;

Hornberger et al., 1985; Lamb et al., 1998).

8. Simulator for Water

Resources in Rural

Basins (SWRRBWQ)

Simulates hydrological processes, sedimentation and nutrient and pesticide transport in a large complex rural

watershed (USEPA, 1993)

9. Water, Heat and

Nitrogen SIMulation

Model (WHNSIM)

Quantification of nitrogen dynamics of agricultural fields and the improvement of agricultural prsctises (Huwe and

Totsche, 1995)

10. Agricultural Non-Point

Source (AGNPS)

Predicts soil erosion rates, sediment yield and nutrient transport in agricultural watershed

11. Decision Support system for River

Environment

Assessment

Management (DESERT)

Generates various alternative management strategies and complements Spreadsheet Tool for River Environment Assessment Management and PLANning (STREAMPLAN) (Somlyody, 1997).

20

Table 2.1: Continued

Model Descriptions

12. Several dynamic and steady

state models

Simulates of short and long-term changes in catchment acidification (Christophersen et al., 1982).

13. Birkenes An extended version of several dynamic and steady state models used to investigate the effects of

hydrological changes on streamwater acidity (Whitehead et al., 1986).

14. Zero-dimensional model Describes the oxygen concentration and major nutrients processes (Schroeder, 1997).

15. STELLA II Simulates the oxygen minimum.

16. Water Quality Analysis

Simulation Program

(WASP)

This model help users interpret and predict water quality responses to natural phenomena and man-

made pollution for various pollution management decision (Burian et al., 2002).

17. Michaelis-Menten Model This model to predicts the reduction in organic matter and NO3- in river water through the floodplain

filtration (Chung et al., 2005)

18. Air, Land, Water Analysis

System (ALWAS)

This prototype effort investigates the feasibility of linking existing media-specific models into a

comprehensive system that could evaluate reasonable range of multimedia exposure assessment

problems (Hedden et al., 1982).

19. Annualized Agricultural

Nonpoint Source(AnnAGNPS)

This is a batch-process and continuous-simulation watershed model (Bosch et al., 1998).

20. Hydrologic Evaluation of

Landfill Performance

(HELP)

Simulates hydrologic processes for a landfill, cover systems, and other solid waste containment

facilities by performing daily, sequential water budget analysis using a quasi-two-dimensional

deterministic approach (Schroeder et al., 1994).

21. A three-dimensional

subsurface model

(PORFLOW)

Simulates ground water flow and contaminant transport away from the site (Bou-Zeid and El-Fadel,

2004).

22. Pesticide

Emission Assessment at

Regional and Local

(PEARL) scales-model

The PEARL model deals with the pesticide transformation and fate and is linked with the SWAP model

for the water cycle and transport (Tiktak et al., 2000; Bouraoui, 2007).

21

2.3 STATISTICAL MODELING

Statistical modeling is about finding general laws from observed data, which amounts

to extracting information from the data. Statistical analysis and modeling involves the

appropriate application of statistical analysis techniques to perform hypothesis tests,

data interpretation, and reach valid conclusions. To be credible, results from

experimentation or testing must be obtained following established statistical

procedures, including experimental design and the appropriate use of statistical

analysis and modeling techniques. Statistical analysis and modeling requires careful

selection of analytical techniques, verification of assumptions, and verification of

data. Descriptive statistics, graphs, and relational plots of the data should first be

examined to evaluate the legitimacy of the data, identify possible outliers and

assumption violations, and form preliminary ideas on variable relationships for

modeling. There are many different statistical analysis and modeling techniques

which have different goals and are appropriate for different types of data.

The most important issue for choosing a statistical model is the presence of adequate

historic data for that model to be effective. Secondly, choosing a model that best

represents the type of data analyzed. A number of basis modeling techniques are

discussed below.

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2.3.1 Time Series Analysis

A time series analysis requires sampling frequency. It also involves the reduction of

the time series which is represented by x1, x2, x3, ………….., xt into a mathematical

expression, where;

xt = f ( Tt, Pt, Et) (2.1)

Tt, Pt, and Et correspond to a trend component, a periodic component and a stochastic

component respectively.

Time series consists of stationary and non-stationary time series. To determine

whether the time series is stationary, i.e that the mean and standard deviation do not

change with time or series length, is by preliminary statistical tests. A non-stationary

time series may contain a linear or non-linear trend component, Tt, which should be

quantified prior to periodic analysis. The long term changes in climate, or the slow

response of a catchment to changes in land use may result in non-stationary time

series. A correlogram of the data may be assembled in order to analyse a time series.

Many publications are available in the application of time series model for

environmental prediction model. For example, (Van Dongen and Geuens, 1998),

successfully applied multivariate time series analysis in modeling the dynamic

behaviour, using only daily routine measurements of biomass and effluent-quality;

Cun and Vilagines (1997), used graphical and statistical time series techniques to

23

analyze the trends and specified time changes, in a 90-year record of annual average

value of Seine river water quality data.

2.3.2 Monte Carlo Analysis

According to Whitehead and Yong (1979), Monte Carlo analysis or simulation in

order to obtain forecast in term of statistically-probalistic distributions with associated

uncertainty to aid decision making in water resources management and planning can

be applied. It involves dynamic stochastic simulation repeated a large number of

times, and for each repetition the values of the stochastic input and uncertain

parameters were selected at random from their estimated probability distribution.

Recently, many studies were conducted by researchers in simulation of environmental

problems using Monte Carlo analysis. Brouwer and Blois (2008), used Monte Carlo

simulation in integrated modeling of risk and uncertainty underlying the cost and

effectiveness of water quality measures. Numerical and visual evaluation of

hydrological and environmental models using the Monte Carlo analysis toolbox at

Leaf River Watershed located north of Fort Collins, Mississippi, USA was developed

by Wagener and Kollat (2006). Verbeke and Clercq (2006) studied in their analyses,

with a number of Monte Carlo experiments, how the properties of environmental and

economic time series might affect the Environmental Kuznets Curve (EKC)

empirical. As reported by Bhattacharya and Malakar (2006), Monte Carlo model was

successfully in simulating the effect of extreme events on the extinction dynamics of

faunal species with 2-stage life cycles. Bormann (2005) applied Monte Carlo

simulation technique and rainfall data to evaluate the suitability of the model to be

24

used for environmental change studies, finally the detected uncertainties were set into

relation to the effects of environmental change scenarios for different regional scale

catchments in central Benin. Many other Monte Carlo techniques applied in

environmental modeling can be found in Sullivan et al. (2004), Qian et al. (2003), Ma

(2002), Bergin and Milford (2000), Hanna et al. (1998), Annan (1997), and Darkins et

al. (1996).

2.3.3 Multiple-Linear Regression (MLR) Model

MLR models have frequently been used for the purpose of deriving relationship

between independent variables (descriptor) and dependent variables (response). MLR

procedure estimates b0, b1,…, bq parameters of the linear equation:

qqo xbxbby +++= ...11 (2.2)

where the regression coefficients b0, b1,…, bq represent the independent contributions

of each independent variable x1,…, xq to the prediction of the dependent variable y.

The global statistical significance of the relationship between y with the independent

variables is analysed by means of an analysis to ensure the validity of the model in a

quantified manner. In environmental sciences, MLR model are widely used in

environmental modeling (Cohn et al., 1992; Helsel and Hirsch, 1992: Christensen,

2001; Lin, 2001; Schlink et al., 2004; Sousa et al., 2007).

25

2.3.4 Design of Experiment (DOE) Model

The principle steps of statistical DOE are selection of response variables, factors and

factor levels, choice of the DOE, and statistical analysis of the data (Bhunia and

Ghangrekar, 2008). Estimation of the effect of individual parameter and two or three

factor interaction effects along with their relative importance in a given process, is

possible using this method. Full factorial design has the advantage in that it is able to

predict response based on few sets of experimental data in which all factors are varied

within a chosen range. Effect of each factor is evaluated under both low and high

range of the remaining factors.

2.3.5 Multivariate Statistical Models (MSM)

MSM are also known as envirometrics or chemometrics and are usually used for

classification of spatial and temporal data. Four main MSM methods widely used for

environmental data are the cluster analysis (CA), discriminant analysis (DA),

principal component analysis (PCA) as well as factor analysis (FA) (Wold, 1987;

Zitko, 1994; Aruga et al., 1995; Momen et al., 1996; Simeonov et al., 2000; Forina et

al., 2002; Gong et al., 2005; Astel et al., 2007). These models are used and

thoroughly discussed in Chapters 4, 5 and 6.

26

2.3.6 Other Statistical Models/Programs

There are also other models and approaches developed for statistical water quality

modeling. Among others are stochastic dynamic methodology (StDM) used in

predicting the richness and diversity of passerine surveys in mountain habitats

characterized by variable and, predominantly, non-standard weather conditions

(Santos et al., 2008). Kriging, regression models – also known as scorpan

(McBratney et al., 2003) were used to collate and integrate various land feature digital

layers to the same resolution and coordinate system, and to develop spatial prediction

models based on scorpan and scorpan-kriging methods, for predicting soil attributes

(Odeh et al., 2007). Redundancy analysis (RDA) model is a direct linear gradient

analysis and have been used to evaluate socio-economic factors of land cover changes

(Hietel et al., 2007).

2.4 RIVER BASIN MANAGEMENT IN MALAYSIA

Maintaining river basin management in a sustainable manner is a major challenge.

One of the challenges is in determining point sources and non-point sources of

pollution. It is easier to control point sources of pollution such as industrial discharges

and domestic wastewater. However, surface runoff from agricultural land, which is

seasonal and highly affected by climate is more difficult to quantify and control.

One of the many aspects of river basin management focuses on managing available

water resources to meet the demands of various consumers located within a river

27

basin. For example, numerous factors have to be considered in order to respond to

drought conditions and increasing demands, and provide sufficient and good quality

water at reasonable cost to the public. According to Kojiri and Teramura (2003), the

river basin management can be formulated as follows: (1) considering whole river

basin environment in long and short-term, (2) evaluating the objective functions and

the related situations and (3) improving the spatial and temporal factors to conserve

the enjoyable and sustainable life.

The need to control the quantity and the quality of pollution sources entering the river

system makes the management of the river basin much more complex. The

introduction of stricter legislative measures for the control of pollution sources may

not provide the most satisfactory solution as it may contribute to unnecessary

excessive expenditure on pollution control and thus affect the economic development

of the industrial sector. Effectively, there is a need for a system optimization approach

in managing the effluent discharge and receiving water quality for sustainable

development.

2.4.1 IWRM/IRBM: Perspective and initiatives of Water Quality Modeling in

Malaysia

Rapidly growing cities and industries have undermined the quality of many rivers in

Malaysia. Rapid changes in land activities such as deforestation and residential

development and the increasing use of chemicals in agriculture have also contributed

28

to the increasing deterioration of river water quality. Thus, there is a pressing need for

a sustainable management of the river basin in this country.

Malaysia has recently adopted IWRM as an innovative approach to manage its water

resources. Clear pronouncements to the effect were found in the Third Outline

Perspective Plan (OPP3) and the 8th Malaysia Plan (MP8) documents. The adoption

provides the necessary impetus to break away from traditional practices characterised

by multiple individualist sector-centred approaches. In line with the current

international trend, the new approach promises to overcome deficiencies in cross-

sector co-ordination, reduce conflicts and inefficiency, and engenders equity.

Countrywide adoption and implementation of IWRM principles and practices have

been hampered by the absence of an enabling environment, which is contributed by

factors including the following:

a) General lack of awareness of IWRM – countrywide;

b) Lack of capacity in implementing agencies (public, private and NGO’s);

and

c) Absence of Best Management Practices (BMPs) in IWRM that is

appropriate to the Malaysian context.

Under the Malaysian Constitution, water is a state matter. Nevertheless when it comes

to water resources development, utilization and management, both the federal and

state governments are involved. This is because the responsibility for water resource

administration is fragmented and is shared among a number of federal and state

agencies, each of them have their own specific involvement in water related issues

29

(Welch & Lim, 1987). Their interest in water related matters could be viewed from

any one or more of the following three aspects:

� The planning, development and management of water resources aspect;

� The protection and conservation of water aspect;

� The land-use control and watershed management aspect.

IWRM plans include actions necessary to develop an effective framework of policies,

legislation, financing structures, capable institutions with clearly defined roles and a

set of management instruments (GWP-ToolBox, 2003). The purpose of such

framework is to effectively regulate the use, conservation and protection of the water

resources, balancing requirements for broad economic development and the need to

sustain ecosystems. The emphasis here is on the process of establishing priorities and

actions for integrated management of water resources. Priorities include ecosystem

protection and conservation in the basin. Although modest progress had been made in

creating public awareness in IWRM, its realisation is still minimal.

The concepts of IWRM and IRBM (Integrated River Basin Management) aspires that

water should be treated as a finite and vulnerable resource, and as an economic good,

and governance should be based on a participatory approach involving all levels of

stakeholders. Integrated management of water quality is the best approach in

gathering all the data involving environmental systems. IWRM and IRBM when

applied to water systems involve integration between freshwater and coastal zones;

land and water; surface water and groundwater; quantity and quality; as well as

upstream and downstream. The principles to be adopted are economic efficiency,

equity and environmental sustainability. There is also a need to develop a structural

30

framework comprising management instruments, enabling environment and

institutional establishment. IRBM which deals with issues of water allocation,

pollution control and flood control is a subset of IWRM which addresses the broader

issues of food self sufficiency, tariffs, cross subsidies, and institutional roles. The role

of the Global Water Partnership in which the Malaysian Water Partnership (MyWP) is

a chapter, promotes and facilitates IWRM / IRBM. The global mission messages the

involvement of all stakeholders, moving towards full-cost pricing of water services,

increasing public funding for research and innovation while promoting cooperation in

management of international basins and increasing the investments of water projects

(Hiew, 2001). Water as an important resource in the coastal zone should also be

managed sustainably within the context of Integrated Coastal Zone Management

(ICZM) as discussed in a paper by Mazlin and Sarah (2003).

In this spirit, environmental modeling has become an important tool for

environmental management. This scenario can be observed from the large number of

scientists and engineers who are highly interested in developing environmental

models. Recently, much discussion on the application of models either as predictive

tools or as a numerical representation of the real environment has been published in

many journals, books and articles worldwide.

An integrated modeling approach provides the opportunity to investigate many water

resources development options and solve conflicts over the utilization of a particular

resource. In this context, water quality modeling provide a basin wide representation

of the water availability and is a very useful procedure in many planning and

operational aspects of the water industry. In Malaysia, the application of water quality

31

and hydrological modeling has increased rapidly. Ahmad Mahir Razali and Hong

(2001) has applied statistical modeling in hydrological data to estimate the frequency

of flood at Langat River, Semenyih River and Lui River at Selangor. Time series

models as forecasting models to long-term stream flow and water table dynamics

(Ayob Katimon et al., 2001) as well as BOD forecasting model (Hafizan et al., 2001)

have also been successfully applied. Maketab Mohamed (2001) and UPUM (2002),

used QUAL2E as a water quality planning tool in studying total maximum daily

pollution loads. The combination of geographical information system (GIS) and

Multi-Criteria/Objectives as spatial planning and decision support system for

modeling water resources was developed at Selangor State (Alias Abdullah et al,

2004). AQUALM simulation software showed advantages in simulating non point

source water pollutants even if only limited data were obtained (Mohd Kamil Yusoff

et al., 2004). There are also other studies that have been published by Malaysian

scientist and engineers on the successful application of water/hydrological modeling

(Mohd Zubir Mat Jafri et al., 2001a,b; Hua, 2001; Ng and Koh, 2001; Patrick Wong,

2004; Wardah Tahir and Zaidah Ibrahim, 2004).

2.4.2 The Langat River IWRM/IRBM Modelling Approach

Integrated management approach was applied for the Langat River Basin in several

water quality improvement programs funded by the Malaysian government. Universiti

of Malaya Consultancy Unit (UPUM) (2002) had conducted Pollution Prevention and

Langat River Water Quality Improvement Program which was funded by The

Ministry of Science, Technology and Environment. In the study by UPUM, the

32

holistic approach to watershed management which includes modeling components

was applied (Figure 2.1). A watershed management plan (WMP) is needed to address

many issues;

i. pollution load estimation, sources and used of geographical information

system (GIS),

ii. pollution load reduction strategies and best management practices (BMPs),

and

iii. communities, institutional and regulatory issues.

The study also suggested an Integrated Langat Basin Management which comprises

coordination of efforts among agencies, as shown in Figure 2.2.

Figure 2.1: Schematic Representation of Holistic Approach

to Managing Water Quality at Langat River.

Regulatory, Enforcement

Institutional, Policy,

Implementation

GIS Database, WQ Modelling

Pollution Load, Sources, Location, WQ Monitoring

Public Communities, Awareness, Education

ENGINEERING LOAD REDUCTION &

BEST MANAGEMENT PRACTICES:

~ River Modelling ~ Pollution Prevention (P2) ~ Effluent Treatment Plant

33

IRBM concept was taken into consideration as well in the study by Mazlin et al.,

(2004a,b,c) entitled Ecosystem Health of The Langat Basin. This study started at the

end of year 2000 and was completed on December 2003 under the IRPA Grant

mechanism. The objectives of the study were:

i. To understand inter and multi-disciplinary approaches to monitoring and

assessing the health of the Langat Basin ecosystem and suggest framework

for integration.

ii. To identify strategies and tools that allow integration of conservation and

development, maintenance of ecological integrity, protection of ecosystem

resilience and satisfaction of basic human needs.

iii. To gather scientists and technical experts to monitor, analyze, evaluate and

make recommendations on the sustainable management of ecosystem

health at the national level based on the Langat Basin as a case study

iv. To suggest a list of environmental health indicators which will assist

planners, policy and decision makers in planning and environmental

management

v. To develop information directory related to Langat Basin Ecosystems to

facilitate finding of information and references

vi. To develop a decision support system (DSS) inclusive of databases, good

management systems, modeling and use friendly interface.

This study advocates the concept of IWRM/IRBM and introduces a general

framework for structuring IWRM/IRBM planning and implementation. It also

involved the development of simulation models using ANNs.

34

Figure 2.2: Integrated Langat Basin Management through Coordination of Efforts Among Agencies

35

Figure 2.3 summarizes the structure of the proposed decision support system (DSS) from

the above study, where the results can be used by decision makers in making decisions

for a more effective and efficient management of Langat Basin.

Within the IWRM/IRBM framework, modeling plays an important role in the

development of a sound DSS. Motivated by the rapid development in information

technology, modeling has become a standard tool in supporting and facilitating objectives

aimed with in the IWRM/IRBM context. Modeling as part of the DSS component is able

to assist managers in their environmental management tasks. In managing water

resources, one of the most important task is to identify and control pollution sources. Two

Figure 2.3: Ecosystem Health of the Langat Basin database

Data

DECISION MAKER

SSoocciioo--

EEccoonnoommiicc

SSttuuddiieess

BBiioollooggiiccaall

EEnnvviirroonnmmeennttaall

SSttuuddiieess

PPhhyyssiiccoo--cchheemmiiccaall

EEnnvviirroonnmmeennttaall

SSttuuddiieess

Centralized Database & Application

INPUT: Multi-discipline expert

Centralized Database

END USER

Local Authorities

JKN

JPS

JMG

Land Office

JPP

Veterinary Dept.

Fire Dept.

Output

Chart, Reports, Completed Forms

DECISION SUPPORT SYSTEM

ANN

Data

INTERNET

Data

INTERNET

36

types of sources of pollutant are point and non-point sources. Modeling non-point sources

of pollution is a more complicated task compared to pollution from point sources. Point

sources of pollution can be easily identified – discreet industrial areas, Indah Water

Konsortium (IWK) ponds, domestic waste, pig farms and others. On the other hand, non-

point sources of pollution are distributed and quite difficult to identify. Nevertheless,

based on the initial analysis of historical data, relating the land use features as a

representation of non-point sources of pollution to water quality, it is possible to model

how non-point sources affect the river. These models are either statistical in nature or

developed with ANNs.

One of the challenges of today’s river basin management is to reduce the cost and

develop more intelligent computer aided tools in IRBM. Under the Pollution Prevention

and Water Quality Improvement Program, UPUM (2002) developed the Langat Pollution

Information System (LaPIS), which serves the objectives below:

“Development of a user friendly Spatial Database Application System and Water

Quality Modeling in the form of GIS maps consisting of:

• a complete information on pollution sources (point and non-point sources)

• data on pollution control and water quality monitoring, and

• the current status of Sungai Langat ecosystem”

Water quality modeling is one of the important components in LaPIS. The integration of

deterministic Qual2E modeling and ArcView was applied in this system.

37

In the Ecosystem Health of the Langat Basin study, the IRBM concept was also applied.

In the study, an attempt was made to develop a sound DSS or river management tools

(Figure 2.4). Mazlin Mokhtar and Ahmad Fariz Mohamed (2003) suggested that the

approach for modeling work to be carried out in the future will have to consider the

following:

• Intrinsic spatial and temporal variations of social and natural sciences sub studies

• Needs to be a synthesized model developed through an integrated quantitative

process, and

• The integrative model will depend on empirical data and sub model observations

of various disciplines.

In the Malaysian context, the availability of historical data, as had been mentioned

before, as well as access to various reasonably priced data analysis software, makes data

analysis, as a precursor to modeling, a natural undertaking. The study proposes to

investigate the ability of Intelligent Predictive Tools (IPT) to estimate the WQI at Langat

River basin (Hafizan Juahir et al., 2004a,b). Approaches based on IPT are highly

desirable in estimating the non-linear behaviour of urban water quality under historical

and future scenarios. The model should be capable of ‘understanding’ the complex

relationships of the Langat Ecosystems. Based on the availability of huge amounts of

historical data (water quality and land use), ANNs are considered to be the best tool

available in representing the complex, non linear relationship between land use activities

and water quality.

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2.5 CONCLUSION

Proper management of river basins is important in reducing water pollution which not

only causes degradation upstream but also downstream as well as the coastal and

estuarine areas. River basin management should take into consideration the integrative

role played by various agencies (both government and private sectors as well as NGO’s),

land use development activities and protection of vital ecosystems. Effective coordination

of various national bodies are required to integrate legislation. All river and water

resources management agencies must be coordinated in making decisions concerning this

matter. This is of utmost importance in order to avoid conflict and duplication of function

and roles between the agencies that are directly involved in these issues. To achieve this,

among the tasks of the integrated approach will include the setting up of water quality

standards, regulation and control of pollution.

In order to achieve the objectives of IWRM and to achieve effective river basin

management, appropriate decision support systems and management tools are

indispensable. A suite of flexible models, allowing for extrapolation, interpolation and

detailed evaluation of data and refinements in description are definitely advantageous in

this context. Flexible and cost effective models can be developed using historical data

employing data driven methods such artificial neural networks (ANNs) as a modeling

tool. In numerous cases around the world these models have demonstrated their

usefulness by providing water managers a powerful decision support system. The

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development and the application of ANNs as water quality pattern recognition will be

discussed thoroughly in Chapter 6.