«Performance Measurement of Water Distribution Systems (WDS).

A critical and constructive appraisal of the state-of-the-art»

by

« Mahdi Moradi Jalal »

A thesis submitted in conformity with the requirements for the degree of «Master of Applied Science»

« Civil Engineering » University of Toronto

© Copyright by « Mahdi Moradi Jalal » « 2008»

ii

Performance Measurement of Water Distribution Systems (WDS)

A critical and constructive appraisal of the state-of-the-art « Mahdi Moradi Jalal »

« Master of Applied Science) » « Civil Engineering Department »

University of Toronto « 2008 »

Abstract Water supply and distribution infrastructures are vital for current life. They have a significant

role in public health, providing safe water for drinking and human consumption as well as for

essential non-potable uses such as fire fighting. These diverse objectives create challenges for

everyone who must address in some way the actual performance of the system.

This research critically evaluates all common objectives of conventional design

approaches and evaluates the advantages and drawbacks of various performance measures. New

ideas for a more realistic and comprehensive approach to the design, operation assessment of

WDS are proposed.

A new approach, called a Risk-based Performance Assessment, for hydraulic

performance evaluation is tentatively proposed. It is based on integration of reliability, resiliency,

and vulnerability as three basic operational indices in the operation of WDS. Furthermore, the

Total Life-cycle Cost evaluation approach is tentatively proposed based on considering all major

costs of a WDS.

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Acknowledgments I would like to express my profound appreciation to my supervisor, Professor Bryan W.

Karney, for his continuous and enthusiastic guidance and encouragement as well as his

invaluable advice and patient throughout this research work particularly and all other of our other

works generally.

I also thank Dr. Chris C. Kennedy for his helpful comments as the second reviewer. Also,

a special thanks to Dr. Andrew Colombo and my friend, Stefanos Karterakis, for their assistant in

evaluating some parts of my thesis.

I would also like to thank my parents, those who dedicated their love and support

throughout my entire life. Special thanks to my wife, Fatemeh Raei, for her understanding,

patience and support in my life.

Many thanks to you all!

Mahdi Moradi Jalal

September 2008

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Table of Contents Abstract ii

Acknowledgement iii

Table of contents iv

List of Figures vii

List of Tables viii

Chapter 1: Overall Description 1

1-1 Introduction 1

1-2 Objectives 4

1-3 Organization 5

Chapter 2: Water Supply and Distribution Systems in Public Domain 9

2-1 Introduction 9

2-2 Customer-Oriented Performance Evaluation 10

2-3 Water Demand Expectations of Major Customers 13

2-4 Water Demand Management 15

2-5 Fire Fighting Demand 19

2-6 Economic Considerations 21

2-7 Transient Events 25

2-8 Performance Classification 27

2-9 Summary 29

Chapter 3: Design and Operation Modeling 31

3-1 Introduction 31

3-2 Modeling of Engineering Systems 31

3-3 Water Demand Modeling 33

3-4 Critiques on Water Demand Evaluation 40

3-5 Optimization Models in Water Resource Management 43

3-6 Optimal Design 44

3-7 Critiques on Conventional Design Methods 46

3-8 Optimal Operation 48

3-9 Critiques on Optimal Operation 50

3-10 Transient Modeling in Design and Operation 51

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3-11 Critical Comments on Transient Design and Operation 53

3-12 Safe-Fail and Fail-Safe Design and Operation 55

3-13 Critiques on Safe-Fail Operation 57

3-14 Evaluation of the Fail-Safe Approach 59

3-15 Summary 60

Chapter 4: Hydraulic Performance and Reliability Measurements 62

4-1 Introduction 62

4-2 Hydraulic Performance and Reliability Criteria 63

4-3 Pressure-based Indices 65

4-4 Demand-Driven and Head-Depended Analyses 66

4-5 Reliability-based Indices 68

4-6 Resilience Concept 70

4-7 Redundancy 73

4-8 Connectivity 74

4-9 Entropy 75

4-10 Total Risk Index (TRI) 76

4-11 Risk-Based Performance Modeling 79

4-12 Summary 80

Chapter 5: Water Quality Performance Measurements 82

5-1 Introduction 82

5-2 Water Quality and Public Requirements 83

5-3 Water Quality and High-risk Groups 86

5-4 Economic Concerns of Water Quality Issues 88

5-5 Mechanisms for Material Transport in Distribution Systems 90

5-6 Drinking Water Contaminants 91

5-7 Water Quality Modeling 94

5-8 Influence of Distribution System on Water Quality 96

5-9 Water Quality Assessments 100

5-10 Summary 103

Chapter 6: Total Life Cycle Cost Evaluation 105

6-1 Introduction 105

6-2 Pipe Materials in WDS 105

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6-3 Influence of Pipe Material on Performance 109

6-4 Performance Indicators for Operation Assessment 112

6-5 Expected Operation Damages 114

6-6 Total Life Cycle Cost Evaluation 117

6-7 Summary 122

Chapter 7: Shared Vision Modeling for Multi-objective Assessment 123

7-1 Introduction 123

7-2 Multiple Decision-makers for Multi-objective Problems 124

7-3 The Complexity of Decision Making 127

7-4 Shared Vision Modeling 129

7-5 Summary 132

Chapter 8: Summary and Conclusions 133

References 137

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List of Tables Chapter 2:

Table 2-1: Typical Fire Flow Requirements. 21

Chapter 3:

Table 3-1: Typical Water Duties 34

Table 3-2: Typical Rates of Water Use for Various Establishments. 35

Table 3-3: Typical Peaking Coefficients. 36

Table 3-4: Typical Service Pressure Criteria. 37

Chapter 4:

Table 4-1: Monte Carlo Simulation Algorithm for TRI Evaluation. 79

Chapter 5:

Table 5-1: Scenarios of Cost Effectiveness Evaluation of Water/Sanitation on

Childhood Diarrhea. 89

Table 5-2: Typical Costs of Illness per Person. 89

Chapter 6:

Table 6-1: Specification of the Most Common Pipe Materials. 106

Table 6-2: Water and Sewer Pipe Market Share, 1993 (% of length). 106

Table 6-3: Cost Comparison Between PVC and DI Pipes. 110

Table 6-4: Comparison of Pipe Materials for WDS. 111

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List of Figures Chapter 2:

Figure 2-1: Average Water Consumption per Billing Period by Customer Type and

Drought Conditions. 14

Figure 2-2: Operations and Maintenance Costs from State and Local Sources

(1978-1994). 23

Figure 2-3: Shortage Categories for Drinking Water Infrastructures. 24

Figure 2-4: Projected Annual Replacement Needs for Transmission Lines and

Distribution Mains (2000-2075). 24

Figure 2-5: Performance Classification of WDS. 28

Figure 2-6: Approaches for Performance Measurement of WDS. 28

Chapter 3:

Figure 3-1: Average Daily Water Demand for a Real WDS. 39

Figure 3-2: Daily Patterns in Hourly Water Demand for the Seven Days of the

Week in: (a) Winter; (b) Spring; (c) Summer; and (d) Fall 40

Figure 3-3: Actual Pressure Waves due to Transient Event in a Pipe. 54

Chapter 4:

Figure 4-1: 3D Feasible and Acceptable Risk Space. 79

Figure 4-2: Flow chart of risk-based performance model 80

Chapter 5:

Figure 5-1: Population with/out Safe Drinking Water source in 1990, 2004 and

2015. 84

Figure 5-2: Population (millions) Without Safe Drinking Water by Region in 2004. 85

Figure 5-3: Outbreak Sources in WDS (1981-2002). 97

Figure 5-4: Change in Customer’s Fees in Ohio (1989-1999). 102

Chapter 6:

Figure 6-1: Example of Life Cycle Deterioration Curve of Pipe. 112

Figure 6-2: Rate of Failures in 97-98 from 30% of Total WDS in Germany,

DVGW Statistics 1999. 112

Figure 6-3: PDF of Pressure, Continuous Damage Function, and Derived PDF of

Expected Damages. 115

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Figure 6-4: PMF of Pressure, Discrete Damage Function, and Derived PMF of

Expected Damages. 116

Figure 6-5: Major Cost Components of Total Life Cycle Cost of WDS 118

Chapter 7:

Figure 7-1: Classical vs Existing structure of systems analysis. 127

Figure 7-2: Shared Vision model for WDS. 131

Chapter 1 Overall Description

1.1 Introduction Both fresh water and energy are vital for life. Indeed, it is no exaggeration to say that supplying

and distributing of water and energy form the foundation of contemporary life. Water supply and

distribution systems serve many critical functions and play a large part in achieving human and

economic health. Despite this, the performance of these systems often goes unnoticed until there

is a major disruption or operational failure. While failure events are likely inevitable and often

dramatic and costly, the day-to-day inefficient performance of a water distribution system

(WDS) also entails great economic, social and environmental burdens. Performance

measurement is a key issue in engineering the behavior and control of any WDS.

Because of significant development of urbanized areas and construction of thousands of

small and large-scale water supply and distribution systems in recent decades, many people have

access to clean water and adequate sanitation. However the quality of service which is provided

by water utilities is often questionable and the cost of new systems is still often prohibitive.

Water supply systems are crucial strategic systems which have physical complexity in

their construction, installation, operation, with enormous economic concerns and environmental

implications. They also contribute significantly to public health. Despite this, design and

operational challenges have often been underestimated by professionals and engineers and their

direct or indirect impacts, although often considered separately, have seldom been investigated

comprehensively.

The most common challenges for the water industry include water quality degradation,

capacity shortages, infrastructure aging and deterioration, demand increases, and their ever-

increasing energy consumption coupled to the global energy crisis.

In September 2000, the one hundred eighty-nine United Nation’s member states adopted

the Millennium Declaration, from which the Millennium Development Goals (MDGs) emerged.

The MDGs form a set of political commitments that focus on the major development issues

faced by the developing world within a fixed period of time. While almost all the MDGs can be

indirectly linked to water supply and sanitation, Goal 7 on environmental sustainability addresses

these issues directly: one of its targets, Target 10, is to “halve by 2015 the proportion of people

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without sustainable access to safe drinking water and basic sanitation”. The baseline year has

been established as 1990. The U.N has recently published a report indicating that 3.4 million

people, mostly children, die from water-related diseases every year. As a result, one MDG goal

is to reduce this rate to half for people without access to clean drinking water and basic sanitation

within next ten years (Toubkiss, 2006).

WHO found that achieving the water and sanitation MDG would have substantial

economic benefits (Hutton and Haller, 2004); US $1 invested would yield a return of between

US $3 and US $34, depending on the region. The benefits include an average global reduction in

public diseases such as diarrhea episodes by around 10%. If the MDG target is achieved, the

health-related costs avoided due to reduced illness are estimated US $7.3 billion per year

worldwide, and the annual global value of adult days gained due to reduced illness can increase

up to US $750 million with respect to formal or informal employment.

One of the major benefits of improving access to water and sanitation derives from the

time saving associated with closer location of the facilities. Time savings occur due to, for

example, the installation of piped water supply to houses. The annual value of these time

savings, if the MDG target is met, is estimated to be near US $64 billion.

The total benefits of such improvements will vary between regions as they depend on the

existing levels of water supply and sanitation access and on local levels of morbidity due to

waterborne diseases. Health benefits and additional benefits are greater in regions where the

number of unserved people is high and where the waterborne disease burden is significant

(Hutton and Haller, 2004).

Additional improvement of potable water quality, such as point-of-use disinfection, in

addition to wide access to clean water and sanitation, can increase the benefit to between $5 and

$60 per $1 invested. Choosing more advanced types of technologies such as provision of

regulated in-house piped water and sewer connection would lead to massive overall gains,

including an average global reduction of diarrhea episodes by around 69%. But this type of

intervention is also the most expensive. Achieving universal access to in-house piped water and

sewer connection might cost more than US$130 billion per year.

From a cost perspective, meeting the MDG target globally requires an estimated

additional expenditure of US $11.3 billion per year based on year 2000, over the current

investments by using basic technologies. Yet wide-ranging estimates of the costs for meeting the

MDG water and sanitation target have emerged. The World Panel on Financing Water

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Infrastructure (2003) also mentioned an extra annual investment cost of about US $10 billion,

using the most basic standards of service and technology to meet this target (Camdessus and

Winpenny, 2003). For example the U.S. Environmental Protection Agency (EPA) estimated the

quantifiable gap between projected clean water and drinking water investment needs over the

twenty-year period from 2000 to 2019 and current levels of spending in United States (EPA,

2002). The analysis found that a significant investment gap that could develop if the clean water

and drinking water systems across the U.S. maintain current spending and operational practices.

Estimates of capital needs to achieve the drinking water goals range from US $154 billion to US

$446 billion with a point estimate of $274 billion over the twenty-year period. But it should be

emphasized that such estimations often involve considerable uncertainties, particularly on global

scale. Difficulties arise because economic evaluation on global issues is not only complicated,

but depends on detailed information that may not be available.

As the majority of the WDS across the world were built decades ago, many are

approaching or have exceeded their design lives. Thus analyzing the safe and secure operation of

the old systems is crucial, particularly since performance has gradually declined and they require

extensive upgrading. Many systems face aging problems over the long-term operation and the

challenges that come with the task of keeping their systems efficient. However these needs far

surpass the available resources (EPA, 2002).

But there are the potential ways to solve the problems through appropriate action. But the

failure to achieve pre-defined goals means that a large number of people around the world will

be in danger of illness and even death due to lack of access to safe and clean water.

How well a water supply and distribution system can satisfy its diverse objectives can be

determined by evaluation of its functional performance. However this evaluation is extremely

complex because it depends on a variety of parameters, some of which vary continually.

Changing parameters include the quality and quantity of water available at the source, the

variation in daily, weekly, and seasonal demands, as well as demand growth over the service life.

But there are some other factors that depend on the characteristics of WDS itself, such as failure

rates of supply pumps, power outages, flow capacity of transmission mains, roughness

characteristics, pipe breaks and valve failures.

Although much attention has been paid to the design and performance assessment of a

WDS, some important areas have not yet been investigated fully. Under explored factors include:

1) Various criteria in social and environmental costs resulting from the performance of WDS; 2)

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The growing awareness of the quality of service requested by all customers; and 3) Assessment

of new approaches for improving performance of current operating systems to include more

comprehensive public and economic issues. However measuring the performance of a WDS as a

multi-purpose system is not a straightforward task, since it can be perceived from different

viewpoints and related to a variety of parameters and properties of the system which are not

usually quantifiable.

The current research cannot reasonably find complete answers to the list of the most

inconvenient and pressing problems. Rather, this research seeks to establish a broad perspective

for the evaluation of the design and operation assessment of a WDS. The main goals are to

challenge designers and decision makers about those issues that have been often neglected in

engineering practice, and also to test and verify new ideas for system design and operation.

1.2 Objectives

The main objective of the current research is to undertake a critical review of the principal

concepts relating to the evaluation of a WDS. This review includes the importance of

construction of such complex systems in all communities, the influence of system operation on

public health and private issues, and the interactions of WDS with other public facilities.

Throughout, the goal is to bear in mind the multiple decision makers that influence the

evaluation of a multi-objective WDS. This critique starts by considering some basic questions:

What is the true nature of a WDS? How does it operate and how should its performance be

evaluated? Who are the major customers and what are their main expectations of such systems?

And finally, how can various decision makers contribute to the evaluation of a multi-objective

WDS?

Engineering knowledge is required to find practical solutions to human needs by applying

scientific approaches. In this light, the following general question is considered and

progressively refined: How can engineering systems respond properly to all requested needs?

The problem of comprehensive water demand evaluation, associated with the optimal design and

operation are essential to system performance.

The advantages and drawbacks of current design approaches of WDS are discussed

herein. It should be noted that there are important questions relating to the effectiveness of

conventional approaches to WDS evaluation. These questions may include: Does the operation

of WDS satisfy all requested needs for current (and next) generation(s)?, and if so, in what

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sense? Are traditional approaches (which are mostly over-simplified) still suitable for design

and performance assessment of current systems with much more complicated conditions and

objectives? Are there better ways to evaluate their performance? How can new issues be

properly reflected in system performance evaluation?

Many of current approaches to the design and operation assessment of a WDS have

considered one major economic or public aspect. They often focus partially on the WDS as a

whole complex system. As a result, their outputs often represent effectively only a part of the

complete perspective and they are unable to evaluate other important aspects. This research aims

to provide a more complete perspective on the design and operational assessment of WDS as a

whole system.

Finally controversial discussions involving WDS often engage multiple decision-makers.

Such problems can be partly analyzed economically, involving the aggregation of individual

costs and benefits. The practical analysis of multi-objective problems, generally involves a series

of solutions that correspond to a variety of objectives and criteria. The necessity of proposing a

comprehensive framework for multi-objective evaluation of WDS by considering multiple

perspectives is also investigated.

1.3 Organization

The work is organized into eight chapters. Following this introduction, the next chapter discusses

the roles of WDS from a public and private perspective. As a result, customer-oriented

performance evaluation is presented. The level-of-service provided by a WDS to all customers

can be used effectively as continuous tool for performance assessment of the system by water

utilities. Customer feedback is an effective monitoring tool that can assist water utilities by

having wide-spread real-time monitoring across the system.

The expectations of major customers including potable and non-potable water users are

discussed in Chapter 2. The full awareness on main priorities of all different customers is

essential for the designer/manager of WDS.

A common characteristic of water demand in urban areas worldwide is for continuous

growth over time. The main influencing factors are population growth, migration, changes in

lifestyle, and demographic structure. Using water efficiently and managing competing demands

are basic steps to ensure that less water is wasted and misused in communities.

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Due to the huge amount of investment required for the design, construction, installation

and operation of a WDS, a comprehensive economic analysis is needed that consider all stages.

This chapter thus discusses the importance of economic analysis of WDS.

Furthermore, if systems are not to fail prematurely, another threat must be considered:

transient or water hammer events. These transient events, although complex, arise when

conditions in a system are disturbed, particularly if changes occur quickly. The major sources of

transient events and their consequences on the operation of WDS are reviewed in Chapter 2.

Finally, having laid the groundwork, chapter 2 presents the first attempt at defining and

classifying WDS. It also illustrates the role of field measurements as performance evaluation

tools for engineers/planners. The approach considers both costumer reports as external

evaluation information and also a Performance Indicators (PI) framework.

Chapter 3 starts with a discussion of the importance of modeling in engineering systems.

The basic questions are: What is a model?, Why they are being used?, and What are their

applications? Advantages and limitations of engineering models for analyzing real systems are

discussed. Then common steps of defining, developing and appropriately using engineering

models are addressed. Next, current optimization models are presented as effective tools in the

design and operation assessment of WDS.

Following this, the conventional formulations of the usual mathematical models used for

the design and operation of a WDS are reviewed briefly. This material starts by addressing

considerations for conventional design methods and then explores more recent developments in

the optimal operation of WDS with their advantages and shortcomings. Design regulations for

transient events are addressed briefly in Chapter 3. Application of transient modeling to this

discussion of the design and operation of WDS are briefly introduced..

Safe-fail and fail-safe paradigms are two major approaches in the modeling of

engineering systems. They are also briefly reviewed with their specifications in this chapter, with

a discussion about their importance in assessing system design and operation..

Chapter 4 reviews the hydraulic performance measurements of WDS. Hydraulic

performance is the main discussion area relating to the operation of a WDS. A broad range of

practical criteria are discussed in this chapter including minimum surplus head index, total

surplus head index, and pressure-related measures.

Reliability is also discussed in Chapter 4. Although evaluation of hydraulic performance

is relatively straightforward, but the concept of reliability is less clear in terms of what exactly

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the analysis is attempting to measure. The major approaches for reliability evaluation are

analyzed in order to tackle that problem. While the direct reliability methods are reviewed at

first, other indirect methods for reliability like redundancy, entropy and connectivity methods are

reviewed later in this chapter.

A key component of this research is to propose a risk-based performance assessment of a

WDS. In this approach, a stochastic process is considered to generate wide range practical

scenarios, such as demand patterns, pump failures, and pipe breaks to apply to subsequent

hydraulic simulations. For each generated scenario, the hydraulic model is simulated to compute

three corresponding hydraulic performance indices, which are ultimately compiled to define

system reliability, resiliency and vulnerability indices. From these, a new proposed total risk

index, TRI, can be computed. The process is repeated for a large number of scenarios in order to

characterize critical operating conditions. The worst-case value of total risk index is a good

indicator for a generalized hydraulic assessment of the system

The next performance area, explored in Chapter 5, relates to the quality of water. Water

quality has implications to public health and private interests. The relationship between water

and sanitation on one hand, and illness costs and prevention benefits on other hand, are discussed

in Chapter 5.

The same criteria for drinking water are often applied for all different groups, regardless

of age, gender and health conditions. Yet different people have different vulnerabilities to poor

water. The water quality criteria for high-risk groups are considered too. Then economic

concerns of water quality problems are discussed based on cost-benefit analysis of safe water.

The main mechanisms for transportation of materials in pipes are briefly discussed and a

broad discussion is provided on common contaminants that often threaten drinking water quality.

Furthermore two major approaches for water quality modeling are discussed: steady and

dynamic water quality models. Interactions of distribution system on the quality of drinking

water are discussed. Finally, major concerns in water quality assessments are reviewed in

Chapter 5.

Chapter 6 considers the most common pipe materials with their specifications in the

water industry. Advantages and drawbacks of each material are listed and their properties

compared. Then the influence of pipe material on performance of a WDS is reviewed.

In order to evaluate operational cost of such a system, performance indicators might be

used to estimate annual operating costs and revenues. They constitute the annual operation and

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maintenance costs of the system. The planner can then evaluate the total life cost of the system

by estimating the expected damage cost due to hydraulic failure and water quality failure. After

this, a new generalized framework for cost analysis, called Total Life-cycle Cost Evaluation, is

tentatively proposed. It includes all major cost components of WDS such as initial cost,

operation and maintenance cost, expected damage cost of hydraulic and water quality

deficiencies over the service life of the system.

Chapter 7 introduces the challenges of a multi-objective assessment when multiple

decision-makers are involved. It starts with modeling requirements for the design of a WDS,

while operation problems are discussed herein as a multi-objective problem with multiple

stakeholders and impacted groups. Practical requirements of multiple decision-makers for multi-

objective systems are investigated. The complexity of decision making for the design and

operation assessment of WDS is critically reviewed. Finally, a possible framework for

evaluation, called Shared Vision Modeling, for comprehensive performance evaluation of water

supply and distribution systems is tentatively proposed.

The final chapter, Chapter 8, summarizes the main conclusions and major achievements

of this research and suggests some areas for further investigation.

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Chapter 2 Water Supply and Distribution Systems in Public Domain

2.1 Introduction

A water supply and distribution system is a complex that exists to satisfy various objectives to

meet public health and environmental constraints, considering the ever-increasing needs for fresh

water and other essential non-potable applications. It consists of various components such as

pipes, pumps, reservoir tanks and hydraulic control elements that collectively supply (at least to

some extent) the required quantities of water with adequate pressure from sources to all

customers. It is generally desired that the water should be supplied in a continuous manner.

However this is an ideal condition, and occasional disruptions by random failures of their

components and unexpected variation of demands may occur over the service life. As a result,

measuring and evaluating a systems’s performance is itself a complex problem.

A WDS is normally designed and operated to satisfy various customer demands over its

service life. The system should be able to effectively satisfy all major routine consumptions

including residential, industrial and others as well as to meet reasonable emergency needs for

demands such as fire fighting. In order to provide a reliable framework for system’s operation, it

is necessary to identify the most critical priorities and preferences of all major customers, and

then try to establish effective management tools to achieve all (or most) predefined goals. For all

these reasons, providing a framework for customer-oriented operation is essential.

Decision makers should also explore innovative and efficient strategies to accommodate

the huge economic requirements of satisfying increasing demands without further destruction of

the environment. Water resource conservation and management are important for sustainable

development goals too.

So how do all these goals and requirements work out in practice? The objective of this

chapter is to review the conventional answers to this question.

Because of significant development of urbanized areas and construction of thousands of

small and large-scale water supply and distribution systems in recent decades, many people have

access to clean water and adequate sanitation. However the quality of service which is provided

by water utilities is often questionable and the cost of new systems is still often prohibitive.

9

Water supply networks are crucial strategic systems which have physical complexity in

their construction, installation, operation, with enormous economic concerns and environmental

implications. They also contribute significantly to public health. Despite this, design and

operational challenges have often been underestimated by professionals and engineers and their

direct or indirect impacts, although often considered separately, have seldom been investigated

comprehensively.

The most common challenges for the water industry include water quality degradation,

capacity shortages, infrastructure aging and deterioration, demand increases, and their ever-

increasing energy consumption coupled to the global energy crisis.

2.2 Customer-Oriented Performance Evaluation Infrastructure is of increasing importance in the urban areas. The term infrastructure generally

refers to a facility or component that is offered for public use, versus one of a private nature. For

example, a household can have its own water purification unit, but a water supply and

distribution system is a public facility which is frequently a cheaper and better option from an

economic and environmental perspective. Infrastructure can be considered either primarily

economic or social. Economic infrastructure, which is also called physical infrastructure,

inevitably involves public economics. Social infrastructure includes those public facilities that

are essential for social aspects of human life such as schools and parks. Both economic and

social infrastructures are essential to ensure productivity, growth and health in urban

communities (Ayogu, 2007).

A water supply and distribution system has close relationships with social and economic

infrastructures. In this regard, it is necessary to identify the major customer groups that require

different services. The following comments illustrate the interdependency of major public

facilities and economic activities with WDS.

• Major public facilities such as hospitals, schools as well as major private customers such as

office buildings, restaurants, and sport complexes require reliable and safe water and

wastewater service. Water is used for drinking, sanitation, heating and cooling and various

other needs. But requirements regarding the amount of water, pressure and quality and also

duration of supply are quite different.

• Most industrial products require water as a component of production and need wastewater

systems to process manufacturing waste. For example, water is required throughout the

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production cycle within the food industry. High standards for water quality are usually

obligatory; however certain activities that do not require water as a component or use water

for non-human consumption purposes sometimes need lower water quality.

• The need for reliable water is critical in some emergency cases such as fire extinguishing.

Appropriate access to water at the right time without any interruptions is essential for fire

fighters.

• Safe water makes an essential contribution to public health and sanitation. Controlling and

preventing the spread of waterborne diseases in public areas are crucial to public health.

Those who are responsible for public health and sanitation have at least equal concern over

the water quality criteria as they do over water quantity. They value safe water with high

quality and minimal contamination during service times (EPA, 2005).

Most water utilities use their revenues to cover their operating and maintenance costs. It

is crucial to provide an appropriate level of satisfaction for all customers. Water utilities

emphasize a greater diversity of services for individual customers who are charged with

explicitly considering customer preferences. They are increasingly aware of customer protection

issues and exposure to public opinion (Coelho, 1997).

The level-of-service provided by a supply system should generally be adjusted to the

requirements, preferences and expectations of the customers. For example, fire fighting needs

huge flow rates with adequate pressure, but only infrequently and for short periods, with much

less regard for meeting high water quality standards.

Residential customers often need safe water with high quality criteria for drinking and

cooking while minor pressure deficits at their taps are usually ignored compared to poor water

quality. Meanwhile for other residential purposes like toilet flushing, the same water quality

criteria are not required as drinking and cooking. In some cases, the volume of supplied water is

important. For example the volume of water for filling bath tubs is desired by some residential

customers, while the possible deficit pressure is acceptable, as the user only needs a full bath tub

with warm water in this case. But if a user wishes to take a shower, having warm water at an

appropriate pressure during washing is obviously of greater importance. Therefore the

satisfaction of the particular customer depends on the specific needs and preferences.

For industrial customers who require water as a component of their products, it is

essential to apply the same high water quality criteria as for drinking water. But in some other

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industries that use water for other purposes, the quantity not quality of supplied water is the main

expectation. It is not always necessary to provide the same high water quality for customers.

The level-of-service not only depends on the accepted industry practice and how this

incorporates quality requirements, but also on the provision of a pleasant product. In fact,

Tansley and Brammer (1993) indicated that the customer is normally willing to pay extra for a

quality-oriented product.

The level-of-service provided by a system can effectively be used as an assessment tool

for water utilities. Customer’s feedback can be an effective monitoring tool that can assist water

utilities to have system-wide and real-time monitoring mechanism. Water utilities have in turn

established procedures to verify their own level-of-service. These procedures are new tools to

guarantee the compliance of their services with legal requirements. In this respect, they are often

even stricter for reasons of self-protection in their management policies (Coelho, 1997).

The basic performance criterion is whether performance is acceptable or not, but a wider

range of parameters is needed. Most utilities keep an internal minimum pressure standard which

is marginally higher than that imposed by the authorities to prevent deficit pressure at customers’

taps. In addition, several types of measures are considered in practice with targeted levels-of-

service often defined more broadly to ensure appropriate response and monitoring. The

minimum level-of-service is often defined in such a way that the utility should compensate its

customers if the service is below it.

As it is not practical to record field measurements for all consumption nodes, including

house faucets and industrial withdrawal nodes, water utilities often rely on the computational

analysis of the systems. They interpret the results of simulation analysis of the system to verify

customer feedback and use associated other data such as limited field measurements, district

metering results and telemetry. Specific customer feedback that is often tracked includes

interruptions supply, pressure violations and traceable quality problems.

One of the most basic set of questions a utility must answer is this: When and how much

water is required to be delivered and to which customer and at what quality? The answer defines

water demand pattern and needs the acquisition of basic information about the customers,

previous records of water usage, population trends, planned growth, topography, and existing

system capacities. Indeed, customer preferences and expectations can be stated as corresponding

water demands. This information can also be used to plan for future extension of the existing

system and to determine required improvements for current operating systems.

12

The diversity of customers and their expectations can in theory be evaluated by multi-

objective and multi-criterion assessment frameworks. While to some customers (e.g., residential

users), the quality of the supplied water is likely the prime objective, pressure considerations are

not unimportant but can function more of a constraint, with penalties for too high or too low

values. Other users, like fire fighters, have the maintenance of a minimum pressure at a given

flow rate as of primary importance, with the quality of the supplied water as a secondary

constraint. Having a way to efficiently trade-off between these various objectives and constraints

is not easy, but can perhaps at least be formulated through multi-objective and multi-criterion

decision making approaches. A more detailed discussion of this topic is presented in Chapter 7.

2.3 Water Demand Expectations of Major Customers In order to evaluate a WDS, it would be ideal to identify all major customers with their

preferences, expectations, needs and requirements and then to explore the ways of meeting their

expectations with consideration to associated consequences. Major customers may include those

facilities that constitute significant portion of supply demand in a region (e.g., residential,

industrial, and fire fighting users, public health officials).

An ideal approach might be to investigate the quantity of water needed for each

individual customer, the period of time they need water for, and the appropriate level of water

quality that is suitable for their need. The overall standard would consider that different

customers have different needs in terms of quantity and quality of water as well as the time that

they need water for. Yet this is clearly not trivial to achieve since one system generally services

all users.

The estimation of the quantity of water should reflect customer preferences and

expectations efficiently. The more closely customer needs are met, the higher the level of

satisfaction for customers and the better the water utility is managed. It is ideal to have an

accurate estimation on water demands of all different customers during service life. But this

estimation initially involves uncertainty, because the customer expectations and preferences

usually depend on various dynamic factors such as land use, population growth and migration,

demographic structure, various development factors including urbanization and, in some places,

rising (falling) standards of living. Water demands are extremely variable over the period of

system operation. For example, the residential water demands have considerable fluctuations

13

over days, weeks, months, seasons and years. Therefore not only the total quantity of water is

interested for, but it its various time variations are still important.

Land use has an important influence on water demand. If a customer’s activity is known,

their associated water demands can be estimated. This estimation might sometimes be carried out

by metering actual water usage for all major types of land use with different densities.

Otherwise, a rough (and more uncertain) estimate of water demand for presumed activities might

be required, as it is discussed in more detail in the next chapter.

The level of water consumption is also related to a customer’s life style which indirectly

depends on the level of income. Recently, Kenney et al. (2008) compared water use for different

groups and for different periods. Households with average summer use of less than 25% of all

households are classified as Low volume users, while those in the highest 25% comprise High

volume users; the rest of the households are designated as Med (medium). When they distinguish

water demands based on the quantity of water for outdoor purposes, they found that high-volume

water users are not surprisingly large outdoor water users (Figure 2-1).

Figure 2-1: Average Water Consumption per Billing Period by Customer Type and Drought

Conditions (Kenney et al., 2008)

These authors showed that the seasonality and weather are also important to the level of

water consumption for all customers. As is intuitively obvious, the demand for water is highly

14

seasonal and dependent on climate and weather conditions. Water use in the irrigation season is

significantly higher than the rest of the year. Demand for water increases as temperatures rise,

and decreases as precipitation increases. They saw that for every one degree Fahrenheit increase

in average daily maximum temperature over the course of the billing period, water use increases

about 2%. Similarly, for every inch of precipitation, water use decreases by roughly 4%.

2.4 Water Demand Management Water is more than just a commodity. It is an essential element for life, basic to most economic

activities and its role in human survival and health is well known. However its economic value

must be recognized and addressed in policy making and human activities especially in large

urban areas. Using water efficiently and demand management are basic steps to reduce how

much water is undervalued, wasted or misused.

While the world population has increased especially in urban populations, most water

resources have remained constant, if not reduced from pollution. While water use has increased

by a factor of six in the past century, the fresh water resources haven’t increased. In fact, it is

estimated that global water withdraws will increase about 35% during 1995 to 2020 (Andresen et

al., 1997) Thus the rate of available water per capita is steadily declining. The inevitable result is

water scarcity.

Meeting increasing demands from existing resources is a challenging task, particularly in

areas with limited water resources. There are typically two potential approaches: supply-side

options which depend on meeting demand with new resources, and demand-side options which

depend on managing consumptive demand to at least delay the need for new resources.

Water demand management is the most important tool for demand-side options. It has

significant benefits to both customers and water utilities including cost and energy saving,

protection of the environment, and reduction of wastewater. Benefits are significant especially in

areas where the capital or environmental costs of new supplies are prohibitive.

Water demand management is a broad concept that is influenced by a variety of different

factors. While some factors are within the scope of water utilities (e.g., price, water restrictions,

or rebate programs), some factors are beyond their control (e.g., climate, weather, or

demographic characteristics). Under control factors generally classified in two categories:

1) - Pricing and Rate Structures: Some researchers have tried to measure how much demand

adjusts in response to price changes and thus to quantify price elasticity of water demand. Espey

15

et al. (1997) reviewed 24 studies and found that 75% of price elasticity estimates were between -

0.02 and -0.75. Based on a survey on 15 studies, Brookshire et al. (2002) proposed a large value

of -0.5 (ranged from -0.11 to -1.59) for price elasticity. Thus a 10% increase in price nets a 5%

decrease in consumption to respond marginal prices. Later Kenney et al. (2008) continued the

previous relationship for price elasticity. They found that given a 10% increase in price of

residential water demand can be expected to 6% decrease in consumption. But evaluation of

pricing system for water efficiently is not possible unless all customers are metered.

The analysis by type of customers confirms that price elasticity vary considerably among

customer groups. High water users often respond more to price (elasticity of -0.75) than low

water users (-0.34). It is important for planners to estimate how existing consumers are likely to

respond to price interventions, and also to assess how long-term changes in demographics and

land-use may alter opportunities for price-based demand management (Goemans, 2006).

2) - Non-price Strategies: There are various non-price strategies for managing water demand

which are often under the control of authorities and water utilities. The range of non-price

strategies can generally be grouped into three options: public education, technological

improvements, and water restrictions. Part of the challenge in assessing the impact of restrictions

programs is that they are usually combined with other price and non-price efforts.

Research on public education programs generally shows that such tools are useful,

especially in the short-term (Michelsen et al., 1999; Syme et al., 2000). However there is no

specific study to show the impact of public education exclusively on water demand as it remains

a challenge to (1) separate the effect of education programs from other pricing and non-price

programs; (2) make meaningful distinctions between the different educational programs; and (3)

assess the long-term value of public education in promoting a conservation ethic.

Municipal demand management has been investigated using three major approaches: 1-

By documenting that pricing and outdoor water restriction policies interact with each other to

ensure that total water savings are not additive of each program operating independently; 2- By

showing that the effectiveness of pricing and restrictions policies varies among different classes

of customers (low, middle, and high volume water users) and operating periods; and 3- In

demonstrating that real-time information about consumption helps customers reach water-use

targets (Kenney et al. 2008).

More attention has been given to exploring the effectiveness of technological changes,

especially indoor retrofitting of water-using devices such as toilets, showerheads, and washing

16

machines. Some of these interventions are based on engineering assumptions of expected

reductions such as Michelsen et al. (1999). But there is an exception by Renwick and Archibald

(1998), that their empirical research on residential water demand in Santa Barbara and Goleta,

California, showed that installing low flow toilets can reduce water consumption by 10% (per

toilet), low flow showerheads by 8% (per fixture), and adoption of water efficient irrigation

technologies by 11%.

Pressure management can be an efficient tool for demand management. For example,

Burn et al. (2002) found that demand management reduces costs by 25-45% and pressure

management increases savings by a further 20-55%.

There are some differences in the pressures customarily maintained in WDS. The water

pressures in residential or business areas should be neither too low nor too high. Low pressures

(less than 20 m head or 30 psi) may cause unwanted flow reductions when simultaneously there

are other active water users. Conversely, high pressures may increase leakage and cause faucets

to leak, valve seats to wear out quickly, or hot water heater pressure relief valves to discharge.

As a result, the Uniform Plumbing Code requires that water pressures not exceed 80 psi (56 m

head) at service connections, unless the service is provided with a pressure-reducing device.

Weather, seasonality and demographic considerations are factors beyond the control of

the water utilities. Weather is the most important uncontrolled factor in water demand

management. It is known that weather can influence on short-term water demand decisions

especially in landscape irrigation. Kenney et al. (2008) also investigated the effect of seasonality

on water demand. They found that in summer, demand management is more important. For

instance, residential demand increases by 30% just because of the arrival or the irrigation season,

regardless of the influence of temperature and precipitation. Therefore influences of weather

variables and price and non-price tools on water demand management are often controlled by

regression-based approaches (e.g., Gutzler and Nims, 2005). The best combination of weather

variables with other factors is investigated. But how to consider exactly these variables is a

challenging question. For example, what is more important in water demand estimation: total

precipitation over a month, the number of precipitation events, or the time between events?

A comprehensive plan for demand management may include the following options

(Butler and Memon, 2005):

1- In general, national and local authorities should test the use of incentives and

sanctions, tax measures, support retrofitting (installation of water conservation technologies) and

17

new technological modifications, initiate water abstraction charges, local water-markets and

tradable permits. The introduction of realistic full-cost pricing of water in a step-by-step action

plan is important for the implementation of water demand management in the urban areas.

2- The industrial and institutional reform can contribute into water conservation plans

like: Reusing wastewater within industries or within an industrial zone, reusing treated municipal

wastes for irrigation of fields and parks, and industrial cooling are some effective examples.

Efficient water processes can result in energy conservation and reduction of pollution.

3- Governments, the public, and NGOs have roles in the water conservation activities.

Most users have wasted water, consumed more than needed water, polluted water and returned it

to nature after inadequate treatment or even no treatment at all. The case can be made that the

time has come to move from constantly augmenting supply to managing demand. Reduction of

water use delays new projects. It reduces pumping and treatment costs, decreases sewage flows

and their disposal costs, increases a utility’s income through a reduction of leakages and

improved water metering and thus provides saving in financial resources, and finally curtails

inefficient distribution and water use.

4- Many cities have often conflicting policies and rules. Low water prices are a

recognized culprit of uncontrolled water use. If municipal and industrial water users pay realistic

water prices, utilities will be able to maintain their systems, minimize losses, and maximize the

quality and the level of service. When local and national officials stop subsidizing the expansion

of existing networks and have to take out loans and sometimes pay considerable interest for new

projects, there may be a turning point in their attitude towards the implementation of demand

management as opposed to a supply augmentation strategy.

5- High rates of leakage or water loss are common in some areas. No utility can

adequately function under such conditions for a long time. Some cities have reached to extreme

levels of leakage of up to 40-60% of total supplied water. A universal and appropriate water

metering system is a good solution for production, block-metering that is essential for leakage

detection and flow management, and billing of micro-metering at households, offices, parks,

restaurants or industrial sites. Without metering system many of the proposed actions will not

achieve their goals.

6- Rate structures have shifted in many utilities from regressive to progressive block-rate-

paying. So the user pays more per unit for the higher water consumption. Progressive block-rates

encourage conservation and reduce waste. An efficient water-metering system is a need.

18

Because of supplying huge amount of water during service life, water demand

management should be seen as an integrated element toward sustainable development. Integrated

resource planning (IRP) is the process of planning services to meet customers’ needs in such a

way that satisfies multiple objectives for using resources. Actually, consumers do not demand

the resources, but they do generate a demand for services, such as end uses for washing clothes,

rather than for liters of water. These end uses can be met by increasing either the supply or the

efficiency of water use (White and Fane, 2001). Increasing the efficiency of using resources has

the potential to be part of a major strategy for countering the looming water scarcity, contrasting

with the old models that are based on “predict the demand and provide the service”.

The Integrated Resource Planning (IRP) is an iterative process in which demand-side and

supply-side options are directly compared to achieve the least-cost outcome. The required steps

of IRP should be undertaken with the full participation of the end-user stakeholders. Many

factors should be considered in preparing an IRP for water supply including (Maddaus and

Maddaus, 2001): 1- Preparation of a water demand forecast based on demographic trends,

historical water use, economic indicators, and climate data; 2- Demand forecasts for different

climatic conditions; 3- Supply-side planning by considering safe yields of existing supplies, and

if inadequate for future needs, location of alternative supplies to meet all or part of future needs;

4- Demand-side planning which identifies additional water conservation measures and

wastewater recycling to reduce demand, and quantifies their costs and savings; 5- Carry out a

supply reliability evaluation which examines the probability of a supply shortage in comparison

with the short-term feasible demand reductions; 6- Come up with resource strategies that

combine new supply development with demand reduction alternatives into a manageable number

of combinations. The strategies should ideally take account of the water quality, economic

considerations, environmental impacts, and the utility policies and goals, including financial

objectives; and monitoring evaluation to keep the process updated. A proposed approach to

incorporate major customers, stakeholders and impacted groups in process of decision making

for a WDS is presented in more detail in Chapter 7.

2.5 Fire Fighting Demands The contribution of fire fighting to water demand is another important aspect of the operation of

WDS. Fire fighting is an emergency event that may potentially happen anywhere. Given enough

time, the occurrence of fire events is inevitable. It is also impossible to predict the location and

19

duration of fire events. Thus implementing efficient systems to protect human lives and

properties in urban areas for fire events is essential.

Fire protection planning in urban areas has multiple components such as fire prevention,

education, inspection, engineering and suppression. While education alone is often an issue for

everyone, other components such as fire prevention, inspection and suppression depend on

engineering knowledge. Fire suppression is a necessary component of fire protection. Most of

death for fire events occurs in residential fires because, for a variety of reasons, the fire is often

well advanced before sufficient action. Delay can happen for several easy to understand reasons:

1- It takes a while before the fires are reported and this causes extension of the fire before

the fire fighting starts. 2- In some areas especially small communities, fire departments are

staffed with volunteers. Response time depends on fire fighters who must be available and must

respond from the local area. 3- Fire fighters must be equipped with fire apparatus and accessory

equipment and it takes time to connect to the nearest hydrants of the local water supply network

to withdraw water for fire fighting. Finally, 4- many urban areas have wide spread households

within their area.

Sophisticated application of engineering tools can help to reduce the occurrence of major

fire events and also to prevent the extension of the fire at an early stage. The most effective way

to reduce fire damages, including human death and property loss, is to prevent fires. Fire

prevention can be effectively implemented within the urban areas by installing and operating

efficient engineering systems such as smoke detectors, residential sprinklers and doing fire

inspections to check the functionality of the system.

It is important to identify adequate sources of water for fire suppression. They can be a

tank or a water delivery truck, a WDS, a direct access to a lake, river, or cistern. Availability of

reliable water supply system should be associated with equipment such as tools, hoses, and

delivery systems.

The head of the fire department should cooperate with the manager of the local WDS to

make sure by periodical inspections that the system equipment is well maintained and the water

supply network is operating properly and can deliver water without interruption when needed.

The main components of a network for fire fighting are the hydrant, the emergency fire pump at

the water treatment facility and the water conveyance system.

Fire fighters need flows with adequate pressure. Yet it is only a few hydrants which are

connected to small pipes of a system are being used simultaneously in fire events. The required

20

pressure in hydrants typically depends on the land use and varies between regions. Fire flow

requirements vary depending on the size of area and the nature of the property. An effective fire

fighting process needs considerable water flows with adequate pressure. For example, the

minimum pressure for residential areas is estimated about 20 psi (138 kPa) during fire fighting.

Typical fire flow requirements for different land uses, quantity of water and duration of fire

fighting events are listed in Table 2-1

Table 2-1: Typical fire flow requirements (Ysusi, 2000)

Duration (Hr.) Fire Flows: GPM (LPS) Land Use

4 5,500 (350) Industrial

4 5,000 (320) High density area

3 4,000 (250) Commercial

2 3,500 (220) Multiple Family

2 2,000 (125) Residential

2 1,000 (65) Others

2.6 Economic Considerations A WDS is usually designed for a long service life, and reality that has many implications for

design, analysis and management. In fact, many systems are a century or older in age, yet are

still in use all around the world. Although the decision to construct this infrastructure is often

made due to social and public needs, the detailed specifications (such as type, size, and other

characteristics) require considerable engineering and technical knowledge.

Due to the huge amount of investment in design, construction, installation and operation

of a WDS, detailed economic analysis is called for. Governments and authorities must constantly

confront the question of how limited resources can be used to produce the greatest individual and

social benefits. WDS influence public health, but also require huge public expenditures.

Therefore economic evaluation of WDS is important.

Economic analysis is carried out to determine whether or not a project is worthwhile for a

community. Such an analysis ideally needs to compare the value of the benefits gained due to a 21

specific option (sanitation training program or waste management system) with the costs of

implementing that option. If all the benefits can be translated into monetary terms, it is possible

to compare the total benefits with the costs. The impact of a system can be measured by the

difference between what the situation in the study area will be with and without the project.

Additionally the impact of an option can be defined for a specific system within an area.

Application of economic analysis in the design and operation assessment of a WDS has

following components and goals:

• Comparison of benefits and costs: Although living in a world with perfectly clean and ample

water is perhaps an easily stated desire; the cost of achieving this is often overwhelming. A

common approach is thus to compare the expected benefits of competing investments with their

respective costs (benefit-cost analysis). If the benefits cannot be measured, a cost-effectiveness

analysis allows one to compare the costs of alternative projects and programs.

• Setting priorities: If the analyst can estimate the expected benefits of different alternatives and

compare them to the costs of other alternatives, the outcome can be effective for setting priorities

and selecting the best option. The main advantages of such analysis associated with quantitative

or in some cases, qualitative results, is to help making more rational decisions on allocating

scarce resources. Public perception of comparative risks or comparative damage from different

environmental problems may be significantly inaccurate. Sometimes the problem that receives

the most attention may in fact have minor problems compared to other issues. Economic analysis

assists to set priorities rationally and to ensure the effective use of scarce resources.

• Getting the attention of decision makers: Decision makers often respond better to

quantitative analyses of alternatives and competing investments than to a single straightforward

proposal. The use of numbers can indicate in a solid way when the health impacts and certain

environmental problems are large and can be addressed in a cost-effective manner. For instance,

when asked how important a threat is in terms of a water shortage or unsafe water, the obvious

reply is: "It is very important!". However this is usually less persuasive than to quantify the

number of people expected to be adversely affected, and the costs associated with these

outcomes. Economic analysis for various options thus assists the decision maker or makers to

allocate investments more effectively.

Financial resources are not always available to completely cover all expenditures such as

capital investment for new systems or rehabilitation cost of aging systems. This is particularly

true of the cost of water is kept artificially low. Limited funds must be distributed among the

22

system's components on the basis of the contribution of each component to the overall system's

functionality. But system’s deterioration is a complex problem that makes the maintenance-

related problems of WDS difficult to handle as a whole.

Like all infrastructure, a WDS needs considerable initial investment. But it requires also

considerable operation and maintenance costs over its service life. It implies that economic

concerns are vital not only in the design and construction stages, but also during service life. For

example, the Environmental Protection Agency (EPA, 2002), released a report considering the

investment condition of WDS across the U.S. over the last three decades. As shown in Figure 2-

2, operating and maintenance (O&M) costs of drinking WDS have increased during the period

from 1974 to 1994. In fact, up to 70% of the total expenditure for WDS was for operating and

maintenance costs in 1994.

Figure 2-2: Operations and Maintenance Costs from State and Local Sources (1978–1994),

Source (EPA, 2002)

Although most water utilities may be assisted by public funding for financing the

investment and rehabilitation costs, private resources from customer payments also play a crucial

role. While no study is available that directly evaluates the total capital investments of a WDS, a

general picture is available by EPA report “Drinking Water Infrastructure Needs Survey” (Figure

2-3). This report estimates the shortage in capital investment in water supply and distribution

systems across the U.S. as up to $150 billion in 1994 (EPA, 2002).

Although it is the least visible component of a public water system, the underground

pipes generally comprise the majority of the capital value of WDS (Jung and Karney, 2006).

23

While the need for new expansions in transmission and distribution systems is the major shortage

with 55% of total shortage, water quality and treatment facilities represented the second largest

category with 25% of the total shortage.

Figure 2-3: Shortage Categories for Drinking Water Infrastructures (EPA, 2002).

Shortage in storage facilities represents 12% of total that needs new projects or

rehabilitation of existing storages. New sources of water constitute 6% of total needs which

include construction and rehabilitation of surface water intakes, raw water pumping facilities,

drilled wells, and spring collectors.

Figure 2-4: Projected Annual Replacement Needs for Transmission Lines and Distribution Mains

(2000–2075), Source (EPA, 2002)

24

Rehabilitation planning is vital for all aging systems. For example, (EPA 2002) reported

a survey on the inventory of pipes and the year in which the pipe were installed for 20 cities

across the U.S. The main goal was to predict the replacement planning of the pipes. The results

revealed that major replacement needs are within the 20-year period of the analysis with peak

annual replacement occurring in 2040 (Figure 2-4). If the current level of expenditure for

operating and maintenance costs remains constant between 2000 to 2019, it is estimated there

will be a gap of up to $495 billion (with a point estimate of $161 billion). Therefore water

utilities may need to increase fees to cover ever-increasing maintenance costs, or greater

government subsidies must be provided.

2.7 Transient Events As mentioned, distributing pipes are one the most common and economically important of

system components. They are classified based on material, size (internal diameter), and wall

thickness. Yet the pipe costs constitute up to 70% of the initial investment cost, (Jung and

Karney, 2006), but the pipe material and wall thickness are two essential parameters respect to

transient evaluation of the system and these factors influence on pipe prices. Therefore it is

important to ask what events or phenomena pose a threat to the distribution piping. One of the

most obvious candidates is transient events which, if uncontrolled, can break pipes and

associated apparatuses. Thus the evaluation of transient events is a necessary element in the

performance assessment of a WDS.

Transient events are known as unsteady flows which can exert extreme pressures, forces

as well as rapid fluid accelerations within the system. They often occur due to various events

such as pump failures and pipe breaks. Transients during pump startup and shutdown can cause

stress within the system to increase leakage. Uncontrolled pump shutdown can also cause

undesirable event of water-column separation which can lead to pipeline failures. Therefore

basic understanding of transient conditions and applying practical methods for their suppression

and control are required. As a result, transient analysis has become an essential requirement for

safe operation.

Transient events often include the banging or hammering noise that is sometimes heard

after rapid closing of a valve. The hammering sound comes from conversion of a portion of the

fluid's original kinetic energy into pressure and acoustic form. This and other energy

25

transformation losses (such as friction) cause the pressure wave to gradually decay until normal

pressures and velocities are restored (Boulos et al., 2005).

In at least the most basic sense that flows must change, transient events are inevitable. All

systems have start ups, switch offs, or undergo flow changes, and experience the effects of

human errors, equipment breakdowns, or other risky disturbances. Although transient conditions

can result in many situations, the engineer should consider those events that might endanger the

safety of the system, or have the potential to cause equipment or device damage, or result in

operational difficulties.

In this regard, the engineer should first define the possible consequences of transient

regimes within the system including: 1) The maximum pressure in the system; 2) The occurrence

of local vacuum conditions at specific locations with the associated risk of contamination from

intrusion or cross-connection; 3) Cavitation either within specific devices or within a pipe; and 4)

Hydraulic vibration, strong oscillations or rapid movement other water masses in the pipe, its

supports, or in specific devices.

Maximum pressures during transient events may destroy pipelines, tunnels, valves or

other components. In other cases, strong pressure surges may cause cracks in an internal lining,

damage connections between steel and concrete, destruction and deformations of equipment such

as pipeline valves, air valves, or any water hammer protection device. Sometimes the damage is

not noticed at the time, but results in intense corrosion that, if combined with repeated transients,

may cause the pipeline to collapse in the future.

Prevention of vacuum conditions in the system is necessary because they can cause high

stresses and strains that are much greater than those occurring during normal conditions. Vacuum

pressures may cause the collapse of thin-walled pipes or reinforced concrete sections,

particularly if these sections are not designed to withstand such strains.

Cavitation occurs when the local pressure is reduced to less than vapor pressure at the

ambient temperature. At this pressure, the gas within the water is gradually released and the

water often starts to evaporate. When the pressure recovers, water enters the cavity created by

the gases and collides with whatever is on the other side of the cavity (i.e., another mass of water

or a fixed boundary) resulting in a pressure surge. In this case, both vacuum and strong pressure

surges are present that may result in substantial damage. The damages of cavitation cannot be

accurately estimated because the parameters describing the process are difficult to determine in

design (Boulos et al. 2006).

26

Hydraulic vibrations, if strong enough, can cause damage to pipelines, tunnels, tunnel

internal linings, measuring and control equipment. The resonance state is characteristic of any

system if forcing occurs near a natural frequency, whether it is civil, electrical, hydraulic, or

mechanical system. If resonance occurs, the entire system may be destroyed. As it is expensive

to design a system that withstands resonance, a good solution is to avoid having an excitation

frequency that matches any natural frequency of the system.

Oscillations of the water masses between reservoirs, basins and water towers may cause

noise, suction of air into the line, and partially loss of control of the system. If another incident

occurs at the same time, the consequences can be disastrous. Accurate modeling is crucial in this

case, since neglecting some influences may lead to the wrong conclusions and poor decisions.

Transient events can also assist serious water quality problems. They can generate high

intensities of fluid shear and may cause re-suspension of settled particles as well as bio-film

detachment. Some events, like red water events, have often been associated with transient

disturbances. Moreover a low-pressure transient event has the potential to cause the intrusion of

contaminations into a pipe at leaky joint or break. Dissolved air can be released from the water

where the local pressure drops considerably to cause the corrosion of steel and iron parts with

rust formation and pipe damage. Even some common transient protection strategies, such as

relief valves or air chambers, if not properly designed and maintained, may permit pathogens or

other contaminants to find a way into the potable WDS (Bolous et al. 2006).

2.8 Performance Classification The subject of system performance has numerous meanings for all engineering disciplines.

Indeed, a variety of approaches to such assessments that are often used in the planning, design

and operation of a WDS. The general goals are almost universally recognized, and it is

commonly agreed that the system should be designed to satisfy a set of demand patterns for

major customers with providing sufficient flows with adequate pressures and acceptable quality.

The pipes, reservoirs and pumps are normally selected based on satisfying requested demands

with considering economic limitations. Therefore any disruptions in qualitative and quantitative

criteria can be considered as poor performance. However, once one goes beyond general

statements and attempts to flesh these issues out in more quantitative detail, a great deal of

variation is observed in the specific way systems are assessed and evaluated. To begin to frame

these issues, it is helpful to go back to a very basic classification.

27

Basically it is helpful to classify performance based on physical and chemical

characteristics of the supplied water into two primary aspects of quantity and quality. Meanwhile

quantity of supplied water can be measured based on two major physical characteristics of

supplied water, as quantity of pressure and quantity of outflows in the service life. The quality of

water in other hand depends on chemical characteristics of the water and its constituents in

service life. Figure 2-5 shows this classification briefly.

Figure 2-5: Performance Classification of WDS

There are two major approaches to evaluate the water quality and quantity variables,

including field measurements and computational predictions. Field measurements are sometimes

supported by customer’s feedback with respect to physical and chemical characteristics of

supplied water. The evaluation can also be achieved through direct sampling by the utility. Such

information can be translated into appropriate indices, called Performance Indicators. PIs are

often used by water utilities to monitor behavior of the system over the service life.

28

Figure 2-6: Approaches for Performance Measurement of WDS

There is another synthetic approach for evaluation of variables that define the

performance of WDS. It uses computational prediction based on simulation models. Figure 2-6

shows approaches for performance measurement of WDS.

Overall, system performance is function of many parameters some of which are

independent (such as physical and chemical characteristics of the water), and some which are

closely linked (such as pressure levels and flow rates). In order to have a more accurate and

reliable evaluation, it is necessary to engaged the sometimes overwhelming task of considering

all major contributing factors in the assessment. As a result, the multi-objective assessment

framework is required, the challenges of which are presented in more detail later.

2.9 Summary This chapter overviews the role of WDS in urban areas. It briefly explores the relationship

between the water system and other public facilities and private systems. Following this, a

framework for customer-oriented performance evaluation of WDS is presented. This framework

aims at finding reliable answers for following questions: who are the major system users? What

are their expectations? And finally if their expectations are satisfied, what are the consequences

of supplying their demands? and finally, the chapter briefly raises the issue of in what ways the

achievement of these expectations are evaluated?

As the primary goal of a WDS is to supply the required amount of water for all customers

with adequate pressures over the service life, providing a framework for considering all diverse

preferences of different customers is essential to performance evaluation of a system. For

example, a household wishes drinking water without contamination, but minor interruptions in

pressure and flow are perhaps acceptable if the duration is sufficiently short. Other users, such as

fire fighters, need huge amounts of water with adequate pressure for a fire event, but much less

strict water quality criteria. The industrial users also have different demand characteristics with

residential, and fire fighting demands. The challenges between various objectives and constraints

for different customers are essential in performance evaluation of WDS.

Following the importance of water demand management are discussed. The inevetable

growth in water demands causes serious problems, particularly in areas with limited resources.

There are typically two potential responses: supply-side options, and demand-side options. While

supply-side options are costly and sometimes impossible, demand-side options are cheaper and

29

more flexible, but harder and need water demand management. Achieving demand-side

management goals depends on establishing adequate policies, strategies and action planning.

Appropriate technological implementations, water metering, and pricing policies are

indispensable elements in this challenge as part of overall legislative, regulatory, and institutional

reforms. This would accommodate demand-side management, on a wide-scale level to achieve

large water savings, reduce the impact of water shortages, and delay the execution of expensive

new water supply projects.

Due to the huge amount of investment in design, construction, installation and operation

of WDS, more cautions needed for economic analysis of WDS in all stages. Therefore the basic

foundations for economic analysis of WDS are discussed in this chapter.

The major causes for transient events are reviewed and their consequences on the

operation of WDS are briefly addressed. Transient events can exert extreme pressures, forces,

and rapid fluid accelerations in the systems and result in events such as pump failures and pipe

breaks. Transients during pump startup and shutdown can cause stress within the system to cause

more leakage and less reliable operation. Thus transient flow analysis has become an essential

requirement for safe operation of WDS. So basic understanding of transient conditions as well as

applying practical methods for their suppression and control is crucial.

Performance of WDS can be classified into two primary aspects of quantity and quality

of supplied water. Meanwhile the quantity of supplied water can be measured based on two

major physical characteristics of supplied water, as quantity of pressure and also quantity of

outflows during service life to all costumers. The quality of the supplied water in other hand

depends on chemical characteristics of the water and its constituents during distribution. And

finally classification of performance of operation of WDS is presented and approaches for

performance measurements are briefly discussed.

30

Chapter 3 Design and Operation Modeling

3.1 Introduction The role of a WDS as an important infrastructure is described in the previous chapter. The major

users of such systems are examined with respect to their diverse needs and preferences. The

expectations of various customers, both with respect to drinking water and non-potable water are

summarized. As a logical next step, this chapter investigates how engineering knowledge can be

used to contribute to the aforementioned targets. In particular, this chapter focuses on the

application of engineering techniques to questions of design and operation of such a system.

The assessment of the system’s operation is complex because of various factors. Not only

it is necessary to have a reliable evaluation of water demands, but the capability of the system to

respond sufficiently to such demands during the service life is required based on the design

criteria. If the system is not efficiently designed, numerous problems will occur during operation

leading to poor performance and increased operating costs over the service life.

The design of the system is often formulated as a single-objective problem. The objective

may target the initial pipe design, operational schedule of the system or the protection of the

system for transient events. While the optimal design emphasize finding the best pipe sizes, the

optimal operation of WDS is generally considered as determining a short-term operation policy

to take advantage of both energy tariffs and reservoir capacity for daily operation.

There are serious considerations in the long-term design life and short-term operating

conditions including demand fluctuations, reliability of individual components and their

locations, fire flow requirements and their locations, transient events and their effects, indirect

damage costs, and public health costs. Further complications arise from the fact that it is difficult

to define unique performance measures and to establish acceptable levels for these parameters.

3.2 Modeling of Engineering Systems The main reason for modeling a system is to assist designers, managers and planners explore the

governing laws of such systems and to accurately analyze their behavior. The models are

employed to resolve problems in system’s design and operation. However most models do not

attempt to describe every detail of a system, but rather implicit and explicit assumptions are

made to simplify the actual system representation. Thus, the results of a model should be

31

evaluated by an analyst. In order to investigate current modeling of WDS, it is better to start by

discussing broad ideas on the modeling process.

The discussion that follows is significantly adopted from Boulos et al., (2006).

Fundamentally, every model is in some sense a replacement, a surrogate, or a deputy for a real

thing. A model is an object, a process, a physical or mental construction designed to represent,

replace, mimic or demonstrate a specific thing. The most important question here is simple and

direct: Why use the replica and not the original? The answer is easy: Models are often more

convenient (easier, cheaper, safer, faster, simpler) to manipulate, test, build, adjust, replace,

understand, communicate or refine than the original thing itself.

Engineering models can not prevent problems from occurring, but they can be used as

effective tools for resolving many problems by following steps:

1. Comprehensive understanding of the problem: Modeling a system requires an

obvious and reliable conceptualization of the system. There is often little attempt to formalize or

systematize an understanding of how WDS function without using efficient models. Every step

in the system’s operation should be clearly defined in models including hydraulic behavior,

water quality disinfections, transient regimes, energy consumption and system’s deterioration.

Even where the understanding represented in a model may be oversimplified or even partially

incorrect, the model's representation becomes a starting-point for improving a systematic

scientific understanding of the problem.

2. Quantifying performance objectives: It is essential to formalize measuring the

system’s performance. This is another aspect of understanding a problem represented by

modeling especially relevant for competing objectives. The modeling outputs must be expressed

in forms that are meaningful in terms of the performance objectives of different customers or

stakeholders. All impacting groups should consider quantitative measures for system’s

performance respect to their own objectives. Without the requirements of engineering modeling,

it is often difficult to encourage impacted groups to specifically articulate their definition of

expectations and preferences and how different trade-offs in system’s performance might or

might not be important.

3. Developing promising alternatives: Engineering models are surrogates of ideal

experimental events for developing new alternatives. A wide range of alternatives can be

evaluated, developed, and refined by modeling at relatively low cost and short time compared to

32

experimenting such alternatives in real. Having more alternatives and the ability to quickly

develop new and hybrid options makes it more likely to satisfy customer desires.

4. Evaluation of alternatives: Engineering models provide relatively easy and

standardized tools for evaluation of competing alternatives. They can tolerate the evaluation of

hundreds or thousands of alternatives in a standardized and reproducible way with the time and

resources typically required to perform a single analysis manually. The fast implementation and

relative completeness of such evaluations can also assist discussions and negotiations.

5. Providing confidence in solutions: The defining role of modeling in the evaluation of

competing objectives is to grant higher confidence to decision makers that the selected solutions

will operate as planned. Modeling should provide confidence that a wide range of alternatives

have been examined, so that the selected solutions are likely to be among the best. Thus, models

are used because they lead to better and better-understood solutions.

6. A forum for discussion: The modeling results can be used as a forum for negotiations

and global problem solving (Sheer et al., 1989; Palmer and Keyes 1993). The goal is to use the

logic of modeling studies and development to structure the discussion process. Models can

contribute to discussions through their classical roles (Lund and Palmer, 1997).

3.3 Water Demand Modeling The first question in the design and operation of WDS is: How much water is needed? The

answer to this essential question is quite difficult because the required water is a function of

various factors, while some of them are completely independent and time varying. Water

demand modeling is one of the most important challenges in the design of WDS. It reflects

changes in population, climate, land use, the number of service connections, and customer life

style. Therefore the challenge of predicting what the demand for water will be during service life

involves extensive uncertainty at the design stage. Recent research has also shown that the higher

the uncertainty in projected future demand, the larger the design costs to ensure an acceptable

performance under future demand scenarios (Lansey et al., 1989; Xu and Goulter, 1999; Tolson

et al., 2004; Kapelan et al., 2003; Babayan et al., 2005, Filion et al., 2006).

There are two main approaches for water demand modeling: deterministic water demand

estimation, and stochastic demand forecasting. In the deterministic approach, the actual water

demand for all major users is estimated implicitly based on predicted water consumption over the

33

service time. But stochastic water demand forecasting considers uncertain fluctuations on water

demand over the time and location spans.

One simple approach for deterministic water demand is to estimate individual needs

based on type of customers and their activities and then to add those to get total water demand.

For example the water demand can be estimated on the basis of per capita demand in small urban

areas. In this regard, U.S. Geological Survey proposes about 400 Lit/day/capita (Lpdc) as an

average water demand for public WDS in 1990. This value is only a rough estimate for

residential areas. While other major customers have different requirements, these differences

should be projected to estimate the water demand.

Rough values of water duties for major land uses are listed in Table 3-1. They refer to

some residential areas within the western U.S. The definitions of land use terms such as low-

density, medium-density residential are not same in different regions and should be examined

carefully before application. The water duties are flexible compared to rough estimation of water

demand proposed by U.S. Geological Survey.

Table 3-1: Typical Water Duties, (Montgomery Watson study of data of 28 cities (gal = 3.78 lit).

Water Duty, (gal/day/acre) Land Use

Low High Average

Low-density residential 400 3300 1670

Medium-density residential 900 3800 2610

High-density residential 2300 12000 4160

Single-family residential 1300 2900 2300

Multifamily residential 2600 6600 4160

Office commercial 1100 5100 2030

Retail commercial 1100 5100 2040

Light industrial 200 4700 1620

Heavy industrial 200 4800 2270

Parks 400 3100 2020

34

Schools 400 2500 1700 Another possible method for estimation of water demands focuses on measuring water

usage for individual major customers separately and then cumulating the measured values in

order to estimate the total water demands. This approach is particularly efficient when an

individual customer consumes a significant portion of the total demand. In this regard, diurnal

varying demand curves are often developed for each major consumption or geographic zones

within a service area. For example diurnal demand curves might be developed for industrial,

commercial establishments, and residences. Large users such as manufacturing facilities may

have unique demand patterns.

Table 3-2: Typical Rates of Water Use for Various Establishments (Ysusi, 2000) Range of Flow Range of Flow User LPDC gal/PDC User LPDC gal/PDC

Airport, per passenger 10-20 3-5 Lodging & tourist home 120-200 32-53 Assembly hall, per seat 6-1 2-3 Motel 400-600 106-159

Bowling alley, per alley 60-100 16-26 Private dwelling on individual well 200-600 53-159

Camp Private dwelling on public water supply, unmetered 400-800 106-201

Pioneer type 80-120 21-32 Factory, sanitary wastes, per shift 40-100 11-26

Children's, central toilet and bath 160-200 42-53 Fairground (based on daily

attendance) 2-6 1-2

Day, no meals 40-70 11-18 Institution Luxury, private bath 300-400 79-106 Average type 400-600 106-159 Labor 140-200 37-53 Hospital 700-1200 185-317 Trailer with private toilet and bath, per unit 500-600 132-159 Office 40-60 11-16

Country clubs Picnic park, with flush toilets 20-40 5-11 Resident type 300-600 79-159 Restaurant (including toilet) Transient type serving meals 60-100 16-26 Average 25-40 7-11 Dwelling unit, residential Kitchen wastes only 10-20 3-5 Apartment house on individual well 300-400 79-106 Short order 10-20 3-5

Apartment house on public water supply, unmetered 300-500 79-132 Short order, paper service 4-8 1-2

Boardinghouse 150-220 40-58 Bar and cocktail lounge 8-12 2-3 Hotel 200-400 53-106 Average type, per seat 120-180 32-48

Store Average type 24 h, per seat 160-220 42-58 First 7.5 m (« 25 ft) of frontage 1600-2000 423-528 Tavern, per seat 60-100 16-26

Each add. 7.5 m of frontage 1400-1600 370-423 Service area/ counter seat (toll road) 1000-1600 53-106

Swimming pool and beach, toilet and shower 40-60 11-16 Service area/ table seat (toll

road) 600-800 159-211

Theater 10-20 3-5 School Indoor, per seat, two showings per day 10-20 3-5 Day, with cafeteria or

lunchroom 40-60 11-16

35

Day, with cafeteria and showers 60-80 16-21 Boarding 200-400 53-106 Outdoor, including food

Stand/car (3 1/3 persons) 1600-2000 423-528 Self-service laundry/machine 1000-3000 264-793

Table 3-2 lists typical water usage recommended for major residential and commercial

establishments. Although they vary widely, they are useful to estimate the total water use for

individual users when no other data are available.

It should be noted that the above approaches only evaluate the average water demand for

drinking water for residential areas and other major customers for a certain duration, for example

daily demand. So any solutions to supply the required daily demand would seem to be

acceptable. For example it would appear to be possible to reserve daily water demand of a

household in a short time in a couple of minutes. So the daily demand of the customer is supplied

fully. In this way, the possible variations in supplying demands would have less impact to

customer needs for a certain period, say 24 hour, and meantime there would be sufficient time to

repair the system and resolve undesired failures.

There are some common terms that are used in defining water demand patterns in the

operation of WDS as following:

Average day demand: The total annual of water demands of all users (potable and non-

potable demands) divided by the total annual number of operating days.

Maximum day demand: The highest daily (24-hour) water demand in a year.

Peak hour demand: The highest water demand in a year that occurs during any one-hour

period. Evaluation of the system at peak hour demand gives the designer better picture of the

operation of WDS, especially for critical scenario.

Peaking factors: Peaking factors are often used as multipliers of the average day demand

to express maximum day and peak hour demands.

Water demand changes with the seasons, the day of the week, and the hour of the day.

Fluctuations are greater in small areas than large communities and during short time rather than

long periods of time. Variations in water consumption are usually expressed based on peaking

factors. They should be developed based on actual consumption data for an individual

community. Table 3-3 lists typical peaking coefficients recommended for design stage.

Table 3-3: Typical Peaking Coefficients (Ysusi, 2000)

Ratio of Rates U.S. Range Common Range

Maximum day: average day 1.5-3.5:1 1.8-2.8:1

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Peak hour: average day 2.0-7.0:1 2.5-4.0:1 Maximum day demand plus fire flow demand: Fire flows are limited demands and are

often superimposed on the average demand of the maximum day. This critical scenario is

assumed, when there is a fire event during the day which the system needs the highest daily

water demand in a year. Placing fire flows at different locations in the system during a maximum

daily operation highlights the system deficiencies. Obviously, it is possible for fires to occur

during peak hour demand, but its possibility is more unlikely than for a fire to occur sometime

during the maximum day demand.

Note that, all different critical scenarios are traditionally considered to be deterministic.

This is true for average day demand, maximum day demand, maximum day demand plus fire

flow demand. Yet more realistically these demands have probabilistic occurrence nature. But

they are assumed deterministic in the design and operation assessment of WDS.

Another pressure criterion related to fire flows commonly requires a minimum of 20 psi

(14 m head) at the connecting fire hydrant. Table 3-4 presents typical service pressure criteria.

Table 3-4: Typical Service Pressure Criteria (Ysusi, 2000)

Condition Service Pressure (psi) Service Pressure

(m head)

Max. pressure 65-75 45-53

Min. pressure during max. day 30-40 21-28

Min. pressure during peak hour 25-35 18-25

Min. pressure during fires 20 14

Considerable effort has also been given to stochastic water demand forecasting. Water

demand forecasting can be classified based on the forecasting span in two categories: Long-term

versus Short-term. As the typical service life of WDS is more than several decades, long-term

forecasting is usually made at the early stage of the design by considering the effects of future

growth in demands. But short-term water demand forecasting is an effective tool for the operator

to adjust the controlling elements of the system to protect the temporal pressure deficits or

transient events within the system. Short to medium timescales (hourly, daily and monthly) have

been investigated more often rather than annual timescale, as long-term forecasting normally

37

relates to life-cycle planning. For example Shvartser et al., (1993); and Zhou et al., (2002)

considered hourly forecasts, Maidment and Parzen, (1984); Maidment et al., (1985); Franklin

and Maidment, (1986); Smith, (1988); and Miaou, (1990) applied daily/monthly timescales.

Advanced methods of water demand forecasting recognize that changes in demand stem

from a variety of factors such as shifting conditions of the future, whether they are

socioeconomic trends, climatic influences and seasonal variations, changing water and sewer

rates, and increasing water-use efficiency.

Factors that affect the water consumption can be projected to develop other alternatives

and to assess the sensitivity of future demands. By considering the variation in each of these

influencing factors, planners can develop probabilistic water demand forecasts that can be used

in risk-based demand simulations. More comprehensive and meaningful water demand forecasts

lead to more accurate, reliable, and cost-effective decisions.

In order to estimate future water demand, information on temporal variations in water

usage over time is required. Spatially different temporal patterns can be applied to the individual

consumption nodes. The best available information should be used for developing temporal

patterns to make water demand forecasting most effective. For example, some users may have

continuous water metering data, while others may use literature values as a first approximation

for estimating residential temporal patterns.

Some forms of demand forecasting are often integrated with the optimal operation of

WDS. The short term forecasting might be used to schedule the pumping arrangements over the

next 24 hours, to take advantage of the electricity tariff structure. For example, in the case of

pump-scheduling (Sterling and Coulbeck, 1975; Zessler and Shamir, 1989; Jowitt and

Germanopoulos, 1992), a short-term demand forecast is required at each morning for the

following 24 hours. They are often based on averaged demand profiles for the particular day of

the week, which may vary based on the season or month of the year. If there is a significant

difference between the demand profile assumed and that which materializes as the day

progresses, it may be necessary to re-run the pump scheduling program with the revised data, but

further assumptions are needed about the future demands for the rest of the 24-hour period.

Figures 3-1 and 3-2 from Alvisi et al., (2007) are typical demand patterns of daily water

needs for a whole year with demand rising over the summer period and during the week.

Likewise, the hourly water demands show a variable diurnal behavior over the day, with

different patterns depending whether it is a weekday or weekend and, to a lesser extent, the 38

season. It should be noted that while daily water demand also has considerable fluctuations over

the 24 hours of operation, the hourly demands in an area vary significantly over each day..

Figure 3-1: Average Daily Water Demand for a Real WDS (Alvisi et al., 2007)

A typical hierarchy for assigning demands includes the following:

1- Baseline Demands usually correspond to customer demands and unaccounted-for-

water associated with average day conditions. This information is often acquired by records of

water utility, such as customer meters and billing records. Although the spatial assignment of

these demands is important and should include the assignment of customer classes such as

industrial, residential, and commercial use. However if actual metering data are available, they

should be considered too.

2- Seasonal Variation varies over the year with higher demands in warmer months. When

developing a steady-state model, the baseline (average day) demand can be modified by

multipliers to reflect other conditions such as maximum and minimum day demand, and peak-

hour demand.

Municipal demand generally comprises base, seasonal, and stochastic components. Base

demand is the water for satisfying minimal residential, commercial, and industrial needs in the

service area. It correlates strongly with indoor water use for these user classifications and it is

calculated as the average water use during the winter months. Base demand tends to increase or

39

decrease over the years due to structural changes such as changing life style or new development.

More details are in Fillion et al., (2007) and Alvisi et al., (2007).

Figure 3-2: Daily Patterns in Hourly Water Demand for the Seven Days of the Week in:

(a) Winter; (b) Spring; (c) Summer; and (d) Fall (Alvisi et al., 2007)

3.4 Critiques on Water Demand Evaluation Although freshwater supplies are adequate to meet human demands for the near future, but they

are poorly distributed around the world within countries and between seasons. Meanwhile there

are more serious challenges concerning the time, space and affordability that lead to a widening

gap between demand and supply in many parts of the world (Memon and Butler, 2006). It is

projected that there will be an increase of urban population of 2.12 billion between 2000 and

2030, with over 95% of this increase expected to be in low-income countries (UNHABITAT,

2004), and also about one-third of the population in the developing world will face severe

shortage by 2050. The water scarcity will be more serious in urban areas where it is projected

that over 50% of the total population will live in urban areas by 2015 (United Nations, 2004).

It is essential to use reliable approaches for the evaluation of future water demands.

Therefore given these changes in the macro environment, utility managers, policy makers and

planners face tougher challenges to ensure that the available water resources are optimally used.

40

The design criteria for the urban WDS were mainly driven by public health

considerations, rather than environmental sustainability. Therefore considering sustainability

issues such as population growth, high levels of urbanization, industrial growth and climatic

change have not been considered essential in past.

The limited and costly options of developing new water resources have induced many

authorities to seek effective approaches on water conservation. National goals can only be

achieved by the collective efforts of all water consumers, and the different levels of

governments. Water conservation provides a unique opportunity for a collaborative national

campaign, but its results often vary considerably from place to place.

Data limitations are common barriers for precise water demand estimations. Complete

datasets are not often available to match residential demands with demographic data about the

people and the houses associated with a residential water account. Nonetheless research to date is

sufficient to suggest that household water demand is influenced by heterogeneity associated with

differences in wealth (income), family size and age distribution, and household preferences

towards water use and conservation (Syme et al., 2000).

Another challenging problem of water demand management regards to the difficulty of

coupling demand management results with design criteria, as these results are often function of

time, location, population served and other uncontrolled factors. This estimation, then, will be

used in design stage.

The system’s design for peak demands has become the accepted norm. It is often

assumed that the water demand is known, and the engineer should design such a system that is

able to meet given demands during service time. This has led to an overcapacity in the system for

the most of the operating time.

Water demand modeling involves with uncertainty considerably, and high sensitivity

with temperature. For example, Kenney et al. (2008) were unable to conclusively determine why

customers were more than twice as sensitive to price during drought as before. They assumed

that these differences in elasticity may derive partially from the wealth of media coverage and

public education programs that accompany drought (Nieswiadomy, 1992). Furthermore these

differences might indicate that the price elasticity of demand is highly nonlinear outside of the

range of prices experienced prior to drought (Pint, 1999).

Residential water demand modeling has focused in recent years, on quantitative and

regression-based studies, in terms of scope and sophistication (Olmstead et al., 2003 and Gaudin, 41

2006). In many cases, development of urban areas have occurred within suburbs rather than

within city centers, as the former often face the strongest growth pressures coupled with the least

robust supply systems. Water demand modeling becomes more complicated in this condition, as

the majority of water demands typically arise from residential water use. Consequently one of

the strongest management needs is to predict how these residential demands are likely to respond

both to demand management options such as price increases and outdoor water use restrictions

and exogenous factors such as weather and demographic changes.

Findings by Kenney et al., (2008) were consistent with previous literature in

demonstrating that residential water demand is largely a function of price, the impact of nonprice

demand management programs, and weather and climate. Their study highlights following

results on residential demand modeling:

1- Pricing and outdoor water restriction policies interact with each other to ensure that

total water savings are not additive of each program operating independently; 2- The

effectiveness of pricing and restrictions policies varies among different classes of customers (i.e.,

low, middle, and high volume water users) and between pre-drought and drought periods; 3-

Real-time information about consumption helps customers reach water-use targets.

Findings that relate climate and weather conditions to residential demand can be useful in

several phases of planning and management, especially in light of research suggesting that

climate change will likely cause fundamental changes in average temperatures, precipitation, and

frequency of extreme events such as droughts and floods (Wagner, 2003).

Although deterministic approaches for water demand evaluation are simple and

straightforward, but they result in higher demand values and their accuracy are more

questionable. As they assumed that the demand conditions are constant and fixed over the

service life which initially is unrealistic assumption. On the other side, the stochastic approaches

for water demand forecasting are able to consider more realistic conditions both in location and

time spans, but the main difficulty here is over-complexity of the problem. Thus for most large

scale systems, solving stochastic models of the system is too difficult. Therefore the main

challenge in selection of deterministic or stochastic approach for water demand forecasting is

function of simplicity of the approach versus of accuracy of the result. This challenge can be

efficiently modeled by a multi-objective or multi-criterion evaluation framework. More

discussion is presented in Chapter 7 in this regards.

42

3.5 Optimization Models in Water Resource Management After considering how much water should be supplied by a system, the next logical question is:

How can required water should be transferred from sources to consumption locations? The

answer determines the design and also operating schedule of a system. Engineering models are

again useful in this regard.

Most of water resource problems, especially in the design, management and operation of

WDS can be formulated by optimization models due to the variability of design components and

diversity of objectives. Some potential areas for optimization techniques include: System design,

operation; Rehabilitation/expansion; Pump scheduling;; Model calibration; Satellite treatment

(booster chlorination station); Leakage reduction (pressure management); and Data collection.

The design of a WDS was initially done based on experience. However due to

engineering advances, sophisticated models have been proposed. The early optimization models

for the design of WDS were often single-objective models since 1970’s. They were typically

limited by pre-defined pipe sizes (Lai and Schaake, 1969; 1977; Quindry et al., 1981; Fujiwara

and Khang, 1990 and Savic and Walters, 1997). In addition to the cost, there are other important

objectives such as water quality, reliability and other performance criteria that should be

considered (Mays and Cullinane, 1986; Kalungi and Tanyimboh, 2003).

Optimization models for WDS are often formulated as a least-cost optimization model

while pipe sizes are represented by decision variables. The main constraints are the desired

demands which should be supplied with adequate pressure head at withdrawal nodes. The pipe

flows and the nodal pressure heads must also satisfy the governing laws of conservation of

energy and mass. In summary, the problem can be typically stated as:

Minimize Cost (capital / operation/ maintenance costs)

Subject to: Meeting hydraulic and water quality constraints,

Fulfilling water demands, and Satisfying pressure requirements

The optimization model should have clear goal or objective functions, and precise limits

or constraints that can be written as computational statements. The designer may also want to

check various constraints or input, such as pressure requirements or demand patterns. The results

of optimization models can provide guidance by sensitivity analysis. However it requires

applying the optimization problem several times to see how the results are changing.

Various formulations have been investigated to solve optimization models based on

objective function and constraints formulations like linear programming, mixed integer 43

programming, dynamic programming, enumeration techniques, heuristic methods, and gradient

search methods (Walski et al., 2003).

Lai and Schaake (1969) first proposed an optimization model to the design of major

transmission lines providing water to New York City. Alperovits and Shamir (1977) applied

linear programming gradient (LPG) in optimization model for the design of WDS. Segmental

length of pipe with differential diameter was used as decision variables. The LPG method was

later improved by Kessler and Shamir (1989). Eiger et al. (1994) used the same formulation to

solve the problem by non-smooth branch and bound algorithms as well as duality theory.

Nonlinear programming (NLP) technique has also applied to solve the optimization

model of the design and operation of WDS (Chiplunkar et al., 1986). However, NLP converges

to local minima due to their reliance on the initial solution and derivatives of the unconstrained

objective function (Gupta et al., 1999). Moreover NLP formulations are based on continuous

variables for pipe diameter. Available pipe diameters are not continuous, so converting them to

the market pipe sizes influences the results (Cunha and Sousa, 1999).

Recently, evolutionary approaches have been used for optimization models of WDS.

Simpson et al. (1994) used simple genetic algorithm (GA), while it was improved later by Dandy

et al. (1996). Savic and Walters (1997) also used simple GA in conjunction with EPANET

network solver. Instead of using a single optimization algorithm, Abebe and Solomatine (1998)

applied GLOBE (Solomatine, 1995) that consists of several search algorithms.Cunha and Sousa

(1999) introduced Simulated Annealing which is based on the analogy with the physical

annealing process with Newton search method, to solve the systems equations. Eusuff and

Lansey (2003) proposed SFLA, a new meta-heuristic algorithm works based on memetic

evolution and information exchange among the population.

3.6 Optimal Design As mentioned, a WDS consists of various components. Adjusting its setting is crucial to establish

an efficient engineering system. Therefore it is necessary to maintain the best configuration of

elements over the service life. In practice, this is a trial-and-error process, where optimization

tools are used to find secure operation with considering a broad range of concerns. Cost is likely

a primary emphasis and includes the construction, operation, and maintenance costs. The initial

investment is for system components: pipes, pumps, tanks, and valves.

44

The conventional format of optimization models for the design, operation, maintenance

and rehabilitation of a WDS can be stated as:

Minimize Cost = F(H,q,DV) (1)

Subject to:

G(H,q,DV)=0 (Conservation Laws) (2)

HHH ≤≤ (Nodal pressure requirements) (3)

wDVHww ≤≤ ),( (4)

(Constraints related to design parameters and pressure heads)

uDVuu ≤≤ )( (5)

(Constraints related to design/operation parameters)

Where DV is the set of design and operation decisions including pipe sizes, tank diameter

and elevations, pump sizes and operations and valve settings. H is the nodal pressures for one or

more demand patterns and q are the nodal demands.

The objective function (Eq. 1) can be a linear or nonlinear function of individual

components to consider influence of installation location or timing on initial cost. In this general

form, some components may already be in place so expansion of an existing system or a

completely new system can be designed.

The conservation laws are one of the most powerful sets of physical laws that can be used

to predict the behavior of complex systems. Ability to predict any quantity usually depends on

the stability of the system that arises from conservation laws. It is the predictability of systems

that makes analysis meaningful and design possible.

The conservation laws, G, include conservation of energy and mass for all nodes and

loops. For the specific condition where the flow regime in the system is steady state and uniform

and also there is no external energy dissipation, the conservation equations for steady state can

be considered as:

∑ ∑ =− eoutin QQQ for all M nodes (Conservation of mass), (6)

∑ ∑ =− 0pf Eh for all loops (Conservation of Energy), (7)

For each feasible solution in steady state condition, continuity of mass must be also

satisfied at all nodes by (Eq. 6) with satisfying some specific demand patterns. The pipe flows 45

into the node, the pipe flows out of the node and an external demand specified by design loads

are presented in (Eq. 6) in terms of Q , Q

46

in out and Qe; respectively. The term Ep indicates the

pumping energy added to the system. Both the continuity and energy conservation constraints are

satisfied externally with the design algorithms described in (Eq’s.1-5) with a hydraulic network

model. The additional constraint in (Eq. 7) is due to conservation of energy for all network loops.

The term hf denotes the head loss across a pipe which can be estimated by Darcy-Weisbach or

Hazen-Williams equations. Therefore steady friction can be expressed as:

22

.

pp

p

AgD

xfR Δ=Darcy-Weisbach: (8)

54.0163.2 )278.0( pCD

xR Δ=Hazen-Williams: (9)

Where f =Darcy-Weisbach friction factor, and C=Hazen-Williams coefficient. p

The conservation equations (Eq's. 6, 7) are the set of linear and nonlinear equations

relating the flow and pressure head distribution within the system. They represent the hydraulic

performance of a system, as they are highly depended on how the system reacts while Qe in (Eq.

6) changes and how they influence on nodal pressures in (Eq. 3). These constraints should be

satisfied for all demand patterns. The external demand patterns are represented by (Eq. 6). These

equations are often used for both branched and looped networks.

Some techniques have been used to simplify these equations or to solve them outside the

regular optimization model to avoid embedding them as constraints, but they have some

difficulties regarding high level of computational burden and time limitation.

Up to this point, a general problem has been presented that can incorporate system’s cost

with budgetary limits and hydraulic constraints. However it has not been solved properly because

of the complexity of the general problem. The majority of effort has focused on cost

minimization subject to simple steady state conditions (Eq's. 6, 7). Clearly, more research is

needed to incorporate practical and realistic concerns to achieve more applicable results.

3.7 Critiques on Conventional Design Methods The classical objective function (Eq.1) that is used for the optimization models of WDS is

usually formulated in terms of investment cost of the pipelines and, less often, of energy costs of

the operation and often includes a fixed head in the source node (Quindry et al., 1981; Goulter

and Bouchart, 1990; Loganathan et aI., 1995; Xu and Goulter, 1999). Most authors consider the

length of pipes as fixed and pipe diameters (pipe size) as decision variables and try to find the

best sizing pattern of the pipes. Yet the pipes lengths and diameters are not the only required

specifications of the pipes, and material and wall thickness of all contributing pipes should be

defined in the design of a system too.

Maintaining pressure range during operation (Eq. 3) is another basic performance

criterion for a WDS. It is assumed implicitly that the pressure deficit is inconvenient for major

customers. Although it is accepted that all major users prefer to have no pressure deficit and they

expect to receive as much as water they want. But in some cases, the volumetric demand during

certain duration is more important than instant demand. For example some large users might

have a storage facility with a reasonable reservoir capacity to pump daily water consumption

during off-peak periods and supply it through peak demand hours within their own withdrawal

taps. So having pressure deficit in certain time during hydraulic simulation of the system does

not necessarily result in pressure deficits in withdrawal taps of individual users.

The results of conventional optimization models often lead to the opening of the loops,

giving rise to a branched or pseudolooped network (Alperovits and Shamir, 1977; Goulter and

Bouchart, 1990; Loganathan et aI., 1995). If a loop is closed by a minimum diameter pipe

(pseudoloop), this loop will rarely behave properly because of insufficient capacity to convey

large flows arising when any of the other loop pipes have to be temporarily drawn out of service.

This type of solution has been criticized elsewhere (Quindry et al., 1981; Awumah et aI., 1991;

Bouchart and Goulter, 1991; Tanyimboh and Templeman, 1993).

The main advantage of looped networks is its more reliable operation compared to

branched networks especially respect to pipe breaks, and mechanical failures. A looped network

can maintain the supply to the majority of users during the time the pipe is out of service.

Researchers have attempted to deal with this problem through indirect approaches (which will be

discussed in more detail in Chapter 4) such as those introducing additional pipe-failure operation

cases (Su et al., 1987). On the other hand, the looped networks need more pipes which mean that

a higher initial investment than branched ones.

The classical objective function has been shown to be multimodal and concave

(stationary points are maxima). The minima of this objective function are not stationary points,

as they are derivative-discontinuous, but are precisely located whenever each of the loop pipes

has a zero flow (Bhave, 1985; Chiong, 1985; Loganathan et aI., 1995).

47

Although a variety of powerful and sophisticated models have been advanced for the

optimal design of WDS (Goulter and Coals 1986; Su et aI. 1987; Goulter and Bouchart 1990;

Bao and Mays 1990; Cullinane et aI. 1992; Tanyimboh and Templeman 1993; Xu et aI. 1998;

Xu and Goulter 1999), most of them are limited to small-size networks due to their high com-

putational load, and some have not even been able to avoid the implicitly of branched network

result (Quindry et al. 1981; Duan et al., 1990; Loganathan et al., 1995).

Multi-objective optimization models are effective solutions to include more cost

components and performance criteria like reliability. This challenging methodology has already

been investigated by several researchers such as (Xu and Goulter 1999) that is discussed in more

detail in the following chapters.

3.8 Optimal Operation After estimating how much water is needed for a particular system and then defining the

structural specification of the system by optimal design of WDS, the next logical step is: How

should the system operate respect to both ever-changing demands and aging structural

components? The answer to this question determines operating specifications of the system.

The optimal operation of a WDS makes real-time adjustment of system’s elements to

ensure customer demands, while minimizing operating costs. This objective can be achieved by

adjusting the control apparatuses such as pumps and valves and also taking account of the energy

tariffs. Most systems at present are operating with experienced operators who use personal

judgment to control system’s components. Although the operators are provided with little or no

assistance in deciding how to meet highly variable demands with the adequate pressure, they

should try to minimize the pumping costs by taking advantage of low and high energy tariffs in

high and low-demand electricity periods.

Scheduling might appear simple at first glance, but the real difficulties arise when the

system is large and complex and the demand patterns are highly variable and often beyond direct

control. However with recent advances in optimization models, online controlling the system is

much easier (Alvisi et al, 2007). It dynamically responds to short-term demand fluctuations

while, at the same time, minimizes the longer-term operating costs. For a complex system with a

known demand pattern, it is almost certain that the objective of controlling the system can

achieve an overall better solution compared to the use of human judgment alone. Yet demand

predictions are difficult and even evaluation of performance is prohibitive in some cases.

48

Even where guidance is available by means of pump-scheduling programs, the actual

operational decisions, related to control apparatus are often adjusted based on the discretion of

the operators. Considering uncertainties associated with the demands and current limitations of

the control techniques, it is not surprising that the tendency is to keep water pressures within the

system higher than the minimum allowable value. As a result, the leakage increases in the system

as it is function of pressure. Furthermore the higher rate of energy consumption results in more

operation costs.

Keeping the pressure as low as possible system-wide while just complying with the

standards of service required by customers (adequate supply with acceptable pressure) generally

decreases the pumping costs and reduces the leakage. Additional savings are achieved by the

reduction in leakage to defer capital expenditure required by a genuine increase in demand. Yet

the risk of crossing pressure boundaries increases (e.g. pipe break or unusual demand).

For the real-time control of a WDS, the objective is optimizing the whole process of the

operation which includes both improved performance and operational-cost reduction. As demand

is continuously fluctuating over the time, it is often necessary to adjust the control apparatuses.

A variety of techniques have been used for optimization of the system’s operation such as

linear programming, dynamic programming, non-linear programming and decomposition-

coordination methods (Ormsbee and Lansey, 1994). Fallside and Perry (1975) investigated

hierarchical decomposition; Sterling and Coulbeck (1975) used dynamic programming; Zessler

and Shamir (1989) proposed iterative dynamic programming; Jowitt and Germanopoulos (1992)

applied linear programming and later Chase and Ormsbee (1993) applied non-linear

programming. Chase et al. (1994) describe a computer program to control energy costs that

incorporates a hydraulic model, a pump optimization program, and an interface. More recently,

evolutionary approaches have been used to achieve the same goal, such as Mackle et al. (1995)

who used genetic algorithms and Sakarya et al. (1999) who applied simulated annealing.

As a result of all operating problems, pump scheduling is revised by water utilities to

reduce energy costs. Estimates of potential cost savings are around 10% (Water Research Centre

1985). While some hydraulic simulation models have been adapted for operational purposes (Orr

et al., 1999; Tiburce et al., 1999), none of them has been used for the optimal control of a WDS.

Ertin et al. (2001) proposed intelligent control policies to evaluate the system’s

performance, based on hybrid system that utilized dynamic programming and rules as design

constraints to minimize average costs over the service life. In the water quality operation

49

management, Uber et al. (2003) used optimization techniques to determine the optimal location

and operation of chlorine booster stations. Jentgen et al. (2003) considered energy and water

quality managements together. This system combines a simplified distribution system model and

an optimization model to adjust the real-time operation of WDS and power generation system.

Finally, optimization models have been used to calibrate the operation of a WDS. It

involves the process of adjusting element’s characteristics such as pipe roughness, junction

demand, and conditions of the pipes, valves and pumps, so that the predicted flows and nodal

pressures comply with the observed field values in an acceptable level (Wu et al. 2002).

3.9 Critiques on Optimal Operation Developing a generic, real-time, optimal control of the system is perhaps an overwhelming

difficult task. Several factors influence this opinion:

(1) The operation of a WDS is usually compromised by a lot of interconnected components with

a variety of operating conditions. Thus adjusting individual components to form one collective

system is more difficult in large systems.

(2) All systems are in state of continuous adjustments. As a result, some components are out-of-

service due to mechanical failure, pipe breaks, and pump shut down. Old components are

generally replaced by new ones with different characteristics that influence the initial hydraulic

specification of the system and furthermore diversion from initial optimal operation. In order to

continue optimal operation, the new system should be analyzed and the new optimal operation

schedule should be investigated.

(3) In some expanded systems due to population growth, or new expansions. The new

components and pipes are often connected to older ones that influence significantly on the

hydraulic integrity of the initial system. This complexity increases when the rate of expansion is

considerable. In such a case, updating the operating schedule is inevitable. Yet the frequency of

new expansion is often less than the frequency of updating the operational schedule of the

system.

(4) Some systems have multiple supply sources with different production costs. Additionally it

is common to have a number of pressure zones within a system, particularly in high level areas.

Whatever their configuration, these systems are used to supply various types of customers with

their different characteristics.

(5) Agreement on the goals of the optimization can not be achieved well.

50

(6) Energy tariffs are often complicated, with different rates for various hours of the day, or

month of the year. Therefore how the strategy for optimal control can be derived in a short

period of time is under question.

The optimal operation of a WDS should consider improvements in the level-of-services

provided for customers in terms of both nodal pressures and the water quality. Improved delivery

pressure does not necessarily imply increasing the pressures but rather ensuring that the

minimum acceptable pressures are met. This can be achieved by locating the critical pressure

points towards the periphery of the system or any other site where pressures tend to be at their

lowest. Similarly, water quality can be improved by imposing minimum flow constraints at

critical points in the system. Any infringement of these operational constraints can result in an

infeasible solution for the control system and will be rejected. Thus pressure and flows are only

increased where necessary.

The resultant savings can be used for other purposes such as environmental enhancement, or

permit the deferment of further system expansions due to future demand growth. Either way, the

environmental benefits of reducing the use of natural resources are more important in the longer

term. Similarly there will be a saving on the amount of energy used, as a consequence of better

pressure management. It should be emphasized that the main benefit of the optimal operation of

a WDS are reducing the overall use of a valuable resources over the long service life and

decreasing direct operating costs. Moreover by transferring as much of the pumping as possible

to off-peak periods, the need for new expansion of energy generation systems is reduced, so the

new investment for energy generation system is further postponed. The extent of these resource

savings is difficult to estimate since they are site-specific and dependent upon existing

conditions. Nevertheless, it is expected that the benefits to the environment are significant.

3.10 Transient Modeling in Design and Operation Transient regimes have an important influence on the operation of WDS. Indeed, since such

events may result in considerable damages, modeling and anticipating transient events is

necessary. The operation of a system has dynamic and time varying characteristics influenced by

unpredicted demand fluctuations, infrequent but almost inevitable pipe breaks, and pump shut off

over the time. Therefore it is common to have sudden changes in the system and it takes a while

to adapt its operation to the new conditions. This period of transition between new and old

operating conditions, in other words, transient state often causes high and low local pressures.

51

Transient events are highly dynamic and complex in detail. Mathematical models are

required to analyze and predict system-wide their movement. Transient analysis for a WDS is at

least as important as other analyses in the design of a system. Transient pressures are most

important when the pipe flow changes rapidly. Such disturbances, whether caused by design or

accident, create traveling pressure and high velocity waves. The transient pressures are

superimposed on the steady state values within the pipes. The total force is obtained by adding

the steady state to the transient pressures. The severity of transient pressures is essential to be

known in the design of system’s components if they are to withstand such additional loads. In

fact, pipes are often characterized by their pressure ratings that define their mechanical strength

and influence on their cost (Boulos et. al, 2006).

Typical design objectives for the unsteady analysis of transient flow are the same as the

steady state analysis of a WDS, namely the minimization of total cost of the system taking into

account capital and operating costs. But the constraints and governing equations to include

unsteady flow regime are different. The overall problem can be stated mathematically in terms of

the nodal heads H and the various design/operational parameters as follows:

52

Objective: Minimize Cost = f(D1, D2, …,D )= , i=1,2,…,P (10) ∑=

P

iii LDc

1

),(n

Subject to the governing transient equations (Wylie and Streeter 1993), and a set of algebraic

constraints that are valid for all operating conditions.

02

=∂∂

+∂∂

xQ

gAa

tH

p (11)

01 1=

Δ+

∂∂

+∂∂ −n

p

QQx

RxH

tQ

gA (12)

, where t=0, for all M nodes (13) 21 )(,)( CtQCtH ii ==

3))(),(( CtQtHf ii = , where t>0, for i=boundary nodes (14)

ii HtH min)( > , where t=0, for all M nodes (long-term operation) (15)

datumii HtH >)( , where t>0, for all M nodes (short-term operation) (16)

{ }DDi ∈ for i=1,2,…,P (17)

where the decision variables D define the dimensions for each element, like the pipe

diameter, pump size, valve setting, and tank volume or elevation; D ,. . ., D1 Npipe= discrete pipe

diameters from the set of commercially available pipe sizes {D}(Eq. 17); C (D ,L )= cost of pipe i i i

i with diameter D and the length Li i; while t= time; x= distance long the centerline of the pipe or

conduit; H= pressure head; Q= flow rate; Dp= inside pipe diameter; a= celerity of the shock

wave; Ap= cross-section of the pipe; and g= gravity acceleration. The friction term in the

momentum equation comprises steady and unsteady portions (Zielke, 1968)

Two hyperbolic partial differential equations, (Eq’s 11 and 12), are subject to initial

conditions in (Eq. 13) and boundary conditions in (Eq. 14), where C

53

1, C and C2 3 are constants.

Simple boundary conditions of constant reservoir level and fixed demand are assumed, but

combined relationships between H and Q are typical for most boundaries. (Eq’s 15 and 16) are

the constraints that involve the pressure performance of WDS. They require that the nodal

pressure H for all nodes, Mnode, is more than the minimum pressure H , and design datum Hmin datum

for unsteady state operation. Finally the design constraint in (Eq. 14) limits the pipe diameters

from a set of available pipes, D (Jung and Karney, 2004).

When evaluating the performance of current systems, in addition to the general constraints

which are often considered for long-term operation, transient events should also be considered so

the system is protected from severe damages and consequences of transient events. Engineers

should carefully consider all potential dangers for the systems and eliminate the weak spots.

Only then should they embark upon a transient analysis since this type of study is usually

expensive in terms of time and economic (Jung and Karney, 2004).

3.11 Critical Comments on Transient Design and Operation A variety of optimization techniques have evolved to achieve economy of design, construction,

operation and maintenance. However most of them are concerned with the optimization of

systems under steady or nearly steady flow conditions. Consideration of transients has often

taken place after assuming that the cost of controlling transients represents a small portion of

total cost of the system.

Recently, Jung and Karney (2006) investigated the influences of transient events by

modeling pressure fluctuations due to system failures or an operational rapid change in valves.

They observed that if there is no transient protection device in the system, after exerting a sudden

surge, the pipe experiences considerable pressure surge with high frequency until it returns to its

normal pressure condition. Figure 3-3 shows the predicted pressure fluctuations in the pipe; the

solid line represents no pressure relief valve installation. Any failure or surge event creates at

least 6 to 7 pressure waves. The range of fluctuations can be critical when the maximum instant

pressure increases to more 250 m head compared to the average pressure head of 150 m.

Now, consider a small size network with the following characteristics: number of pipes= 200;

total length of pipes= 50 km; pipe material= DIP (ductile iron pipe); number of pipe breaking=

26 failures in 100 km length per year; and service life= 100 year. These data are selected based

on a typical real system.

Due to the realistic water demand variation over the service life, it is reasonable to assume

different hourly demand values, estimated around 876,000 values over the 100 year service life

(Figures 3-1, 3-2).

As the assumed service life is 100 years, the total length of the pipes is 50 km, and the rate of

26 failures/ 100km/ year, so the total number of pipe breaks can be estimated as 1,300 failures

over the service. To put this figure in context, note that the assumption of a constant failure rates

is likely conservative as the true rate would likely increase.

Figure 3-3: Actual Pressure Waves due to Transient Event in a Pipe (Jung and Karney, 2006)

The frequency of different pressure heads that each pipe in this system may experience

during service life is beyond the current expectations (876,000 different demand values, 1,300

failures, and approximately 10 times pressure fluctuations per each demand change or transient

events). Yet the impacts of frequency of transient events on failure rates with respect to pipe

materials have not even been investigated yet.

54

There are important relationships between the steady and transient regimes within the

system. The selection of pipe diameter, material and wall thickness strongly influences the nature

of the pipeline transient response. The system should also supply large and sudden fire flows at

adequate pressures. While fire events occur infrequently at different nodes, they may be the

constraining factor in the design of some systems. The designer should also consider the ability

of the system to supply fire flows at all nodes. Even though it is unlikely to see simultaneous

fires at all nodes, there is still a wide range of fire fighting demand patterns to be considered.

Water quality risk posed by transient intrusion is another challenging problem in a WDS is. It

was often assumed until recently that the quality of water that entered at one end of a distribution

system was essentially equivalent to that leaving at the other. However all WDS experience

leakage and hydraulic transients. In fact, low pressure transient waves have considerable risk of

drawing untreated and possibly hazardous water into the system (Karney, 2003). More recently,

soil and water samples were collected near drinking supply system and then tested for the

presence of some important pathogens, viruses. Indicator microorganisms and enteric viruses

were detected in more than 50% of the samples analyzed (Karim et al., 2003).

The results of this study suggest that during negative- or low-pressure events,

microorganisms may directly enter the treated drinking water through leakage of the system.

Therefore the designer should not overlook the effect of water hammer or surge pressures in the

design of WDS and the determination of total system cost. Thus, any “optimal” design that fails

to efficiently consider the water hammer effects is likely to be, at best, suboptimal, and, at worst,

completely inadequate. Therefore, despite its difficulties, transient analysis is essential for WDS

design (Jung and Karney, 2006). More detailed information regarding to water quality and

transient events is provided in Chapter 5.

3.12 Safe-Fail and Fail-Safe Design and Operation Most engineering systems are designed, built and operated based on one of two paradigms: either

using a safe-fail approach or a fail-safe approach. The fail-safe approach is more conservative

and the objective is to design and operate engineering systems in such a way that they never

experience failure in operating period or life-span; in other words, these systems should be safe

in any failure (Filion et al., 2003). The other approach is for the system to be safe sail, or in

order words, to be safe in failure.

The safe-fail framework is selected based on three criteria: 1- the imposed loadings of the

system have large span values, so its design based on largest load is impractical and

economically rejected; 2- it is difficult to estimate the values of extreme loads (lower and upper

limits), and; 3- users and people being served adopt low risk levels in the design and operation of

55

such systems so that repetition of system failures that cause rigorous social and economic

damages (loss of life and property) can be kept appropriately low.

As an example, the safe-fail framework is often adopted in the design of residential

buildings, hydrologic systems such as flood levees, spillways, flood-control reservoirs for several

reasons. These motivations include earthquakes as well as random events and loads

(earthquakes, severe precipitation or runoff). Such events often span a wide range which makes it

impractical and economically inefficient to design and operate for such extreme loading.

Moreover the extreme earthquakes and hydrologic loads recur infrequently and so are typically

difficult to infer from a record of finite duration. So it is required that the structure be designed

and operated to fail frequently and yet also safely; moreover, the design and its failure should

provide conditions that prevent severe social and economic damages (Filion et al., 2003).

On other hand, the fail-safe approach generally requires that a system be designed and

operated for the most extreme and extraordinary loadings. If one or all of the above criteria are

not met, a system is designed and operated based on fail-safe paradigm. For instance, the design

of most kinds of spillways for dams and nuclear reactors close to high population areas follow

the fail-safe approach. In these cases, possibilities of even infrequent or improbable failures must

be considered before, due to the huge amount of loss of human lives and the impairment of the

health of countless others.

Water supply systems have been often designed with the assumption of the fail-safe

approach. Indeed the traditional design and operation of a WDS is based on a deterministic

approach, where loading patterns are assumed to span a limited range with pre-defined extreme

values. Systems are usually designed for some reasonably-assumed worst-case loading patterns

and so the specifications of failure damages have not often been evaluated.

Arguably, the circumstances surrounding the design and operation of such systems fully

satisfy the three safe-fail criteria. Firstly, customer demand is inherently random with stochastic

behavior, so it can be measured based on different time steps such as hourly, daily, and weekly

periods. The loading patterns are also randomly and stochastically correlated with other

surrounding natural factors such as daily temperature season and rainfall. Furthermore

unexpected mechanical failures (i.e., pump failures, or pipe breaks) have also a stochastic

behavior and can create a wide range of hydraulic, water quality and demand patterns when

considered in combination with customer demand. Second, the repetition frequency of demand

patterns associated with device failures may exert extreme or extraordinary loadings to the 56

systems which are difficult to evaluate based on system records of finite duration. Third, some of

those loading conditions may in fact result in unacceptable physical and chemical conditions that

can produce undesirable social and economic damages even loss of human life due to pathogenic

contamination of the system. Although the nature of demand patterns is random and stochastic,

safe-fail design of WDS needs also probabilistic and stochastic framework. For more

information, please refer to Filion et al., (2003).

3.13 Critiques on Safe-Fail Operation Changing from a fail-safe to a safe-fail framework would constitute a basic alteration in the

design paradigm. In the fail-safe approach, the main purpose is reducing the possibility of failure

by considering the most extreme loads, worst-case in the design and operation. But in safe-fail

approach, it is implicitly allowed to have minor failures with economically acceptable

consequences during service life of the system.

This conversion, no matter how attractive the motivation, has many important implications.

First, it is essential to define and differentiate the failures categories clearly. A first level of

differentiation may include: 1) physical performance deficiency such as inadequate pressure or

discharge; 2) chemical performance deficiency such as water quality failure, where one category

of deficit performance or failure can result in others too. (i.e., hydraulic failure violation of upper

pressure bound can mean structural failure of pipe or deficit pressure may have water quality

problems). Second, it is necessary to define and differentiate categories of failure consequences.

For example, a first level of differentiation may include: 1) inconvenience to customer; 2)

damage to system and its economic loss consequences; 3) damage to system and surrounding

urban areas with economic loss, and; 4) injury and/or loss of life. Third, there is a need to

determine and attribute repetition frequencies and cost/penalty indices to failure-consequence

states. Fourth, the designer/operator must decide which events and probabilities are potentially

essential for the design/operation. The third step presents a sizeable challenge, while the fourth

step presents a moral challenge. Successful completion of steps three and four likely requires an

earnest collaboration between governments, public and multi-disciplinary experts to set

guidelines on how to establish these measures (Plate and Duckstein, 1988; Von Thun, 1987).

These considerations have almost never been made explicit in the design and operation of a

WDS (Filion et al., 2003). The nature of operational constraints (i.e., pressure corridors,

constituent concentrations) and the minimal consideration of failure consequences in most

57

operation and design approaches maybe best emphasize this issue. For example, the pressure

corridor criteria are typically unclear about issues such as failure type and severity of

consequences. A comparison of two scenarios serves to explain the point. Suppose two different

scenarios that may happen in common systems.

Scenario one: Consider a drinking WDS for various residential customers. By applying peak

residential demand on common simulation models like EPANET, one can observe temporary

pressure deficits in some points that may inconvenience customer at those specific nodes. So in

reality only a few nodes in neighborhood may have acceptable consequences of pressure deficit

or may not have any consequences if they do not use the water at the same time.

Scenario two: Consider a fire event with rapid fire flow that causes a low-pressure wave to

travel in the system, hence the consequences are not only violating the minimum pressure

constraint, but actually causing transient negative pressures in larger elements. It is almost

inevitable that the lower pressure wave will draw at least some contaminated water into the

system and even causes the collapse of some pipes, both clearly results with serious damages.

It should be noted that all failure scenarios differ in nature and therefore have different

consequences. However above two failure scenarios are considered the same, because they both

transgress the minimum pressure constraint. This kind of consideration has significant

implications for the design and performance evaluation of WDS. By considering different failure

types and corresponding damage costs explicitly, the design and operation evaluation of WDS

results in more accurate outcomes.

As discussed before, the system is designed in the fail-safe approach to a high degree of

confidence and the secure operation without any interruption. But in safe-fail approach, there is

an implicit understanding that a particular design and operation may result in specific types of

acceptable failures. These approaches, design-for-failure and operation-possible-failure, are

expected to establish the equilibrium between the design and operation considerations in one

side, and performance considerations in other side. For example in selection of pipe size, the

most important trade-off is done between capital, maintenance costs and hydraulic performance.

In fail-safe approach, selection of pipe size is typically done by satisfying extreme loadings

while establishing minimum pressure level. It implicitly means that cost savings mostly target at

improved hydraulic performance. However in safe-fail approach, where a certain degree of

hydraulic failure is acceptable, a range of capital and operation cost savings can be established

with corresponding hydraulic performances (marginal increase in costs vs. marginal decrease in 58

benefits), leading to greater flexibility in how design and operation challenges are addressed

(Filion et al., 2003).

3.14 Evaluation of the Fail-Safe Operation The fail-safe operation has been the standard operating paradigm, given the context of system

failure. Until recently, the fail-safe operation for WDS has been supported by design

assumptions that were justified and necessary given the relative infancy of the field. For example

it is argued that the range of extreme and critical loading patterns which usually considered in

design process should be defined accurately. The belief behind this assumption is expressed as:

If the system can operate well in such extreme peak loads that reflect the worst-case scenarios,

corresponding damages of systems failures (inadequate pressure, unacceptable service) are

reasonably accepted (Filion et al., 2003).

Providing new techniques like looped system and increasing redundancy in the operation of a

WDS can mitigate failure consequences in the system. Here, the delicate trade-off between

minimizing costs through design efficiencies (smaller pipes, fewer loops) and benefits achieved

in system performance (acceptable or improved level of service) should be investigated in more

detail. These design assumptions were both justified and necessary in the past given the limited

demand data available, the relative lack of knowledge and understanding of system operation,

the inadequacy of available models for system analysis and the early steps of computing science.

The safe-fail design of WDS starts with estimation of future extreme loads over the service

life, typically the worst case between maximum day demand and fire or peak hour demand. Then

the system elements such as pipes, pumps and reservoirs are selected to meet a set of physical

and chemical performance criteria described before, with respect to present and future worst-

case scenarios. It is implicitly assumed that if the system operates well with respect to extreme

loads, so it will perform well when subjected to all other possible loadings that may appear

throughout rest of operation life (Filion et al., 2003).

There are two major sources of errors that bear on these matters. First, the process of

estimating extreme loading patterns inherently has a high level of uncertainty, given that there

are often limited data records on past and present demands. In fact, extreme loading patterns can

rarely be measured in the system at any given time. It follows that a system which operates to

yield an optimal performance when subjected to a restrictive set of design loads (e.g., peak loads)

will likely perform at a sub-optimal level during most of its operating time since the design loads

59

are never exactly encountered (Hashimoto, 1980). Therefore the optimal operation should

consider the uncertainty associated with both short-term and long-term demand patterns.

Second, some types of failures that are well below the extreme loadings, sometimes still

result in severe damages especially in water quality failures. Systems operate to meet a minimum

pressure threshold when subjected to the greater of two critical loading patterns, while it is also

expected to establish a certain water quality standard such as minimum residual chlorine

concentration. But sometimes when the water demand is less than peak values, residence times

can be long and thus it can take a long time for water to travel within the system. As a result,

residual chlorine can be degraded considerably which can result in significant health problems.

While it is a hypothetical sample, catastrophic failures may occur while the system is

experiencing loadings much less severe then their supposed safe “design” values. The trade-off

between physical and chemical performance of WDS reveals that system failure is a multi-

dimensional event, while the acceptable operation of the system to mitigate one type of failure

may assist to increase the likelihood of another one. So it is not correct to assume that if the

system is protected over a limited range of extreme loads, it will be secure for all other lower

loading patterns (Filion et al., 2003).

Note that the full spectrum of appropriate loading patterns has not yet been defined, nor has

been shown that applying some specific loading patterns to the system will necessarily reveal

any information about frequency of repetition and return period of extreme loadings. Therefore it

is impossible to assess the frequency of common failure types with a specific system design.

The limited range of extreme loading patterns often associates with high degree of

uncertainty. This range can be extracted from a record of demand-device events of typically

short duration. Consequently the critical loading patterns do not necessarily result in the worst

performance loadings. A combination of rare events may exceed peak loading patterns and lead

to more dramatic system failures and socio-economic damages. By contrast, the complete

consideration of socio-economic consequences of system failures will be partially ignored by

accepting the fail-safe approach (Filion et al., 2003).

3.15 Summary This chapter begins with a brief discussion about modeling and its applications in engineering

sciences. Then application of water demand modeling is discussed.

60

The world population is expected to cross 10 billion by 2050, with the bulk of the growth in

urban areas. So water demand modeling is essential for authorities challenged with the task of

satisfying ever-increasing water consumption. Demand forecasting is critical for WDS that

experience considerable fluctuations over the service life. While the long-term forecasting is

required for the estimation of further system-wide expansion over the service life, the short-term

forecasting is referred for the operation of the system to take advantage of electrical tariffs. The

relationships between water demand estimation and other important factors such as land use, fire

demand, and peaking factor coefficients are discussed in this Chapter.

A variety of optimization models have been advanced for the design and operation of WDS

that can be classified into three groups: optimal design, optimal operation; and optimal transient

design and operation of WDS. All of the above models aim at optimizing some important aspects

of the system with more complicated operation. But the main outcome of such models just

partially covers the whole system. Therefore each of aforementioned optimization models has

advantages and drawbacks itself. A brief critique for all three major optimization models was

presented.

Transient event is a complex phenomenon in the operation of WDS. The major consequences

of transient events within the system are reviewed in this chapter. Then the application of

transient modeling in the design and operation of WDS is presented. Furthermore it is followed

by critiques about current consideration of transient events in the operation of WDS.

Safe-fail and fail-safe paradigms are two major approaches in the modeling of

engineering systems. These two different approaches are also briefly reviewed with their

specifications in this chapter, with a discussion about the importance of these two strategies for

the design of water supply systems.

61

Chapter 4 Hydraulic Performance and Reliability Measurements

4.1 Introduction The main goal of traditional WDS design is to satisfy an estimated demand for water at

acceptable pressure and quality while appropriately minimizing capital and operating costs.

System performance throughout its service life may often be relegated to secondary role, in part

because its evolution over time is not known with certainty and capital costs tend to take

precedence despite widespread appreciation of how significant recurring future costs can be.

Fortunately, more sophisticated physical and economic modeling is gaining away and early

network conceptualization increasingly entertains considerations of the temporal dimension of

network’s performance, which are particularly important in modern systems characterized by

complex topologies and multiple and changing demand patterns.

The optimal operation of a WDS is a key consideration in the early design stages. Despite

this, it is not rare to find networks plagued with functional and operational difficulties which

originated in hydraulic shortcomings as early as the design phase. The rapid growth of many

urban areas calls for better tools to facilitate diagnosis of system weaknesses and support

decision making without having to rely exclusively on the insight and conjecture of experienced

utility managers.

Hydraulic performance is the first and most obvious element to address in improving

performance evaluation. The procedure of designing, building and running a WDS is primarily

driven by the need to satisfy a given set of demand points with sufficient flow and pressure

(Eq’s. 6, 7) avoiding wide fluctuations of these parameters. A new approach called risk-based

performance assessment is also proposed in this chapter for hydraulic performance evaluation. It

is conceptually based on the three fundamental criteria of reliability, resiliency and vulnerability.

The assessment of the system’s operation is complex because of various factors. Not only

it is necessary to have a reliable evaluation of water demands, but the capability of the system to

respond sufficiently to such demands during the service life is required based on the design

criteria. If the system is not efficiently designed, numerous problems will occur during operation

leading to poor performance and increased operating costs over the service life.

The design of the system is often formulated as a single-objective problem. The objective

may target the initial pipe design, operational schedule of the system or the protection of the 62

system for transient events. While the optimal design emphasize finding the best pipe sizes, the

optimal operation of WDS is generally considered as determining a short-term operation policy

to take advantage of both energy tariffs and reservoir capacity for daily operation.

There are serious considerations in the long-term design life and short-term operating

conditions including demand fluctuations, reliability of individual components and their

locations, fire flow requirements and their locations, transient events and their effects, indirect

damage costs, and public health costs. Further complications arise from the fact that it is difficult

to define unique performance measures and to establish acceptable levels for these parameters.

4.2 Hydraulic Performance and Reliability Criteria A distribution network should be able to deliver water to all customers in the required quantity,

quality and pressure over its whole service life for all reasonable operating conditions and

preferably free of interruption. Unfortunately, interruptions still occur due to mechanical failures,

hydraulic instabilities and compromised water quality. Although the operation of a WDS is often

linked to its reliability characteristics (Hashimoto et al., 1982; Tanyimboh et al., 1993), there is

no universally accepted definition of reliability. One approach is to define the hydraulic

reliability of a WDS as the probability that a system can satisfy the customer demands for a

specific period of time under certain conditions (Ostfeld, 2001; Setiadi et al., 2005).

In order to account for possible supply failures, Goulter and Coals (1986) suggested the

use of Node Isolation Probability, i.e. the probability of simultaneous failure of pipes connected

to a node. Later, Su et al. (1987) evaluated reliability by using the minimum cut set, an ensemble

of system components that invokes failure when disconnected from other components. Most of

the analytical approaches presume, unrealistically, that, as long as a node remains connected to a

source through at least one pipe, the demand at the node is satisfied. Wagner et al. (1988a)

recognized this drawback and introduced the concept of reachability. They stated that connection

to a source was only a necessary, and not sufficient, condition to ensure that a demand node was

functional. They also defined the concept of connectivity, which requires that every demand node

is connected to at least one source node. Expanding on this, Wagner et al. (1988b) introduced the

terms service head and minimum nodal head. Demand is met if the nodal head is equal to or

above the service head while, below the minimum allowable head, flow is unavailable and

demand remains unsatisfied. When it falls between the two heads, some flow (calculated

according to a square-root law) is available to partially satisfy demand. However since the

63

proposed analysis assumed that all nodal demands were satisfied (and then nodal heads were

calculated based on such flows), system behavior is not well depicted for a partial flow scenario.

Cullinane (1989) introduced nodal availability which is the time proportion when the

nodal pressure is higher than the required value for all demand patterns and different system

component failures. Fujiwara and De Silva (1990) also considered service and minimum nodal

heads in their approach and found that the flow capacity defined in the maximum flow model did

not follow a clear physical meaning and that estimated system reliability failed to account for

hydraulic consistency along each loop. The simulation approach of Bao and Mays (1990) also

suffered from the same problem. In it, nodal reliability is a joint probability of both outflow and

the pressure head to be satisfied. Unfortunately, it is difficult to derive and compute because

discharge and nodal pressure head are not independent. Goulter and Bouchart (1990) proposed a

chance-constrained model in which probabilities of pipe failure and surplus demand are

combined into a single reliability measure, the probability of no node failure.

Cullinane et al. (1992) examined the intermediate stage of partial pressure failure using

the nodal availability concept but, instead of assuming a binary index (i.e., zero when available

pressure is less than required and one otherwise), they assumed a continuous fuzzy relationship.

Gupta and Behave (1994) defined three reliability indices: Nodal; Volume and Network

Reliability for nodal flow analysis as part of reliability evaluation of a WDS.

A significant improvement to hydraulic reliability assessment was advanced by

Tanyimboh et al. (2003) in which they employed pressure-driven simulation to capture the

variation of supplied water to customers as a function of pressure. The quantity of supplied water

had already been applied indirectly for describing reliability by Tanyimboh et al. (2001);

however, this time, reliability was defined as a time-averaged value of the ratio of flow-delivered

to flow-required. Kalungi and Tanyimboh (2003) considered both reliability and redundancy as

two indices that could be used for improving total WDS performance.

Note that the reliability of a WDS can be contemplated from two different perspectives: i-

the customer’s view: the main goal of modeling is to analyze and evaluate system reliability in

the case of water delivery cut-offs and the duration of these suspensions. ii- the planner’s view:

the main goal is to analyze and evaluate failures, examine various reliability states and undertake

assessment of the system’s reliability. Although efforts have generally been premised on the

second perspective, customer-based evaluation is also important. As a result, Kwietniewski

(2006) recently entertained field reliability tests where the water deficiency of system failures 64

was expressed in terms of the volume of unsupplied water. This approach treated the water

deficit volume as the chief measure of system reliability.

4.3 Pressure-based Indices Establishing system-wide pressure limits is essential for ensuring safe and efficient network

operation. While nodal pressures should be sufficient to satisfy end-user consumption, they need

to reflect other concerns such as transient robustness, burst susceptibility, leak control, energy

use, etc. Thus, their determination reflects a trade-off between consistent demand provision and

safe and efficient system operation. In this context, pressure-based indices are introduced as

useful tools for guiding network operation.

The minimum nodal pressure, hmin (in Eq. 6), is one of the basic hydraulic requirements

of any system. This level is based on the average height of the buildings that the utility supplies

without additional pressure boosting and is typically 12-20 m above that height. Surplus pressure

is required to avoid the very low or negative pressure heads that could arise during transient

quality degradation from contaminant intrusion.

Measuring the nodal pressures within a system can be effected directly by means of

pressure gauges which are installed at selected nodes, or it can be indirectly achieved with

mathematical modeling or hydraulic simulations. The first is simple but expensive, while the

later is complicated and cheaper, but need more sophistication.

The simplest index of hydraulic performance is the lowest observed system-wide nodal

head. Keeping the node which reveals this pressure above the minimum head limit guarantees

adequate head at the other nodes. The surplus head at a node is equal to the difference between

the actual head H at which the demand Q is supplied and the minimum required head or design

head Hl at that node. It also indicates how much energy can be dissipated during failure

conditions. Increasing the available surplus head at the most depressed node improves the

hydraulic stability of the system to some extent.

Accordingly the minimum surplus head index Im is defined as:

{ }ljjm HHI −= min j= 1,2,…,n and (18) l

jj HH ≥

Im indicates which node has the least surplus pressure and it may implicitly define the

first node that would experience deficit pressure if any interruption were to occur. The greater its

value, the lower is the possibility that the system will experience deficit pressures at all

consumption points. 65

The main drawback of Im is that it only depends on one specific node. There is also no

guarantee that, if any failure occurs, another node will not prove to be more vulnerable to deficit

pressure. Since Im represents the lowest surplus pressure, there is no information about the rest of

system. Therefore, it is difficult to compare it with the other nodes and, as a result, Im is not a

perfect index for summarizing the overall system’s hydraulic performance.

Another potential index of system reliability is the sum of surplus pressures experienced

at all demands nodes. In mathematical form, the total surplus head index, I can be expressed as: t

for all j=1,2,…,n and (19) ∑=

−=n

j

ljjt HHI

1

)( ljj HH ≥

It represents the extent to which all consumption nodes meet pressure expectations.

Increasing It improves the system’s capacity to continue supplying water at all demand nodes

after a failure. Although, it can sometimes represent the reliability of a system better than Im, it is

still unable to confirm secure operation in emergency conditions. For example, in a system with

only one pumping unit, severe consequences can occur after a pump’s mechanical failure

regardless of a large I because of topographic/topological factors. t

The above hydraulic performance measurements summarize network capacity for single

demand patterns. It is necessary, however, to consider various operating conditions and

especially multiple demand patterns. These indices also provide no practical information on how

to improve system’s operation.

4.4 Demand-Driven and Head-Dependent Analysis Outflows and pressures are the state variables that must be modeled in order to assess the

hydraulic performance of any system. Outflows and pressures are interdependent and influenced

by both internal and external factors. For example, outflow depends on pressure, which in turn is

influenced by frictional losses (an internal factor). Headloss is in large part governed by demand

(an external factor) and affects pressure and outflow. As previously discussed, because demand

values are not known with certainty, being determined in large measure by factors outside the

control of system operators, they need to be estimated and then varied according to likely

consumption scenarios in order to test the system’s ability to maintain adequate pressures.

Typically in simulation, one state parameter (such as demand) is fixed and the other (in this case

pressure) is calculated according to the hydraulic model.

66

Hydraulic simulation is a straightforward and flexible approach for evaluating pressures

and flows under a wide range of demand patterns; however, it entails simplifications that may

significantly influence results. Traditionally, hydraulic simulation has assumed demand

satisfaction at all nodes and then computed system pressures in an approach called Demand-

Driven Analysis (DDA). Mathematical models traditionally used for WDS analysis are largely

premised on DDA. This method is satisfactory if the nodal heads are sufficient. In some

references (Bhave 1991), this approach is also called Node Head Analysis, (NHA).

This family of models gives acceptable results when systems are subject to normal

operating conditions. Clearly, the relationship between the nodal outflows and the pressure heads

is not modeled precisely, because when the nodal pressure drops below the required level, the

analyst has no information about amount of supplied water. Consequently, some customers

might face reduced supply or even no supply at all (Ackley et al., 2001, Tanyimboh et al., 2003).

All water supply systems are subject to component failures and fluctuating demand and

wide pressure variations are sometimes inevitable. When this happens, DDA results may

erroneously indicate that the system is still supplying the full demand at lower, and sometimes

even, negative pressures. The validity of such results is obviously questionable and, in order to

remedy the modeling artifice that gives rise to this unrealistic system behaviour, and more

accurately capture the pressure-demand relationship, Head-Dependent Analysis, HDA, has been

advanced as a better hydraulic simulation platform (Ackley et al., 2001, Tahar et al., 2002). It is

also known as Node Flow Analysis, NFA, by Bhave (1991) since it determines the resulting

nodal flows considering the minimum required nodal heads.

There are numerous potential occasions when all of the nodal demands may remain

partially unsatisfied due to deficit pressures, such as occurs during fire fighting, pump-failures,

pipe bursts and other unforeseen and excessive demands. Therefore, hydraulic simulation with

low pressure should be based on HDA since its main objective is to establish the actual supply

quantity and pressure at each withdrawal node. In NFA, the minimum-required and available

nodal heads ( respectively); as well as the required and available nodal flows

( respectively) are considered simultaneously in one of the following conditions:

;,min avljj HH

;, avlj

reqj qq

1. When (supercritical node), (adequate outflow); (20) minj

avlj HH > req

javlj qq =

2. When (critical node), (partial outflow); and (21) minj

avlj HH = req

javlj qq <<0

3. When (subcritical node), (no outflow). (22) minj

avlj HH < 0=avl

jq

67

In NFA it is assumed, like in NHA, that even if the available nodal flow is more than that

required, the actual outflow does not exceed that required. Moreover, as in NHA, the nodal

velocity heads are neglected. Since the available nodal flows are compatible with the available

nodal heads, the problem of jointly considering the probabilities of heads and flows is avoided

(Bao and Mays, 1990). Bhave (1991) proposed assigning categories to all consumption nodes

and changing them after each DDA simulation according to a predefined scheme involving

constraint violation in every iteration, with the discrepancy being reduced in successive passes

until all constraints are satisfied. The Head Driven Simulation Method (HDSM) by Tabesh

(1998) is based on the Newton-Raphson technique and explicitly incorporates the head-outflow

relationship of (Eq's 20-22) in the continuity equations.

When deficit pressures arise, the system can be upgraded by increasing pipe sizes,

substituting material, relining conduits to reduce roughness and altering other physical

characteristics until the required nodal pressures are met. However, during pump failure, pipe

break, or an exaggerated demand episode such as firefighting, the system may experience deficit

pressure in several nodes but, since these are temporary, system modification may not be an

obvious response. Nonetheless, system behaviour during such episodes should be considered as

part of the reliability analysis and it is better to consider both nodal pressure heads and outflows

simultaneously for hydraulic performance assessment.

Finally, it should be noted that the actual relationship between nodal head-outflows is

quite complicated and is function of several internal and external factors. Therefore, it is

necessary to verify the nodal pressures and outflows of both DDA and NFA with corresponding

field measurements. If there is no significant discrepancy, model results are acceptable.

4.5 Reliability-based Indices Because supply continuity with adequate pressure is influenced considerably by various events

during typically protracted service lives, it is necessary to consider the impacts of all possible

operational failures. Reliability-based indices can facilitate this endeavor.

System’s reliability depends first on identifying and categorizing likely system failures

and their water supply consequences. Since customers are essentially interested in the final

outputs of system operation, failure can be perceived as a deficit of either pressure or flow. The

deficit itself is a random variable because water demands are spatially and temporally stochastic

and supplying water is inherently vulnerable to unexpected incidents.

68

While the Im and It indices pertain to surplus pressures at a single node and at all nodes,

respectively, other reliability indices incorporating the quantity of nodal outflows based on (Eq.

6) have been proposed. In this capacity, two major indices, the node-reliability and volume-

reliability indices, have been derived and relate to outflows from individual nodes and from all

demand nodes, respectively.

The node-reliability index, Rnj, is defined as the ratio of the total volume of available

outflow at a particular node to the desired volume of outflow at that node for all the states of the

service period. Thus for node j:

ss

reqjs

ss

avljs

tq

tq

.

.

WaterRequested WaterSuppliedR nj ∑

∑== for individual node j (22)

In which q

69

avl =available outflow; qreq = required outflow; ts = operation time (same for all

nodes); j = subscript of demand node; and s = subscript of state.

Rnj summarizes the water supply picture at only one node and it is, thus, useful to define

anther reliability index corresponding to the supply status all demand nodes. One such measure

is the volume-reliability index, Rv, and is defined as the ratio of the total volume of available

outflow to the required volume of outflows for all demand nodes and for all states in a given

service period. Therefore:

ss j

reqjs

ss j

avljs

tq

tq

.

.

WaterRequested WaterSuppliedR v ∑∑

∑∑== for all nodes j (23)

The node- and volume-reliability indices describe the supply conditions at particular node

and for all demand nodes, respectively. Unfortunately, they can not complete the entire system’s

reliability picture since they are both functions of nodal demand and it is assumed that the nodal

heads are satisfied regardless of nodal outflows, skirting the head-outflow interdependence.

The definition of system’s reliability can be based on three basic criteria: i) the proportion

of demands covered (Rv), ii) the duration for which demand is satisfied (Ft) and iii) the total

number of satisfied nodes (Fn). For reasons of tractability and modeling convenience, it is

preferable to define an integrated reliability index that can describe the reliability of the whole

system. As the above three criteria are independent, the probability of simultaneous occurrence is

equal to the product of individual event probabilities. Thus, the system reliability index, Rnw, can

be defined as:

(24) ntvnw FFRR ..=

In which F = time factor and F

70

t n = node factor. The time factor is defined as:

TJ

taF s j

jsjs

t .

.∑∑= (25)

In which J = the total number of demand nodes; T = period of analysis (= ); a∑ st js = 1,

if the discharge ratio, reqj

avlj

qq

at a node for a particular state is equal to or more than an acceptable

value, and ajs = 0, otherwise. Thus, if the acceptable value of discharge ratio is 0.5, a node is

included in evaluation of the time factor if it satisfies at least 50% of demand during the state.

The node factor is the geometric mean of the node-reliability factors:

JJ

jnjn RF

1

1⎥⎦

⎤⎢⎣

⎡= ∏

=

(26)

If the available outflow at specific node is less than required outflow, Rnj is set to zero in

(Eq. 26), making R equal to zero and rendering system performance unacceptable. nw

There are some difficulties in using reliability as an indicator of looped system’s

operation. These may arise due to following reasons: 1- the lack of a comprehensive and realistic

definition of reliability; 2- lack of practical methodology for large network optimization and

reliability calculation; 3- uncertainty over which value should be assigned to reliability, and 4-

the trade-off between cost and reliability.

4.6 Resilience Concept Although the above outlined reliability indices can represent approximately the ability of a

system to supply water in terms of nodal pressures and outflows, they are describe hydraulic

behavior following interruption or failure. Furthermore, direct measurement of all nodal

pressures and outflows for numerous demand patterns is difficult in large-scale systems and it is,

thus, necessary to explore other indirect approaches for assessing reliability. To this effect, the

resilience concept has been introduced recently.

Todini (2000) first proposed the resilience index, which is strongly linked to the intrinsic

capability of the system to overcome failures. It is based on the network’s ability to equalize

power fluxes. It states briefly that the total input power of a system is equal to the total power

lost internally to overcome the friction plus the total power that is delivered at withdrawal points:

P =P

71

inp int + Pout (27)

The total input power is supplied by reservoir nodes and pumps as follows:

∑ ∑= =

+=r pn

k

n

iikkinp PHQP

1 1

..γ (28)

In which Qk and Hk= outflow and pressure head corresponding to each reservoir node k;

n = number of reservoir nodes; P = power supplied by pump i; and nr i p = number of pumps in a

system. The total output power of the system is given by:

∑=

=nn

jjjout HQP

1

..γ (29)

= outflow at node j; and H = pressure head at node where QWhere Qj j j is supplied. The

resilience index, Ir ,of the system is then defined as:

⎟⎟⎠

⎞⎜⎜⎝

⎛−= max

int

int1PP

I r (30)

Where Pint= amount of power dissipated in the system; and = maximum power that

would be dissipated internally in order to satisfy design demand Q and design head H

maxintP

l at the

junction nodes. In other word, Ir links current power dissipation at one specific node with the

worst case situation of power dissipation at the same node. Values of the Ir index fall between

zero and one, with zero corresponding to the worst case scenario in which the system exhibits the

lowest performance. Higher values of I denote better performance at the specific demand node. r

Improving the reliability indices simply increases surplus pressure at the demand nodes,

but does not reflect the effect of redundancy in the network. In this sense, application of the

resilience concept offers some advantages over direct reliability approaches. It does not require

statistical inference from the probability distributions of the different failure modes and thist

measurement only calls for knowledge of a few simple parameters at the withdrawal nodes and

pumping locations and is not highly dependent on time steps.

While a looped network is more reliable, it alone does not guarantee delivery at different

nodes under modified or stress conditions. Failures and increased demands result in elevated

internal energy dissipation. Thus, if a surplus of energy is unavailable, service interruption

occurs regardless of cause and the probability of occurrence.

Although a branched network with adequate surplus nodal heads may respond well to

increased demands, there are some situations, such as pipe break or pump failure, that bear

important consequences for downstream nodes. In order to reduce the impacts of single point

failure on system operation as a whole, improving resilience is one solution. However boosting

surplus pressures or input power alone is not enough. The following reliability parameter,

referred to as network resilience, In, is a measure of both the nodal surplus power and the

uniformity in diameters connected to that node. This index incorporates the effects of both

surplus power and reliable loops. The surplus power at any node j is given by:

)(. ljjjj HHQP −= γ (31)

Reliable loops can be ensured, if the pipes incident to a node have similar diameter. If D1,

D , and D (where D

72

2 3 1>D >D2 3) are the diameters of three pipes connected to node j, then

uniformity of that node is given by:

1

321

3)(

DDDDC j

++= (32)

In generalized form, the uniformity of a node can be expressed as:

{ }ip

np

ii

j Dn

DC

j

j

max1

×=

∑= (33)

Where npj =number of pipes connected to node j. A value of C=1 is applicable if pipes

connected to the node have the same diameter and C<1 if their diameters differ. For nodes

connected with only one pipe, C is taken to be one. The combined effect of both surplus power

and the nodal uniformity of node j, called weighted surplus power, is expressed as:

X =C .P (32) j j j

For a network, it is given by:

∑∑∑===

−===nn

j

ljjjj

nn

jjj

nn

jj HHQCPCXX

111

)(.. (33)

(Eq. 33) can be normalized by dividing with maximum surplus power to obtain network

resilience as:

∑∑ ∑

∑

== =

=

−⎟⎟⎠

⎞⎜⎜⎝

⎛+

−==

nn

j

ljj

nr

k

npu

i

ikk

nn

j

ljjjj

n

HQP

HQ

HHQC

XXI

11 1

1

max

).(.

γ

(34)

Where = maximum surplus power. The network resilience is

equal to the resilience index with surplus power at each node j given a weight of C

∑=

−=nn

j

ljjinp HQPX

1max ).(

j based on the

uniformity in diameter of incident pipes. Theoretically, the value of network resilience may vary

between 0 and 1.

Increasing resilience is a practical solution to improving reliability regardless of the

mechanisms stressing the system. The increment in resilience can be expressed as an

augmentation in the energetic redundancy which means a decrease of the internal energy

dissipation for a given topology. The designer can investigate the trade-off between resilience

and cost since a small increase in cost may lead to a large increase in resilience or vice versa.

The output of trade-offs should be considered in system’s performance evaluation.

Resilience measurement considers adverse consequences and evaluates network

vulnerability to attacks, failures, and disasters. Prasad and Park (2004) treated the maximization

of network resilience as one objective and the minimization of investment cost as a second

objective in a multi-objective design framework. Filion et al., (2006) applied hydraulic resilience

in performance evaluation of WDS. Qiao et al. (2007) proposed a max-min linear programming

method for allocating a security budget to maximize minimum resilience of a system for a

predetermined attack level by considering network resilience in their approach.

Most Recently, Jayaram and Srinivasan (2008) proposed a new modified resilience index

in multi-objective WDS design and rehabilitation. Minimization of life-cycle cost and

maximization of minimum modified resilience index over the service life were the objective

functions. The modified resilience index is proposed as an improvement on the resilience index

of Todini (2000) because of its applicability to networks with multiple sources. Although the

resilience index introduced by Todini (2000) was a simple and useful measure for the system’s

performance, it may not be applicable if the system is supplied by multiple sources. Using a

sample network, Jayaram and Srinivasan (2008) found that a system exhibiting a high value of

the minimum modified resilience index can handle uncertainties arising from demand growth

and pipe roughness degradation during service life better than a system with a lower index value.

4.7 Redundancy The previous reliability indices are often used to explore network performance during normal

operating conditions but investigating operation under stressed circumstances, and scrutinizing

73

system reaction to sudden failures, is essential. Redundancy is another hydraulic aspect often

neglected. It contextualizes system resilience more effectively because it relates to network

performance under partial failure conditions, such as pump failures, pipe bursts and the sudden

unavailability of certain components. The basic premise of a looped system is that nodal

demands are supplied through a number of alternative paths. After failure modes, there is often

insufficient pressure at the demand nodes to fully satisfy all nodal demands. Therefore head

driven analysis (HDA) is preferred in this case.

Because redundancy is the existence of alternative paths between sources to demand

nodes, and/or excess capacity in normal operating conditions, it can be used as an effective

metric of performance evaluation when entertaining possible network interruptions and looped

networks are typically assumed to exhibit redundancy. The interaction between supply paths, the

degree to which various paths contribute to nodal supply and the multiplicity of these paths are

factors that complicate redundancy evaluation. In general, it is preferable that flow distribution

be as even as possible in order to increase the reliability. If the distribution is very uneven, the

failure of a link that supplies the largest discharge to the node may have serious consequences.

Redundancy captures this effect and thus entails greater descriptive ability than the other indices

described earlier.

Note that, as the system is composed of more elements, the total number of failure modes

increases significantly and it becomes difficult to predict which component will go off-line or

experience failure. As a result, redundancy analysis for large-scale systems is still challenging.

4.8 Connectivity A WDS is a system with a collection of various components which can be described as a graph

according to the axioms of graph theory. The nodes represent sources and consumption points

and the arcs connect nodes with elements such as pipes, pumps, and valves. A graph with one or

more directed links is called directed graph or digraph. Theoretically, the flow in each arc can

travel in either direction resulting in 2n possible digraphs, where n equals the number of arcs

(links). However, this number is typically much less In reality due to the governing continuity of

mass and energy within the system, and also in certain arcs (links) the flow is constrained to only

one direction (e.g., the pipe leading out of a well).

Only a few experts have performed connectivity analysis based on the concept of backup

selection. Satyanarayana and Wood (1982), Rosental (1977) and Wagner et al. (1988b) applied

74

analytical methods for computing both Connectivity and Reachability. The concept of backup

selection was introduced at first by Ostfeld and Shamir (1996). Backups are subsystems (of the

entire network) which remain active when a failure occurs. Ostfeld (2005) proposed a

generalized approach for the backup selection and connectivity analysis of the entire layout of a

system, identifying the most flexible pair in the system: the operational and backup digraphs.

The digraphs with the maximum number of paths between sources and withdrawal nodes are

selected to achieve a one-level system redundancy so that, if one arc/link fails, at least one path

from one source to all demand nodes is available via the operational or backup digraphs, while

maintaining continuity equations of mass and energy. Agrawal et al (2006) used node- and

volume-reliability indices to optimally design a Level 1 redundant WDS (i.e., networks that can

sustain a single pipe failure without interruption and supply water to all customers, either in part

or in full).

The main advantage of connectivity analysis is its ability to find substitute arcs that can

continue supplying water in failure conditions, especially when water quality issues are also

considered. There are; however, serious obstacles to the practical application of connectivity

analysis since it is limited to failure and also, due to computational challenges, only small

systems have hitherto been investigated. Actually, only the first steps of WDS connectivity

analysis have been developed.

4.9 Entropy Considering all failures modes is essential in reliability measurement, but it is too difficult for

large and complex systems. As a result, an alternative approach called entropy has been

investigated (Yassin-Kassab et al., 1999, Setiadi et al., 2005). Its strengths stem from ease of

computation, as it can be easily incorporated within optimization models and has minimal data

requirements.

At first, Shannon (1948) suggested an entropy function to quantify the levels of

information or uncertainty of different probability distributions as follows:

(35) ∑−=i

ii ppKS ln./

In which S is the entropy, K is an arbitrary positive constant often taken as 1, pi is the

probability associated with the ith outcome. Awumah et al. (1990 and 1991) then developed

Shannon’s entropy formulation with several functions while Awumah and Goulter (1992) also

75

tried to incorporate entropy within an optimization model as a tool for quantifying system’s

reliability. Tanyimboh and Templeman (1993) found that higher system flexibility is achieved by

increasing the entropy of the system. They also realized that it is possible to enjoy resilient

designs without a substantial cost increase. These efforts encourage further investigations about

the relationships between the entropy and hydraulic reliability of WDS and suggest that systems

with maximum entropy are more reliable.

Tanyimboh and Sheahan (2002) also used an entropy formulation to find the optimum

layout of a system. Templeman and Yassin-Kassab (2002) suggested that entropy could be used

in calibrating WDS and to find the most likely pipe characteristics. Later, Ang and Jowitt (2003)

explored the relationship between energy loss and entropy, finding that the importance of a pipe

can be related to the amount of energy that the system dissipates resulting from the removal or

closure of that pipe.

4.10 Total Risk Index (TRI) Although all of above reliability approaches hinge on a specific criterion for the evaluation of

system’s operation, none is able to fully portray the reliability of the whole system with respect

to all the different failure categories during the long service life of the system. To circumvent

some of these limitations, a new and comprehensive approach, called Total Risk-based

performance assessment is proposed.

Hashimoto et al. (1982) offered a new approach for evaluating hydraulic operation based

on output values in violation of performance thresholds. System performance can be defined

based on three different viewpoints: 1) Reliability, how often the system fails; 2) Resiliency, how

quickly the system recovers from failure; and 3) Vulnerability, how serious the consequences of

the failure may be.

1- The reliability (Rel) is introduced to account for the potential of a system to remain in

acceptable operation for a given period and specific conditions (e.g. 14 m head, 20 psi). Time-

dependent reliability can be mathematically defined as the ratio of the duration of acceptable

operation to the total operation period as:

76

∑=

=T

ttT Zl

1

1Re (36) ⎩⎨⎧

<≥

=min

min

0,1

hhhh

Zt

tt

where T = total number of time steps in service life of the system; t = time step number, Zt =

random variable that takes the value of 1 when there is surplus pressure, and 0 when there is

deficit pressure. The System-Reliability Index (Rnw) (Eq. 24) can be considered here for the

reliability of the whole system.

2- Resiliency (Res) is the ability of the system to recover from failure and continue with

acceptable operation. A number of resiliency formulations have been applied to water resources

(Siminovic et al., 1992). Jinno et al. (1995) defined resiliency as the conditional probability of

entering a failure state at time step t, given that the state was acceptable at previous time step t-1.

The Time-dependent resiliency index of a system can be defined as the inverse of the

expected failure period and may be formulated as the inverse of the average period of

experiencing deficit pressure:

⎪⎪⎩

⎪⎪⎨

⎧

=

≠= ∑

=

01

01

1

1

Fif

Fifdf

F

F

ii (37) Res

Where F is the total number of failures, and dfi is the number of time periods of pressure

deficit during the ith failure. Here, the Network-Resilience Index (In) (Eq. 34), is considered to

estimate the general resilience index, which represents the resilience of the whole system.

3- Vulnerability (Vul) is defined as the ratio of the average deficit pressure to the minimum

pressure during the operation period (T), where h and ht min are the nodal deficit and minimum

allowable pressures, respectively.

min

1min

.

.)(

hT

Zhh t

T

tt∑

=

−=

77

Vul⎩⎨⎧

=01

Zt <≥

min

min,hhhh

t

t (38)

In order to integrate the reliability, resiliency, and vulnerability indices, a new index

called total risk index, TRI , is defined as (Merabtene et al., 2002):

TRI = w (1-Rel) + w (1-Res) + w1 2 3 Vul (39)

Where w is subject to ( ). ∑=

=3

1

1i

iwi

The TRI was introduced at first by (Merabtene et al., 2002) in risk-based performance

assessment of reservoir operations, but is adapted to the performance measurement of WDS here.

As shown in (Eq. 39), TRI is a linear weighted function of the risk of failure (1 - Rel), the risk of

non-recovery from failure (1- Res), and the level of pressure deficit, vulnerability (Vul). The

terms w (1- Rel), w (1- Res) and w Vul define probabilistic measures to analyze systematically 1 2 3

all contributing modes of component failure and to identify the resulting effects on the system. In

addition to a direct indication of the relative performance with respect to each risk criterion, the

weightings w , and w, w1 2 3 reflect the conflict between system stability and system failure mode.

In practice, although a system may exhibit a high degree of reliability, it may in fact be

extremely slow to recover once a failure has occurred (low resiliency). The evaluation process

requires a full understanding of the risk concept as well as advanced statistical methods and other

tools currently applied in risk management like ruin probability, zero-order analysis, and

classical risk process (Novosyolov, 1998).

The estimation of efficient weightings for the TRI function is not a straightforward task.

Such decisions can be made only with a robust knowledge of the network. Furthermore, efficient

evaluation of the degree of vulnerability requires an understanding of the physical performance

of each system component under normal and deficit pressure conditions, and knowledge of the

levels of acceptable outflow shortage. In other words, accepting that a risk exists, the objective is

to define its characteristics quantitatively. These include the magnitude, spatial scale, duration,

intensity of adverse consequences with their associated probabilities, as well as a description of

the cause and effect links. It should be emphasized that the weighting values reflect judgments

about the significance and acceptability of the risk associated with an individual component of

the system, as well as the performance of the entire system. For example, if equal values are

assigned to the weights (w = w = w1 2 3=1/3), the system is characterized by a particular

equilibrium among the risk criteria.

Concurrent with a proper definition of the risk weights, risk thresholds must be defined to

evaluate actual risks for practical applications:

⎪⎩

⎪⎨

⎧

≤≤≤

max

max

max

(Vul) VulRes)-(1 Res -1Rel)-(1 Rel -1

(40)

78

In application to WDS operation, the risks are correlated with demand patterns, operating

requirements, and increase as the age of system advances. As depicted schematically in Figure 4-

1, the risk may evolve from simple performance failures to major catastrophes (0≤ 1- Rel ≤ 1, 0 ≤

1- Res ≤ 1, and 0 ≤ Vul ≤1). The primary concern is to manage the nodal pressure level so as to

maintain the maximum risk below the risk threshold limits. In Figure 4-1, this threshold is

marked by the solid line, which represents the maximum acceptable values of the risk criteria.

The thresholds of the three risk indices are combined to derive the maximum total risk index

TRImax as:

79

Maximum Acceptable Vulnerability

Maximum Acceptable Failure in WDS

Maximum Acceptable Non-recovery

Vul

1-Rel

1-Res

Acceptable solution space

Feasible solution space

TRImax = w (1-Rel) + w (1-Res) + w Vul (41) 1 2 3

Only those systems for which TRImax is less than pre-defined thresholds can be recognized

as safely operating system.

Figure 4-1: 3D Feasible and Acceptable Risk Space (Merabtene et al., 2002)

4.11 Risk-Based Performance Modeling The risk-based performance assessment of WDS follows a number of steps as indicated in Figure

4-2. First, system information is generated including length, diameter, roughness and node

elevations along with all other geometric and hydraulic information needed for hydraulic

simulation. Then, stochastic operating conditions are generated for investigating the total risk

index. This algorithm performs 1, 2, 3, ..., m independent runs corresponding to generated

demand patterns, fire flows, pipe breaks, and other practical conditions. At the beginning of each

run, generated information is fed into EPANET2 (Rossman 2000) to obtain pressure heads. Thus,

each run comprises a sequence of pressure heads h , as indicated in Table 4-1. , h0 1, h , h , ..., h2 3 n

Table 4-1: Monte Carlo Simulation Algorithm for TRI Evaluation

Node Rel Res Vul TRI Run

1 2 3 . n

1 h h h . h Rel Res Vul TRI1,1 1,2 1,3 1,n 1 1 1 1

2 h h h . h Rel Res Vul TRI2,1 2,2 2,3 2,n 2 2 2 2

. . . . . . . . .

m h h h . h Rel Res Vul TRIm,1 m,2 m,3 m,n m m m m

TRImax

Figure 4-2: Flow chart of risk-based performance model

At the end of each run, the sequence of pressure heads is used to compute reliability,

resilience and vulnerability indices for all m independent runs. Then the total risk index, TRI, is

calculated for each individual run. These steps are repeated for all practical operating scenarios.

The run with highest TRI is selected as the maximum total risk of system’s operation and, if this

risk is further than pre-defined thresholds, the system can be considered as high-risk. This means

that it needs some sort of urgent intervention, such as a new rehabilitation plan, a modified

operating schedule or a new maintenance program.

4.12 Summary WDS performance evaluation can be summarized according to a variety of useful metrics, such

as the probability that the system is operational (reliability), the fraction of time that the system

80

is operational (availability), and in terms of indices or surrogate measures that are determined to

reflect the operational requirements of the system (serviceability).

This chapter reviews current approaches for hydraulic performance assessment of

networks via several indicators such as pressure-based indices and reliability-based indices.

In addition, in assessing the hydraulic behavior of specific components, or the entire

system, WDS performance can also be measured by how consistent or reliable it actually is.

Although the hydraulic performance indices described in first parts of this chapter are relatively

straightforward to evaluate, the concept of reliability is less clear. The major approaches to

reliability evaluation are found in the literature are described and scrutinized in order to clarify

exactly what is being considered by each index.

Failure occurrence, regardless of type, duration and reoccurrence, is inevitable.

Therefore, it is essential to evaluate the performance of WDS under abnormal conditions in order

better reveal the nature and impacts of common failures. To this effect, indirect approaches such

as use of resiliency, redundancy, maximum entropy, and connectivity analyses have been

developed.

The direct reliability methods were reviewed firstly, followed by other indirect methods

which are related to reliability. Application of the theory of maximum entropy flows for the

indirect measurement of hydraulic reliability was then reviewed and followed by a discussion of

the suitability of the entropy concept for reliability evaluation. Finally, a key contribution of this

research was featured, the proposal of risk-based performance assessment of WDS. In this

approach, a stochastic process is considered to generate a wide range of practical scenarios, such

as demand patterns and pipe breaks, to apply in the hydraulic network simulation. For each

generated scenario, the hydraulic simulation is solved to compute three corresponding hydraulic

performance indices, which are the reliability, resiliency and vulnerability indices of the system.

Then, a total risk index, TRI, is computed based on these three indices. The process is repeated

for a large number of scenarios to cover the system’s most probable critical operating conditions.

The worst-case value of the total risk index is a good indicator for a generalized hydraulic

assessment of the system.

81

Chapter 5 Water Quality Performance Measurements

5.1 Introduction Water and oxygen are two vital elements that are needed for life. Indeed, in one way or another,

water is needed for almost every activity in modern life. Due to this importance, water utilities

are continually striving to provide clean, safe, reliable and economical water to all customers.

Water is also known as a universal solvent, Capable of dissolving at least a small amount

of almost all substances. Moreover, water can also carry many materials in suspension.

Unfortunately from the engineer’s standpoint, water is not particularly selective in dissolving or

suspending compounds. For instance the water that dissolves sugar or various chemicals in

coffee and tea can also dissolve other contaminants such as lead residuals from pipes or toxic

compounds from soils. Even chlorinated water, created to improve the biological safety of

water, can contain small concentrations of chloroform and other disinfection byproducts.

In this regard, the question here needed to ask is not: does the tap water contain

contaminants? In fact, all water outside of specialized laboratories will have some form of

contaminations and will not be completely pure water. The real questions are: What are the

major contaminates in the water? What are their concentration levels? Do they pose either short

or long term health risks at those levels? And the related issue is do concentrations exceed

legislated limits?

The answers to above questions are not easy, as they depend on various conditions such

as: 1- where the user lives (country, city ); 2- the primary source of drinking water (confined or

unconfined aquifer or surface water);3- what supply agency supplies the water, including

whether it is a private or public agency, small or large municipal water utility, and iv- what

happens in treatment and distribution process, the possible events when water travels from its

source(s) to the treatment and distribution system and finally to consumption locations.

Consequently, the majority of harmful contaminants that can be found in drinking water

are often present in very small amounts. Yet accidental events can introduce a large amount of

contamination even in a short time. They may contribute to serious health problems only after

many years of exposure which make it difficult to detect.

Water treatment and quality measurement are complex problems. There is an ongoing

debate about associated costs, benefits and the risks of every aspect of the water treatment and 82

the subsequent water quality changes in the distribution system. All actions related to the

treatment of water have their own costs, but provide specific benefit in the form of reduced

levels of the targeted contaminants, and decreased risk of diseases. The treatment process may

also add substances to the water that increases risks of other certain diseases.

Prior to the promulgation of the Safe Drinking Water Act (SDWA) of 1990, the hydraulic

performance was the primary concern in the design of a WDS; the goal was merely to ensure that

outflow, pressure, fire flow, and demand requirements were satisfied. Water quality requirements

were usually applied only at the water source(s), thus water treatment issues were considered

only at production points, thus excluding the distributing system. The 1990 SDWA changed the

perspective by requiring adequate disinfection levels at the most distant point of withdrawal.

Thus exploring water quality variations within the distribution system is now a required activity

of water utilities.

This chapter discusses water quality issues in the distribution systems. This includes the

interaction of water quality and public health especially for high-risk people, and briefly touches

on the economic concerns arising from water quality. It reviews the challenge of modeling water

quality, the mechanisms of material transport in pipes, and the identity of the major water

contaminants.

5.2 Water Quality and Public Requirements Safe drinking water, sanitation and good hygiene are fundamental to health, growth and

development. However these basic necessities are still a luxury for many poor people in many

parts of the world. It is estimated that nearly 1.1 billion people, 16% of the world’s populations,

are still without some form of safe water. While 2.4 billion people, close to 40% of the global

population, are living without adequate sanitation. Additionally, inadequate access to water and

sanitation is also unequally distributed between urban and rural areas, and across geographic

regions. Rural coverage of water and sanitation is 71% and 38%, respectively; whereas the

coverage in urban areas is 94% and 86%. Most of people with poor water and sanitation are in

Asia and Africa, 66% of those without water supply and 80% of those without sanitation live in

Asia (Figures 5-1, 5-2). Poor water and inadequate sanitation continue as threats to human

health. Disease analysis shows that poor water, sanitation and hygiene are the third most

significant risk factor in countries with high mortality rates (WHO/UNICEF, 2000).

83

Waterborne diseases are transmitted by poor water, and can affect other illnesses

transmitted by the faecal-oral route. Efforts to reduce diseases are useless unless people use safe

water and basic sanitation. There are an estimated 2.4 million deaths per year resulting from

diarrhea diseases, mostly among children under five. For instance, Murray and Lopez (1996)

stated that, 5.3% of all deaths and 6.8% of all disabilities are associated with diarrhea and selected

parasitic infections in 1990. They estimated that 88% of that burden is attributable to unsafe

water supply, sanitation and hygiene.

Figure 5-1: Population with/out Safe Drinking Water source in 1990, 2004 and 2015

(WHO/UNICEF, 2006).

The Millennium Development Goals (MDGs) have been defined to push back poverty,

inequality, hunger and illness around the world. This means the nations of the world have

pledged to reduce by half the proportion of people without sustainable access to safe drinking

water and basic sanitation (WHO/UNICEF, 2006). Achieving the MDG drinking water and

sanitation target poses two major challenges: a rapid urbanization rate and a huge backlog of

rural people without access to basic sanitation and safe drinking water. It is a major challenge

that requires building new WDS to provide services to an additional 1.1 billion people and

sanitation to an additional 1.6 billion people by 2015 around the world (Figures 5-1 and 5-2).

Construction of modern WDS with efficient water treatment facilities is only the first

necessary step, but not sufficient to control waterborne diseases. The issue is that pathogens and

other dangerous contaminants can also be delivered to water users through distribution systems

and through contamination in transit. 84

Figure 5-2: Population (millions) Without Safe Drinking Water by Region in 2004

(WHO/UNICEF 2006).

Clean water that is reasonably contaminant free and safe at one moment can become

dangerously contaminated a moment later because of accident, neglect, some natural event, or

sabotage. For example, one-fourth of the people in Milwaukee area, U.S., became ill in summer

1993. It was found that the water supply and distribution of the city had been contaminated by

Cryptosporidium, which various testing and filtering systems had failed to detect. It caused a

massive outbreak of gastroenteritis affecting over 400,000 residents, including as many as 100

deaths among immune-compromised people. The main reason was a combination of natural

events such as heavy rains, and accidental events such as improperly installation of monitoring

equipment that allowed the parasite to pass though the purification system and into the

distribution system. Giardia causes the beaver fever, which had also caused some other

waterborne outbreaks in North America like Banff and Edmonton incidents in the early 1980s.

In another water outbreak, Sydney, Australia experienced a series of announcements on

the safety of drinking water in August 1998 just before the 2000 Olympics. The people were

informed that two types of microscopic pathogen, Cryptosporidium and Giardia, had been

detected in their drinking water and three successive episodes of boil water notices affected up to

three million residents during July to September 1998. It ended up by costing tens of millions of

dollars of taxpayers in subsequent public inquiry costs and liability settlements with affected

water consumers (Stein, 2000).

85

5.3 Water Quality and High-risk Groups Like all standards, those for drinking water have some drawbacks. To isolate effects, research

often tends to consider one specific material rather than multiple combinations of contaminations

in water. The same regulations are later applied for a variety of contaminants, and also the

standards for drinking water are often the same for all people regardless of age, and gender.

There are three specific individuals, classified as high-risk exposure groups, including: i)

infants and young children; ii) pregnant women; and iii) elderly people, sick persons and/or

those who has an immune system that has been impaired by disease or treatment (Immune-

compromised people). The higher the exposure to chemical and microbiological contaminants in

water, the higher the vulnerability is for high-risk individuals. Nevertheless current standards

generally do not propose higher protections for more vulnerable people, partly because of the

overwhelming complexity of doing so.

i) Risk for Infants and Children:

Children need more drinking water relative to their size than adults. For example one-year-old

child usually drinks more than twice as much as water compared with adult considering the

difference in size. EPA (1995) announced for the first time new policy to protect infants and

children from exposure to toxic substances. Yet only two substances (lead and nitrate) of the

more than eighty drinking water standards have been set explicitly to protect infants and children

from immediate or long term health risk. No standards have been modified respect to children

and infants since the EPA policy was announced (EPA 1995).

Major water contaminations which are known to have serious impact on infants and

children are lead, nitrates and pesticides. High levels of nitrites or nitrates pollutants in the water

can interfere with infants’ ability to absorb oxygen and thus to cause blue-baby syndrome (called

Methemoglobinemia), that can result in death. EPA has regulations for Nitrates and Nitrites

(EPA, 1995). Pesticides may cause Malignancies in children, in some reports result in Leukemia,

Neuroblastoma, Wilms' tumor, Soft-tissue Sarcoma, Ewing's Sarcoma, non-Hodgkin's

lymphoma, cancers of the brain, Colorectum, and testes (Zahm and Ward, 1998). Biological

contaminations such as E. coli, Giardia and Cryptosporidia cysts can all cause gastro-intestinal

problems where dehydration from diarrhea and vomiting in children and infants may be more

severe and rapid than in adults and even cause death (Cohen, et al., 1996).

Contaminated water may result more Disinfection Byproducts (DBP) in children, while

the risk varies depending on the DBP. Some epidemiological studies indicate a link between 86

certain DBP and a slight increased risk of reproductive and developmental effects. Contaminated

water with moderate level of DBP in long term increases the risk of some cancers (Cohen et al.,

1996).

ii) Risk to Pregnancy:

There are ongoing studies to investigate possible problems of pregnancy with different

contaminations of drinking water such as Arsenic, Nitrates and Lead. Arsenic may cause low

birth weights and spontaneous abortions. Exposure to Nitrates during pregnancy is possibly

linked to neural tube defects (EPA, 1998).

Disinfection Byproducts: EPA (1998) listed several studies to link drinking water with

disinfection byproducts. Trihalomethanes (THM) increase early term miscarriage and the risk of

neural tube defects in the developing fetus.

Neural tube defects: EPA (1998) also reported that elevated odds ratios (ORs), are

generally between 1.5 and 2.1, for the association of neural tube defects with Trihalomethanes

(THMs). If odds ratio is 2.1, it means that the odds of a pregnancy with neural tube defects are

about double what they have been without exposure to THMs. However the only statistically

significant results were observed when the analysis was isolated to those subjects with the

highest THM exposures (greater than 40 parts per billion) and limited to those subjects with

neural tube defects in which there were no other malformations. This is also approved by

Nieuwenhuijsen et al., (2000), and Dodds and King, (2001).

Miscarriages: The rate of early term miscarriage rate for women with low THM

exposure in home tap water is about 9.5%, compared with a rate of 15.7% for women with high

THM exposure. Drinking more than 5 glasses per day of tap water containing more than 75

micro grams per liter of THM, will result significant increase in miscarriage rate in long term

(EPA 1998).

iii) Risk to Elderly people, sick persons and Immune-compromised people:

Old people, patients, and immune-compromised people undergoing major stress or surgery are

more vulnerable to infection, particularly infection from opportunistic pathogens. E. coli

infection can also cause a complication called hemolytic uremic syndrome particularly in

children and old people, in which the red blood cells are destroyed and the kidneys fail.

Therefore greater attention is required to these special groups (Jones, 1991).

87

5.4 Economic Concerns of Water Quality Issues The UN MDGs confirm the central role of water and sanitation in sustainable development.

Having safe drinking water and adequate sanitation can decrease the level of poverty. Therefore

the health and socio-economic benefits of safe water and sanitation are essential to support

resource allocations towards this goal. The costs and benefits of increasing access to clean water

and sanitation vary considerably depending on the type of service. Thus it is necessary to do

efficient economic evaluation on all feasible options (Toubkiss, 2006).

Clean water has different costs and benefits, which are not often considered efficiently in

purely economic evaluation of systems. All major costs and benefits resulted directly and

indirectly from WDS should be considered for a reliable economic evaluation. Certainly, the

direct costs and benefits can be evaluated effectively by current engineering approaches; but

evaluation of indirect costs and benefits are almost invariably more complicated.

There has been considerable debate on the efficiency of traditional cost analysis in the

field of water and sanitation (Hutton, 2001). Water and sanitation programs generally are applied

over the long period for large populations with significant public/private health effects. But the

main concern is that, these programs are often much more costly compared with other options.

Some studies have investigated the influence of safe water in a community. They

demonstrate that safe water, hygiene and sanitation facilities radically reduce public diseases and

population illness. For instance safe water can reduce diarrhea morbidity by 21%. Improved

sanitation also reduces diarrhea morbidity by 37.5%. Additional water quality improvements at

home such as point of use disinfection using chlorine, are simple and cheap measures that make

an immediate difference to the lives of the worst affected. They can lead to a reduction of

diarrhea episodes up to 45% (Varley et al., 1998). They also reassessed the cost effectiveness of

water and sanitation on public diseases especially childhood diarrhea. They considered four

different scenarios (Table 5-1) with combinations of hardware improvements, such as the addition

of basic infrastructure, and software improvements, such as management, regulation, and health

promotion tools. The costs of adding software to existing hardware, or just adding one component

were compared with other health interventions. The non-health benefits were not considered here.

It was observed that expansion of a system is better than improving current management and

health promotion.

The indirect and potential benefits of safe water are sourced from, i- Prevention of health-

related costs; ii- The time previously lost for illness can be saved and translated to more 88

productive activities such as attending schools for students, work for adults. But on the other

side, the costs of achieving public health are highly variable and depend on the level of water

quality and sanitation. The direct costs include initial investment cost and operation and

maintenance costs such as: 1- Planning and supervision; 2- Hardware construction; 3- Water

treatment and distribution; 4- Monitoring and protection of water sources; and 5- Maintenance of

hardware and replacement of components. It should be noted that each activity may be financed

by a number of different agencies, including governmental organization, the community and the

households, depending on the country and depending on which intervention is being considered.

Table 5-1: Scenarios of Cost Effectiveness Evaluation of Water/Sanitation on Childhood

diarrhea, (Varley et al., 1998)

Scenario I Scenario II Scenario III Scenario IV Adding software

Adding software and

hardware

Adding software to existing hardware

Adding hardware only

only Cost/case averted 12.47 60.58 168.81 6.46

Cost/death averted 4,891 14,253 39,720 1,520

Cost/DALY averted 140 413 1,152 44

Economic costs of illness vary in different areas. For instance the direct and indirect costs

of an illness based on U.S medical sector are listed Table 5-2.

Table 5-2: Typical Costs of Illness per Person (Ostro, 1992)

Respiratory hospital admission: RHA

Emergency room visit: ERV

Lower respiratory Illness(kids): LRI

average stay 10.13 days 1 day 14 days

average cost of stay $26,898 $133 $15

Lost day wage rate $ 125 $125 $125

Total cost $28,164 $258 $326 Evaluation of cost is more complicated, when the poor water quality and/or failure in

water treatment cause a disease breakout in an area. For example, Corso et al. (2003) estimated

the total medical costs and productivity losses associated with Milwaukee event based on the

average cost per person with illness. They applied retrospective cost-of-illness analysis on

epidemiologic data from 11 regional hospitals during the outbreak. The medical cost,

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productivity losses and total cost of outbreak were estimated $31.7, $64.6 and $96.2 millions,

respectively. The average total costs per persons with mild, moderate, and severe illness were

estimated $116, $470, and $7,808, respectively.

The evaluation of indirect costs and benefits is much more complicated. Clean water is an

effective tool to reduce the illness rate and even the morbidity rate of waterborne diseases in an

area. But quantifying health impacts ideally should include both the out-of-pocket costs of illness

such as medical costs, lost income and averting expenditures, and the less tangible effects of

illness such as pain, discomfort and restriction in non-work activities.

Health impacts evaluated by willingness-to-pay (WTP) incorporate all of these impacts,

whereas a cost of illness (COI) approach only includes out-of-pocket expenses such as medical

costs and lost income. WTP estimations are often used to evaluate prevention or accepting small

changes in the risks of death. They are usually completed based on empirical evidence gathered

from different people around the world. Additionally for example, some respondents are asked

directly what they are willing to pay to reduce risks associated with work or traffic accidents.

Considerable controversy exists over the value of life or the monetary value of preventing early

death. One commonly used value in the US is $300 for a 0.0001 reduction in risk. Thus the

reduction in the risk translates to $3 million per death avoided (Larsen and Rosen, 2002).

5.5 Mechanisms for Material Transport in Distribution Systems Having discussed various health problems due to wide range of water contaminations, it is useful

to consider the physical mechanisms for transmission of contaminations in distribution systems.

Water quality is related to a system’s chemical performance. Generally, transportation of

a general fluid property happens through some combination of five mechanisms, including:

advection, molecular diffusion, turbulent diffusion, dispersion and radiation.

Advection. Advection is the most common and important mechanism for contaminant

transportation within system pipes and defined as the movement of a constituent with and in

direction of flow with the magnitude of the main velocity component (Boulos et al., 2006).

Molecular diffusion. Movement due to random motion is called diffusion or conduction.

Molecular diffusion is mass transport caused by Brownian motion of molecules, and is often

very small. The velocity of water in a pipe is on the order of feet or meters per second while

molecular diffusion is on the order of feet per day. The additional spreading in the direction of

flow (longitudinal spreading) due to molecular diffusion is not often detectable unless flow is

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very slow. However diffusive movement is important in slow flows that occur, for example, in

dead-end pipes under low constant or intermittent flow conditions.

Since the flow is generally turbulent with a relatively high velocity, molecular diffusion

and turbulent diffusion are normally neglected in WDS. Thus most water quality models just

consider advection. Details of other transport mechanisms are in Lansey and Boulos (2005).

Turbulent diffusion. Turbulent diffusion is the transport of species by the random

movement of fluid parcels induced by turbulence; it is usually much larger than molecular

diffusion. Parcels of water with high constituent concentrations mix with parts of the water that

have low concentrations until the constituent is uniformly distributed throughout the pipe.

Turbulent diffusion also occurs in a pipe during transient flow. Water can pass over and around

the imperfections on the pipe wall at the pipe wall under laminar flow with low velocities. As

velocities increase, water runs into the “bumps” and bounces away from the pipe wall forming

eddies. This mechanism distributes a constituent through the water across the pipe section more

rapidly than molecular diffusion since the fluid is mixing. The momentum and velocity are

mixed in the same way resulting in a more uniform velocity distribution in the pipe.

Dispersion. The axial, or longitudinal, spreading of a constituent mass due to non-

uniform velocities is called dispersion. While advection is the transport at the mean fluid

velocity, the velocity is nearly uniform across a section and nearly equal to the mean value in

turbulent flow. So spreading the constituent-laden mass in the axial direction is small. The non-

uniform velocity distribution causes variations in axial transport across the pipe at low flow rates

and laminar flow. The center of the pipe has a velocity greater than the mean. If only advection is

considered, the additional transport above the mean velocity, that stretches a pulse of constituent,

is not considered (Boulos et al., 2006).

Radiation. Radiation is restricted to energy transport by electromagnetic waves and is

not applicable for typical WDS (Boulos et al., 2006).

5.6 Drinking Water Contaminants What might be discharged from a faucet besides water? This is a simple question but the answer

is seldom simple. Water is capable of dissolving or suspending a tremendous variety of

materials, so there is simply no way to get pure water (H O and nothing but H2 2O) out of taps.

Even distilled water in plastic bottles eventually has thousands of molecules of other constituents

including carbon dioxide (CO2) from the air dissolved in it.

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Nor are all water contaminants bad for health. Many naturally occurring compounds in

water are in fact beneficial for human health. Some minerals, like calcium and magnesium are

essential to health and even drinking water can provide a dietary source for some minerals. For

simplicity, the discussions that follow focus on harmful contaminants. The major

contaminations can be considered in various classes.

1- Inorganic compounds: Compounds without element carbon are called inorganic compounds.

They normally have the ability of dissolving in water and originated from natural sources or as

the result of human activity.

2- Organic Compounds: Organic compounds contain the element carbon. There are some

naturally organic compounds such as sugars, proteins, alcohols that are synthesized in the cells of

living organisms, or they are formed by natural processes acting on the organic chemicals

including substances such as raw petroleum and coal.

2-1- Synthetic Organic Chemicals: Organic chemicals can be synthesized in

laboratories of chemical and industrial companies. There are growing varieties of synthetic

organic compounds such as pesticides, plastics, synthetic fabrics, dyes, gasoline additives like

MTBE, and solvents like Carbon Tetrachloride (MCL=0.005). Many synthetic organic chemicals

vaporize easily in air and are classified as Volatile Organic Chemicals (VOCs) such as benzene

(MCL=0.005), carbon tetrachloride, and vinyl chloride (MCL=0.002).

Significant attention was given to the issue of organic chemicals contaminating water

supplies in January 2000. The potential for water contamination by synthetic organic chemicals

was announced by Denver Water tests which analyzed for 54 VOCs (21 with MCLs established

by the EPA), 73 different pesticides (23 with MCLs), 25 different chemicals classified as

synthetic organic compounds (5 with MCLs), and 7 as non-specific organics. Nearly all the

chemicals were below the levels of detectability. Yet the Denver water tests were done for only

150 out of the thousands of current synthetic organic chemicals, while EPA has established

MCLs for even fewer (Carter et al., 2008).

2-2- Trihalomethanes (THMs) (MCL=0.1): Chlorination is one of the most common

and economical processes used to kill harmful pathogens in water. THMs are formed when the

chlorine interacts with organic materials of the water, like leaf or other biological fragments. The

level of THMs in water is usually higher in those systems that use surface water as a supply

source. Such levels typically vary seasonally with the organic content of the source water supply.

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The most common THM is Chloroform which varies from about 10 to 50 micrograms per

liter from winter to summer respectively, with an average around 20-25 micrograms per liter.

These levels are well under EPA's MCL of 100 micrograms per liter. Driedger and Eyles, (2003),

and Villanueva et al., (2004) found that even drinking water with THM levels below 100

microgram per liter over a 40-50 year period might increase the risk of certain cancers. They

reported that disinfection byproducts can cause adverse reproductive outcomes. But it is

important to know that most people use chlorinated water with small quantities of chloroform.

3- Dissolved gases: Commonly occurring dissolved gases include oxygen, carbon dioxide,

nitrogen, radon, methane, and hydrogen sulfide. They do not often have appreciable health

effects, except for hydrogen sulfide and dissolved radioactive gases like Radon.

Radon is a radioactive gas which comes from the natural breakdown (radioactive decay)

of radium that is itself a decay product of uranium. The major source of radon at homes comes

from the underlying soil and bedrock. However an additional source can be the water supply,

particularly if the house is served by a private well or a small community water system.

4- Metal and Positive Ions: Some positive charged ions of interest include lead (MCL=0.015),

mercury (MCL=0.002), arsenic (MCL=0.05), aluminum, zinc, and copper (MCL=1.3). They are

extremely dangerous even at low concentrations and can be introduced into drinking water either

though natural processes or as a result of human activity. Other ions such as calcium,

magnesium, sodium, and potassium are essential to human health in correct amounts. Calcium

and magnesium are interesting ions, although they may have some health benefits, but they are

the prime culprits in most hard water. They may be undesirable contaminants for those

consumers that observe deposits of calcium carbonate on their faucets and in their pipes and

water heaters. Calcium carbonate may cause problem in bathing not to get the soap to lather.

5- Negative Ions: Negatively charge ions of importance include chloride, phosphate, sulfate, and

cyanide (MCL=0.2). As with positive ions, some of these negative ions are thought necessary to

life in proper amount like Fluoride (MCL=4.0) and carbonate; others can be harmful at moderate

levels such as nitrites (MCL=1.0) and nitrate (MCL=10.0).

6- Suspended Materials: Countless varieties of materials can be suspended in water, but only

those that affect the health or drinking water quality are important. If there are enough particles

suspended in water, it becomes cloudy or turbid. Light bounces off the suspended particles

giving the water a milky or muddy appearance. Gases dissolved in water can also cause

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turbidity. Gas bubbles eventually rise to the surface and disappear. But other materials suspended

in water neither rise nor settle, so the water does not clear.

7- Pathogens: These are organisms that cause diseases such as E. coli, Cryptosporieia, and

Giardia.

7-1-Viruses: Although, most viruses in water cause gastrointestinal illnesses such as

diarrhea, vomiting, and cramps. Viruses that cause Hepatitis A and E can also be transmitted in

water, but they are relatively difficult to detect.

7-2-Bacteria: More than one century ago cholera (caused by Vibrio cholera) and typhoid

fever (caused by Salmonella typhi) were responsible for epidemics caused by poor water and

killed many thousands of people. But because of chlorination and other water purification

processes, occurring cholera outbreaks are rare unless an accident or natural disaster has disabled

water treatment plants. Fecal bacteria, E. coli, are the most pathogenic bacterial contaminant in

North America, which enters the water from human and animal wastes. EPA regulates the MCL

of these bacteria in drinking water.

8-Other suspended solids: Unless the materials in water are themselves dangerous, suspended

solids in water are typically a nuisance rather than hazardous. Suspended materials in the water

can interact with the disinfection processes making them less effective (Johnson, 2005).

5.7 Water Quality Modeling After an overview of common contaminations of drinking water and their physical mechanisms

for movement within the distribution systems, it is helpful to consider how these processes can

be modeled from sources to end points.

Water quality assessment of WDS can be done by two complimentary approaches: direct

sampling and computational predictions. The first approach is straightforward and simple, but

needs considerable time and money. It is suitable and practical for monitoring the quality of

water based on predefined standards. By contrast, computational predictions are based on

mathematical modeling. They are cheaper and do not waste material and need less time, but

require more sophistication and have obvious concerns of accuracy particularly when the

underlying phenomena are complex. The level of uncertainty in final result due to not accounting

all contaminations is higher than in the direct approach. Yet these models can be of assistance in

obtaining a sufficiently accurate picture of the quality of water through the system, which can

later be verified or confirmed through limited sampling.

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Mathematical models have been increasingly used as efficient tools for forecasting water

quality variations both in space and time. They are classified into two major groups: 1) those

concerned with modeling the processes that influence the movement and transformation of

chemical, biochemical and physical properties of the water, and 2) those that aim to translate the

processes governing microbiological life within the system.

The first group uses the equations of advection, mixing and transformation of

conservative and non-conservative substances and parameters, such as turbidity, and chlorine

residuals. These models are based on purely physical processes that were described in previous

sections. They are basically built on the hydraulic equations of the system and the transformation

phenomena such as decay, growth and reaction. There are adequate documents with useful

results for many chemical and biochemical substances (Boulos et al., 2006)

The second group conversely is more difficult to develop and calibrate. It is because of

additional degrees of freedom and the number of extra factors that are introduced by the

biological processes which rule the presence of bacteria and other forms of life. Not only the

advection and mixing processes are involved, but the life patterns and interaction with the

environment are important. For example growth of biofilm on pipe walls is washed away when it

grows beyond certain proportions, or flow velocity increases abruptly. For developing efficient

models, these reactions are considerably more complicated. They depend more on practical

experiments and field calibration, as compared with the validation requirements of the first group

of models (Boulos et al., 2006).

The water quality modeling can be classified into two major groups: Steady state models

and Dynamic water quality models based on flow regime of contaminations and water quality

flows during the operating period.

Steady state water quality modeling: This situation is achieved when water quality at

all withdrawal nodes does not change over the time. If steady conditions are not established, the

source contributing to water withdrawal from a node or the percentage of water that is provided

by a source may change and then causing changes in water quality. But equilibrium water quality

only occurs when flow conditions are time invariant and full flow from all sources has reached

all withdrawal nodes along all paths. Thus the time needed to reach steady state water quality is

equal to the longest travel time from any source to any node. Under steady flow conditions, the

paths from sources to withdrawal nodes can be identified and the mixing of waters at junction

nodes can be evaluated to compute the water quality conditions (Boulos et al. 2006). 95

It should be noted that, flow paths are reasonably easy to trace for a small system. But

identifying paths and setting up mixing equations for steady state analysis are complicated in

large systems. Boulos and Altman (1993) developed a general analytical approach to determine

source contributions, water age and conservative constituent concentrations in a WDS by

extending early approaches by Males et al., (1985); Chun and Selznick, (1985); Clark et al.,

(1988); and Boulos et al., (1992).

Dynamic water quality modeling: Steady state water quality modeling is of interest

from a global perspective of understanding how a system reacts about the variation of water

quality respect to location and distance to source(s). However a system rarely experiences

equilibrium water quality condition due to fluctuation of water demand and constituent

concentrations. Dynamic simulation is an effective tool for modeling water quality variations and

evaluation of potential water quality problems over the service life.

The equations for nodal mixing and advective transport in pipes describe the water

quality condition within the system. These equations can be solved analytically for some simple

systems. But numerical approaches are more effective than other methods for large and complex

systems. Dynamic water quality has been modeled by using Eulerian and Lagrangian

approaches. Eulerian methods consider fixed grids or cells and move water to the grid locations

or through the cells to represent the movement of a constituent in the pipe. Chemical reactions

are included during transport. Lagrangian methods track locations of discrete changes in water

quality known as fronts. Front locations are updated at a fixed time step or when a front reaches

a junction. Rossman and Boulos (1996) compared these methods and their conclusions regarding

the computation time, accuracy and computer memory requirements.

5.8 Influence of Distribution System on Water Quality The chemical, microbiological and aesthetic quality of drinking water usually well adjusted at

the treatment plant prior to distribution to ensure that the water is both safe and tasty to all

customers. However water often travels through long distributing pathways before delivering to

final destination. This procedure may take considerable time and this period certainly has side

effects on the quality of the water. Water quality is variable in space and time across the system.

Aesthetic properties such as odor, taste, color, and its chemical, physical and microbiological

contents are often deteriorated by the water’s movement through the distribution pipes. Various

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transportation, mixing and transformation processes occur during the period of time between

production and consumption.

Yet in these considerations, it is well to bear in mind that no system exists without

occasional failure. Accidents happen, mistakes are made, systems wear out, toxic chemicals are

released into the environment to find their way into the surface and ground water, regulators fail

to operate well, population growth in a region puts more stress on a water system than it can

handle, and many other weak elements are in the chain of water supply and distribution process

from sources to final withdrawal points that are often customers’ faucets (Johnson, 2005). The

only real way to approach such matters is through explicit consideration of likelihood, but the

industry has been slow to accept these assessments as routine.

In this regard, EPA announced that the number of annual waterborne outbreaks has

declined since 1982, but those problems that are related to distribution systems have been

increased significantly. Improvements in water treatment technology may be the main reason,

but the water quality problems in distribution systems clearly need greater attention.

Recently, EPA surveyed main reasons for reported outbreaks of distribution systems

during 1981-2002 (Figure 5-3). Cross-connections and backflows are the most common water

quality problems in distribution systems. It is based on the long history of recognized health risks

posed by cross-connections as well as clear epidemiological and surveillance data that relate

these events with outbreaks or sporadic cases of waterborne diseases.

Figure 5-3: Outbreak Sources in WDS (1981-2002), (Kirmeyer and Dewis, 2007)

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Cross-connections are temporary or permanent connections between either public water

system or consumer’s potable water system with any source containing non-potable water or

hazardous substances. They are the links through which it is possible for contaminations to enter

a potable water supply. The contaminants enter the potable water system when the pressure of

the polluted source exceeds the pressure of the potable source. For example, the piping between

a residential plumbing system and an auxiliary water system, cooling system, well, and irrigation

system are cross-connections (EPA, 2003).

The following is a list of cross-connection fixtures in urban drinking systems: agricultural

mixing tanks; auxiliary water supply; dialysis equipment; dishwashers; garden hoses; fire

protection systems; lawn irrigation systems; photographic developers; sinks; solar energy

systems; swimming pools; toilet flush valves; watering troughs; and water softeners.

Another source of water quality problems is backflows. They can happen due to

undesirable reversal flow of non-potable water or other substances through a cross-connection

into the public water supply system or consumer’s potable water system. Backpressure and

backsiphonage are two common types of backflows in distribution systems (EPA, 2003).

Backpressure is kind of backflow which is often caused by a downstream pressure that is

greater than the upstream or supply pressure. It can happen due to an increase in downstream

pressure, a reduction in the potable water supply pressure, or a combination of both. Turning on

and off the pumps and other similar events can increase downstream pressure. Pressure

reductions often occur when the demand for water is higher than the water being supplied, such

as during water line flushing, fire fighting, and pipe breaks.

Backsiphonage is another kind of backflow that is often caused by a negative pressure

(i.e., a vacuum or partial vacuum). This event is similar to drinking water through a straw.

Backsiphonage can occur when there is a stoppage of water supply due to nearby fire fighting, a

break in a water main, or any other situation that causes a significant loss in system pressure.

Backflow within a public water system can contaminate the potable water in that system,

and threat consumer’s health. Therefore it is necessary to take precautions to protect distribution

system against backflows (EPA, 2003).

Residential, commercial or industrial facilities with potential backflows may have

following conditions: 1) there is an auxiliary water supply which is connected to the drinking

WDS; 2) there is piping for conveying liquids other than potable water, where that piping is

under pressure and is installed in proximity to potable water piping; 3) there is intricate plumbing 98

which makes it impractical to ascertain whether or not cross-connections exist; and 4) there are

cross-connections or potential cross-connections within the system.

The above list approves that a significant variety of residential, industrial and rural water

users may have serious water quality problems, if their plumbing and distribution systems do not

operate safely. It may result because: First; plumbing is frequently installed by persons who are

unaware of the inherent dangers of cross-connections. Second; such connections are made as a

simple matter of convenience without regard to the dangerous situation that might be created.

Third; they are made with reliance on inadequate protection such as a single valve or other

mechanical device.

The criteria for selection of the best cross-connection control elements depend on the

physical situations of the system and vary in different locations. The practical solutions for cross-

section and backflow problems are implementing effective controlling elements in critical points

such as ensuring or installing: (a) an air gap separation; (b) reduced pressure principle by

backflow prevention assembly; (c) atmospheric vacuum breaker; (d) pressure vacuum breaker

assembly; (e) double check valve assembly; and (f) residential dual check (EPA, 2003).

Corrosion is the second major cause for water quality problems in WDS by 15% of total

outbreaks during period of 1982-2002 (Figure 5-3). It may include water chemistry, electric

grounding and microbially-induced. The proposed solutions for corrosion include use of newer

pipe materials and implementing corrosion control systems (Rotert, 2001).

Transient events can accelerate water quality problems by surging high pressures within

the system (EPA, 2003). They may spread settled particles through the pipes. On the other side,

the low pressure transient may cause the intrusion of contamination into the pipes at leaky joints

or pipe breaks. It is necessary to prevent such circumstances that the distribution system became

a vehicle for transmission of contamination rather than safe and clean water. Intrusion is in third

place of water quality problems in WDS with approximately 10% share events (Figure 5-3).

Common reasons for pressure drops in WDS include pump operation, flushing operations,

hydrant operations, changes in demand, main breaks, valve operation, and power failures.

Increased leakage and more pipe breaks which assist backflow and intrusion events, are

the primary results of aging WDS. Leak sites can become a portal for contaminant intrusion. The

problem is a dynamic one, because piping systems are continually being installed, altered, or

extended. Effects of improper repair and replacement may result microbial, chemical

99

contaminants introduced; problems of large releases of Chlorine; and accelerated corrosion

(Rotert, 2001).

Although penetration of contaminants into distributing pipes has serious problems, but

the influence of internal factors such as pipe walls on water quality issues should be considered

too. For example chlorination is the common treatment and disinfection process. The minimum

level of chlorine residual is required within the distribution system to preserve both chemical and

microbial quality of treated water and due to potential chlorine’s oxidizing (Vasconcelos et al.,

1997). But chlorine has the ability to react with different materials inside the pipe when it travels

through the pipes. The level of chlorine is decreased due to water oxidation of dissolved organic

compounds as well as interaction with internal walls of pipe (Frateur et al., 1999). In systems

with unlined cast iron pipes, the impacts of wall materials play an important role in kinetics of

chlorine reaction. Some studies indicate that the decay rate of chlorine in the pipe is several

times greater than the decay rate of the same water in a flask (Wable et al., 1991). It is thought

that the pipe wall and material attached to the pipe wall contribute to the total chlorine demand in

distribution systems (Clark et al., 1995).

There are several major reasons that might increase residence time and thus increase the

risk of water quality changes: i) Improperly sized distribution systems; ii) Low flow areas (e.g.,

dead ends); iii) High demand storage; and iv) insufficient flushing or inadequate exercise or

valves. Problems associated with excess residence times include: 1) Formation of disinfection

and corrosion byproducts; 2) Increase in proliferation of microbes; 3) loss of disinfectant

residual; 4) It can accumulate contaminants for later release especially for inorganic materials;

and 5) Other problems such as taste, color and odor.

There are some other sources that may cause water quality problems in WDS such as

permeation and leaching. But their contributions are less important than others. Permeation may

result due to plastic pipes in close proximity to gas tanks (VOCs) and runoff, pipe replacement

and the use of compatible pipe materials. But leaching may result because of contact with

linings, including asphalts with high VOCs, or improperly cured pipe materials (Rotert, 2001).

5.9 Water Quality Assessments There is no doubt that water pollution often leads to serious health problems and significant

economic costs. But quantifying these impacts is difficult and values are often under-estimated

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or even ignored. In fact, it is extremely difficult to establish direct cause-effect relationships, and

it is usually impossible to place precise monetary values on health impacts or productivity losses.

Nevertheless, recent advances in quantifying environmental impacts provide new

opportunities for the monetary evaluation of health impacts. This awareness has increased the

application of environmental data and statistics in evaluation of poor water impacts on public

health. The major impacts of poor water on public areas include the following.

Health impacts are the most important impacts that need the most attention. It is often

easier to estimate economic costs of health outcomes; this information is effective for influence

on public decision makers. However they deserve initial attention as they are often large,

measurable, and quantifiable.

Productivity impacts are often important, but can be estimated roughly. They are

measurable economic costs which are used, especially when individuals or firms need to install

special equipment or take special measures to protect themselves from water pollution. Poor

water often causes reduction in productivity, and even restriction or preventing certain activities.

The final result is increasing both economic and social costs of the community.

Ecosystem impacts may occur when the environment is hurt such as contamination of

water reservoirs. They are harder to quantify and the real impacts may not be felt for long time.

They are often evaluated in qualitative form.

Aesthetic impacts are the last, but not the least, of water pollution impacts. People feel

hurt or injured if they live in a polluted environment, which results in a loss of social welfare.

Due to increasing public awareness, most people have willingness-to-pay for a cleaner

environment and safe water, but low income levels of poor people do not allow them to take

effective counter measures. Whittington and Lauria (1991) noted that people, even poor people,

are willing to spend considerable amounts for safe water supply. For example household in India

and in southern Africa are willing to invest in basic sanitation by themselves four times as many

cases as the households that are only served by government projects. It appears that the non-

health benefits of safe water and sanitation are great, and can be more popular. However

wealthier people have a larger ability to pay for an improved aesthetic environment.

While there is no reliable data to evaluate how much customers are willing to pay for safe

drinking water over the long period, the state of Ohio released a survey data and information

regarding the changing rates of customer fees for the period of 1989-1999. There has been an

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upward shift in the number of communities paying higher annual fees over the time. It approves

that paying more for safe water is accepted in urban areas (Figure 5-4).

Figure 5-4: Change in Customer’s Fees in Ohio (1989-1999) (EPA, 2002).

Damages arising from death and human injury are not traded in the market and thus have

no directly measured economic value. Non-market valuation techniques such as willingness-to-

pay and contingent valuation developed in the field of economics may be useful to estimate the

value of human life and the costs of human injury and illness. Despite this, these techniques are

unable to fully account all impacts of a quality failure on a human life (Hutton, 2001).

Rapid progress in the economic art of quantifying means that many environmental

impacts can be valued and placed within the framework of traditional economic analysis. Direct

impacts are generally easier to identify monetary values. Health and productivity effects are in

this category. But quantifying ecosystem and aesthetic impacts are much more difficult due to

complexity of the impacts and nature. Various contingent valuation methods have expanded the

ability of quantifying values for ecosystem or aesthetic impacts.

The investment or action for improving public health obviously makes sense. But when

an action, for example improving water quality criteria like asbestos removal from some

manufacturing processes, is it a good investment? The answer, of course is "it depends"; it

depends on the value of a life saved, and alternative actions that can reduce premature death.

Problem of quantifying cost of death or mortality is more complicated. There is no

perfect technique. However information on the cost-effectiveness analysis (CEA) of preventing

deaths is useful, but does not quantifying value of a life. Human capital approach is a known

approach to estimate the value of a human life. This approach is based on foregone earnings. It 102

treats a life as a piece of productive capital and estimates the production lost from premature

death. This approach is full of methodological and has moral problems.

But there is another approach which is based on information on the willingness-to-pay of

individuals to avoid premature death. They are based on contingent valuation methods which use

survey questions to determine values and other available data such as observing the risk premium

individuals demand to do riskier jobs and yield estimates of the value of a statistical life.

It is important to note that a statistical life is not equal to any individual life, it represents

the change in premature mortality across a population from any given cause. In addition,

willingness-to-pay measures reflect the whole range of costs associated with premature death,

loss of production as in the human capital approach, suffering, losses imposed on other family

members and society, and all complex attributes associated with a human life.

This willingness-to-pay estimation is often higher than what derived from the human

capital approach. For instance, a statistical life is valued on average several million dollars,

versus a commonly used value of about $3 million in U.S.

Finally, the economic valuation of health impacts is a dynamic field that demonstrates the

potential for using economic analysis of health outcomes to help identify priority environmental

problems. After completing the estimation of expected changes in health status and pollution

exposure is completed, the economic valuation of these health outcomes can be done. The

valuation of morbidity effects is fairly direct, at least for lost production and health care costs.

But premature mortality poses difficult analytical and moral issues. In the economic analysis of

poor water with health impacts, it is recommended that values for morbidity be quantified and

expressed in monetary terms in form of numbers of deaths involved or avoided (Hutton, 2001).

5.10 Summary Water quality is crucially important performance area for all WDS and, as such, it has been

investigated intensively. Water utilities need to meet service standards relating to the potability

and aesthetic aspects of the water. Potable water must pass strict restrictions on its

microbiological contents, as well as on the concentration values of chemical, biochemical and

constituents carried with it. Additionally the latter may influence certain characteristics of the

water that results in poor appearance, odor or taste and therefore should be controlled to ensure

consumer satisfaction.

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This chapter reviews issues relating to a system’s water quality performance. It starts with

a discussion about the relationships of public health and quality of water. The importance of

access to safe water and sanitation systems are discussed. Then a discussion relating to water

quality requirements for high-risk exposure groups is provided. These groups include, children

and infants, pregnant women, elderly people, as well as sick persons and those who have weak

immune systems. As these people are more vulnerable to poor water, they deserve more attention

regarding to water quality criteria.

Furthermore economic concerns of water quality problems are investigated based on

cost-benefit analysis of safe water on communities. WDS are costly systems with a variety of

different benefits. It is often difficult to perform precise cost-benefit cost analysis, because

quantifying all contributing costs and benefits of WDS is too difficult.

Later, a brief description about the main physical mechanisms for transportation of

materials in pipes is presented. Then several major contaminants that may be found in drinking

water with their possible sources in WDS are considered. Then several recommended standards

of major constituents are compiled.

It is impossible to have a WDS system without any failure. Due to accidents, mistakes,

system deterioration, some form of contaminants may enter to system. It is obviously helpful to

know how the contaminants can be transmitted through the system. As a result, water quality

models are used to predict the behavior of spreading the pollution within the system. The current

water quality modeling approaches are briefly summarized.

The distribution systems usually receive clean water from treatment plants and then

transmit it to consumers’ locations. The influence of transmission procedure on water quality is

essential to guarantee safe water for customers. Cross-connections as well as backflows

including backpressures and backsiphonage, are the major reasons for water quality outbreaks in

recent decade. A cross-connection is a direct arrangement of a piping line which allows the

potable water supply to be connected to a line which contains a contaminant. Backpressure is the

reversal of normal flow in a system due to an increase in the downstream or customer’s pressure

above that of the supply pressure. Backsiphonage is the reversal of normal flow in a system

caused by a negative pressure (vacuum or partial vacuum) in the supply system.

And finally, major concerns in water quality assessments of WDS are reviewed to

complete other performance areas that were described before.

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Chapter 6 Total Life Cycle Cost Evaluation

6.1 Introduction As discussed in Chapter 3, there are various approaches for optimizing the design and operation

of WDS. Although considerable improvement has been recently achieved in mathematically

formulating and solving such optimization models, most of them address one, or at most two,

major cost components, highlighting the need for developing a new framework that incorporates

additional economical aspects of the system.

The efficient operation of the system should be based on a number of premises, such as a

proper and adequate knowledge of public health indicators, an understanding of system

performance, the level of service provided and required, intervention options and their

implications, the costs associated with system performance, and the consequences of service

failures. Moreover, an intellectually robust, transparent and auditable methodology should be

used for combining these aspects to ensure that all customers are satisfied and enjoy the highest

level-of-service possible. Hence, there is a need to develop a comprehensive approach that

considers system performance within a sensible economic and engineering decision support

framework. Economic representation of the system should be clear and reflect all major costs

and benefits of the actual infrastructure.

This chapter investigates the total life-cycle cost evaluation of WDS by exploring a

generalized framework for considering all major cost components of a WDS and proposing

efficient trade-offs between all major contributing costs and performance criteria.

6.2 Pipe Materials in WDS A variety of materials such as metal, concrete, and plastic, are employed in the water industry.

Some composite materials are also used in pipe manufacturing. Criteria for pipe material

selection are often based on economic considerations, but other elements, such as environmental

impacts, the system’s physical and ambient conditions, hydraulic and water quality properties,

and construction, installation and operational parameters should be considered.

Table 6-1 lists the most common pipe materials used in the water industry along with

their defining characteristics, including size, recommended pressure range, and design codes.

Table 6-2 lists the approximate market share for all major pipe materials in the Canadian water

105

industry, confirming that all materials appearing in Table 1 are currently present in the domestic

infrastructure. Clearly, PVC is the prevalent material for small size pipes (4"-12"), while ductile

iron and concrete pipes find their application in conduits of larger diameter. What follows is a

brief description of the most popular pipe materials.

Table 6-1: Specification of the Most Common Pipe Materials (Ysusi, 2000)

Sizes mm(in.)

Pressure range kPa(lb/in2)

Standard & Design Guidelines Name of Pipe Material

DIPRA, 1984 100-1350 1380- 2400 (200-350) Ductile Iron Pipe (DIP) Cast-iron

AWWA C150,151 (4-54)

ASTM D 1784 100-900 (4-36) Polyvinyl Chloride (PVC) Polymer Up to 2400 (350)

ASTM F1483-05

100-3600 (4-144)

690-17,000 (100-2500) Steel Pipe Alloyed steel AWWA C200

Reinforced Concrete Pressure (RCPP)

Reinforced Concrete

300-3600 (24-144) Up to 1380 (200) AWWA C300-303

AWWA C901&906 High-Density Polyethylene (HDPE)

100-600 (4-63) Polymer Up to 1750 (250)

ASTM D 3350

Cement ASTM C 296 100-1050 (4-42)

Asbestos-Cement Pipe (ACP) Up to 3100 (450 )

+Asbestos fiber AWWA C401-403

Table 6-2: Water and Sewer Pipe Market Share, 1993 (Environment Canada)

Type of Pipe Water main Sanitary and Sewer Pipe

4"-12" (Small)

14"-36" (Large)

4"-15" (Small)

18"-36" (Medium) 36"+ (Large)

PVC 88% 25% 85% 34% 0%

HDPE 0% 10% 5% 2% 0%

Ductile iron 12% 35% 0% 0% 15%

Concrete 0% 30% 10% 64% 85%

Total 100% 100% 100% 100% 100% 106

i. Ductile Iron Pipe (DIP)

The oldest pipe material used in the water industry is ductile iron pipe (DIP), a cast iron product.

Cast iron pipe is manufactured from an iron alloy which is centrifugally set in sand or metal

molds. The strength, durability, and long service life of ductile iron’s predecessor, gray cast iron,

are widely recognized.

Cast Iron was used in Germany about six centuries ago and first appeared in the U.S. in

1817 where it was used in the Philadelphia water system. Since then, more than 600 utilities have

deployed cast iron pipes with more than 100-year service life across North America, and at least

21 utilities have employed cast iron pipes for more than 150 years (Ysusi, 2000).

ii. Steel Pipe

Steel is another popular conduit material, appearing in pipes of all almost every size found in

WDS. The main advantages include high strength, the ability to deflect without breaking, ease of

installation, shock resistance, lighter weight than ductile iron pipe, relatively easier fabrication of

larger diameter sections, the availability of special welded configurations, and easy field

modification.

Recently, stainless steel has been used as a pipe material because it offers greater strength

and ductility than either steel or ductile iron. The higher strength permits reduced pipe wall

thickness and the coating and cathodic protection necessary for steel and iron is often not

required. Stainless steel is also more resistant to erosion when accommodating high flow rates

and can tolerate high velocities and turbulence with a lesser degree of pipe wall degradation.

This in part allows stainless steel pipes to retain more of their original hydraulic capacity than

pipes with aged cement lining or steel pipes, resulting in less headloss and energy dissipation.

Other advantages of stainless steel pipes include ease of shape fabrication, greater permissible

length, recycleability, depressed leakage rates, extended service life, and diminished

environmental impacts (Ysusi, 2000).

iii. Reinforced Concrete Pressure Pipe (RCPP)

Reinforced concrete offers good pressure and tensile resistance. Various RCPP are manufactured

in North America, such as steel cylinder (AWWA C300), pre-stressed, steel cylinder (AWWA

C301), non-cylinder (AWWA C302), and pre-tensioned steel cylinder (AWWA 303).

While pre-tensioned steel cylinder pipe is suitable for semi-rigid design, the other types

of RCPP are suitable for rigid design. AWWA M9 introduced the terms rigid and semi-rigid to

differentiate between two design theories. Essentially, rigid pipe does not depend on the passive 107

resistance offered by adjacent soil for the structural support of vertical loads, semi-rigid pipe

requires passive soil resistance for vertical load support (Ysusi, 2000).

iv. Polyvinyl Chloride Pipe (PVC)

A recent pipe material which has often been used especially in municipal WDS is Polyvinyl

Chloride Pipe (PVC). It is a polymer which is extruded under heat and pressure into a

thermoplastic that is quasi inert when exposed to most acids, alkalis, fuels, and corrosives. It

claims about 66% of the WDS and 75% of sanitary sewer markets in the U.S. (Ysusi, 2000).

There are several other types of plastic pipe, but PVC is the most common variety in

municipal systems since the 1960’s owing to its high corrosion resistance, high strength-to-

weight ratio, low reactivity, ease of installation, and smoother interior wall surface. In addition,

PVC pipes can be fused together using various solvent cements, creating permanent joints that

are impervious to leakage. Despite such advantages, metals are still preferred when very high

strength and ease of disassembly are required.

Environmental issues are the major limitation for PVC pipes despite numerous

investigations undertaken to assess the impact of PVC on water quality. The California Building

Standards Code approved chlorinated polyvinyl chloride (CPVC) pipes for residential water

supply systems in February 2007. CPVC has been a nationally-accepted material in the U.S.

since 1982. Yet its use has been permitted only for limited applications since 2001 (Ysusi, 2000).

v. High-Density Polyethylene Pipe (HDPE)

HDPE is another new plastic pipe material which has found relatively widespread

application in the water industry. Low-density polyethylene was first introduced in the 1930’s

and 1940’s in England, and later in the U.S. Initially, it was mostly used for cable coatings until

pipe grade resins were developed in the 1950’s. Today, high-density, extra-high-molecular

weight materials have been developed for other applications. Polyethylene pipes constitute

almost all natural gas-distribution pipes installed in the U.S. since 1970. However, the AWWA

has only just approved it for water distribution systems.

Using polyethylene pipe in municipal WDS was often limited to water services. HDPE

pipe is gaining acceptance for use in municipal water systems due to its exceptional corrosion

resistance, light weight, high strength-to-weight ratio, resistance to cracking, smoother interior

wall surface, and demonstrated resistance to damage during seismic events.

108

HDPE pipe is rated to pressure classes in design standards according to AWWA C906.

The pressure classes allow for pressure rises above working pressure due to occasional transients

which do not exceed twice the nominal pressure class and recurring pressure surges not

exceeding one and a half times the nominal pressure class.

HDPE Pipe has better hydraulic characteristics than other common pipe materials. The

Hazen-Williams coefficient for HDPE pipe is 150 and changes minimally over time due to its

high corrosion resistance and impropensity to tuberculation. The combination of butt-fused, leak

free joints and flexibility render its fabrication and installation more straightforward than for

rigid pipes and its viscoelasticity is more able to absorb pressure surge energy, countering the

tendency for oversized design and yielding cost savings (Ysusi, 2000).

vi. Asbestos-Cement Pipe (ACP)

ACP is another common pipe material which has been in use since the 1930s. It is made by

mixing Portland cement and Asbestos fiber under pressure and heating them to produce a hard

and strong pipe. Over 480,000 km (300,000 mi) of ACP is estimated to be in service in the U.S.

Concern about the environmental hazards of asbestos emerged in the late 1970s,

provoking a significant debate about its use in drinking water applications. Some experts

advised about the potential dangers associated with asbestos while others claimed that pipes

made with asbestos do not result in elevated asbestos concentrations in drinking water. Studies

have revealed no link between water supplied by ACP and any specific diseases; however,

fallout from the controversy had serious impacts on ACP and PVC use recently (Ysusi, 2000).

6.3 Influence of Pipe Material on Performance

Selection of pipe material is undertaken according to several criteria which are often related to

the physical traits of the pipes and interactions with their surrounding environmental. It should

be noted that pipe size is often the only parameter incorporated in conventional WDS design but

other important criteria should also be considered when selecting pipes and pipe material:

• Service conditions: 1- Pressure (surges and transients); 2- Soil loads, bearing capacity of soil

and potential settlement; 3- Corrosivity of soil particles; and 4- Potentially corrosive nature of

some waters.

• Availability of material: 1- Local availability and experienced installation personnel; 2-

Sizes and thicknesses (pressure ratings/classes); and 3- Compatibility with available fittings.

109

• Properties of the pipe such as: 1- Strength (static and fatigue); 2- Ductility; 3- Corrosion

resistance; and 4- Fluid friction resistance.

• Economics that include: 1- Installation; 2- Service life; and 3- maintenance and repair costs.

Total pipe cost is the summation of real costs including purchase cost, shipping,

installation, maintenance and expected repair costs over the service life of the pipe. As the pipe

expense constitutes a significant part of the initial cost, it is advisable to do a comparison

between the total cost of different pipe materials and their performance attributes. For example, a

simple cost comparison with installation equipment costs between DIP and PVC pipe reveals

that the equipment to tap PVC involves about $90 more than the equipment to tap Ductile iron,

even though the tap cost of PVC is $45 cheaper than that for DIP (listed in detail in Table 6-3).

Table 6-3: Cost Comparison Between PVC and DI Pipe.

PVC Pipe Ductile Iron Pipe

Tap (3/4") $70 Drill/tap (3/4") $115

Shell cutter (3/4") $91

Removal tool (3/4") $18

Safety blanket (estimated) $25

Total $204 Total = $115

The criteria for pipe material selection may be changed if other specific factors such as

service conditions are essential. For example, steel pipe and RCPP are both available in 300 mm

(12 in) diameter. However, the installation cost of DI or PVC pipe is typically lower for

municipal use in the 300 mm (12 in) size. Therefore, if the service conditions do not call for the

high-pressure capabilities of steel or RCPP, DI and PVC pipes are good candidates for 300 mm

pipe (12 in). Conversely, if the proposed pipeline is 900 mm (36 in) in diameter, the installation

cost of both steel and RCPP tend to be more competitive, ignoring the particulars of location. A

general comparison of various pipes is summarized in Table 6-4 which highlights the major

advantages and limitations of pipe materials in the water industry.

The useful life of a pipe depends on several factors including material, the soil

conditions, and the chemical characteristics of the water flowing through it. In addition, pipes do

not deteriorate at a constant rate. During the initial period following installation, deterioration is

110

likely to be slow and may unfold over several decades; therefore, repair and maintenance costs

are low. With advanced age; however, deterioration rate and maintenance costs can pick up pace

(Figure 6-1).

Table 6-4: Comparison of Pipe Materials for WDS (Ysusi, 2000). Pipe Advantages Disadvantages/Limitations

Yield str.= 290,000 kPa (42,000 lb/in2); Maxi. Press.= 2400 kPa (350 lb/in2); E = 166 X 106 kPa (24 X 106 lb/in2); high cost especially for long freight hauls ductile, elongation » 10% no diameters above 1350 mm (54 in)

111

difficult to weld good corrosion resistance Ductile iron

may require wrapping or cathodic protection in corrosive soils good resistance to waterhammer

(DIP)

high strength for supporting earth loads Yield strengths: 207,000-414,000 kPa (30,000-60,000 lb/in2)

Poor corrosion resistance unless both lined and coated or wrapped,

Tensile str.= 338,000-518,000 kPa (49,000-75,000 lb/in2);

May require cathodic protection in corrosive soils

E = 207 X 106 kPa (30 X 106 lb/in2); higher unit cost in smaller diameters ductile, elongation varies from 17 to 35%

diameters to 3.66 m (12 ft); Steel

widest variety of available fittings and joints, custom fittings can be mitered and welded, excellent resistance to waterhammer, low cost, high strength for supporting earth loads

Tensile str.=26,400 kPa (4000 lb/in2); Max. press.=2400 kPa(350 lb/in2)

waterhammer not included in AWWA C905; E = 2,600,000 kPa (400,000 lb/in2) limited resistance to cyclic loading light weight, very durable, very smooth,

Polyvinyl chloride

unsuited for outdoor use above ground liners and wrapping not required, (PVC) can use ductile iron fittings with adapters, Tensile str.=11,000 kPa (1600 lb/in2) Max.press.=1750 kPa (250 lb/in2);

relatively new product, E = 896,000kPa (130,000 lb/in2) 750 mm (30 in) is largest size available for municipal system pressures lightweight, very durable, very smooth,

High-density polyethylene

thermal butt-fusion joints, liners and wrapping not required, (HDPE)

can use ductile iron fittings, requires higher laborer skill Several types available to suit different conditions

Attacked by soft water, acids, sulfides, sulfates, and chlorides, often requires protective coatings high strength for supporting earth loads,

Reinforced

waterhammer can crack outer shell, concrete exposing reinforcement to corrosion and destroying its strength with time

pressure (RCPP)

Max. press. = 138OkPa (200 lb/in2) Yield strength: not applicable; Attacked by soft water, acids, sulfates; design based on crushing strength, needs thrust blocks at elbows tees& dead ends E = 23,500,000 kPa (3,400,000 lb/in2)

Max.press.= 1380 kPa (200 lb/in2) for pipe up to 400 mm (16 in); rigid, lightweight in long lengths, low cost;

Asbestos-

health threats in potable WDS compatible with cast-iron fittings, cement (ACP)

Press. ratings 1600-3100 kPa (225-450 lb/in2) (18 in) or more

Figure 6-1: Example of Life Cycle Deterioration Curve of Pipe (EPA, 2002)

The relationships between pipe material and breaks are complicated and not thoroughly

known. Nonetheless, repair cost can be estimated based on historical records of previous failure

rates for the same pipe material. As different pipe materials exhibit different break rates, the

expected repair costs of any WDS depends on the different proportions of pipe material

composing the network and should be considered in design stage. For example, Figure 6-2,

presents the failure rates for various pipe materials.

Figure 6-2: Rate of Failures in 97-98 from 30% of Total WDS in Germany, DVGW

Statistics 1999 (Huelsmann, 2008)

6.4 Performance Indicators for Operation Assessment Performance indicators (PIs) are effective tools to facilitate management options. They allow

planners, key stakeholders, utility managers and consumers to make sound decisions for the most

effective use of resources and also serve as diagnostic tools. PIs allow for quick assessment at

112

minimal cost and effort than in-depth analysis, even though they are no substitute for

comprehensive assessment such as computational analysis and simulation models. General

evaluations via the use of indicators are nevertheless useful complements to detailed analysis. PIs

play an important role in all stages of WDS performance determination, including identification,

design and preparation, implementation and supervision and post completion evaluation.

PIs, like other benchmarks, enjoy relatively wide application. Recently, the International

Water Association (IWA) proposed new standardized performance indicators (Alegre et aI.,

2000; Lambert and Hirner, 2000) to give utilities and analysts a ‘lingua franca’ when comparing

performance among different systems in disparate jurisdictions, often with positive reception

(Brothers, 2001; Carpenter et aI., 2003). They are used to assess the physical and chemical

characteristics of supplied water and to evaluate operating conditions in a variety of scales

(spatial and temporal).

The IWA classifies PIs for WDS in six major groups: 1- Water Resource indicators; 2-

Personnel indicators; 3- Physical indicators; 4-Operational indicators; 5- Quality of Service

indicators; and 6- Financial indicators. Some of them are related to performance of WDS.

i) Physical indicators link the physical characteristics of supplied water to performance of

WDS. For example, the energy consumption rate for pumping unit volume of water in 100 m

head. This indicator watches variation of energy consumption over the service time for different

loading patterns.

ii) Operational indicators propose regulations for the annual inspection of components

like pumps, hydrants, as well as surveys of networks, leakage control, rates of mains failures, and

power failures.

iii) Quality of service indicators summarize the extent and consistency of water supply

for the consumers in a given jurisdiction over a fixed period. For example, the percentage of

population coverage, continuity of supply, fraction of the system receiving an acceptable

pressure, the number of interruptions, and the quality of supplied water.

iv) Financial indicators can be used to demonstrate accountability to municipalities and

consumers as well as in support of loan contracts such as billed metered consumption.

PIs are the simplest way for water utilities to assess their general performance on a

regular basis and can help to expose obvious areas of improvement and adopt realistic working

targets. They can also facilitate project description and presentation to regulatory and public

agencies and allow customers to exercise their voice in a more simple and standard framework. 113

Credible and comprehensive indicators also help private investors to identify costs and benefits

and make more informed decisions about their potential entry into water supply industry.

PIs are useful tools for evaluating project output and their application assists in

ascertaining system performance relative to their objectives. PIs can be used to investigate how

well investment goals are achieved and serve as guides for measuring the successful degree of

public policy implementation. Public agencies are also able to assess the impact of their

assistance for the benefit of their own taxpayers and prioritize their future interventions on the

basis of such assessments. Regulators find indicators indispensable in making transparent

decisions, while protecting the interests of both customers and the utilities.

It should be noted that, although PIs are effective performance evaluation tools, they are

beset by certain limitations. While PIs can indicate relative success in meeting objectives, they

do not propose remedial strategies.

Although WDS have a strong relationship to public health, PIs directly representative of

water quality are often absent. Water quality is just one of the many factors that influence health

care, and to isolate the impact of water supply and water quality alone is complex and

challenging with PIs. Finally, PIs cannot be used in a rigid and prescriptive fashion and their

interpretation depends on managerial experience to set acceptable or desirable targets.

6.5 Expected Operation Damages Up to now, two major direct cost components of WDS have been investigated, initial capital and

operating-maintenance costs over network service life. But indirect costs and damages are also

important in the economic evaluation of WDS. For example, supplying water with a minimum

pressure level is an important service criterion for the hydraulic performance of WDS, so the

consequences of deficit pressure during long-term operation should be considered. Thus, it is

necessary to explore the relationship between different pressure levels and their consequences.

Expected operational demerit can be evaluated by defining a specific function that maps a deficit

pressure level to a unique average level of damages. This function may have a bathtub shape, as

indicated in Figure 6-3. For all deficit pressures (e.g., pressure below Slow), possible costs such as

inconvenience to customers, interruption of industrial production, pipe collapse, and loss of life

and property in fires can be plotted. High pressures (e.g., pressures above Shigh) also entail their

own set of burdens such as an elevated incidence of pipe breaks and associated property damage.

Meanwhile, the middle pressure range between S

114

low and Shigh involves negligible damage.

The operational damage function reflects two simplifications. First, no attempt is made to

associate a response pressure level to the precise level of damages observed in the field. In

reality, if a particular response level occurs frequently (e.g., pressures below Slow), damages are

not the same and vary from place to place and throughout the service life. Instead, the damage

function only associates an average level of damages to each response variable level. Second,

different damage functions must be scaled to a common unit (monetary value) if a damage

function is to be amenable to numerical treatment. Damage of a pipe burst can be estimated

effectively because its economic or utility value can be defined, whereas the loss of a human life

is not easily scalable. Expected damages can be represented by Eq. 36

∫=1

)()()(S

Ss

o

dssfsDDE (36)

where E[D]=expected annual damages ($/year); S0, S1 = lower and upper limits of response

variable (pressure) observed (m); D(s)=damage function that associates response variable s to an

average level of damages ($); and f (s) = probability density function of response variable s. s

Figure 6-3: PDF of Pressure, Continuous Damage Function, and Derived PDF of Expected

Damages (Filion et al., 2007)

In practice, scarcity of field data may limit the designer to estimate only a few points on a

damage function. Thus, expected annual damages in Eq. (36), can be changed to a discrete form.

115

The PDF of the response variable (pressure) is transformed into a probability mass function by

integrating probability densities over S ,S

116

1 2,S ,…,S ranges of the response variable (Figure 6-4). 3 n

Each range of the response variable can be considered as a state variable with a finite

probability mass p (ss i). Expected annual damages can be estimated by summing damages over all

variable states:

∑=

=S

iisi spDDE

1)()( (37)

Where D =damages incurred when pressure state S occurs ($); p (si i s j)= probability mass

function of variable state s; and S=number of pressure states.

Figure 6-4: PMF of Pressure, Discrete Damage Function, and Derived PMF of Expected

Damages (Filion et al., 2007)

It is implicitly assumed in Eq. (37) that damages Di result for each incidence of system

state S . But when a system finds itself in pressure state Si i, damages may not necessarily arise.

For example, fire does not happen at each node for every pressure deficit. Similarly, pipes do not

burst for every episode of high pressure. Therefore, expected annual damages in Eq. (37) should

include a conditional probability mass function to reflect damages incurred only a fraction of the

time in a particular variable state S . i

∑∑= =

=S

iisijiSK

KK

jji spsKpDDE

iji1

,/1

, )()/()(,

(38)

where = conditional probability that variable state Siji SKp /,

results in consequence j, Ki i,j

with damage level Di,j and KK = number of possible consequences when system is in variable

state S . Expected annual damages as computed with Eq. (38) imply that each variable state Si i

can result in j = 1, 2, 3, ..., KK different consequences. For example, a pressure below 14 m head

at a node can result in the loss of property if there is a fire while the node is experiencing a low

pressure. At the same time, a pressure below 14 m head may cause delays in industrial

production resulting in economic losses. For further simplification, it can be assumed that each

variable state, S , results in only one consequence K whose damages Di i i dominate all other

damages. This important assumption simplifies Eq. (38) to yield:

∑=

=S

iisiiSKi spsKpDDE

ii1

/ )()/()( (39)

Where = conditional probability that variable state Sii SKp / results in consequence Ki i

with damage level Di. Note that expected annual damages in Eq. (39) only compute the mean of

damages by the full PDF shape of damages in Figures 6-3 and 6-4. The damage functions

assume logical shapes but remain theoretical since they are unique to particular systems and can

only be ascertained through field data. However, so long as a damage function follows a logical

shape, estimation of the expected damage is plausible (Filion et al., 2007).

6.6 Total Life Cycle Cost Evaluation When water distribution systems are modeled and designed, the system components are typically

analyzed using sophisticated analysis tools. Although the components themselves may be

modeled in great detail, evaluation of system behavior as an integrated whole is in some cases

difficult. Because it is not enough to understand only the behavior of the individual components

of complex systems, interactions of all contributing components are essential. Moreover, human

interactions and organizational behavior play important roles in the functionality of such

engineering systems.

Realistic analysis of complex engineering systems is best facilitated by a total system

model which represents the interactions, interdependencies and feedbacks between the various

117

components and also includes human factors. Without such a model, it may not be possible to

identify all potential bottlenecks, risks, failure mechanisms, and system inefficiencies.

In order to achieve efficient resource use, it is necessary to consider all economic aspects

of WDS. Total life-cycle cost evaluation is an effective tool that can be used to satisfy this need.

The proposed comprehensive framework should consider all major cost components either

private-source or publicly-funded for the initiation, provision, operation and maintenance over

the service life of the system. All major cost components of WDS can be expressed in annual

form, with initial investment cost being annualized and coupled with depreciation. Therefore, the

total annual cost of construction and operation of WDS includes annualized investment capital,

energy and expected failure costs as well as the annual cost of shortfall of water quality damages

(Figure 6-5). As initial capital cost usually takes place at the start of service life, but other cost

components often occur during long service life, as r result they have different time origins. In

order to combine two cost components with different time origins, it is necessary to apply

appropriate discount rate to enable annualizing total cost.

Figure 6-5: Major Cost Components of Total Life Cycle Cost of WDS

Several attempts have been made to include more cost components in WDS design while

still using conventional optimization models. They often consider initial cost and at most one of

the other cost components as objectives, either in the form of a multi-objective optimization

model or by merging two objectives by introducing an appropriate penalty function to facilitate

one general objective function. This limitation forces consideration only one objective from

among optimal pipe sizing, optimal operation schedule for energy cost, optimal control for 118

transient protection, optimal social and environmental damage estimation which correspond to

major cost components of WDS.

Chiong (1985) was the first to enumerate the advantages of including both energy costs

and excess pressures as decision variables of the optimization model, considering minimum

annualized cost which includes annualized investment and energy costs over the service life. As

there was no hydraulic simulation solver at that time, entropy theory was employed for the

hydraulic simulation of the system. The solution method was classical differential calculus and

the exponential function was used to measure the excess pressures. Due to the strictly positive

character of the function, and its derivative that facilitates convergence of the nonlinear system

of equations, the Newton-Raphson algorithm was selected to solve the equations. This

substitution approach was complicated by the need to deal with the non-convexity of this

problem. It is known (Bhave, 1985; Loganathan et al., 1995) that for fixed values of headloss,

the objective function is concave and multimodal. While for fixed values of pipe flows, the

objective function is convex and the minimum is global.

Later, Savic and Walters (1997) discussed how conventional optimization methods and a

sustainability framework can be considered together to explore management options and reduce

resource consumption. Kleiner et al. (1998) developed an optimization model to investigate long-

term economic sustainability criteria in the form of pipeline operation, maintenance,

rehabilitation, and replacement while ignoring energy costs as part of total operational cost.

Colombo and Karney (2002) also considered the relationships between pipe leakage and energy

cost in the broader context of WDS operation and environmental burden.

Engelhardt et al. (2002) and Skipworth et al. (2003) developed a whole life costing

(WLC) framework for determining long-term maintenance cost requirements for WDS. They

explored a rigorous framework which included operating costs, capital expenditure (cost of

replacement), public costs (social and environmental costs) and costs associated with leakage

and pipe bursts. They also applied factors such as demand projections, leakage, changes in

hydraulic capacity and structure capacity, customer interruptions and water quality through inter-

connected modules of their optimization model.

Later, Fillion et al., (2004) tried an indirect approach to conduct a life-cycle energy

analysis (LCEA) for WDS. Their methodology included energy costs for fabrication, use, and

end-of-life stages of the system. Their model was applied to the NYC primary water supply

system to determine energy expenditures associated with four different pipe-replacement 119

strategies (10-, 25-, 50-, and 100-year). Their results illustrated the tension between the energy

costs incurred in the fabrication and end-of-life stages of a system and those realized during the

use stage. They observed that a pipe-replacement period about 50 years had the minimum energy

expenditure among all other life stages.

Recently, multi-objective evolutionary optimization techniques have been used to

evaluate trade-offs of the least-cost design problem with other operational objectives. Prasad and

Park (2004) presented a multi-objective genetic algorithm approach to the optimal design of

WDS with minimizing the initial cost while maximizing the system resilience. System resilience

was defined as a reliability surrogate measure which incorporates excess pressure heads at

consumption nodes and loops with practicable pipe diameters.

Later, Farmani et al. (2005) compared three evolutionary multi-objective optimization

algorithms in the design of WDS by visualizing the non-dominated fronts of each of the

optimization methods as well as two performance indicators. They considered an expanded

rehabilitation problem with total maintenance costs which included pipe rehabilitation decisions,

tank sizing, tank setting, and pump operation schedules. They analyzed the case study under

multiple loading conditions for practical reasons. Inclusion of pump scheduling necessarily

requires consideration of water system operation over an extended period simulation. The total

cost of the system included the initial capital costs of pipes and tanks as well as the present value

of the energy cost during a specified period. They featured a resilience index as a second

objective of the optimization model to bolster the reliability and availability of water during pipe

failures and presented the optimal results of the payoff characteristics between total cost and

reliability for only 24 h operation and five loading conditions.

Furthermore, Vamvakeridou-Lyroudia et al. (2005) employed a genetic algorithm

multiobjective scheme to trade-off the least cost design of WDS, with the maximum benefits

evaluated by fuzzy logic reasoning. Dandy and Engelhardt (2006) also tried to perform a trade-

off between total cost and system reliability for optimal rehabilitation planning for pipe

replacement decisions during service life. In their approach, they expressed total maintenance

cost in terms of the present value of replacement cost and the expected repair and damage costs

associated with pipe breaks. System reliability was measured as the expected number of

customer interruptions per year to account for customer inconvenience. Results were developed

by generating curves for a variety of performance measure definitions and scenarios, with the

trade-off curves for two planning scenarios being presented. The first identified the trade-offs 120

necessary for the system’s present conditions while the second allowed determination of the

required levels of future cost incorporating funding limitations to meet a specified level of

service over the planning horizon.

It should be noted that the trade-off curves themselves are not what is critical, but rather

the information about performance criteria like pipe replacement scheduling, which is essential.

This helps operators more readily understand the factors driving WDS operation and

maintenance.

Later, Martinez (2007) applied a two-step optimization approach to the earlier problem

advanced by Chiong (1985). The first step involved a simple algorithm to hydraulically solve the

system and obtain pipe flows with maximization of their uniformity, implying that, for a given

set of nodal demands, pipe flows are calculated only once during the first step. In the second

step, the Chiong formulation was used to optimize the operating costs of flow distributions

obtained in the previous step so that the global total minimum cost including design and

operation costs is obtained. The entropy approach was applied with a similar goal of maximizing

flow uniformity. In order to do this, the set of pipe flows was treated as a statistical series and the

flows were calculated in such a way as to minimize the variance of the series. In the new

formulation, it was assumed that if a pipe goes out of service it can be isolated by closing valves

at the extremes and only those customers located along the isolated pipe are affected. Thus, when

this happens, affected customers are supplied by other means. The new objective function was

obtained by adding a new term to the previous function which accounts for the expected annual

cost due to pipe breaks. The expected damage cost included the costs of pipe repair and of

supplying affected customers by using an empirical formula to express the frequency of failures

(Su et al. 1987). Since the final results were in continuous diameters, they were assigned to

corresponding available values.

Recently, Ostfeld and Tubaltzev (2008) performed optimization on the least-cost design

of looped newtork, including sources, pumping units, tanks and pipes under multiple extended

period loading conditions. The objective was to minimize total design cost (sizing) and network

operation while supplying required demands at acceptable pressures. They considered different

energy tariffs and implemented tank reservoir within the system.

Finally, Jayaram and Srinivasan (2008) applied a new multi-objective formulation for the

optimal design and rehabilitation of WDS, with minimization of life-cycle cost and maximization

of minimum modified resilience index as two major objectives. The life-cycle cost included the 121

initial cost of pipes, the cost of replacing old pipes with new ones, the cost of cleaning and lining

existing pipes, the expected repair cost for pipe breaks and the salvage value of the pipes that are

replaced. The performance measure was a modification to the resilience index to consider a

system with multiple sources.

Although numerous models have been used for the optimal design and operation of

WDS, all face some practical limitations. For example, if the system analysis proceeds with

insufficient capacity to respond to pipe breaks or allows demands that exceed design values

without violating required performance levels, optimization models often tend to reduce total

costs by reducing the diameter of, or completely eliminating, some pipes, often resulting in

branched topologies rather than looped networks which entail greater reliability and less

vulnerability. They are also unable to consider indirect cost components which are a significant

portion of total life-cycle cost of the system. Therefore, it is necessary to propose such a

framework that cover above mentioned drawbacks.

6.7 Summary This chapter begins with a brief discussion about pipe materials since purchase constitutes a

major part of initial cost and it is, thus, important to select the best available material for pipes

based on operating conditions and environmental considerations. Pipes should be able to resist

internal pressure, external loads, differential settlement, and corrosive factors of both soil

particles and also the water it carries.

In order to evaluate the operating costs of WDS, it is necessary to have accurate estimates

of operating costs, energy consumption, repair cost, and revenue over the service life of the

system. Performance indicators are an effective tool for estimating the costs and benefits of the

system and were described in the mid-section of the chapter.

The chapter concludes with an outline of total life-cycle cost evaluation of WDS, which

is presented as a way to introduce a new framework for determining annual operation,

annualized capital investment, and annual expected damage costs. Furthermore, this approach

tries to account for capital investment by selecting best pipe size, while also attempting to

evaluate annual system operation by taking advantage of electrical tariff and short-term water

demand forecasting in order to reduce annual expected costs of damages. Nonetheless, further

research is needed to more effectively evaluate the indirect costs and benefits of WDS and obtain

a more comprehensive picture.

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Chapter 7 Shared Vision Modeling for Multi-objective Assessment

7.1 Introduction As was discussed previously, the main objective of a WDS is to provide a reliable water supply

of desired quality and quantity to all customers under a variety of operating conditions. Yet, this

set of goals is difficult to achieve, particularly taking into account the varying characteristic of

demands, the multi-objective nature of the operation and multi-institutional characteristics of the

physical system. Planners, managers and other decision makers require a set of flexible tools that

can assist them in making wise decisions on a range of problems, particularly when confronted

with a variety of competing objectives and demands and strict or legislated constraints.

WDS have been developed and implemented all around the world. The application of

engineering techniques for the design and operation of WDS has shifted for the originally quite

simple processes in the past to complicated procedure now. The system’s operation was initially

required to do little more than to merely supply water from sources to demand points taking into

account economic issues for like capital and operational costs with an eye to rehabilitation. By

contrast, newer system must also consider social purposes like water quality and sanitation; and

recent environmental objectives like water conservation planning and full life cycle costs.

Thus it is necessary to provide a comprehensive framework to facilitate the process of

decision making for the construction and operation of WDS over the service life. They are often

complex problems with difficult optimal solutions, but not many efficient decision support

systems yet fully support such problems.

The new framework should be able to consider all major impacted individuals, whether

direct and indirect or public and private components. The principal requirements of such a

methodology include:

• It should be flexible enough to integrate with different sensitivities impacted, interpretations

or objectives, given the open nature of the performance of a WDS.

• It should follow up on a certain degree of standardization for facilitating a multi-disciplinary

approach to consider all major aspects of WDS.

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• It should be quantitative and numerically based and computationally convertible in order to

afford intensive use. Although it is possible to computationally deal with non-numerical values,

numerical approaches are usually still preferred, especially if integration with analysis and

modeling techniques is also of interest.

This chapter investigates the requirements and challenges of more explicit multi-

objective assessment for multiple decision-makers. It also tentatively proposes a newly

developed approach, called Shared Vision Modeling, to advance the discussion. This approach is

based on the concept of incorporating all relevant ideas of all stakeholders in water resource

management (Palmer and Keyes, 1993).

7.2 Multiple Decision-makers for Multi-objective Problems Conversations relating to WDS are often engaged with multiple decision-makers.

Theoretically such problems can be analyzed using public economics which is the aggregation of

individual costs and benefits. The practical tools for multi-objective problems that involve with

multiple decision makers generally include a series of solutions that correspond to a variety of

objectives and criteria. The classical paradigm for a systematic approach to decision making

contains five steps:

1.Definition of Objectives: There is a person, group of people, and customer who is affected and

also responsible for making decision in relation to what (if anything) gets done and when. Each

decision maker or customer may have a set of objectives, preferences or desires that should be

considered. These objectives dictate a set of criteria to assess all possible options. WDS

problems usually have a broad range of objectives and thus assessment criteria. Therefore a

scoping exercise is required to identify all their attributes. They may be related to 1- Supplying

drinking water; 2- Providing non-potable water, such as to meet industrial needs or for fire

fighting; 3- Environmental limitations; 4- Public sanitation; 5- Efficient use of resources

(money); 6- Equity in the distribution of public benefits; and 7- Regional and national

development.

2. Measures of Effectiveness: Efficient procedures should be established for assessing each

objective or criteria. They may be either in form of quantitative such as cost and level of

contamination or in qualitative form such as odor, taste, and recreation.

3. Generation of Alternatives: It is essential to consider as complete a list of alternatives as

possible based on local and global requirements.

4. Evaluation of Alternatives: All of the possible solutions should be evaluated respect to the

measures of effectiveness for each criterion. This invariably requires modeling and produces an

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assessment matrix. Each alternative design is modeled and its performance and impacts are

predicted (Bruen, 2002).

The characteristics, factors, qualities, performance indices, or parameters of all different

alternatives or other decision processes are often referred as Attributes. An attribute provides a

means for evaluating all feasible levels of an objective. It can be defined as a measurable aspect

of a judgment by which a dimension of the various decision variables or alternative management

options can be characterized. The process involves the selection of the best alternative from a

given number of alternatives described in terms of their attributes.

A decision maker is an individual or a group of individuals whose desires are supposed to

be satisfied by the outcome of the multi-criterion decision process. A criterion may represent

either an attribute or an objective. In this sense, Multi-criterion decision making means either a

multi-attribute or a multi-objective decision problem or both. Therefore multi-criterion decision

making is used to indicate the general field of study which includes decision making in the

presence of two or more objectives and/or decision analysis process with two or more attributes.

Decision variables are the vehicles used to specify decisions made by a decision maker.

In mathematical programming, they represent the numerical variables, whose values are

determined during process of decision making. The quantities whose values are fixed are called

Parameters. There are restrictions on attributes and decision variables, which may or may not be

expressed mathematically. Constraints describe restrictions or dependencies between decision

variables and parameters, which may be stated in the form of equalities, inequalities, or

probabilistic statements.

Goals, aspiration levels, and ideal points also reflect different aspects of the decision

maker’s desire respect to a multi-criterion problem. Goals, known as Targets, are conditions

desired by the decision maker and expressed in terms of a specific state in space and time.

Aspiration levels are special cases of goals. If the level of goal points can not be achieved

simultaneously for all objectives, they do not in the feasible region. But when the goal point is in

the feasible objective space, it is considered as an aspiration level. If the optimal values of a

problem are determined for each objective without regard to the other objectives, the point

having these optimal values as its coordinates in the objective space is called an Ideal Point. The

ideal point of a multicriterion problem must lie outside the feasible region in the objective space.

Interaction between the analyst and decision maker is required to find the final satisfying

solution. The decision maker identifies the decision problem and specifies the objectives. An 125

analyst is responsible for defining the decision model, conducting a multi-criterion decision

process and presenting results to the decision maker. It should be noted that the priorities often

attached to each one of the various criteria under consideration in MCDM process. These

priorities may be represented as quantitative numbers usually referred to as weights, or by means

of ordinal expressions, which are denoted by priorities. The weights and priorities in the decision

maker’s view represent the relative importance of the objectives of a problem to one another

There are a number of possible solution types to multi-objective problems. The

differences among the solution types are usually related to following factors: 1- the type of

problem and required solution; 2- the type of techniques utilized to arrive at the solution; and 3-

the number of decision makers involved in the process. For example the problem and selected

technique may be decision analysis or mathematical programming, but the required solution can

be a preference ordering of alternatives or determining the magnitude of the value of each

objective and selecting alternatives accordingly. Likewise the decision-making unit may consist

of a single individual or a group of individuals with conflicting interests. These kinds of

differences in multi-objective problems can lead to different kinds of solutions.

Approaches for comparing the multi-objective performance of WDS have a wide range of

alternatives that involve the various decision makers. At first, the key contributing objectives of

each individual customer should be represented. Then, interactions of contributing groups on

other alternatives should be considered.

The multi-objective comparisons provide quantitative trade-offs that are useful in ranking

of alternatives. This trade-off assessment can aid discussions to select the best alternative, and

also to assist contributing groups to develop new efficient alternatives.

This is a linear procedure, illustrated in the left hand side of Figure 7-1. The final two

steps depend on the results of the three preceding steps; therefore the steps should be completed

in the order listed. This paradigm is valid in certain circumstances, but does have some

limitations, particularly when applied to complex problems with large environmental

considerations. Because it assumes that the decision makers are readily identifiable and that their

objectives and priorities can be readily obtained at the outset of the analysis. This is true in some

cases such as most private companies and some public agencies. However many decisions

related to large scale systems such as WDS infrastructure have significant public and

environmental impacts and the objectives and priorities of the general public are complicated.

Public consultation activities are important and essential features of decision making in these

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situations. What happens in practice at the moment is more closely represented by the steps

shown on the right hand side of Figure 7-1. The ultimate aim is to find an acceptable

compromise between the various and invariably competing objectives and this involves

negotiation, compromise and perhaps even some rethinking of the project objectives.

Figure 7-1: Classical vs Existing structure of systems analysis (Bruen, 2002)

7.3 The Complexity of Decision Making The design and operation of a WDS as a key urban infrastructure has complications due to its

considerable influence on public health, the environmental and economic constraints to which it

is subject, and arising from its long time service life. It is an excellent test case for multi-

objective problems influenced as it is by multiple groups and interests.. Although such systems

need huge amount of resources for initial investment, they also have high operating and

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maintenance costs over the long time service life. This entire picture can create concern among

the different customers who are directly or indirectly responsible for those costs.

In summary the main sources of difficulties in multi-objective performance measurement

of WDS are initiated basically from Lund and Palmer (1997):

1. The system’s objectives may not be precisely defined. As it is discussed before, water

distribution systems have maintenance and rehabilitation problems over their service life. It

typically occurs that some parts experiences serious problems while other parts may need

expansion and development. The influence of new parts (added pipes and components) with the

older parts is often poorly reflected in the analysis. In some cases, the objectives of new expanded

or rehabilitated system are not completely the same as the initial system, because of advances in

of new criteria relating to system design and operation. In such cases, the integrated and new

models that are often used may have precise results in some objectives and criteria, while they

have poor results in other objective and criteria.

2. The solutions to the water issues are often driven by external factors such as political

considerations. The system objectives which are modeled may include traditional engineering,

economic, and environmental criteria. The real political objectives in the short term may result of

the need for support of a particular stakeholder groups in jurisdiction, or even further application

of privatization or public control of WDS throughout the jurisdiction. Such objectives may be

considered illegitimate for public policy analysis conducted by government agencies or academia,

although they might be pursued by political advisors and dominate some decision processes.

3. Because of huge amount of initial and rehabilitation costs of a WDS, it almost invariably

depends on public funding to cover such expenditures. Approval of political and governmental

officials is often needed for the making decisions on public funding and resource allocation.

Public decision makers are often sourced from political policy level and they are often unfamiliar

with scientific modeling. Political decision and policy makers may not also be interested in

scientific or technical approaches and typically they do not have enough time to learn what is

needed or for understanding the problem and potential solutions. Despite this, they may be more

comfortable and familiar with simple forms of decision making.

4. The chief objectives of public decision makers may not be technical nor including within

normal engineering considerations. Even if public decision makers are truly concerned with

solving the problem well, their decisions are influenced by political not engineering concerns. For

example, social concerns and political objectives may well over-shadow solutions to the narrower 128

engineering problems. The solution to WDS problems is often part of a broader set of urban

infrastructures that are pursued in political level.

5. The temporal scale of specific policy decisions and analysis are poorly matched. Policy

decisions are often made before the time that the modeling analysis is completed. Outlines of the

decisions often exist before a comprehensive evaluation is done. Policy decisions typically are

motivated by water quality outbreaks, fire events, or other episodes which require public

attention. Such events and their consequences are often too short to conduct new analyses from

scratch. It means that the technical basis for policy decisions is usually based on studies

conducted before the most recent event.

6. There are insufficient public resources to act on the recommendations of a comprehensive

study. The attention of high level policy-makers is often distracted by non-water resource

management issues. Even when attention is available, it may be insufficient to craft enough of a

consensus for action to be taken. Even where sufficient public attention and consensus exists,

there may be insufficient financial resources to implement the agreed-upon solution.

7. Performance measurements may not be quantified well to gauge improvement toward an

objective. Decision makers often can not identify quantitative indicators of preferred performance

of objectives. For example the influences of an investment in a WDS on public health in terms of

a reduction in disease or an improvement in sanitation can seldom be measured efficiently in all

communities due to lack of required data.

8. Stakeholders and customers’ contribution often come too late in the analysis. This may

cause serious consequences, not least of which is that all alternatives, comments and preferences

may not be heard by decision makers.

9. The discussion forum is not about the underlying objectives or criteria which it

represents, but it is primarily about the final decision (design) itself. Final designs are the results

of many complex analyses. Even if such a design is optimal in terms of one measure of

performance, there is no guarantee that small perturbations of the design will cause it to remain

close to optimality, nor even that they do not violate any constraints which may have been

imposed on the original design.

7.4 Shared Vision Modeling Shared Vision Modeling is the development of a single, common model or modeling framework

by a diverse group of stakeholders (Palmer and Keyes, 1993). Its basic concept is that for all

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impacted individuals, it should provide the opportunity to participate in model design,

development, and evaluation, where and when their contributions are most appropriate in the

modeling process. Thus the goal of the modeling process is to provide all interested parties with

a tool that increases understanding of the objectives and the ability to evaluate potential

tradeoffs.

The “Shared Vision” approach has been used since 1990 (Loucks, 1990; Theissen and

Loucks, 1992; Keyes and Palmer 1993), typically when nothing else has resolved public and

controversial issues over the water use. This approach does more than just presenting the effects

of different plans to the public. It is essential to connect every objective to every concern, to

make it more likely that information gaps are noticed more quickly and that all concerns are

expressed most clearly. The model is typically developed by a single, often neutral, entity with

close consultation and review by technical representatives from each stakeholder or customer

group. The model should be approved by the individual stakeholders.

The first step in the creation of shared vision models is to define the major users and also

main applications of the system. This step ensures that essential modeling decisions, such as the

time-step, level of detail, data needs, geographic region included, platform and hardware

requirements, model type, and user interface, are in sync with the intended users and

applications. This step implicitly suggests the need to develop a team of participants early in the

modeling process to ensure that the answer to these questions represents the interests of a wide

range of participants. The result of this effort is a clear definition of the questions that the model

will answer and how well they must be answered to be useful in the particular decision making

environment.

For the next step, the modeling functions and requirements are translated into a common

model. The first intention is to create a shared understanding and vision of the whole system in

the form of this common model. The model development process allows the technical personnel

from the different interests to work together to develop a higher degree of consensus on how the

system works and to identify and quantify relevant performance criteria.

The model development process is intended to take most of technical decisions out from

the political spotlight. If one can arrive at agreement on what is contained in the model, then later

efforts can focus on interpretation of the results, rather than discussion on model content.

Arriving at a consensus about model construction is not easy, and model development will

progress much more slowly than if performed by a single group with a single perspective. 130

However the advantage of the shared model building process is the development of a tool which

can be approved by all contributing groups.

Once a common vision of the system is obtained while this is typically based upon the

status quo or current condition, the model can be used to develop and evaluate alternatives. The

process of developing this model often is seen as a prelude to the process of developing and

evaluating promising solution alternatives and informed and meaningful negotiations among

stakeholders.

The second intent of this approach is to create a technically-based method where the

major decision makers can negotiate. These negotiations are facilitated, once confidence in the

common model has been achieved. Once a shared Vision model has been developed and agreed

upon, it can be used as a basis for developing, evaluating, and refining the details of management

alternatives as a part of negotiating process. The overall approach is seen as an extension of

classical engineering planning to more pluralistic decision-making circumstances.

The Shared Vision model establishes a triangulation approach that interconnects three

most important aspects of an engineering system which are design, criteria and performance

indicators of WDS (Figure 7-2). This approach is the effective tool for performance

measurements of WDS because of its ability to include a repeatable process in the design and

operation of WDS over the service time.

Figure 7-2: Shared Vision model for WDS

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One technique that has been found useful in shared vision modeling is careful

prototyping. This calls for the systematic development of a series of increasingly more detailed

models, rather than the development of a single model version. It encourages a number of

desirable features over the time that can be critiqued and improved. Incorporation of increasing

model’s details are achieved only when they are important in decision making and the

opportunity to train people in the details of the system efficiently is provided too.

7.5 Summary Multi-objective decision making with multiple decision makers depends on following steps: 1)

effective technical understanding of the problem; 2) defining solution objectives; 3) developing

promising alternative solutions; 4) evaluating the performance of alternative solutions; 5)

providing technical confidence in the solution agreed upon; and 6) perhaps provide a forum for

discussions. The modeling efforts must make these contributions within a larger technical

planning process. Modeling can be organized effectively to provide these services within a

comprehensive planning with multiple decision makers.

Management of a WDS often encounters complex decision problems with various

objectives. The process of solving a problem with two or more objectives that may be non-

commensurable, is known as multi-objective decision making (MODM).

There is a need to involve all stakeholders and impacted groups as early as possible in the

analysis of WDS and to focus that involvement on refining objectives and criteria, rather than on

adjusting a proposed solution.

For each individual objective of a WDS, there is at least one specific group or customer

that directly interested or affected. Ideally, every influenced person, stakeholder should be able

to see how the system is likely to affect them. In reality even the experts have trouble

understanding in measuring actual impacts of WDS. Shared Vision approach tries to combine all

information in a single model in such a way that decision makers and stakeholders can ask "what

if" questions and get answers about how important issues to them are affected.

How can Share Vision Model for WDS contribute to resolving these complex and

difficult problems? Or can such models help at all? The implicit assumption that models

inherently reduce problems is called into question. Shared Vision Modeling, like other consensus

building processes, requires a strong motivation among decision makers to include expectations

and desires.

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Chapter 8 Summary and conclusions

Water is universally recognized as a valuable resource for health and well-being of the society

and its sustainable development. Yet it is also a finite resource, which must be used efficiently

and wisely. It is a renewable resource, which must be kept clean and its quality protected. It is a

shared resource, which must meet the needs of competing users and future generations (Bakir,

2004).

This research reviews and reflects on the current state-of-the-art relating to the design and

operation of water supply systems and how performance of water supply and distribution

systems should be assessed, with a critical discussion about advantages and shortcomings of

various published and available approaches. Most of the current approaches to design and

operation of water supply and distribution infrastructures only focus on only some portion of

such a complex and multi-objective systems, while the pressing need is clearly for a

comprehensive design and operation evaluation approach.

Water demands in developing countries are rapidly increasing due not only to growing

populations, but also as a result of the higher standards of living leading to increased per capita

use, rapid industrialization, and the expansion of irrigation to supply the agricultural needs of the

population growth. This projected growth has serious implications for environmental

sustainability of the urban areas, both locally and globally. Economic developments as well as

environmental management are essential for all communities. They must be integrated into a

wise strategy that reconciles economic implications and environmental health.

Water supply and distribution systems are often seen as one of the most important

component of a larger urban infrastructure system that fosters public health and economic which

underpins most economic activities in communities. Urban populations typically rely on WDS to

provide reliable, clean, potable and non-potable water. Potable water is needed to perform basic

domestic activities such as washing, cooking, and cleaning; non-potable water is required

emergency events like fire fighting, some industrial activities, and outdoor irrigation. The

occasional compromise of water safety in such systems, and the loss of life sometimes associated

with such failures, serves as reminders of the fundamental role these systems play in modern life.

Performance analysis is a key issue in all engineering disciplines including water

resource management. A WDS in particular requires huge amounts of natural resources to serve

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considerably different customers with a wide variety of demands and costs over the service life.

Although at first glance, design and operational challenges might look simple, a more

comprehensive perspective is required to deal with all different problems of such systems.

Identifying major customers with their specific priorities and their requested level-of-service are

essential. As a result, evaluation of the performance of a WDS is challenging process, because it

can be perceived as multi-purpose complex system from different view points of multiple

decision makers to achieve various objectives which are often not easily quantifiable.

There are systems serving the utilities all around the world are more than 150 years of

age. The older part of the systems has been built based on standards, construction practices, and

with technologies that are no longer appropriate. Nevertheless, to replace all the older parts of a

system is usually beyond the reasonable economic capabilities of the water utilities and

governments. The logical consequence is that it is necessary to operate aging and non-ideal

systems more efficiently and effectively. In order to maintain or improve the performance of

current systems, both old and new, it is necessary to use effective technologies for inspection and

control, operation and maintenance, and rehabilitation planning. Additionally, basic knowledge

on influence of WDS on public health and sanitation is important. Operational factors such as

deterioration rates and models for forecasting failures and rehabilitation needs are also important

for long-term performance assessment.

In addition to technical goals and objectives, the sustainable development of a WDS must

increasingly be guided by planning approaches that push back the analytical boundaries to

include the economic, environmental, and even social dimensions. Arguably, the tightly linked

nature of social institutions and infrastructure systems within urban areas, energy production and

consumption, water supply, rural and urban communities, and so on mandates the development

and use of holistic planning approaches to trace the interactions between these factors.

Application of the conventional water resource management to contemporary times has

not met the required results. Water authorities, managers and policy makers are faced with

enormous challenges of effectively managing resources to supply water and sanitation services

while minimizing the negative impacts on the environment. The alarming rate of water scarcity

and considerable environmental degradation has brought the need for effective planned actions to

manage water resources. Many water utilities do not fully appreciate the impact of their

operations on environment where there are efforts to mitigate against environmental degradation.

The efforts are often ad-hoc, and fragmented. But environmental sustainability is not 134

implemented efficiently in the operational management of urban water utilities (Kayaga and

Smout 2006).

The deterioration of a WDS is influenced by changes in demand pattern and water quality

over the time and the existing environmental conditions. This is reflected by increased

interruptions to customers due to higher pipe breakage rates, lower hydraulic capacities because

of increase in roughness of pipes, and reduction in quality of the water received.

The undisputed importance of a WDS along with gaps in the understanding of their

performance has necessarily led to their conservative design through fail-safe approach.

However some of previous barriers have been gradually removed by ever-increasing knowledge

of system analysis and design through research efforts. A considerable change from fail-safe to

safe-fail design approaches is desired with the increased importance of resource conservation and

more efficient design and operation of systems.

The general guideline-based models made no attempt to prioritize all important

requirements. Also the assessment is centered on a single performance measures. Although the

prioritization models are suited for the whole-life costing ideology, they do not explicitly account

for the budget and are not able to consider extended planning horizons. Moreover the levels of

service (performance) that will be provided by the system after rehabilitation cannot be

predicted. Multi-objective optimization approaches are found to overcome the drawbacks of the

other early approaches mentioned and can be utilized effectively to formulate total life-cost

evaluation model.

The need for comprehensive design and operation of such complex systems coupled with

concerns about environmental degradation and resource scarcity are spurring governments and

other officials to conduct such activities on a more realistic and sustainable approach. Ideally,

future efforts to develop a WDS as a sustainable infrastructure should consider objectives of to

”meet the needs of the current customers as well as compromising the ability to respond to needs

of future generations”. More pragmatically, the design and operation of a WDS is likely to be

guided by specific criteria such as resource efficiency, minimization of total annualized cost,

reducing expected damage costs by decreasing total operating risk, increasing of reliability of the

system for continuous operation during entire life cycle of the system, and so on. Governmental

agencies should incorporate some of these criteria and objectives in future design and operation

management of WDS efforts.

135

WDS are designed typically based on considering only one major cost components, often

initial cost of system without considering other contributing cost components such as operating

cost, rehabilitation cost over the service life. Indeed optimal strategies for all major cost

components of WDS are somehow need-based (in case of system necessities and increased

demands) rather than pre-planned. However since high initial investments may result in a low

maintenance and rehabilitation cost, while compromising on the initial investment may lead to

increased rehabilitation costs. It is prudent to consider all major contributing costs in early design

stage and include operating schedule and rehabilitation processes together. Thus the optimal

strategy that maximizes the performance of the system over the service life, while minimizing

the life-cycle cost is tentatively proposed.

As the main conclusion, importance of the total life-cycle cost evaluation of WDS. Total

life cycle cost evaluation of WDS is presented in order to define a comprehensive framework for

considering all major cost components of water supply and distribution systems in form of

annual operation as well as annualized capital investment with consideration to annual expected

system costs. This framework not only considers the influence of capital investment on the

system by selecting optimum pipe size, but also it attempts to value operating an maintenance

costs of the system by taking advantage of electrical tariff and short-term water demand

forecasting in order to reduce expected of failure costs. Yet implementation of such a scheme

will be difficult given all the needs for data, the conflicts with other intentions, and the inevitable

scarcity of quality data. Yet without a view of the desired endpoint, all actions become merely

random guesses, a poor substitute for careful planning when critical systems are at stake.

136

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