Amir Water Supply

Full Terms & Conditions of access and use can be found athttp://www.tandfonline.com/action/journalInformation?journalCode=gcee20

Download by: [Universiti Teknologi Malaysia] Date: 22 September 2015, At: 08:04

Civil Engineering and Environmental Systems

ISSN: 1028-6608 (Print) 1029-0249 (Online) Journal homepage: http://www.tandfonline.com/loi/gcee20

Developing efficient management strategies fora water supply system using system dynamicsmodelling

Suwan Park, Dae-Hoon Jeon & So-Yeon Jung

To cite this article: Suwan Park, Dae-Hoon Jeon & So-Yeon Jung (2014) Developing efficientmanagement strategies for a water supply system using system dynamics modelling, CivilEngineering and Environmental Systems, 31:3, 189-208, DOI: 10.1080/10286608.2013.820281

To link to this article: http://dx.doi.org/10.1080/10286608.2013.820281

Published online: 06 Aug 2013.

Submit your article to this journal

Article views: 374

View related articles

View Crossmark data

Civil Engineering and Environmental Systems, 2014Vol. 31, No. 3, 189208, http://dx.doi.org/10.1080/10286608.2013.820281

Developing efficient management strategies for a water supplysystem using system dynamics modelling

Suwan Park*, Dae-Hoon Jeon and So-Yeon Jung

Department of Civil and Environmental Engineering, Pusan National University, Busan 609-735,Republic of Korea

(Received 3 July 2013)

In this paper, the causal feedback relationships among the components that make up the working mechanismof water supply systems management, including key factors and their relationship to the management ofwater pipes, were identified based on the conceptual framework established for water supply systemsmanagement. Subsequently, a system dynamics computer simulation model, which can be used to aidefficient management of water supply systems, was developed. The computer model consisted of watersupply, pipe maintenance, and water supply business finance sub-models. The model was verified usinghistorical data from a water supply service case study. Using the verified model, long-term managerialand operating conditions of the case study system were predicted under optimistic, basic, and pessimisticmanagement condition scenarios. In addition, sensitivity analyses on major indicators of the case studysystem have been presented to show that the developed model can facilitate identification of the best policyfor achieving a specific management objective of a water supply system.

Keywords: causal feedback; computer model; management; simulation; system dynamics; water sup-ply system

1. Introduction

Water supply systems are one of the most significant engineering accomplishments in historyand have greatly contributed to the wellz-being of mankind. However, due to waters status as apublic service, many water supply systems around the globe are confronted with problems relatedto their overall efficiency. South Koreas water supply system has also faced these problems andsuffered from inefficient operations and poor monetary management.As a result, the Korean watersupply service incurred debts equivalent to 28% of their total income in 2006 (Korea Ministry ofEnvironment 2007).

Given the advancement of international standardisation of water and wastewater services(ISO/TC224), and the addition of environmental services to the agenda of the World TradeOrganisations multilateral trade negotiations, developed countries, such as the EU member states,continuously demand the participation of their companies in the Korean water supply servicesmarket. To cope with current internal problems and external pressures, the water supply services inKorea seek to improve the efficiency of their systems. They hope to accomplish this by employing

*Corresponding author. Email: [email protected]

2013 Taylor & Francis

Dow

nloa

ded

by [U

nivers

iti T

ekno

logi M

alays

ia] at

08:04

22 Se

ptemb

er 20

15

190 S. Park et al.

various strategies such as reducing the gap between consumer price and production cost, bench-marking operation and management strategies, and evaluating system performance based on thelevel of water supply services and financial management.

Determining sound and sustainable policies is of the utmost importance for the efficient oper-ation and management of a water supply system. The application of poor policies which are notbased on an understanding of the underlying relations among various components that make upthe working mechanism of a system may only lead to a short-lived, partial, or superficial solutionto the problem. Moreover, determining an appropriate policy based only on experience may beproblematical. This is because human experience alone may not be able to identify the inher-ent mechanisms connecting the input (or policy) to the output of a system. Consequently, anyconnections between a policy and indicators that represent the state of a system (e.g. physicalcondition of system components and financial performance and service improvement indicators)established during the evaluation process of a policy may not be ascertained.

Therefore, an understanding of the working components of a system, as well as the correlationsamong them, is essential for appropriate analysis of problems associated with water supply sys-tems and establishment of policies that address problems of interest. A very useful and efficientmethodology suited for modelling such multiple component systems, where these componentsinfluence each other, is system dynamics (SD).

Grigg and Bryson (1975) and Lee, Park, and Park (2007) modelled water supply service man-agement using the SD methodology. However, these manuscripts did not explicitly considermodelling the effects of pipe maintenance on other aspects of system operations and manage-ment, such as mechanisms for modifying the condition of pipes and the corresponding effects inwater supply systems management.

Given that the majority of investments made by water utilities are for pipe rehabilitation,extension, and replacement, one cannot neglect the significance of pipe maintenance on watersupply services. In Korea, the main policy orientation until the end of the 1990s was to expandwater supply service with low investments in pipe rehabilitation and replacement. As a result ofthis policy, the amount of water lost from deteriorated water pipes between 1998 and 2007 wasestimated at 8.4 billion tons (Korea Ministry of Environment 2009). Due to the enormous waterlosses caused by leakage, and the heightened public awareness of tap water quality, the watersupply authorities in Korea have spent large amounts of money on pipe renewal in recent years.

In this context, an SD computer simulation model has been developed to aid the efficientmanagement of water supply systems. To develop the SD computer model, the conceptual frame-work for the working mechanisms of water supply systems was established, and then the causalfeedback loop relationships among the components of the systems management, including themanagement of water pipes, were identified.

Data from a water supply service in South Korea were used as a case study to validate the modelsstructure. Some important indicators of the system under the study were analysed via managementscenario simulations, with sensitivity analyses conducted on some major indicators of the casestudy system. This study was conducted in order to illustrate the developed SD models ability tofacilitate the identification of the best policy for achieving a specific management objective for awater supply system (i.e. identify decisions which provide policy leverage).

2. The SD methodology

The SD Methodology is a simulation methodology based on systems theory. It deals with theinterpretation of the dynamic nature of systems in which information and material feedback arepresent. The characteristics of systemic approaches adopted in the systems theory were wellpresented by Beard (1999) in which 14 systemic ideas were provided, with each idea explained in

Dow

nloa

ded

by [U

nivers

iti T

ekno

logi M

alays

ia] at

08:04

22 Se

ptemb

er 20

15

Civil Engineering and Environmental Systems 191

terms of the associated philosophical concepts. The methodology can facilitate understanding of asystem by extracting structures essential to its working mechanisms, and, based on an analysis offeedback structures inherent to the system, lead to development of efficient management strategies.

Computer simulation models that are developed based on an SD methodology are composedof four basic components: stocks, flows, converters, and interrelations among them, which aregraphically represented as arrows and mathematically modelled as the finite difference equations.The value of each component is calculated at each delta time for a specified simulation timeperiod defined in a model, starting at the initial values of the stocks and based on the functionalrelations among components. Computer simulation experiments using an SD methodology arerealised using object-oriented modelling software such as Vensim, Powersim Studio, AnyLogic,STELLA, etc. and generally take the following steps (Richardson and Pugh 1981).

First, relevant problems are identified and defined, in order to provide assurance that theestablished model functions consistently with the purpose of the model. During the second step,feedback phenomena inherent in a systems working mechanisms are conceptualised and a causalloop diagram representing the cause and effect relations among the systems components is drawn.A computer simulation model is created in the third step, based on the conceptualised workingmechanisms of a system. In the fourth step, the temporal behaviour of a system is analysed viathe computer simulation model and compared to the recorded behaviour of the system. In thefifth step, the established model is calibrated and validated. Finally, the validated model is usedto analyse the behaviour of the system through various management scenarios. After this processone can develop management strategies that meet the long-term operation goals of the system.Figure 1 provides an example of an SD computer model that shows a causal feedback loop dia-gram of a reservoir system with outflows by gravity and the corresponding computer simulationmodel using STELLA.

The plus (+) sign in Figure 1 implies that an effect is changed in the same direction as thecause. In other words, the plus sign indicates a situation in which an effect is increased whenits cause is increased and vice versa. The minus sign () represents the opposite, a situation inwhich the difference of the effects in the current and the previous time step is decreased when thecause is increased and vice versa. The finite difference equations generated by STELLA for theexample model are given in Table 1.

Hannon and Ruth (2001) provide a general introduction to modelling dynamic systems andmany real-world examples involving chemistry, genetics, ecology, economics, and engineering.There are also many textbooks that introduce the methodology for more specialised types ofsystems such as economic (Ruth and Hannon 1997), business (Sterman 2000, McGarvey and

Figure 1. Causal diagram and corresponding computer simulation model using STELLA.

Dow

nloa

ded

by [U

nivers

iti T

ekno

logi M

alays

ia] at

08:04

22 Se

ptemb

er 20

15

192 S. Park et al.

Table 1. Finite difference equations for the example model.

Stocks Outflows

WaterVolume(t) = WaterVolume(t dt) + (OutFlow) dt OutFlow = WaterVolume FlowRateINITIAL VALUE: WaterVolume = 100 [m3] FlowRate = 0.2 [m3/hr/m3]

Hannon 2004), biological (Hannon and Ruth 1997), and environmental (Ford 1999, Deaton andWinebrake 2000).

3. Previous studies on the applications of SD for water resources and water supplyservices management

After Forrester (1958) introduced the concept of SD to model systems with complex feedbackstructures, the applications of SD methodology have focused on modelling of management strate-gies and, personnel management and inventories in industrial fields. More recently, SD has beendeveloped into a general methodology that has been used to address management problems notonly in industrial areas, but also in other fields, such as public policy evaluation, engineeringproblem solving, and social issues.

Drew et al. (1985) applied the SD methodology to the planning of a hypothetical civil andenvironmental system that included water resources, sewage treatment, land use, and urban trans-portation. Water resources planning and management problems have especially benefitted fromthe use of this methodology. Examples in this field include: Crawford and Linsleys (1966) com-prehensive watershed simulation model, which included physical processes of water flows andsocio-economic factors of water management in a watershed, drought analysis by Keyes andPalmer (1993) using SD, Shawwash and Russells (1997) SD model dealing with water manage-ment problems in Jordan, Palmers (1998) application of SD methodology to watershed planning,application of the SD methodology as a decision support system for management of surface waterby Fletcher (1998), and studies by Simonovic, Fahmy, and Elshorbagy (1997) and Simonovic andFahmy (1999) on long-term planning and policy analyses for the use of water resources in theNile watershed, Egypt.

Application of SD methodology to the field of water resources management has continuedinto the twenty-first century, including Ahmad and Simonovics (2000) work on applying SDto reservoir operations for flood prevention, Teegavarapu and Simonovics (2000) SD model formultiple reservoir operations for hydroelectric power generation, Staves (2000) application ofSD to policy determination for water resources problems in Las Vegas, Ahmad and Simonovics(2001) study on SD as a decision support tool for evaluating the effects of flood protection policies,the SD model developed by Li and Simonovic (2002) for prediction of floods due to snowmeltin the prairies in of North America, Simonovics (2002) world water model, and a study bySimonovic and Li (2003) that analysed the effects of climate change on the performance of floodprotection systems. Simonovic (2009) also presented the SD simulation along with optimisationand multi-objective analysis as an integrated modelling tool for contemporary water resourcesmanagement problems. A comprehensive review of the applications of SD methodology for waterresource management problems can be found in Winz, Brierley, and Trowsdale (2009).

Applications of SD methodology to modelling of urban water supply service managementwas initiated by Grigg and Bryson (1975), where a computer programme was used to developan SD model of the water supply system in Fort Collins, Colorado. The model consisted ofwater balance, water use, population, and financial accounting sectors; it focused on controllingthe price of water while meeting the water demand of the growing population. Although Grigg

Dow

nloa

ded

by [U

nivers

iti T

ekno

logi M

alays

ia] at

08:04

22 Se

ptemb

er 20

15


and Brysons (1975) model captured several factors inherent to urban water management, someimportant causal relations, like the one between condition of capital and investments, were notconsidered. In addition to this, the model was not verified via calibration processes, even thoughit was based on actual data.

Recently, Lee, Kim, and Park (2006) presented an SD model, used to assist with the managementof water supply systems; it modelled the dynamic nature of the water revenue ratio, suppliedpopulation and financial status of water supply businesses, and mainly analysed the effects oftransparency of operational and managerial status of a case water supply system. Lee, Park, andPark (2007) modified the model of Lee, Kim, and Park (2006) to more comprehensively analysemanagerial conditions of a case water supply system. However, the modifications to the modelmade in Lee, Park, and Park (2007) were not of any significance. Even though Lee, Kim, andPark (2006) and Lee, Park, and Park (2007) modelled the causal feedback mechanisms of pipeextension and rehabilitation, they did not explicitly model the dynamic nature of pipe conditiondue to deterioration, rehabilitation, and extension, or the causal feedback mechanisms among pipecondition and other working components in water supply system management.

4. Developing causal relationships of working mechanisms in water supply systemsmanagement

4.1. Conceptual Framework for the MechanismThe goal of establishing the underlying causal relationships in water supply systems managementwas to identify key drivers of managerial action and the corresponding consequences of action.As a result, the fundamental working mechanism of a water supply system was establishedconceptually, in relation to the causal relationship between each component of the mechanism(Figure 2).

In Figure 2, the state of a water supply system is represented using a Managerial Indicator.A system may also have a Target for the indicator of interest. The gap between the Target andcurrent value of the Managerial Indicator plays a key role in driving the mechanism, as thisdifference defines the Required Amount of Action. If the required amount cannot be fulfilled

Figure 2. Conceptual framework for the working mechanism of water supply systems management.

Dow

nloa

ded

by [U

nivers

iti T

ekno

logi M

alays

ia] at

08:04

22 Se

ptemb

er 20

15

194 S. Park et al.

in a given year, the Yearly Amount of Action is calculated based on the specified PlanningTime Period. During this period the indicator is to be incrementally improved, so that that theYearly Amount of Action will enable the indicator to reach target levels after the specified timeperiod. The Yearly Amount of Action put on a system will produce corresponding Conse-quences of Action and, as a result, the current state of the system represented by the ManagerialIndicator will be changed. After examining the polarity of the relationships between the causesand effects, it was determined that the fundamental causal relationship of water supply systemsmanagement has the form of a negative feedback loop with variables that exhibit stabilisingbehaviour.

Typical Managerial Indicators of a water supply system are water supply ratio [dimension-less or %], revenue water ratio [dimensionless or %], and water price [the US dollar/m3 orthe Korean currency 1000 Won/m3]. Action to improve managerial conditions of water sup-ply system may include capital investments or water price increases. Consequences of Actionare dependent on the nature of the Action and may include changes in the state of the sys-tems pipelines. Through additional causal relationships between Consequences of Action andManagerial Indicator, the Consequences of Action eventually contribute to changes in theManagerial Indicator.

4.2. Realisation of a conceptual framework as a causal feedback loop diagramA causal feedback loop diagram for water supply systems was developed based on a previouslyestablished conceptual framework that represents the working mechanism of water supply sys-tems management. Figure 3 shows the essential causal feedback loop relationships among thecomponents of the management mechanism of water supply systems identified in this study.

Figure 3. Causal feedback loop relationships among the components of water supply systems management.

Dow

nloa

ded

by [U

nivers

iti T

ekno

logi M

alays

ia] at

08:04

22 Se

ptemb

er 20

15


The three main Managerial Indicators used to establish causal relationships were (water)supply ratio [dimensionless], Revenue water ratio [dimensionless], and water price[1000 Won/m3]. (Water) Supply ratio [dimensionless] represents the portion of the popula-tion that has access to a water supply system and has a value between 0 and 1. Revenue waterratio represents the ratio of the amount of billed (or consumed) water to the total amount ofwater produced and also has a value between 0 and 1. The differences between supply ratioand target supply ratio, revenue water ratio and target revenue water ratio, and water priceand target price increase trigger the working mechanisms of water supply systems and play keyroles in driving the management mechanism of water supply systems. Therefore, the dynamics ofwater supply was assumed to be driven by the differences between the targets and actual valuesof the three main Managerial Indicators. The actual water demand was considered indirectly byconsidering the causal effects among yearly consumed water, serviced population, and dailyconsumed water per person.

As shown in Figure 3, the causal feedback relationship between supply ratio and (yearly)investment costs for pipe extension [1000 Won/yr] was determined to be a negative feedbackloop in which the variables exhibit stabilising behaviour. This feedback loop was establishedby examining the following causal relationship between investment costs for pipe extensionand supply ratio. Increases in investment costs for pipe extension will result in an increasedsupply ratio. These increases in the supply ratio will in turn reduce the difference betweenthe supply ratio and target supply ratio which was initially assumed to be 1. Therefore, theincrease in required costs for supply ratio increase [1000 Won] in the current simulation timestep will be reduced compared to the previously calculated value for a specified one simula-tion time step earlier. Subsequently, increases in the investment costs for pipe extension in thecurrent simulation time step will also be reduced compared to the previously calculated valuescompleting the stabilising negative feedback loop that stems from investment costs for pipeextension.

The causal feedback relationship between the revenue water ratio and (yearly) investmentcosts for pipe rehabilitation [1000 Won/yr] was also determined to be a negative feedback loop.In this stabilising feedback mechanism, increases in investment costs for pipe rehabilitation willresult in increased rehabilitated pipe lengths[km], reduced (yearly) leakage (amount)[m3/yr],increased revenue water ratio, decreased required pipe rehabilitation length[km], and reducedincreases in or stabilised investment costs for pipe rehabilitation. The difference between therevenue water ratio and target revenue water ratio [dimensionless] plays a key role in thisstabilising feedback mechanism.

The causal feedback relationship between the water price and investment costs for pipe reha-bilitation was modelled based on the following causal feedback mechanisms. First, an increasein investment costs for pipe extension will yield an increase in supply ratio and revenuewater ratio but a reduction of increases in, or stabilisation of, prime cost (of water production)[1000 Won/m3]. This will also stabilise (water) price increase (per year) [1000 Won/yr] dueto a decreased difference between the prime cost and water price. This in turn will stabilisethe variables water price, recognition of profitability [dimensionless], and investment costsfor pipe extension. However, the actual behaviours of the variables in the causal relationshipbetween prime cost and income (per year) [1000 Won/yr] may be dominated by the con-sumer price indices (CPI)-trend [dimensionless], which can cause the values of these variablesto continuously increase.

In addition, there are two other counteracting causal feedback loops affecting cost of invest-ment: an increase in investment due to increased income and a decrease in investment dueto increased production costs (per year) [1000 Won/yr]. These two counteracting feedbackmechanisms are represented by the bifurcating routes from prime cost to total balance ratio[dimensionless] via production costs and income. Total balance ratio was calculated using

Dow

nloa

ded

by [U

nivers

iti T

ekno

logi M

alays

ia] at

08:04

22 Se

ptemb

er 20

15

196 S. Park et al.

Equation (1).total balance ratio = income

investment costs for pipe rehabilitation + investment costs for pipe extension (1)

[1000 Won/yr] was used for the units of the variables in Equation (1) that are income,investment costs for pipe rehabilitation, and investments costs for pipe extension.

5. Description of the developed SD computer simulation model

An SD computer simulation model was developed based on the causal feedback loops shown inFigure 3. The model was composed of three sub-models: a water supply sub-model, a pipe main-tenance sub-model, and a water supply business finance sub-model. Figure 4 shows the essentialstock and flow diagram of the computer model constructed using STELLA. The ghost compo-nents, which have italicised names, shown in Figure 4 can be tracked to find inter-connectionsbetween the sub-models.

The water supply sub-model examined changes in supply ratio due to population changes andpipeline extension. It also examined long-term changes in total (volume of) water produced (peryear) [m3/yr], resulting from changes in leakage due to pipe deterioration. In this sub-model,supply ratio was modelled as a stock variable, whereas yearly supply ratio change [1/yr] wasmodelled as yearly extended length of pipe [km/yr] divided by supply ratio improvement ratio[km]. Supply ratio improvement ratio represents improvements made in the supply ratio, withrespect to changes in yearly extended length of pipe. The variable yearly (volume of) consumed

Figure 4. Stock and flow diagram of the SD computer simulation model.

Dow

nloa

ded

by [U

nivers

iti T

ekno

logi M

alays

ia] at

08:04

22 Se

ptemb

er 20

15


water [m3/yr] was calculated as the product of serviced population [persons] and (average)daily consumed water (volume) per person [m3/(dayperson)]. In this sub-model, the variableyearly consumed water was also designed to influence the variable income in the water supplybusiness finance sub-model. Serviced population was modelled as the product of supply ratioand total population which can be provided by the modeller as predicted values. Therefore, theSD model developed in this study simplified modelling of water demand by considering only thesimple relationship among yearly consumed water, daily consumed water per person, servicedpopulation, total population, and supply ratio. A more sophisticated modelling is needed tofully incorporate the effects of changes in water demand in the SD modelling of water supplysystems management.

In the pipe maintenance sub-model, condition of the pipes was examined, and pipes wereassigned one of three possible groups: deteriorated pipes [km], non-deteriorated pipes [km], ordisposed-off pipes [km]. Deteriorated pipes represented those pipes that were assumed to haveleaks caused by deterioration, while non-deteriorated pipesrepresented pipes not in a deterioratedcondition. It should be noted that the group non-deteriorated pipesincluded pipes ranging in con-dition from newly installed pipes to pipes in the last stage of non-deteriorated condition. In orderto account for change of condition over time non-deteriorated pipes was modelled to becomedeteriorated pipes at a rate of deterioration rate of (non-deteriorated) pipe [dimensionless].Disposed-off pipes represented pipes that were discarded during rehabilitation or replacementactivities. Based on consultation with management officials regarding pipe maintenance history inthe case study Water Supply Authority, pipes in the group non-deteriorated pipeswere also mod-elled to become the group disposed-off pipesconsidering the historical non-optimal managementpractice of the Authority that eliminated non-deteriorated but unnecessary pipes.

The three types of pipes were affected by the following indicators: disposal rate for deterioratedpipe [dimensionless], disposal rate for non-deteriorated pipe [dimensionless], deterioration rateof pipe, yearly deteriorated pipe length [km/yr], and yearly rehabilitated pipe length [km/yr].The yearly deteriorated pipe length and yearly rehabilitated pipe length were themselvesaffected by investment costs for pipe rehabilitation and investment costs for pipe extension inthe water supply business finance sub-model. The unit cost of pipe rehabilitation [1000 Won/km]and unit cost of pipe extension [1000 Won/km] in the pipe maintenance sub-model were used, inturn, to calculate investment costs for pipe rehabilitationand investment costs for pipe extensionin the water supply business finance sub-model.

The transition from non-deteriorated pipesto deteriorated pipesand from deteriorated pipesto non-deteriorated pipes were modelled using yearly deteriorated pipe length and yearlyrehabilitated pipe length, respectively. The yearly rehabilitated pipe length was modelled as theproduct of 1/(unit cost of pipe rehabilitation) and investment costs for pipe rehabilitation.

In the water supply business finance sub-model, the indicators able to represent the financialstatus of a water supply system were modelled; they included income, production costs, invest-ment costs for pipe rehabilitation, and investment costs for pipe extension. In this sub-model,total balance ratio was modelled to have a direct relationship with income and an inverse rela-tionship with investment costs for pipe rehabilitation and investment costs for pipe extension.Target price increase [1000 Won/m3] was estimated as the difference between prime cost andwater price. Yearly price increases [1000 Won/(m3yr)] were determined by dividing targetprice increase by TAG3 [yr]. TAG3 represented the period of time over which target waterprice increase is realised, or alternatively the period of time over which water price was to beincrementally increased. The resulting model was structured such that yearly price increaseswill enable the water price to reach target price increase after TAG3.

Investment costs for pipe rehabilitationwere determined based on required pipe rehabilitationlength [km], which was calculated by utilising revenue water improvement ratio [km] (whichrepresents the improvements in revenue water ratio with respect to changes in rehabilitated pipe

Dow

nloa

ded

by [U

nivers

iti T

ekno

logi M

alays

ia] at

08:04

22 Se

ptemb

er 20

15

198 S. Park et al.

length). Investment costs for pipe extension were modelled using target supply ratio, supplyratio, and supply ratio improvement ratio.

6. Model verification and scenario analyses

6.1. Description of model input dataThe developed SD computer model was verified using data from the water supply service of Busan,South Korea. Using the necessary input data and results of the model simulation, predicted valuesof the systems operational and managerial indicators were compared with historical data reportedby the Statistics on Busan Water Supply Services in 2008 (Busan Metropolitan City Water SupplyAuthority, 2009), abbreviated to Busan (2009) in the following texts. Specifically, data in Busan(2009) from 1995 to 2007 were used to run and verify the model.

Necessary input data for the model included initial stock variable values and appropriate valuesor trends as functions of time for the exogenous variables. Exogenous variables may be interpretedas inputs supplied beyond the assumed system boundary, independent of the working mechanismof the system. Data for 1995 in Busan (2009) were used as the initial stock variable values, anddata from 1995 to 2007 were analysed to estimate the values or trends of the exogenous variables.

Since some of the necessary input data were not available, these were estimated using trial-and-error during the model verification processes. Determination of these values consisted of testingassumed values for unavailable input data until the simulated results of the model closely followedreported data for the case study system. Tables 24 show the stock and exogenous variables usedin the model as well as their values or trends. The time scale used in the computer simulationmodel was designed so that time 1 in the model represented 1995. This definition of time wasthe same across all variables, and has been named time in the following tables.

Table 2. Stock and exogenous variables of the water supply sub-model.

Variable Variable Initial value/ Source of initial value/type name function of time Unit basis for estimation

Stockvariable

Supply ratio 0.974 Not applicable(N.A.)

Busan (2009)

Accumulated leakage 95,378,573 m3 Busan (2009)Accumulated water

meter underregistration and othernon-revenue water

42,444,546 m3 Busan (2009)

Accumulated non-revenue water

172,049,104 m3 Busan (2009)

Accumulated total waterproduced

540,694,859 m3 Busan (2009)

Exogenousvariable

Total population Figure 5 Persons Busan (2009)

Daily consumed waterper person

270 /(personday) Estimated duringthe calibration(model verification)processes

Supply ratioimprovementratio

6 109 loge(time) + 6 106

1/km Analysis of Busan(2009)

Yearly leakage per unitdeteriorated pipelength

38,225e(0.087time) (m3/yr)/km Analysis of Busan(2009)

Dow

nloa

ded

by [U

nivers

iti T

ekno

logi M

alays

ia] at

08:04

22 Se

ptemb

er 20

15


Table 3. Stock and exogenous variables of the pipe maintenance sub-model.

Variable Variable Initial value/ Source of initial value/type name function of time Unit Basis for estimation

Stock variable Non-deterioratedpipes

5982 km Estimated as 65% of theinitial value of totalpipe length during thecalibration processes

Deteriorated pipes 3221 km Estimated as 35% of theinitial value of totalpipe length during thecalibration processes

Disposed-off pipes 92 km Assumed as 1% of theinitial value of totalpipe length

Exogenousvariable

Unit cost of piperehabilitation

If time 8 then7957.7e0.1749timeelse 150,000

1000 Won/km Analysis of Busan (2009)

Deterioration rate ofpipe

0.06 1/yr Estimated during thecalibration processes

Unit cost of pipeextension

58,856loge(time) +18784

1000 Won/km Analysis of Busan (2009)

Disposal rate ofnon-deterioratedpipe

If time < 7 then 0.08else if time < 10then 0.095 else iftime < 13 then 0.03else 0.005

1/yr Estimated during thecalibration processesutilising expert opinionfrom personnel in theBusan water authority

Disposal rate ofdeteriorated pipe

If time 13 then0.015 else 0.004

1/yr Estimated during thecalibration processesutilising expert opinionfrom personnel in theBusan water authority

Income per unitdisposed-off pipe

200 1000 Won/km Consultation with thepersonnel in the Busanwater authority

Table 4. Stock and exogenous variables of the water supply business finance sub-model.

Variable Variable Initial value/ Source of initial value/type name Function of time Unit basis for estimation

Stockvariable

Capital 141,577,480 1000 Won Busan (2009)

Water price 0.320 1000 Won/m3 Busan (2009)Recognition of profitability 0.4 N.A. Assumed

Exogenousvariable

TAG1 (time duration duringwhich required costs forpipe rehabilitation is spent)

If time 7.5 then 2.5else if time 10then 9 else 6

yr Estimated during the calibrationprocesses

CPI-trend 2.6797 time +70.161

yr Statistics Korea (2009)

Delay time 1 yr AssumedTAG2 (time duration during

which required costs forsupply ratio increase isspent)

If time 7 then 5 else2.5

yr Estimated during the calibrationprocesses

Target revenue water ratio 1 N.A. Maximum probable revenuewater ratio

Revenue water improvementratio

5902.75 km Analysis of Busan (2009)

TAG3 (time duration duringwhich target water priceincrease is realised)

4.5 yr Estimated during the calibrationprocesses

Target service ratio 1 N.A. Maximum probable service ratio

Dow

nloa

ded

by [U

nivers

iti T

ekno

logi M

alays

ia] at

08:04

22 Se

ptemb

er 20

15

200 S. Park et al.

Table 5. Values or trends of exogenous variables for the scenario simulations.

Variable name Optimistic scenario Basic scenario Pessimistic scenario

Deterioration rate ofpipe (1/yr)

Fixed as 0.03 during thescenario simulation period



Disposal rate ofnon-deterioratedpipe (1/yr)




Disposal rate ofdeteriorated pipe(1/yr)




Total population(persons)

Figure 5 Figure 5 Figure 5

CPI-trend Stabilised from 105 in year2007 to 138 in year 2030(52.299 time0.2699)

Increased linearly from105 in year 2007 to167 in year 2030(2.6797 time + 70.161)

Increased exponentiallyfrom 105 in year 2007to 199 in year 2030(73.362 e0.0277time)

TAG1 (yr) Decreased linearly from 6 inyear 2007 to 4 in year 2030(0.087 time + 7.1304)

Fixed as 6 during the scenariosimulation period

Increased linearly from 6 inyear 2007 to 10 in year 2030(0.1739 time + 3.7391)

TAG2 (yr) Decreased linearly from 2.5in year 2007 to 1.5 in year2030 (0.0435 time +3.0652)


Increased linearly from 2.5in year 2007 to 4.5 inyear 2030 (0.087 time +1.3696)

TAG3 (yr) Decreased linearly from 4.5in year 2007 to 2.5 in year2030 (0.087 time +5.6304)


Increased linearly from 2.5in year 2007 to 6.5 inyear 2030 (0.087 time +3.3696)

The value for supply ratio improvement ratio was obtained by analysing data on length ofnew pipe installed yearly and corresponding improvements in supply ratio. Similarly, the valueof revenue water improvement ratio was estimated based on available data on length of piperehabilitated yearly and corresponding improvements made in the revenue water ratio. Values forunit cost of pipe rehabilitation and unit cost of pipe extension were also based on actual datafor yearly rehabilitated/extended length of pipes and corresponding investments.

6.2. Management scenario analysis

Management scenarios for the case study system were created for the period between 2008 and2030 using different values for the exogenous variables to predict the status of the system underoptimistic, basic, and pessimistic managerial conditions. The optimistic scenario simulated a man-agerial condition that would improve the systems financial status compared to the basic scenario,while the pessimistic scenario was designed to simulate the opposite managerial condition.

Table 5 shows values or trends of the exogenous variables used to create the scenarios. Exoge-nous variables not given in Table 5 were assumed to retain the same values or functions given inTable 4 during the scenario simulation period from 2008 to 2030. Figure 5 shows the value oftotal populationof Busan in each of three scenarios; since the citys population has continuouslydeclined for more than 20 years, population for the optimistic scenario was assumed to be thesame as for the basic scenario, with the exception of the period after 2020, in which populationwas assumed to increase.

Figures 610 show the results of the scenario simulations for supply ratio, revenue waterratio, total balance ratio, cost recovery ratio, total pipe length, and deteriorated pipes ineach of the three scenarios. Figures 610 also show the simulation results before 2008, whichwere obtained using the data in Table 4. Since the input data used for the period before 2008 were

Dow

nloa

ded

by [U

nivers

iti T

ekno

logi M

alays

ia] at

08:04

22 Se

ptemb

er 20

15


Figure 5. Scenarios for total population.

Figure 6. Scenario simulation results for supply ratio.

the same as those used for the basic scenario, the label for the simulation results before 2008 wasalso given as basic in Figures 610.

Equations (2) and (3) show equations used to calculate revenue water ratio and cost recoveryratio, respectively, which were, together with total balance ratio, selected as important indicatorsin the representation of managerial conditions of the case study system.

revenue water ratio = yearly consumed watertotal water produced

, (2)

cost recovery ratio = water priceprime cost

. (3)

Dow

nloa

ded

by [U

nivers

iti T

ekno

logi M

alays

ia] at

08:04

22 Se

ptemb

er 20

15

202 S. Park et al.

Figure 7. Scenario simulation results for revenue water ratio.

Figure 8. Scenario simulation results for total balance ratio.

Units of the variables in Equations (2) and (3) are as follows: [m3/yr] for yearly consumed waterand total water produced; and [1000 Won] for water price and prime cost, respectively.

By comparing historical data and corresponding simulation results for the model variables, asshown in Figures 610, it was determined that the developed SD model succeeded in reasonablysimulating historical trends of the case study system. Figure 6 revealed that, in the basic scenario,the systems supply ratio should essentially reach a state of dynamic equilibrium around 2015;results of the optimistic and pessimistic scenarios were almost identical. Under the basic scenario,cost recovery ratiois also expected to reach a state of dynamic equilibrium around 2015, as shownin Figure 9. However, as shown in Figures 7 and 8, revenue water ratio and total balance ratioare predicted to continuously improve under any assumed scenario.

Dow

nloa

ded

by [U

nivers

iti T

ekno

logi M

alays

ia] at

08:04

22 Se

ptemb

er 20

15


Figure 9. Scenario simulation results for cost recovery ratio.

Figure 10. Scenario simulation results for pipe length changes.

Figure 10 shows the scenario simulation results for total pipe length and deteriorated pipes.Since there are no historical records for deteriorated pipes in the system, only the simulationresults of deteriorated pipes are shown in Figure 10. From Figure 10, total pipe length wasobserved to remain within the range of 75008000 km during the planning horizon, but the generalcondition of pipes in the system will deteriorate in any of the assumed scenarios.

7. Analysis of exogenous variable sensitivity and identification of policy leverage

The policy leverage for a system may be defined as the policy that most efficiently directs agiven system to its desired state; this policy can be identified using experiments with various

Dow

nloa

ded

by [U

nivers

iti T

ekno

logi M

alays

ia] at

08:04

22 Se

ptemb

er 20

15

204 S. Park et al.

Table 6. Results of the sensitivity analyses of the exogenous variables.

Exogenous Change rate of Maximum changeIndex variable corresponding index occurrence time (yr)

Revenue water ratio TAG1: 6 yr 1.5 yr 2% (91% 93%) 2014Deterioration rate of pipe: 0.06/yr 0.015/yr 2% (92% 94%) 2017

Cost recovery ratio TAG3: 4.5 yr 1.125 yr 7% (90% 97%) 2013TAG1: 6 yr 1.5 yr 1% (90% 91%) 2010Deterioration rate of pipe: 0.06/yr 0.015/yr 1% (90% 91%) 2011

Total balance ratio TAG3: 4.5 yr 1.125 yr 7% (83% 90%) 2020TAG2: 2.5 yr 10 yr 3% (70% 73%) 2008Deterioration rate of pipe: 0.06/yr 0.015yr 2% (80% 82%) 2015

management alternatives on a constructed SD model. Exogenous variables may be regarded asinputs to a system, and work as operational or managerial decisions. Additionally, exogenousvariables in an SD model trigger changes in the systems behaviour by providing a means forpolicy implementation, and can, therefore, be regarded as candidates for policy leverage.

The policy leverage that will enable a system to reach goals most efficiently can be determinedby conducting analyses of the sensitivity of the exogenous variables on major operational ormanagerial indicators within a system. These sensitivity analyses record the behaviour of majorindicators within the system according to changes in the exogenous variables values.The indicatorsanalysed to obtain policy leverage of the case study system were revenue water ratio, totalbalance ratio, and cost recovery ratio. The exogenous variables selected to analyse the behaviourof these indicators were TAG1, TAG2, TAG3, deterioration rate of pipe, disposal rate ofnon-deteriorated pipe, and disposal rate of deteriorated pipe.

The changes in the selected system indicators were observed after calibrated values of theexogenous variables used during the model verification (given in Tables 24) were multiplied by1/4, 1/2, 1, 2, and 4. Only one of the selected exogenous variables was allowed to change in agiven model run; this method allowed modellers to find the exogenous variable with the mostsignificant effect on an index.

Table 6 shows results of the sensitivity analyses of the exogenous variables on revenue waterratio, total balance ratio, and cost recovery ratio. For example, a 2% maximum increase inrevenue water ratio (i.e. from 91% to 93%) was observed in 2014 after reducing TAG1 from6 to 1.5 years (6 being the previously calibrated value of TAG1). Decreasing deteriorationrate of pipe from 0.06 to 0.015/yr had a similar impact on revenue water ratio, resulting ina 2% maximum increase in 2017, i.e. from 92% to 94%. Improvements made by changes inother exogenous variables on revenue water ratio were not as significant as those in TAG1 anddeterioration rate of pipe.

Although TAG1 and deterioration rate of pipe had similar effects on revenue water ratio,adjusting TAG1 may be a more practical policy than changing deterioration rate of pipe,given the obvious difficulty in controlling deterioration rate of pipe. Since TAG1 determinesinvestment costs for pipe rehabilitation, a policy to adjust TAG1 may be stated in more generalterms as an adjustment in yearly allocation of funds for pipe rehabilitation. It was concluded thatadjusting TAG1 or the yearly allocation of funds for pipe rehabilitation is the policy leveragethat would most effectively improve revenue water ratio. The important role of TAG1 indetermination of revenue water ratio can also be found through the causal relations of the modelshown in Figure 3. In this model TAG1 determines investment costs for pipe rehabilitation,rehabilitated pipe length, yearly deteriorated pipe length, leakage, and revenue water ratio.

Results of the sensitivity analyses on cost recovery ratio and total balance ratio are alsosummarised in Table 6. Results for cost recovery ratio reveal that a reduction of TAG3 or

Dow

nloa

ded

by [U

nivers

iti T

ekno

logi M

alays

ia] at

08:04

22 Se

ptemb

er 20

15


Table 7. Effects of deterioration rate of pipe (1.5% and 24%).

Calculation Deteriorated Leakage Total balance Revenue watercriteria pipes (km) (m3/yr) ratio (%) ratio (%)

Absolute maximum differences 2215; 2337 7,611,940; 16,318,230 3; 5 2; 4Absolute maximum change ratios

with regard to base condition (%)37; 48 26; 47 3; 6 2; 4

Absolute max occurrence times (yr) 2030; 2016 2015; 2012 2015; 2012 2017; 2012

an increase in price of water (achieved by yearly price increases in the model) was the policyleverage that most effectively improved cost recovery ratio.

Results of TAG2on total balance ratioin Table 6 show that total balance ratiowas increasedby 3% in 2008 when a TAG2 of 10 years was used. However, differences in the values of totalbalance ratio between TAG2 values of 2.5 and 10 years gradually diminished over time, andafter 2014 values of total balance ratio for a TAG2 of 10 years became lower than that for aTAG2 of 2.5 years. Therefore, even though total balance ratio was improved when TAG2 wasincreased, i.e. when investment costs for pipe extension was decreased, the improvements madein total balance ratio by increasing TAG2 were only temporary.

Observations of the long-term behaviour of total balance ratioas a result of changes in TAG2,combined with improvements made in total balance ratio as a result of changes in TAG3, leadto the conclusion that an increase in income that comes from raising water price more efficientlyincreases total balance ratio than a decrease in investment spending. Therefore, TAG2 maynot be considered an effective policy to improve total balance ratio of the system, as TAG3 isconsidered to be the policy leverage for improving total balance ratio.

A sensitivity analyses of TAG1versus total balance ratioshowed that the effects of decreasingTAG1 were a decrease in total balance ratio and an increase in revenue water ratio. However,further analyses revealed that the decrease in total balance ratio caused by a decrease in TAG1gradually recovered during the planning period. Therefore, it was concluded that reducing TAG1,or increasing the yearly allocation of funds for pipe rehabilitation, is the policy leverage to improverevenue water ratio as well as an efficient policy for overall improvement of the managerialconditions of the system.

Additional sensitivity analyses were conducted to analyse the effects of the exogenous vari-ables related to pipe management. Calibrated values for deterioration rate of deteriorated pipe,disposal rate of deteriorated pipe, and disposal rate of non-deteriorated pipe, which are givenin Table 3, were multiplied by 1/4, 1/2, 1, 2, and 4, and changes in the selected system indica-tors were observed after each model run, each incorporating variation in only one of the givenexogenous variables.

The second rows of Tables 79 represent the absolute maximum differences between systemindicator simulation results under the base condition (given in Table 4) and those under the alteredcondition (shown in the titles of the corresponding tables). The third rows of Tables 79 representthe absolute maximum ratios of change in the corresponding indicators, while the fourth rows inTables 79 represent the times at which the absolute maximum differences were observed.

To illustrate the results given in Tables 79, results for deteriorated pipes in Table 7 areexplicated below; the other results in Tables 79 follow a similar convention. In Table 7, 2215 kmof deteriorated pipes were predicted for 2030, which was the maximum among the absolutedifferences for deteriorated pipes; the absolute difference was calculated for every year in theplanning horizon. These absolute differences are the differences between the base condition, withdeterioration rate of pipe value of 6% and the case with a deterioration rate of pipe value of1.5%. The corresponding ratio of maximum difference for the deteriorated pipes in the case ofa deterioration rate of pipe of 1.5% compared to the base condition was calculated to be 37%.

Dow

nloa

ded

by [U

nivers

iti T

ekno

logi M

alays

ia] at

08:04

22 Se

ptemb

er 20

15

206 S. Park et al.

Table 8. Effects of disposal rate of deteriorated pipe (0.1% and 1.6%).

Calculation Accumulated disposed-off Deteriorated Revenue water Total balancecriteria pipes (km) pipes (km) ratio (%) ratio (%)

Absolute maximum differences 342; 1182 327; 1127 0.2; 0.7 0.2; 0.8Absolute change ratios with regard

to base condition (%)6; 20 5; 20 0.2; 0.7 0.3; 1

Absolute max occurrence times (yr) 2030; 2030 2030; 2030 2023; 2022 2022; 2019

Table 9. Effects of disposal rate of non-deteriorated pipe (0.125% and 2%).

Accumulated Non- Revenue TotalCalculation disposed-off deteriorated Deteriorated water balancecriteria pipes (km) pipes (km) pipes (km) ratio (%) ratio (%)

Absolute maximum differences 252; 863 130; 447 123; 434 0.06; 0.2 0.08; 0.3Absolute change ratios with

regard to base condition (%)4; 15 7; 20 2; 7 0.07; 0.2 0.1; 0.4

Absolute max occurrencetimes (yr)

2030; 2030 2030; 2025 2030; 2030 2026; 2025 2023; 2023

The values 2337 km, 48%, and 2016 in Table 7 corresponded to results associated with the casein which a deterioration rate of pipe value of 24% was used.

As given in Table 7, noticeable changes in deteriorated pipes and leakage were observed dueto changes in deterioration rate of pipe. However, other important indicators, such as revenuewater ratio and total balance ratio, were not significantly changed. Also, analyses on the effectsof disposal rate of deteriorated pipe, the results of which are given in Table 8, revealed thatnoticeable changes only occurred in accumulated disposed-off pipes and deteriorated pipes,as no significant changes occurred in other important indicators, such as revenue water ratio andtotal balance ratio.

Similar to deterioration rate of pipe, changes in disposal rate of non-deteriorated pipeonly had noticeable effects on accumulated disposed-off pipes, deteriorated pipes, and non-deteriorated pipes (Table 9). Based on these observations, deterioration rate of pipe, disposalrate of non-deteriorated pipe, and disposal rate of deteriorated pipe were not considered asthe policy leverage for the major system indicators, such as revenue water ratio and totalbalance ratio.

8. Summary and conclusions

In this paper, a conceptual framework for management of water supply systems was established,leading to identification of the working mechanisms inherent in water supply systems manage-ment based on causal feedback relationships.An SD computer simulation model was subsequentlypresented based on these causal feedback relationships. In particular, the key factors and interre-lationships associated with management of water pipes were identified and modelled based on theSD modelling principles. An example of a modelled causal feedback relation is the causal feed-back loop, which includes income, investment costs for pipe rehabilitation, rehabilitated pipelength, yearly deteriorated pipe length, leakage, non-revenue water, total water produced,revenue water ratio, prime cost, and water price.

The aim of model calibration in this study was to simulate the historical data reported in Busan(2009), which were used to calibrate the model as closely as possible. Among the required inputvalues for the model, those that could not be obtained from the case study data, such as the initial

Dow

nloa

ded

by [U

nivers

iti T

ekno

logi M

alays

ia] at

08:04

22 Se

ptemb

er 20

15


values of the stock variables, and values or trends of the exogenous variables, were assumedthrough the model calibration and verification processes. The calibration process also took intoaccount expert opinions of managerial personnel of the case study system. During calibration, acomparison of the simulated results and historical data of the model variables showed that theconstructed model reasonably simulated the historical trends of the case study system.

The analyses conducted on model simulation results were based on assumed managementscenarios, and showed that supply ratio and cost recovery ratio of the system were predicted toreach a state of dynamic equilibrium under the basic scenario in approximately 2015. However,revenue water ratio and total balance ratio were predicted to continuously improve in all of theassumed scenarios. Meanwhile, pipes in the system were found to be in a deteriorating stage, evenin the most optimistic scenario. Therefore, major investments for pipe rehabilitation, even greaterthan those assumed in the optimistic scenario, may need to be implemented to reduce amounts ofpipe deterioration in the system.

To identify the policy leverage for the case study, system changes for revenue water ratio, totalbalance ratio, and cost recovery ratio, with respect to changes in exogenous variables, such asTAG1, TAG2, TAG3, deterioration rate of pipe, disposal rate of non-deteriorated pipe,and disposal rate of deteriorated pipe were analysed. These analyses showed that a reduction inTAG1 or an increase in yearly allocation of funds for pipe rehabilitation was the policy leveragefor improvement in revenue water ratio. A reduction in TAG3 or increased water price wasthe policy leverage for improvement in cost recovery ratio and total balance ratio.

Further sensitivity analyses of the exogenous variables showed that deterioration rate of pipe,disposal rate of non-deteriorated pipe, and disposal rate of deteriorated pipehad no considerableimpact on some of the major indicators, such as revenue water ratio and supply ratio. It wasconcluded that the limited effect of deterioration rate of pipemay have resulted from the modelsown structure, since the direct costs of pipe repair and indirect costs related to pipe failure, suchas traffic congestion and flooding of nearby building basements, were not considered. Furtherstudy will be required to model the working mechanism of indirect costs in water supply servicesmanagement. The model may need to be further expanded to include both direct and indirect costsrelated to pipe failure.

Through the sensitivity and scenario analyses illustrated in this paper, the SD model developedfor water supply systems was shown to be sufficient in identification of policy leverage, leadingto efficient water supply system management; the model could also be utilised to determine long-term effects of policy change on the status of a water supply system. The principles associatedwith establishing the causal relationships used in the SD computer modelling and the sensitivityanalysis methods for exogenous variables used for identifying policy leverages are also expectedto work as prototypical methods for modelling and solving the management problems of otherwater supply systems.

Acknowledgements

This subject is supported by Korea Ministry of Environment as Projects for Developing Eco-Innovation Technologies(GT-11-G-02-001-1).

References

Ahmad, S., and S. P. Simonovic. 2000. System Dynamics Modeling of Reservoir Operations for Flood Management.Journal of Computing in Civil Engineering 14 (3): 190198.

Ahmad, S., and S. P. Simonovic. 2001. A Decision-Support Tool for Evaluation of Impacts of Flood ManagementPolicies. Hydrological Science and Technology 17 (1): 1122.

Beard, A. N. 1999. Some Ideas on a Systemic Approach. Civil Engineering and Environmental Systems 16 (3): 197209.

Dow

nloa

ded

by [U

nivers

iti T

ekno

logi M

alays

ia] at

08:04

22 Se

ptemb

er 20

15

208 S. Park et al.

Busan Metropolitan City Water Supply Authority. 2009. 2008 Statistics on Water Supply Services. Busan: Republicof Korea.

Crawford, N. H., and R. K. Linsley. 1966. Digital simulation in hydrology: Stanford Watershed Model IV. TechnicalReport 39. Stanford, CA: Civil Engineering Department, Stanford University.

Deaton, M. L., and J. I. Winebrake. 2000. Dynamic Modeling of Environmental Systems. New York: Springer.Drew, D. R., L. C. Wadhwa, I. B. Santoso, and S. M. Moussavi. 1985. Scenariopolis: The Civil Engineering Systems

Policy Research Laboratory. Civil Engineering Systems 2 (2): 6676.Fletcher, E. J. 1998. The Use of System Dynamics as a Decision Support Tool for the Management of Scarce Water

Resources. International conference on new information technologies for decision making in civil engineering,Montreal, Canada, Vol. 1 (2), 909920.

Ford, A. 1999. Modeling the Environment. Washington, DC: Island Press.Forrester, J. W. 1958. Industrial Dynamics A Major Breakthrough for Decision Makers. Harvard Business Review 36

(4): 3766.Grigg, N. S., and M. C. Bryson. 1975. Interactive Simulation for Water System Dynamics. Journal of Urban Planning

Division 101 (1): 7792.Hannon, B., and M. Ruth. 1997. Modeling Dynamic Biological Systems. New York: Springer.Hannon, B., and M. Ruth. 2001. Dynamic Modeling (Modeling Dynamic Systems). New York: Springer.Keyes,A. M., and R. Palmer. 1993. The Role of Object-Oriented Simulation Models in the Drought Preparedness Studies.

20th annual national conference of Water Resources Planning and Management Division, New York, 479482.Korea Ministry of Environment. 2007. 2006 Statistics on Water Supply Services. Seoul: Republic of Korea.Korea Ministry of Environment. 2009. Press Release: 2nd International Symposium for Integrated Water Network System.

Ministry of Environment Web. Accessed June 25, 2012. http://www.me.go.kr/web/288/me/common/board/detail.do?idx=170537&decorator=me&searchOption=all&boardId=notice_02&categoryId=02&searchWord=%BB%F3%BC%F6%B0%FC%B8%C1. (in Korean)

Lee, S., H. Kim, and H. Park. 2006. Analyzing Effects of Transparency on a Water Business Case Using SystemDynamics. Journal of the Korean Society of Water and Wastewater 20 (4): 605616. (in Korean)

Lee, S., H. Park, and H. Park. 2007. Application of System Dynamics Methodology for Comprehensive Analysis of theWater Business System. Journal of the Korean Society of Water and Wastewater 21 (1): 6573. (in Korean)

Li, L., and S. P. Simonovic. 2002. A System Dynamics Model for Predicting Floods from Snowmelt in North AmericanPrairie Watersheds. Hydrological Processes 16 (13): 26452666.

McGarvey, B., and B. Hannon. 2004. Dynamic Modeling for Business Management. New York: Springer.Palmer, R. 1998. A History of SharedVision Modeling in theACT-ACF Comprehensive Study:A Modelers Perspective.

In Special session of ASCEs 25th Annual Conference on Water Resources Planning and Management, edited byW. Whipple Jr., 221226. Reston, VA: ASCE.

Richardson, G. P., and A. L. Pugh. 1981. Introduction to System Dynamics Modeling with DYNAMO. Cambridge, MA:The MIT Press.

Ruth, M., and B. Hannon. 1997. Modeling Dynamic Economic Systems. New York: Springer.Shawwash, Z. K., and S. O. Russell. 1997. Use of Interactive Simulation Environments for Managing Water in Jordan.

The 1996 international system dynamics conference, Cambridge, MA, July. CSCE, 156169.Simonovic, S. P. 2002. World Water Dynamics : Global Modeling of Water Resources. Journal of Environment

Management 66 (3): 249267.Simonovic, S. P. 2009. Managing Water Resources: Methods and Tools for a Systems Approach. London: Earthscan.Simonovic, S. P., and H. Fahmy. 1999. A New ModelingApproach for Water Resources PolicyAnalysis.Water Resources

Research 35 (1): 295304.Simonovic, S. P., H. Fahmy, and A. Elshorbagy. 1997. The Use of Object-Oriented Modeling for Water Resources

Planning in Egypt. Water Resources Management 11 (4): 243261.Simonovic, S. P., and L. Li. 2003. Methodology for Assessment of Climate Change Impacts On Large-Scale Flood

Protection System. Journal of Water Resources Planning and Management 129 (5): 361372.Statistics Korea. 2009. 2008 Consumer Price Index Survey. Seoul: Republic of Korea.Stave, K. A. 2000. Using System Dynamics Models to Facilitate Public Participation in Water Resources Management: A

Pilot Study Using the Las Vegas, NV Water System. 18th international conference of the System Dynamics Society,Bergen, Norway, August 610.

Sterman, J. D. 2000. Business Dynamics: Systems Thinking and Modeling for a Complex World. New York: Irwin/McGraw-Hill.

Teegavarapu, R., and S. P. Simonovic. 2000. System Dynamics Simulation Model for Operation of Multiple Reservoirs.10th World Water Congress: Water, the Worlds Most Important Resource. Melbourne, Australia. International WaterResources Association, 10091016.

Winz, I., G. Brierley, and S. Trowsdale. 2009. The Use of System Dynamics Simulation in Water ResourcesManagement. Water Resources Management 23 (7): 13011323.

Dow

nloa

ded

by [U

nivers

iti T

ekno

logi M

alays

ia] at

08:04

22 Se

ptemb

er 20

15

IntroductionThe SD methodologyPrevious studies on the applications of SD for water resources and water supply services managementDeveloping causal relationships of working mechanisms in water supply systems managementConceptual Framework for the MechanismRealisation of a conceptual framework as a causal feedback loop diagram

Description of the developed SD computer simulation modelModel verification and scenario analysesDescription of model input dataManagement scenario analysis

Analysis of exogenous variable sensitivity and identification of policy leverageSummary and conclusions

Amir Water Supply

Documents

Transcript of Amir Water Supply