Comparative Assessment of Water Losses Performance Indicators - Case Study Western Part

of Jakarta, Indonesia

Handy Salim MSc Thesis WM.10.18 April 2010

UNESCO-IHE INSTITUTE FOR WATER EDUCATION

Comparative Assessment of Water Losses Performance Indicators – Case Study Western Part of

Jakarta, Indonesia

Master of Science Thesis by

Handy Salim

Supervisors

Prof. Meine Pieter van Dijk, PhD. Marco A. C. Schouten, PhD.

Examination committee

Prof. Meine Pieter van Dijk, PhD (UNESCO-IHE), Chairman Jacques Labre (SUEZ Environnement)

Maarten W. Blokland, MSc (UNESCO-IHE)

This research is done for the partial fulfilment of requirements for the Master of Science degree at the

UNESCO-IHE Institute for Water Education, Delft, the Netherlands

Delft

April 2010

The findings, interpretations and conclusions expressed in this study do neither necessarily reflect the views of the UNESCO-IHE Institute for Water Education, nor of the individual members of the MSc committee, nor of their respective employers.

For Mom and Dad, the best parents, and

My Family, the reasons for living

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Abstract

The use of water losses performance indicators is essential in the management of a water utility. Traditional water losses PIs such as the Non Revenue Water expressed in percentage of the total system input is one of the widely used indicators in the world, including also several other expressions such as volume / time / length of network or number of connections. The Infrastructure Leakage Index as a new PI that is promoted by many water loss consultants and practitioners has raised the debates over its applicability and comparisons are made between the traditional PIs and the ILI in various issues. This paper documented a study to apply these PIs in a real water utility in the developing country to put opinions to the debate from the perspective of a water utility that is facing high water losses problem.

Keywords: Water losses, performance indicators, non-revenue water, leakage, real loss, apparent loss, infrastructure leakage index, water utility, developing country, benchmarking,

urban water supply

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Acknowledgements

Suez Environnement and PALYJA, for making this possible, Philippe Folliasson, Tunc Erk, Luc Martin, Pablo Vizioli, Jacques Labre, Anne Pedretti, Christine Tavelet, Wahyu Wibowo, Yunita Hastuti, Nancy Manurung, Hidayat Turahim, Hetty Astuty, Indradi Kridiasto, Achmad Hattary, Rosita Halim, Ira Larasati, Wisnu Prabakti, Azhar Levi Sianturi, Roby Kurniawan, Januar Dwi Saptono, Syibli, Johnson Hutagaol, Agus Putranto, and all the colleagues from PALYJA and Suez Environnement for the opportunities, inputs, assistance, data and financial support, Friends from Indo-IHE 2008 for the morale support and togetherness, Cat2 for the choco, The staff and colleagues of UNESCO-IHE for just everything...

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Table of Contents

Abstract ..................................................................................................................... i Acknowledgements..................................................................................................iii

1. Introduction ....................................................................................... 1 1.1 Water Losses in Water Supply Sector ............................................................ 1 1.2 Water Losses Performance Indicators ............................................................ 1 1.3 Research Objectives and Questions ............................................................... 2 1.4 Chapters Overview ........................................................................................ 3

2 Scholarly Overview on Water Losses Performance Indicators ...... 5 2.1 Adopted Definitions ...................................................................................... 5

2.1.1 Non Revenue Water and Unaccounted for Water ................................... 5 2.1.2 Infrastructure Leakage Index.................................................................. 6

2.2 Applications of Water Losses Performance Indicators ................................... 6 2.2.1 Brief History of the Water Losses Performance Indicators ..................... 6 2.2.2 Case in Geneva, Switzerland................................................................ 10 2.2.3 Case in Trinidad and Tobago ............................................................... 12 2.2.4 Case in Sandakan, Malaysia................................................................. 13

3 Research Methods & Object ........................................................... 15 3.1 Methods ...................................................................................................... 15

3.1.1 Data Collection.................................................................................... 15 3.1.2 Data Analysis ...................................................................................... 16

3.2 Object.......................................................................................................... 17 3.2.1 PALYJA.............................................................................................. 17 3.2.2 Permanent Areas.................................................................................. 17 3.2.3 Non Revenue Water Division............................................................... 18 3.2.4 Water Losses Reduction Strategy in PALYJA...................................... 19

4 Results .............................................................................................. 21 4.1 Performance of PALYJA According to Traditional PIs................................ 21

4.1.1 Water Losses in the Whole Service Area.............................................. 21 4.1.2 Water Losses at Permanent Area Level ................................................ 25

4.2 Application of Infrastructure Leakage Index (ILI)........................................ 27 4.2.1 Night Flow Analysis ............................................................................ 28 4.2.2 Statistical Analysis............................................................................... 30 4.2.3 ILI in P7 .............................................................................................. 32 4.2.4 ILI vs NRW......................................................................................... 33

4.3 New Water Losses Performance Indicator ................................................... 36

5 Discussion & Conclusions ............................................................... 43 5.1 Traditional PIs............................................................................................. 43

5.1.1 Percentage to System Supply ............................................................... 43 5.1.2 Volume / Time / Size of Area .............................................................. 43

5.2 Infrastructure Leakage Index (ILI)............................................................... 44 5.3 Distribution Performance Index (DPI) – Final Words .................................. 45

References............................................................................................... 47

Appendices.............................................................................................. 51 Appendix A Map of the Permanent Areas in PALYJA ............................................ 51

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Appendix B Night Flow Analysis in P7................................................................... 52

List of tables

Table 2-1: Limitations of Traditional Water Losses Performance Indicators.................. 7 Table 2-2: Details of Selected Key PIs .......................................................................... 8 Table 2-3: Evolution of NRW in SIG.......................................................................... 10 Table 3-1: Water Losses Indicators and Required Data ............................................... 15 Table 4-1: PALYJA Water Losses 1998 – 2009 .......................................................... 21 Table 4-2: Improvement Level 1998 - 2009 ................................................................ 22 Table 4-3: Improvement of Water Losses Level in % of Supply.................................. 22 Table 4-4: Improvement of Water Losses Level in m3/day/km .................................... 23 Table 4-5: Improvement of Water Losses Level in m3/day/conn.................................. 23 Table 4-6: Water Losses Level by Permanent Areas (as of September 2009)............... 26 Table 4-7: Highest Losses Permanent Areas (as of September 2009)........................... 27 Table 4-8: Pressure and NRW in P7 for Jan 08 – Oct 09 ............................................. 30 Table 4-9: ILI in P7 for Jan 08 – Oct 09...................................................................... 32 Table 4-10: PA Ranking System in PALYJA (as of September 2009) ......................... 37 Table 4-11: Distribution Performance Index Data and Calculation – September 2009 . 37 Table 4-12: Apparent Losses Target vs Real Losses Target......................................... 38 Table 4-13: NRW in % and Modified DPI .................................................................. 39

List of figures

Figure 1: IWA ‘Best Practice’ Water Balance and Terminology.................................... 5 Figure 2: World Bank Institute Physical Loss Target Matrix ......................................... 9 Figure 3: IWA Standard Water Balance Result in SIG for year 2006........................... 11 Figure 4: NRW Breakdown in Trinidad & Tobago...................................................... 13 Figure 5: Water Balance in Sandakan, Malaysia for 2002............................................ 14 Figure 6: Non Revenue Water Division Organization Chart ........................................ 19 Figure 7: PALYJA Total Length of Mains 1998 - 2009............................................... 24 Figure 8: PALYJA Total Number of Connections 1998 - 2009 ................................... 25 Figure 9: P7 Linear Regression Model ........................................................................ 31 Figure 10: ILI vs NRW in P7 ...................................................................................... 33 Figure 11: ILI vs CARL in P7..................................................................................... 34 Figure 12: NRW vs CARL in P7................................................................................. 35 Figure 13: ILI vs Number of Connections in P7 .......................................................... 35

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1. Introduction

1.1 Water Losses in Water Supply Sector

In the business of supplying water through piping system, water loss has been an unavoidable aspect in the operation and management of a water utility. To some extent, these losses might not be considered major problem, while in some cases this might have great influence in the overall performance of the utility. Uncontrolled losses lead to inefficiency of water supply system and like domino effect cause other problems to occur such as low system pressure, water quality issues, legal issues, unwillingness to pay, low revenue / profitability and cost recovery level, low staff motivation, poor performance, and like a closed loop eventually lead to higher losses. In the developing countries, water loss has been one of the major issues faced by most water utilities (Kingdom, 2006), particularly in areas where water resources are scarce or are economically expensive. In general, these losses may include leakage from the pipe system, water theft, metering inaccuracy, human or administrative errors, etc. (Thornton, et al., 2008). Dealing with these losses often requires relatively expensive investments, while in oppose to that, rationally speaking, water utilities facing the problem with water losses usually are also under somewhat financial pressure. While financial constraints might be solved through various financial schemes such as loans, intensive water loss reduction actions also involve some particular practices, techniques and skills that are sometimes not entirely available where they are most needed. Dealing with these losses is one thing, quantifying the losses is another. However it is believed that the first step to deal with the problem is to know to what extent the problem is; that is to quantify how big the losses are in the system (Farley & Trow, 2003). This is where the water losses performance indicators play an important role.

1.2 Water Losses Performance Indicators

Water losses performance indicators are figures used to show the scale of water losses in a water supply system. They are important for decision makers to decide in which aspects of the operation the utility should invest more, which actions should be undertaken to address particular problems, evaluate the shortcomings and therefore what kind of improvements should be made, and to benchmark the performance of one system with another; to mention some of the essential uses of the indicators (Alegre, 2006). There are plenty of water losses indicators that are available. Several widely used water losses indicators are namely Unaccounted for Water (UfW) and Non Revenue Water (NRW), commonly expressed in percentage to system supply, that are considered traditional indicators. In the recent development, one water loss indicator has been promoted by IWA, which is the Infrastructure Leakage Index (ILI), which as being recent, the use has not been as common as the first two mentioned. One issue is that a lot of focus has been given to the external use of the indicators, which is for benchmarking between water utilities. While contradictory, a water utility

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that is facing high water loss issue would normally be more interested to how an indicator can be used internally to drive actions for intervention and improvement, showing ‘how’ an improvement can be made, rather than comparing themselves with others, to see ‘how far’ or even whether an improvement should be made (Berg & Lin, 2008). Liemberger (2002) argues about how some of the performance indicators can be misleading when used without proper caution, since each of the indicators is designed to give only a limited information. Indeed, there have been debates among water losses specialists regarding the use of some performance indicators that are considered not sufficient to assess the performance of a distribution system, to mention several: the use of percentage of water losses to water supply - NRW (Ismail & Puad, 2007; Kunkel, 2003; Liemberger, 2002; RS Mckenzie, et al., 2008; Winarni, 2009), the use of volume of losses per length of pipes or connections (Liemberger, 2005) or issues with terminology (Kunkel, 2003). However, larger part of the debates argue on the interpretation of the figures when used for benchmarking and not the use of the methods itself, which more or less is agreed (Lambert, 2000a). Interestingly, almost all of these arguments typically are concluded with promoting ILI as the recommended indicator for water loss management performance, claimed as being “robust and practical” (Lambert, et al., 2005) or “smarter” (Liemberger, 2005), while some admit that still some limitations have been reported (Hunaidi, 2007). Reading all these discussions and recommendations, it is therefore very difficult not to conclude that ILI has been somewhat pronounced by some as “the best” water loss indicator available at the moment. This is one statement yet to be challenged.

1.3 Research Objectives and Questions

This research aims to address the issues mentioned above: to prove how an indicator can be misleading; to prove whether ILI is applicable, robust and practical; to try to apply water losses indicators for more internal use – driving actions for intervention; and finally, to find a suitable indicator that is useful for a water utility that is facing high water loss problem. Summarized in one question, the research is aimed to answer, ‘how good are the available water losses performance indicators for a water utility with high water losses in a developing country?’. This main question is sharpened and made scientific in order to meet the above mentioned objectives by several research sub questions that have been designed, they are:

1) How is the performance of water losses management in a water utility according to different indicators?

2) What are the difficulties in applying these indicators in the water utility? 3) Which is the more practical indicator for the water utility? 4) How can the indicator be used to drive the utility’s actions for intervention?

Answer to question a) should provide different perspectives in evaluating the performance of a water utility, that might lead to assess whether an indicator provides a “fair” picture of the condition, or in other words, misleading or not. Answering question b) combined with a definition of “being practical” should prove whether an indicator is

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acceptably practical and applicable in a water utility. And lastly questions c) and d) should lead to explaining the methods for utilization of the indicators for internal use.

1.4 Chapters Overview

The following part of the documents discuss deeper on the research. Chapter 2 details the definitions that are used as the basis of discussion and analysis, and some other case studies based on literatures on the application of water losses performance indicators to bring about the basic knowledge and background story of the research. Chapter 3 outlines the methods and materials used for the research, steps to answer the research questions, the selection criteria and description of the object where the research takes place. Chapter 4 features the data analysis and results of the assessment, while detail discussions of the result are presented in Chapter 5, ended with conclusions and recommendations to bring to a close.

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2 Scholarly Overview on Water Losses Performance Indicators

2.1 Adopted Definitions

2.1.1 Non Revenue Water and Unaccounted for Water

Non Revenue Water (NRW) is defined as the difference between the system input volume and billed authorised consumption (Lambert & Hirner, 2002), commonly expressed as a percentage of total water supply / system input. The following figure shows the definition according to International Water Association (IWA):

Figure 1: IWA ‘Best Practice’ Water Balance and Terminology [Source: Developing a Strategy for Managing Losses in Water Distribution Networks (Trow & Farley, 2006)] Calculation of NRW is therefore simple:

NRW = Total Input - Billed consumption (expressed in volume), or

NRW = (Total Input - Billed consumption) x 100% / Total Input (expressed in %) UfW is basically the ‘Water Losses’ as shown in the figure, which excludes Non-revenue Water from unbilled authorized consumption. As seen, if unbilled authorized consumption is zero then NRW is equal to UfW, it indicates all water losses related to

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both "real losses" (leakage) and "apparent losses" (water theft, metering / administrative errors). NRW and UfW are the traditional water losses indicators that are well known and applied by most water utilities, because of its simplicity and applicability. With proper metering of both supply and billed consumption, the calculation of the NRW figure should be fairly easy. These indicators are good to show the total losses in an area as basic indicators of performance, but are not sufficient for strategic decision making tool related to investment, since being very general, they do not clearly indicate where the water is being lost, therefore in which field that actions should be undertaken.

2.1.2 Infrastructure Leakage Index

Infrastructure Leakage Index (ILI) is a more complicated indicator that shows the ratio of real losses compared to the estimated leakage figure that is unavoidable in a normal system, taking into account various variables such as system pressure, length of network, number of connections, and other physical aspects in which the system is operating under (Lambert, et al., 1999). ILI can be calculated as follows:

ILI = CARL / UARL Where: CARL = Current Annual Real Losses, which indicates the actual leakage level in the current system being assessed annually, UARL = Unavoidable Annual Real Losses, which indicates the unavoidable level of leakage in the system, as no system is 100% leakage-free.

UARL (litres per day) = (18 x Lm + 0.8 x Nc + 25 x Lp) x P Where Lm = length of mains (km), Nc = Number of service connections, Lp = length of private service pipes from property boundary to the meter (km), and P = average pressure (m). UARL is a standard theoretical figure for an accepted leakage level in a system. As indicated by the terminology used in the components, ILI shows only the physical aspect of the water losses (leakage), and therefore can be used mainly to assess the performance of the water infrastructure and leakage management.

2.2 Applications of Water Losses Performance Indicators

2.2.1 Brief History of the Water Losses Performance Indicators

2.2.1.1 The Traditional PIs

The typical questions that are often quoted in the discussion of water loss reduction strategies are (Farley & Trow, 2003):

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How much water is being lost? Where is it being lost from? Why is it being lost? What strategies can be introduced to reduce losses? How can we sustain the achievement? The water losses performance indicators fit into answering the first and the second question, which indicates the important role that they play in the losses reduction strategies. It has been for long time that a very simple method of indicating how much water is being lost in a distribution system is used. This method calculates the total loss by subtracting the total water produced with consumption and compares the value with the water produced to generate a value of distribution efficiency expressed in %. For many years, this figure was referred to as the Unaccounted for Water (UfW), which refers to the part of water distributed that the water utility are unable (or unwilling) to account for. This method was then upgraded by the Non Revenue Water (NRW) which has a clearer meaning and way for calculating. The NRW simply refers to the amount of water that is not generating revenue for the water utility, the water that is not billed for. However, as well described in the thesis by Hardeman (2008) in the development the percentage system was found to be inadequate since it tells nothing about the second question, where the water is being lost from, whether the loss is of real or apparent. The following table provided summarised arguments about the limitations of the traditional indicators. Table 2-1: Limitations of Traditional Water Losses Performance Indicators

[Source: Review of Performance Indicators for Real Losses from Water Supply Systems (Lambert, et al., 1999)] Liembeger et al. (2007) stated that,’For almost 30 years, reputable working groups and National Organizations have been recommending against expressing NRW or its components as % of system input volume’, and claimed that the percentage system is unsuitable for target setting for regulation, environmental protection, contract supervision, financial optimization, and operational management.

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In July 2000, as is outlined by McKenzie and Seago (2004) the IWA Task Forces on Performance Indicators and Water Loss published a standard international best practice water balance, as is shown in Figure 1. Further, quoting from the same paper, ‘While “percentage by volume” is still recommended as a basic financial PI for non-revenue water, and a basic PI for real losses from a water resources viewpoint, it should definitely not be used for assessing any aspect of operational performance management of water losses’. The recommended detail applications of the water losses performance indicators are then as shown in the following table. Table 2-2: Details of Selected Key PIs

[Source: Assessment of Real Losses in Potable Water Distribution Systems: Some Recent Developments (R McKenzie & Seago, 2004)]

2.2.1.2 The Modern PI

It is argued that basic PIs are insufficient, because some factors are not taken into account, such as the connection density, length of service pipe between the main and customer meter and the average pressure. These are the basic arguments that initiated the development of the Infrastructure Leakage Index (ILI) by the IWA Water Loss Task Force. ‘The ILI measures how effectively a utility is managing real losses under the current operating pressure regime, and is simply the ratio of the actual estimated real losses, divided by the UARL’, as quoted from McKenzie and Seago. Liemberger (2007) argues that ILI, ‘identifies not only what the current losses are, but also permits an initial estimate of the maximum potential for reduction in real losses at the current pressure’ and ,’It has proved to be robust in application, with many hundreds of ILIs having been calculated in numerous countries’.

2.2.1.3 Recognition by the World Bank Institute

And further after, following the active campaign by the ILI promoters, the World Bank Institute adopted the ILI into their international banding system to identify Technical Performance Category to assess the management of real losses in a water utility and to guide actions that should be undertaken by the respective utility. The following figure shows the World Bank Institute target matrix

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Figure 2: World Bank Institute Physical Loss Target Matrix [Source: Water Loss Performance Indicators (Liemberger, et al., 2007)]

2.2.1.4 The Critics Against ILI

However, the ILI is not without critics. In the same paper, Liemberger presents the two main critics that were posted in the IWA PI manual, they are: ILI ‘ contains a judgement in itself and is based on an empirical expression (and for this reason does not fit all the PI requirements)’, and ‘Shortcomings relate to the meaning and confidence level when the variability of the operating pressure and/of the service connection length in the system is high (e.g. hilly regions, systems with significant daily pressure fluctuations, systems with apartment blocks and individual apartment meters.’ Nevertheless, in the other paper, Liemberger (2005) also admits other issues related to the application of ILI, particularly in the developing countries. These issues include questions on the accuracy of UARL formula, availability of data to calculate UARL, the limited use and application of this PI in the industry, and questions regarding the need to use this indicator instead of the old volume/time/size of area. Furthermore, it was then recognised that the UARL formula indeed has limitations, as stated by Liemberger in the paper, ‘Practical limitations placed on applying the UARL formula were, originally, that systems should not have less than 5000 service connections, not less than 20 connections/km of mains, and not less than 25 metres of pressure. Following recent research, the lower limits for number of service connections is now 3000 and the lower limit on density of connections has been removed.’

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Following this, the situations in the developing countries then do not appear to be suitable for the implementation of ILI, because in these countries often there are: 1) No reliable information on the network length, 2) Number of service connections is unknown, 3) Neither pressure data nor pressure loggers are available, 4) High level of apparent losses. Here it can be seen the term of apparent losses comes to surface, something that appears to might have been forgotten in the assessment of the PIs for implementation in the developed countries. Rizzo et al. (2007) promotes a similar concept to ILI that is applicable to the apparent losses, called Apparent Loss Index (ALI), by introducing the ‘apparent losses version’ of UARL. This standard value is 5% of water sales as the recommended reference, which is considered the minimum acceptable level of apparent loss. The formula is then as follows:

Apparent Loss Index (ALI) = Apparent Loss Value / 5% of water sales

2.2.2 Case in Geneva, Switzerland

Based on the study by Guibentif (2006), the Geneva Water (SIG) did not really have an accurate calculation (and definition) of their water losses level until 2004. All the figures previous to the year were based on estimation, while it is not clear what estimation method was used, and were expressed in the percentage method. Since 2004 and on, the company started to implement the standard water balance as recommended by IWA (see Figure 1), calculated using the components based method, while still in some reports express the value in percentage. The result that was provided by the previously estimated method and the newly applied IWA standard yield some differences, while fairly small, as seen in the following table. Table 2-3: Evolution of NRW in SIG

[Source: Acceptable Level of Losses in Geneva(Guibentif, et al., 2006)] Interestingly, the increase of the NRW level that exceed the ‘psychological’ level of 10% (based on estimation) was the main factor that initiated the urge to introduce a new and reliable standard of calculation, which eventually turned up to yield even higher

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losses (10.40% based on estimation and 13.30% based on IWA standard). However, a very important achievement is the ability to produce a quite detail water balance figure, as shown in the next figure for year 2006.

Figure 3: IWA Standard Water Balance Result in SIG for year 2006 [Source: Acceptable Level of Losses in Geneva(Guibentif, et al., 2006)] This water balance is the basis that was used to further apply another recommended best practice indicator, the Infrastructure Leakage Index (ILI), applying the amount of real losses for the Current Annual Real Losses (CARL) figure. The result, as reported, was at the level of 2.5 to 3 in the year 2004 to 2006. Furthermore, breakdown of the calculation was also done in smaller network basis, but without detail water balance as it was said not to be possible. CARL for smaller networks was estimated using the night flow analysis method. Nevertheless, there are two questions that arise regarding the implementation of the water losses indicators in Geneva. The first is related to the method that was used to produce the detail of the water balance. Very limited information is provided in the report to explain how such figure was able to be produced. The presence of apparent losses that is significant in the share of the total losses, as seen in the figure, should have made the quantification of real losses difficult, if not impossible. Detailing the components of real losses such as reported bursts, unreported bursts, background leakage, reservoir leakage and overflow should have been based on the findings on field, which automatically also means that these losses were repaired immediately and therefore should not have been included in the water balance anymore. The questions remains: how was it really done if it was not based on another estimation method? And if in fact it was based on estimation, how much better is the method compared to the method that was previously used before 2004?

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This automatically brings to the second question that is related so much with the accuracy issue. This appears to also have been recognised by SIG as seen with the ranges that are provided next every figure in the water balance. Apparent losses appear to have the lowest accuracy with the range of +/- 50%, which means that the number might go from 1.1 Mm3 to 3.3 Mm3 per year. Such result unfortunately is not reliable because the lower the accuracy of the result of one component in the water balance is, the lower also the accuracy of the result of the other components, because by definition they are supposed to be a balance in reality.

2.2.3 Case in Trinidad and Tobago

The thesis by Balkaran and Wyke (2003) presents the application of water losses performance indicators in Trinidad and Tobago. The local Water and Sewerage Authority (WASA) was reported to lose about 45% of the distributed water in 2002. However, unlike the case in Geneva, this paper describes the method for acquiring the NRW value in 7 steps, following the IWA water balance standard. Step 1 obtain system input volume, Step 2 obtain billed authorised consumption from the metered and unmetered (estimated?) consumption to get revenue water, Step 3 calculate Non Revenue Water, the difference between result of step 1 minus step 2, Step 4 obtain unbilled authorised consumption from metered and unmetered (estimated?) consumption, Step 5 calculate total authorised consumption by adding the result of step 2 with step 4, Step 6 calculate the total water losses which is the difference between the result from step 1 and step 5 And finally Step 7 assess components of apparent losses by ‘best means available’ This process appears acceptable until step 6. The last step however raises question: what is the best means available to assess components of apparent losses? In the further discussion of the paper, the researcher presents an interesting breakdown of the losses, being 39% to real losses and the remaining 9% to apparent losses. The following figure shows the result of the breakdown for the period of 1995 – 2002. Such figures are apparently the result of the ‘leakage assessment’ that was done by WASA as part of phase 1 in the leakage reduction campaign. The researcher quoted the methods used from a case study in England and Wales while admitted that these methods were not applicable in the country, they are: 1. Leakage = distribution input – consumption (which is NOT true, because there is

also the components of apparent losses), 2. The minimum night flow analysis, which also is based on assumption that apparent

losses do not exist (during the night).

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Figure 4: NRW Breakdown in Trinidad & Tobago [Source: Managing Water Loss: Strategies and Assessment, Reduction and Control of Non-Revenue Water (NRW) in Trinidad & Tobago (Balkaran & Wyke, 2003)] With the lack of proper metering and zoning, it was then decided that the assessment of the components of real losses was to be done based on more and more estimation, as is described in the paper. It has become less and less clear in the further discussion of the result of the assessment regarding the process that lead to such result. It is difficult to accept that if the ‘best means available’ refers to a rough estimation method, that a breakdown of the losses components is fairly accurate. Furthermore, the result of this questionable assessment method appears to be used as the basis for the calculation of ILI, and claimed was done and ready for a benchmarking indicator with other utilities in the world, while no result is presented.

2.2.4 Case in Sandakan, Malaysia

A report by Pilcher (2010) based on his work in Sandakan, Sabah, Malaysia in water loss management also provides another example of the application of the different water losses performance indicators. Similar to the other water utilities in the region, the Jabatan Air Sabah utilised also the traditional percentage system to express their level of water loss. The figure was at the level of 55% in 2002. This figure is said to have a reasonably high accuracy level since the calculation was based on good metering data from both the supply and consumption, while it is recognized that the level of unauthorized consumption was also likely to be high. However, as part of the water loss reduction project that was started in 2002, the water utility was also introduced to the IWA standard water balance system and had somehow managed to breakdown the water losses into the components, real loss to account for 42% and apparent loss 12%. The result for year 2002 is as shown in the following figure.

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Figure 5: Water Balance in Sandakan, Malaysia for 2002 [Source: A Practical Approach to Developing a Sustainable Water Loss Reduction Strategy in Sandakan, Sabah, Malaysia (Pilcher, 2010)] Further, it is only mentioned very briefly that an ILI figure was available, at the level of around 30 based on the ratio between CARL and UARL. Very limited explanation is provided on the losses breakdown method that managed to produce such result. Overall, the three case studies from Switzerland, Trinidad and Tobago, and Malaysia are only very few of the more or less similar stories of the implementation of the water losses performance indicators in the world, particularly the developing countries. Valuable information regarding the method for acquiring the results appears to always have been left out, whether intentionally or unintentionally. Such cases have been the major obstacles for the other water utilities to learn the method and try to apply similar approach so that they will not be left behind.

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3 Research Methods & Object

3.1 Methods

3.1.1 Data Collection

In order to answer the research questions, it is inevitable that the calculation and processing of real numbers and figures, quantitative data, have to be involved. An actual research object must be selected, in which actual data are mined. This research is conducted using the mixed method research (MMR) (Fidel, 2008), that combines both quantitative and qualitative data and is approached using inductive reasoning where observation is made to find patterns in order to build hypothesis, and finally a conclusion (Saunders, et al., 2007). The systematic process that is adopted is outlined in steps as follows:

1) Select the research object and the performance indicators that are to be assessed, 2) Determine the data components required to calculate these indicators, 3) Collect the data from the research object, 4) Process the data, calculate the water losses indicators (RQ#1), 5) Analyze results, explain anomalies / inconsistencies based on data, 6) Evaluate the process, assess difficulties and problems during the process (RQ#2), 7) Assess options to overcome the difficulties and problems (RQ#3), 8) Promote explanations of the patched results (RQ#4)

The research object is as discussed in the next section, while the performance indicators to be assessed are the most commonly used indicators: Non Revenue Water (NRW) expressed in percentage to total supply, in volume over time, in volume over time over length of network and/or number of connections and the recent widely promoted ILI. The following table presents the quantitative data that are required to calculate the selected indicators. Table 3-1: Water Losses Indicators and Required Data

No Indicator Required Data Data Period

1. NRW at company level in % of system input

Total supply, total billed consumption

Monthly (5-11 years)

2. NRW at PA level in % of system input

Supply by PA, billed consumption by PA

Monthly (5 years)

3. NRW at company level in volume / time / length of network

Total supply, total billed consumption, total length of network

Monthly (5-11 years)

4. NRW at PA level in volume / time / length of network

Supply by PA, billed consumption by PA, length of network by PA

Monthly (5 years)

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No Indicator Required Data Data Period

5. NRW at company level in volume / time / number of connections

Total supply, total billed consumption, total number of connections

Monthly (5-11 years)

6. NRW at PA level in volume / time / number of connections

Supply by PA, billed consumption by PA, number of connections by PA

Monthly (5 years)

7. ILI at PA level Length of network by PA, number of connections by PA, average length of service pipes by PA, average pressure by PA, CARL by PA

Monthly / Annual (5 years)

Apart from the quantitative data, some qualitative data are also required, particularly for the evaluation process. These involve information on the intervention actions that have been undertaken in a studied area, types of customers or social condition of an area, customers behaviour, etc., which depend so much on the result of data analysis. The collection of all the required data is done by direct inquiries to the related department within the research object, stating the study purpose nature of the research. However, it also acknowledged that some internet based research would also be necessary to mine the public information from other water utilities for comparison and presentation.

3.1.2 Data Analysis

Once data are collected, processing of the data can be done by using spreadsheet software, in this case Microsoft Excel® is used. Formulas are applied to calculate the figure of a water losses indicator and then copied to acquire the periodic (monthly or annual) figures. The results are presented as a spreadsheet table and charts, while exceptional points or trends require further explanations from the qualitative information gathered. Interpretation however is a little bit more delicate. Defining an indicator to be whether applicable or not, practical or not, process is difficult or not, is most of the time subjective. Therefore, standards are set for the interpretation in this research, they are:

a) Not applicable, when the data / component required to acquire the result is not present,

b) Not practical, when the process to acquire the result requires more effort or resource or time than other existing methods,

c) Difficult, when the process to acquire the result requires in-depth knowledge or skills in a certain field.

Handy Salim 17

3.2 Object

The research object is selected from the water utilities that are currently fighting problem with high water losses, preferably from the developing countries where techniques and human resources skills are assumed relatively limited; financial aspects are considered a very important constraint (tariff is low); and other conditions as described in previous chapter apply.

3.2.1 PALYJA

Jakarta is the capital of Indonesia in South East Asia, and among the largest and densely populated cities in world. The water supply utility is split into two, both operated by private companies under concession contract since 1998, one serving the western part of the city (PALYJA) and another the eastern part (AETRA). The research is done in the part served by PALYJA. A subsidiary of SUEZ Environnement, PALYJA is currently serving over 370,000 connections; spread in 5100 kilometres of pipe network (PALYJA, 2008a), with approximately 60% of the population is covered (PALYJA, 2008b) and all are metered. Established in 1998, the company has been facing the problem that also the city faces, the vast population growth and at the same time, the water resource is limited. Exacerbating the condition, the private operator is operating a network inherited from the previous public water utility owned by the municipality which is in a very poor condition. The system operates under a considerably low pressure (below 20 m in average) while is still losing about 45% of the water being supplied, data at the end of 2008; more than 55% at the beginning of the private takeover in 1998 (PALYJA, 2009d), which shows a major improvement and indicates the considerable efforts by the company to solve the problem. Indeed, NRW has been viewed by the management team as one of the key indicators that drives the performance of the company. While the reduction of roughly 10% of NRW level in about 10 year time should be appreciated, this achievement was the result of a huge investment including inexhaustible pipe replacements, leak detection and reparations, metering actions, commercial actions and campaigns, and extensive studies. It should be noted that since the beginning of the operation, only NRW has been generally used by the company for indicators, while ILI has not been common.

3.2.2 Permanent Areas

Since 2002, PALYJA has been breaking down the water distribution system into smaller and controllable zones, known as Permanent Areas. The Permanent Areas (PAs) are hydraulically and administratively isolated zones with master meter and which supply and consumption are regularly monitored. This system is also often referred to as District Metered Area (DMA) (Garcia, et al., 2003). In PALYJA, each PA has different characteristics than the others, including size of the areas, number of connections, type of customers, social aspects, service levels, etc. One of the main purposes for the establishment of these zones is to have a more detail understanding of a particular area and for better control. This is related very closely with the water losses management strategy undertaken by the company, since it has

18 MSc thesis

been known that the source of losses (and other problems) in an area may differ from the others. Having these understanding, the operation of the PAs in PALYJA can be seen as if they are the representations of different water utilities from hydraulic point of view because each PA has its own supply regime, although in reality the management is done centrally. Each PA can be analysed independently from the others. The naming of the PAs is done in the order of the design consecutively and is coded by area. The first letter in the name shows where the PA is located in the service area, P indicates that it is located in “Pusat”, the central distribution area that covers parts of Central and North Jakarta, B refers to “Barat”, an Indonesian word for west, it refers to the western service area that covers all West Jakarta and some parts of North Jakarta. S refers to “Selatan”, indicating the location of the PA being in the southern service area that covers parts of South Jakarta. Therefore PAs are named P1, P2, P3, B1, B2, B3, S1, S2, S3 and so on accordingly. Some areas have been split into smaller areas, called Sub Areas, for various purposes, i.e. for better control and monitoring. These sub areas are named with additional number behind the PA number, for example S9/7 means it is a sub area number 7 that is part of the PA number 9 in the southern service area. While the establishment work is still ongoing, the PAs are equipped with monitoring and control tools for inflow and service pressures. The records are available daily in about 5 to 15 minutes logging interval, and therefore a huge amount of data. However, infrastructural operations for such system was only started since 2007, and still to date a lot of troubleshooting works need to be done. To date, there are in total 35 PAs that are designed and spread throughout the whole PALYJA service area. The map of designed PAs can be found in Appendix A (PALYJA, 2010b).

3.2.3 Non Revenue Water Division

One somewhat unique about PALYJA compared to other water utilities is the existence of a so called Non Revenue Water Division, a specialised unit dealing particularly with water losses. It is unique because the unit presents in the company organization chart as a permanent unit and not an ad hoc entity or unit that exists only in a project organization that will end once the job is done. This shows that PALYJA consider water loss a very serious matter and an everlasting work that needs to be done continuously, that even when the level of the losses is already considered low, works still are required to maintain the level and improvements are always possible in such case. The NRW division’s task is to collect, maintain, and study all the information related to water losses, and coordinate the intervention actions to reduce the level of losses. This unit acts more like a ‘think tank’, centre of water losses reduction coordination, than an operator. The operation itself is distributed to and undertaken by other units, depending on the types of work needed in the proposed action. The following shows the organization chart of Non Revenue Water Division in PALYJA:

Handy Salim 19

[Source: Modified from NRW Division Organization Structure January 2010 (PALYJA, 2010a)] Figure 6: Non Revenue Water Division Organization Chart The Non Revenue Water division consists of 3 different units that handle different aspects of water loss. The Commercial Losses and Metering department specialises in the data collection, study, and coordination of the field investigation works. Under this department is also the metering specialised unit that takes care of all the metering works, including the investigation of suspect unauthorised consumption by meter tampering. The Permanent Area Study department, as already well described in the name, coordinates the PA establishment works in the PA Creation team. It also includes special study and coordination units, the PA Services, that each manages several PAs in order to gain specific knowledge of the area situation to propose improvements. It can be seen here that the PAs are indeed the basis for PALYJA in undertaking the NRW reduction strategy. The Physical Losses and Reporting section collects and study the data of reported, found, and repaired leaks throughout the whole service area, and utilise these information to improve physical losses reduction strategy. Looking at the job description, it is natural that the NRW division holds a considerable bulk of information related to water losses and the history of actions that have been undertaken in the company.

3.2.4 Water Losses Reduction Strategy in PALYJA

As mentioned, PALYJA utilises the district metered area method as the basis for the water loss reduction strategy. The basic concept is to establish smaller independent zones, called the Permanent Areas, which water supplies are controlled and fully monitored. Using this system, the level of water losses in each area as well as the progress of the NRW reduction works can be monitored. Many studies have been made within the company by making use of the bulk of data collected from the PAs, such as supply flow pattern, pressure in the network pattern, consumption pattern, NRW trend, leakage report, finding and reparation, customer

NRW Division

Permanent Area Study Dept.

Commercial Losses &

Metering Dept.

Commercial Losses Section

Metering Section

PA Creation Section

PA Services Section

Physical Losses & Reporting

Section

20 MSc thesis

complaints, etc. All these studies have been very useful in driving the strategies so far in the achievement of water loss reduction, while clearly more could be achieved (Halim, 2009). In the field of real loss management, the water utility implements active leakage control strategy. This means, briefly, that the utility actively search and repair leakage, and not only waits for leakage report from the communities. There are specialised team on field to search for leakage, apart from also the desktop monitoring and assessment methods to detect possible burst. Similar active strategy also applies to the apparent (commercial) loss. In dealing with an area identified as having high water losses, PALYJA implements the so called Integrated Actions, which refers to the simultaneous actions by several units to undertake water losses detection and reduction works. These actions include altogether the leakage detection, leakage repair, unauthorised consumption survey, meter replacements, pressure management, pipe replacements, and many more; practically almost every thinkable possible action to reduce water losses, and therefore it involves considerable resources and investments. Since this requires enormous resources to implement, it is impossible that PALYJA can do this simultaneously in every Permanent Areas. A system to prioritize is developed, using basic information such as the water losses indicator, in this case the NRW figure expressed in % and in absolute value such as volume/time or volume/time/size of area. However, for the larger investments needs such as the network rehabilitations, a specialised tool called Scoring Tool was developed. This scoring tool incorporates various elements that need to be considered for the project to take place. These elements are for instance the level of losses, size of the area, cost of the project, the payback period, potential future sales, etc. As seen, the water losses performance indicators always play very important roles in the water losses reduction strategy in PALYJA.

Handy Salim 21

4 Results

4.1 Performance of PALYJA According to Traditional PIs

4.1.1 Water Losses in the Whole Service Area

With the records of monthly total water supplied to the system and billed consumption since the beginning of the concession contract in 1998 (PALYJA, 2009e), one can easily calculate the water loss level. The difference between system input / supply and billed consumption is in PALYJA referred to as Non Revenue Water, while in fact according to IWA definition is not the case because NRW should also include the unbilled authorized consumption (refer to the IWA definition in Chapter 2). This is done in PALYJA however because the figure of the unbilled authorized consumption is nearly none and therefore neglected from the calculation. Combined with information of pipe length and total number of connections per month, the table below provides the result for total water losses at PALYJA level expressed in m3/day, % to total supply, m3/day/km of pipe length, m3/day/number of connections (assuming there are always 30 days in a month), calculated using spreadsheet. The results in the middle of each year are not shown. Table 4-1: PALYJA Water Losses 1998 – 2009

Losses Month

m3/day % m3/day/km m3/day/conn Jan-98 351,337.3 56% 104.7 1.7 Dec-98 353,396.0 57% 105.3 1.7 Jan-99 425,872.6 62% 120.2 2.0 Dec-99 332,655.7 52% 93.9 1.5 Jan-00 407,014.9 60% 100.1 1.8 Dec-00 357,277.0 54% 87.9 1.4 Jan-01 298,416.9 48% 68.8 1.1 Dec-01 314,969.1 51% 72.6 1.1 Jan-02 259,743.9 41% 58.6 0.9 Dec-02 296,333.6 46% 66.8 0.9 Jan-03 283,407.7 43% 59.7 0.9 Dec-03 312,275.1 47% 65.8 0.9 Jan-04 308,794.3 45% 63.5 0.9 Dec-04 345,645.6 48% 71.1 1.0 Jan-05 355,275.4 49% 72.3 1.1 Dec-05 379,886.2 52% 77.3 1.1 Jan-06 377,896.9 51% 75.1 1.1 Dec-06 344,421.4 48% 68.4 1.0 Jan-07 332,688.1 48% 65.2 0.9 Dec-07 326,081.3 44% 63.9 0.9 Jan-08 357,374.0 50% 71.1 0.9 Dec-08 336,160.2 48% 65.4 0.8 Jan-09 320,011.3 46% 62.0 0.8 Sep-09 276,714.1 43% 52.8 0.7

22 MSc thesis

In a simple explanation, m3/day shows the average volume of water lost in 1 day, % shows the relative volume of water lost compared to the volume supplied, m3/day/km shows the volume of water lost in a day on average 1 km of pipe, and lastly m3/day/conn shows the volume of water lost in a day on average 1 customer’s connection. The result shows an almost constantly improving water loss level from year to year. Another indicator can be generated from this result, it is the improvement level, calculated by deducing the figure in the beginning of the period (January 1998) with the figure at the end (September 2009), compared to the figure at the beginning. This will show the percentage of improvement made by the company in water losses reduction.

Improvement Level = (figure at the beginning – figure at the end) / figure at the beginning.

The result is as follows: Table 4-2: Improvement Level 1998 - 2009

Indicator Fig. at beginning Fig. at the end Improvement m3/day 351,337.3 276,714.1 21% % 56 43 23% m3/day/km 104.7 52.8 50% m3/day/conn 1.7 0.7 61% As seen, the improvement level made by the water utility may interestingly vary from 21% (1.9% per year in average), which might seem relatively low, to the level of 61% (5.5% per year in average) which is a very good improvement (more than half of the losses in the beginning). This leads to the further question: how is PALYJA performing compared to other water utilities, according to these indicators? Extracting the data from an international benchmarking web site, the tables provided below show how well PALYJA have been performing relatively to other selected water utilities in South East Asian region. Data limitation made the comparison is done only for the improvements made between year 2000 and 2002. Table 4-3: Improvement of Water Losses Level in % of Supply Rank Water Utility 2000 2002 Improvement

1 Vietnam - Vung Tau Water Supply Joint Stock Company 19 14 26%

2 Vietnam - Kien Tuong Water Supply Company 55 41 25%

3 Malaysia - LAKU 23 18 22%

4 Vietnam - Cao Bang One Member Water Supply Company Ltd., 44 36 18%

5 Malaysia - Syarikat Air Terengganu SDN. Bhd 39 33 15%

6 Indonesia - PALYJA 54 46 15% 7 Malaysia - Kedah 46 42 9%

8 Malaysia - Sarawak 22 21 5%

9 Vietnam - Binh Thuan Water Supply And Sewerage Company

27 26 4%

10 Malaysia - Kelantan state 44 45 -2%

[Source: Modified from The International Benchmarking Network for Water and Sanitation Utilities - Indicator Search (IBNet, 2010)]

Handy Salim 23

Table 4-4: Improvement of Water Losses Level in m3/day/km Rank Water Utility 2000 2002 Improvement

1 Vietnam - Vung Tau Water Supply Joint Stock Company 19 13 32%

2 Indonesia - PALYJA 87.9 66.8 24%

3 Vietnam - Binh Thuan Water Supply And Sewerage Company 30 23 23%

4 Malaysia - LAKU 32 25 22%

5 Vietnam - Kien Tuong Water Supply Company 81 70 14%

6 Malaysia - Kedah 47.8 43 10%

7 Vietnam - Cao Bang One Member Water Supply Company Ltd.,

29 27 7%

8 Malaysia - Kelantan state 21.8 22 -1%

9 Malaysia - Syarikat Air Terengganu SDN. Bhd 27.7 28 -1%

10 Malaysia - Sarawak 7 9 -29% [Source: Modified from The International Benchmarking Network for Water and Sanitation Utilities - Indicator Search (IBNet, 2010)] Table 4-5: Improvement of Water Losses Level in m3/day/conn Rank Water Utility 2000 2002 Improvement

1 Indonesia - PALYJA 1.4 0.9 36% 2 Vietnam - Vung Tau Water Supply Joint Stock Company 0.3 0.2 33%

3 Vietnam - Cao Bang One Member Water Supply Company Ltd.,

0.4 0.3 25%

4 Malaysia - Kedah 1.1 0.9 18%

5 Malaysia - LAKU 0.6 0.5 17%

6 Vietnam - Binh Thuan Water Supply And Sewerage Company

0.2 0.2 0%

7 Vietnam - Kien Tuong Water Supply Company 0.4 0.4 0%

8 Malaysia - Sarawak 0.3 0.3 0%

9 Malaysia - Kelantan state 0.7 0.7 0%

10 Malaysia - Syarikat Air Terengganu SDN. Bhd 0.7 0.7 0% [Source: Modified from The International Benchmarking Network for Water and Sanitation Utilities - Indicator Search (IBNet, 2010)] If one to evaluate the improvement made by PALYJA based only on the percentage of water losses to total supply in the period of year 2000 - 2002, it may seem that the water utility made only mediocre achievement compared to other utilities in the region, giving it rank 6 out of 10 utilities, as seen in the first table. However, the result in the second table gives a different perspective, if does not completely change the picture, with PALYJA ranking second out of the 10, which is a very good achievement. The third table is even more shocking, showing PALYJA as the best performer in water loss reduction among the selected water utilities in the region. An opposite version of story can be told about i.e. Kien Tuong Water Supply Company in Vietnam that appears in the first table as one of the best performers (rank 2), but would suddenly drop to rank 5 when assessed using m3/day/km indicator and even lower to rank 7 before the indicator expressed in m3/day/connection. How could this happen?

24 MSc thesis

The charts provided below answer the question for the case of PALYJA:

PALYJA Total Length of Mains (km)

3356.264

5244.773

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Length of mains (km) Water losses (m3/day)

Figure 7: PALYJA Total Length of Mains 1998 - 2009 [Source: Modified from PT PAM Lyonnaise Jaya Volume of Water Delivered and Water Billed (PALYJA, 2009e) ] The water utility started at around 3,300 km of total pipe length on January 1998 and had extended the distribution network to about 5,200 km by September 2009 (almost 60% improvement in 11 years). At the same time, as shown in Table 4.1 and 4.2, water losses absolute level (volume) is also constantly decreasing. This resulted in the higher overall improvement when the utility is assessed using indicator that is expressed in m3/day/km. The discussion in Chapter 2 has made it clear that there is a relation between water loss and length of network. The longer the pipes are, the higher the chances for losses to occur. Therefore it is considered a better achievement to have a growing network (meaning more service area is covered) but at the same time water losses are decreasing, compared to ones which network is not growing (or even shrinking – i.e. likely be the case in Malaysia - Sarawak).

Handy Salim 25

PALYJA Total Number of Connections

410,146

201,662

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Number of connections Water losses (m3/day)

Figure 8: PALYJA Total Number of Connections 1998 - 2009 [Source: Modified from PT PAM Lyonnaise Jaya Volume of Water Delivered and Water Billed (PALYJA, 2009e) ] The figure shown in the total number of connection chart explains the phenomena of leaping performance ranking in Table 4.5. In the beginning of 1998, PALYJA started with around 200 thousand connections within its service area. This number was doubled in 2009, giving the figure of 410 thousand connections in total. Similar to pipe length, the more connections there are, the higher the risk is for losses to occur. Therefore it is a better score for PALYJA that the utility was able to connect more customers (meaning more people have access to water) and still not only maintain, but reducing the level of losses, compared to the ones which also managed to reduce losses but without any improvement in the number of people served (i.e. Malaysia – Syarikat Air Terengganu SDN. Bhd). Since network length and number of connections are the very important attributes of the size of water utility, it is therefore safe to say that when size is not considered, PALYJA is rank 6 in water losses reduction achievement among the 10 water utilities assessed in the region. But when size is taken into account, it is fair to say that PALYJA is actually one of the best performers in water losses reduction among the 10 assessed water utilities. An interesting question that arises from this result is ‘would PALYJA have reduced more of its water losses if it had not expanded the network and connected more customers during that period?’

4.1.2 Water Losses at Permanent Area Level

The story for the Permanent Areas is not much different, with different indicators showing different performance level, as seen in the following table that presents the result for water losses indicators in 30 PAs or sub areas in PALYJA, processed from data of the PA Services Database Report (PALYJA, 2009b).

26 MSc thesis

Table 4-6: Water Losses Level by Permanent Areas (as of September 2009)

Rank PA % PA m3/day PA m3/day/km

PA m3/day/conn

PA

100 m3/day/km/co

nn

1 B6/4 6% S9/2 71.2 S9/2 4.4 B6/4 0.1 S12 0.1

2 S9/2 7% B9/2 165.8 B6/4 7.2 B2 0.1 B6/4 0.1

3 S9/11 12% S9/11 178.5 B13 7.6 B6/3 0.1 B14 0.1

4 B6/3 15% S9/8 204.0 B6/2 8.6 S9/2 0.1 B2 0.3

5 B2 16% B13 210.3 B2 8.7 B9/2 0.2 P4 0.3

6 B9/2 20% B8 242.4 B9/2 9.7 B14 0.2 P10 0.3

7 B6/2 21% B6/2 327.3 S1 10.0 B6/2 0.2 S1 0.4

8 B8 23% B6/4 338.7 S9/11 10.2 B13 0.2 B6/2 0.5

9 B13 26% B2 359.8 S14 11.7 S1 0.3 S8 0.5

10 B14 30% B6/3 394.1 S9/8 12.5 B8 0.3 B15 0.5

11 S9/8 30% S14 543.5 S12 13.9 S9/8 0.3 P3 0.5

12 S1 34% B10 564.9 B10 14.1 S9/11 0.3 P5 0.6

13 P4 35% S11 597.6 S11 14.3 S12 0.3 S14 0.7

14 S14 36% S1 629.0 S4 15.4 B10 0.3 S9/2 0.7

15 B10 38% S9/3 808.6 B14 18.6 S14 0.3 B10 0.8

16 P10 39% S4 1181.1 B8 20.7 P10 0.4 S4 0.8

17 S12 40% S9/7 1461.4 B6/3 27.7 P4 0.4 B6/3 0.8

18 P7 44% S9/5 1523.6 S8 31.0 S11 0.5 B13 0.8

19 P5 45% S9/9 1559.4 P10 31.7 S4 0.6 B9/2 1.0

20 S9/9 47% B14 2367.4 S13 37.9 S8 0.7 S11 1.2

21 P6 47% S13 2831.4 P4 43.1 P5 0.7 S13 1.2

22 S8 51% P7 3243.2 P5 55.3 P7 0.8 S9/11 1.6

23 S11 51% P10 3497.6 S9/9 59.2 B15 0.9 S9/8 1.7

24 S4 52% P6 3880.3 S9/5 63.8 S13 0.9 P7 2.2

25 S9/5 56% S12 4078.0 S9/7 69.1 P3 1.0 P6 2.2

26 P3 56% S8 4300.7 P6 79.7 P6 1.1 B8 2.3

27 B15 60% P4 5936.7 S9/3 84.8 S9/7 2.3 S9/9 9.4

28 S13 61% P5 6229.7 P7 91.1 S9/9 2.5 S9/7 10.8

29 S9/3 69% B15 18844.1 P3 100.5 S9/5 2.9 S9/5 12.0

30 S9/7 75% P3 19319.3 B15 103.4 S9/3 3.0 S9/3 31.2

The ranking system of PA water losses performance can be used for prioritizing areas for actions when resources are limited. Areas where losses are high should normally be given the first priority, not only because the potential gain is higher but also because losses are easier to find, therefore improvements can be easier made, compared to the ones where losses are already low. However, assessing the table, the question is then ‘which ranking should be used?’. As seen, the first column ranking (orange) suggests S9/7, S9/3, S13, B15 and P3 consecutively as the five highest losses PA (lowest ranking) and therefore should be prioritised for immediate actions. Second column ranking suggests P3 & B15 (similar with first column), P5, P4, and S8. This goes on with the other column suggesting different PAs.

Handy Salim 27

Applying similar understanding as the benchmarking result between PALYJA and the other water utilities in South East Asian region, it is now clear that this inconsitencies in performance level occur simply because the areas have different attributes of area size, that is length of pipe and number of connections. Nevertheless, when size of the area is taken into account there is also the need to see the efficiency of the distribution which is indicated by the percentage of losses. Therefore another result can be developed by combining the % and m3/day/km/conn columns, to show the PAs that occur at the lowest rank in both columns. This new list should therefore show the PAs that are worth prioritising, as follows: Table 4-7: Highest Losses Permanent Areas (as of September 2009)

Rank PA % PA m3/day/km/conn

20 S9/9 47% S11 1.2 21 P6 47% S13 1.2 22 S8 51% S9/11 1.6 23 S11 51% S9/8 1.7 24 S4 52% P7 2.2 25 S9/5 56% P6 2.2 26 P3 56% B8 2.3 27 B15 60% S9/9 9.4 28 S13 61% S9/7 10.8 29 S9/3 69% S9/5 12.0 30 S9/7 75% S9/3 31.2

And the top 6 priority PAs therefore should be S9/9, P6, S11, S13, S9/3 and S9/7, that each loses above 47% of the water supplied which amounts over 1.2 m3/day/km/connections. However, such result is still somewhat confusing and not sufficient for a decision making tool since it does not show clearly which area should be number one priority should resources availability limits only the actions to be undertaken in one area.

4.2 Application of Infrastructure Leakage Index (ILI)

Applying ILI in PALYJA takes more efforts than the traditional water losses indicator such as NRW for which data are all collected and ready to be used. ILI, as mentioned in the previous chapter, is not yet common in the water utility. Furthermore, calculation of ILI requires many technical components such as length of network, length of service pipe, number of customers and system pressure, as described in the formula. The more components there are, the higher the degree of error that is possible in the calculation. For illustration, if there is just a slight 5% error in network length data, another 5% in service pipe data, and another 5% in the system pressure data, this would make the total error of ILI become magnified to over 5% error as the result. This understanding is the basis for applying such calculation in a smaller area, the PAs, where data are expected to be reasonably more accurate and representative, compared to applying this in the whole service area where variability is extremely high.

28 MSc thesis

Nevertheless, understanding this does not automatically mean the work gets simpler. Going through the steps for calculating ILI, firstly the Unavoidable Annual Real Losses (UARL) figure is needed. This figure is a theoretical figure, as discussed in Chapter 2, it can be easily calculated by applying the given formula, despite some accuracy limitations when this formula is applied in a developing country (Liemberger & McKenzie, 2005). Secondly, there is the Current Annual Real Losses (CARL) which is not a theoretical figure and should be based on actual real loss data collected from the field. The big question is how to get this figure. As mentioned earlier, water losses consist of 2 components, real (or physical) loss and apparent (or commercial) loss. Real loss refers to the loss caused by leakage, water that leaves the pipe physically without reaching the customers. Apparent loss refers to the loss caused by water theft and administration or metering errors. However, the contribution of each of these losses to the total figure of losses is not very well known, particularly in the case of PALYJA where apparent loss might appear to be as high as real loss. This according to PALYJA is due to; firstly, the system pressure that is very low at some areas which sometimes prevents water meter to work at the optimum designed performance therefore might increase metering error. Secondly, unauthorised consumption (water theft) is high especially in the urban poor settlements. While the exact balance of real loss and apparent loss is not clearly known, the water utility has been assuming that both losses have equal contributions of 50% each to the total losses. But again, this statement is yet to be proven and therefore the accuracy is highly questionable. For PALYJA, figuring out the balance of real and apparent loss in an area would unquestionably be a great knowledge. When it became clear where the water is lost to, it became also very easy to find it. Investment decisions could be made very easily: replacing the pipes where leakage is higher and law enforcement actions in areas where water theft is higher. However, one would argue: how would you know how much is the leakage without finding the leakage first? But if you found them, you would directly repair them and therefore they do not exist anymore and the knowledge became obsolete. This is the main argument why CARL data is currently not present. However, in answer to that argument, there are several techniques to estimate the amount of real loss, without having to find the leakage on field first, despite the questions of reliability and applicability in a water utility like PALYJA. These techniques, as part of this research to assess the applicability of ILI, are applied in the Permanent Areas in PALYJA, which results are presented in the following.

4.2.1 Night Flow Analysis

As briefly mentioned in the earlier chapter, the night flow analysis is a technique to estimate the level of leakage (real loss) by utilizing the flow and pressure measurement in the assessed area during the period when water consumption is assumed the lowest (night time). The logic is that during the night when people are not using water (or consumption is at minimum), the recorded flow into an area should represent the flow of leakage and not consumption. This amount of instantaneous flow is then interpolated to the figure of the full day (24 hours) using the pressure-leakage relationship (Lambert, 2000b; Thornton & Lambert, 2005) equation.

Handy Salim 29

L1/L0 = (P1/P0)

N1 Where L1 is the leakage level at a given time (in this case is CARL), L0 is the leakage level at the basis of assessment (in this case leakage during the night), P1 is the pressure at the given time (in this case the annual average pressure), P0 is the pressure at the basis of assessment (in this case pressure during the night), and finally N1 is the exponent that shows the relationship between pressure and leakage. The key is N1, as seen in the formula. Leakage and pressure during the night (L0 & P0) can be estimated using the record on water flow into the assessed area. P1 is also a known variable that can be acquired from pressure record. But without knowing N1, L1 (which is the CARL) cannot be obtained. Lambert (2000b) shows that N1 may vary from 0.5 to 2.5, which is quite a big range, depending upon the mixture of leaks and pipe materials, and suggests that an assumption of linear value (N1 = 1) be used in absence of knowledge of leakage level and pipe materials. This is the case for PALYJA. Nevertheless, the sample result of a night flow analysis that was done internally by PALYJA in year 2006 on P7, one of the Permanent Areas, using the assumptions and approach as discussed above, is presented in Appendix B (PALYJA, 2006). While such method indeed produces an estimate of the level of real loss, a figure that is needed to proceed with the calculation of ILI, it can be seen from the result that the accuracy is questionable. In fact, the exercises of this method in other Permanent Areas do not yield acceptable results, with sometimes the level of leakage being higher than the total loss, which is by default impossible in reality. Tracking back on the process, some weaknesses can be outlined. The first issue is related to the use of the assumption that night consumption is low. True that this assumption can be valid for some area, but in the case of PALYJA where system pressure is generally low for the whole day, customers in some areas have adapted to the situation by utilizing underground water storage, therefore collecting water during the night, leaving their taps on, which means night consumption might not be necessarily lower in these areas. The second issue is related to the instability of the supply pattern into an area which happens from time to time in PALYJA. As noted, the night flow analysis method assumes the real loss that occurs during a 24 hour period to constantly occur for the rest of the time in the year, while at the same time consumption and supply regime change abruptly from time to time as well as the level of leakage. This at the end results in over or underestimation of the level of real loss. The third issue is definitely related to the use of linear relationship between pressure and leakage, by using N1 = 1, which is based solely on pure assumption. Even a slight modification to the N1 figure, for instance increasing it to 1.1, would change so much the end result of leakage level. Analysing the result and the limitations it confines, it is of no doubt that using this method to complement the calculation of ILI would only yield unreliable and possibly

30 MSc thesis

misleading result. It is therefore decided that night flow analysis is not suitable for PALYJA to quantify its leakage level.

4.2.2 Statistical Analysis

Another method is then developed out of the absent of available and applicable method to quantify real loss in order to proceed with ILI calculation. It is not yet a common method that can be found in the literatures, and therefore the validity of the process and result is yet to be challenged, as already noted. It is already understood that a close positive relationship exists between pressure and leakage. In fact, such close relationship exists largely only between pressure and leakage and affects only a small proportion of apparent loss. This however is again an assumption that is going to be the basis of the next leakage level quantifying method. Coming from the assumption that the part of losses that increases together with the increase of pressure is leakage, one can develop a pattern of leakage versus pressure based on the data of monthly pressure and monthly NRW figure in an area. This pattern is then used to build a regression model to develop a formula that explains the relationship between the increase of pressure and the increase of losses (leakage). The result for P7 is presented in below. Table 4-8: Pressure and NRW in P7 for Jan 08 – Oct 09

Month Pressure (m)

NRW (lps) dP (m) dNRW

(lps) CARL (lps)

Jan-08 7.5 30.4 24.4 Feb-08 8.5 33.7 1 3.4 27.7 Mar-08 8 37.0 -0.5 3.2 26.1 Apr-08 7.5 31.9 -0.5 -5.1 24.4 May-08 8 38.4 0.5 6.5 26.1 Jun-08 8.5 36.2 0.5 -2.2 27.7 Jul-08 9 33.0 0.5 -3.2 29.3 Aug-08 7.5 32.3 -1.5 -0.7 24.4 Sep-08 8.5 42.8 1 10.5 27.7 Oct-08 8.5 37.3 0 -5.5 27.7 Nov-08 8.5 35.9 0 -1.3 27.7 Dec-08 7.5 35.6 -1 -0.3 24.4 Jan-09 8 41.6 0.5 6.1 26.1 Feb-09 8 40.5 0 -1.2 26.1 Mar-09 9 41.9 1 1.5 29.3 Apr-09 8 40.6 -1 -1.4 26.1 May-09 7.5 36.2 -0.5 -4.4 24.4 Jun-09 8 42.4 0.5 6.2 26.1 Jul-09 8.5 40.5 0.5 -1.9 27.7 Aug-09 10 49.6 1.5 9.1 32.5 Sep-09 9.5 37.5 -0.5 -12.1 30.9 Oct-09 8.5 42.2 -1 4.7 27.7

Handy Salim 31

dP stands for the difference of pressure between one month and the previous, while dNRW expresses the difference of losses between one month and the previous. Pressure data are from the online 24 hour monitoring of the network pressure (PALYJA, 2009a). The data for a period of almost 2 years are used for the area and are the only reliable data that are available for the respective PA. Data prior to this period are not valid due to the changes that have been made physically to the PA that change its properties and hydraulic performance. However, using the available data, a simple linear regression model is developed using Ms Excel® as shown in the following.

Linear Regression of dP and dNRW in P7

y = 3.205x + 0.4104

-15

-10

-5

0

5

10

15

-2 -1.5 -1 -0.5 0 0.5 1 1.5 2

dP

dNR

W

R2 = 0.22

Figure 9: P7 Linear Regression Model The equation y = 3.205x + 0.4104 is then used to calculate the CARL figures (but monthly not annually) as presented in Table 4-8, with y being the level of real loss and x being the pressure. Nevertheless, it is again noticed that this method also bends some limitations. Looking at the result, the first thing that would raise question is the value of R2 being very low for a valid regression model. This shows that there is indeed a positive relationship between pressure and leakage but the influence of pressure to leakage level is only about 22%, while the remaining balance is of some other variables that do not exist in the model. In other words, the model is weak. It is explainable simply because, firstly, this method bases itself upon assumptions that only leakage is affected by pressure, while as explained previously that in some parts of the service area in PALYJA people are leaving their taps open 24 hours a day, which

32 MSc thesis

means that the consumptions have leakage-like behaviour and therefore would also increase together with the increase of pressure. This shows that even a proportion of apparent loss, such as water theft, might also be affected by the change of pressure. Secondly, there is high possibility that during the monitoring period (2 years) some intervention actions were done on field, i.e. leak repair, which would affect the level of losses in a reverse way (leakage reduces, pressure increases) than the basic assumption that this method is built upon (pressure increases, leakage increases). Lastly, the use of linear regression again means that this technique assumes somewhat linier approach to the relationship between leakage and pressure, very much similar with applying night flow analysis method using N1=1. This method is applied to 12 PAs in PALYJA with P7 is the only one that yields reasonable result. Even so, ILI needs to be calculated, and figures of CARL are needed, therefore this result is used in the next process.

4.2.3 ILI in P7

The first step to calculating ILI for P7 is done with the definition of the CARL. The second step is to define the value of the theoretical leakage level, the Unavoidable Annual Real Loss (UARL). This can simply be done by applying the formula:

UARL (litres per day) = (18 x Lm + 0.8 x Nc + 25 x Lp) x P Lm is the length of network in P7 (km), which data is available, Nc is the number of connections in P7, also available (PALYJA, 2009b), and Lp is the total length of service pipe, which in this case is the average length of service pipe (6.5 m or 0.0065 km for PALYJA) multiplied by the number of connections. P is the system pressure in P7 (m), which data is also available (PALYJA, 2009c). When CARL value is defined, ILI is simply the CARL / UARL. The following table shows the result of monthly figure of calculated UARL, CARL (converted to litres per day) and ILI for P7. Table 4-9: ILI in P7 for Jan 08 – Oct 09

Month Lm (km) Nc Lp (km) P (m) UARL CARL ILI

Jan-08 34.3 4015 26.1 7.5 33613.8 2112299 62.8 Feb-08 34.3 4032 26.2 8.5 38234.7 2389211 62.5 Mar-08 34.3 4038 26.2 8.0 36031.8 2250755 62.5 Apr-08 34.3 4056 26.4 7.5 33909.8 2112299 62.3 May-08 34.3 4061 26.4 8.0 36208.9 2250755 62.2 Jun-08 34.3 4063 26.4 8.5 38488.3 2389211 62.1 Jul-08 34.3 4072 26.5 9.0 40830.3 2527667 61.9 Aug-08 34.3 4140 26.9 7.5 34516.1 2112299 61.2 Sep-08 34.3 4166 27.1 8.5 39331.0 2389211 60.7 Oct-08 36.0 4176 27.1 8.5 39672.9 2389211 60.2

Handy Salim 33

Month Lm (km) Nc Lp (km) P (m) UARL CARL ILI

Nov-08 36.0 4185 27.2 8.5 39746.5 2389211 60.1 Dec-08 36.0 4191 27.2 7.5 35113.8 2112299 60.2 Jan-09 36.0 4182 27.2 8.0 37385.4 2250755 60.2 Feb-09 36.0 4162 27.1 8.0 37231.4 2250755 60.5 Mar-09 36.0 4165 27.1 9.0 41911.3 2527667 60.3 Apr-09 36.0 4169 27.1 8.0 37285.3 2250755 60.4 May-09 36.0 4179 27.2 7.5 35027.2 2112299 60.3 Jun-09 36.0 4182 27.2 8.0 37385.4 2250755 60.2 Jul-09 36.0 4186 27.2 8.5 39754.7 2389211 60.1 Aug-09 35.6 4197 27.3 10.0 46802.9 2804579 59.9 Sep-09 35.6 4206 27.3 9.5 44545.0 2666123 59.9 Oct-09 35.6 4212 27.4 8.5 39905.2 2389211 59.9

4.2.4 ILI vs NRW

The table shows a very interesting result, where ILI appears to be decreasing from January 2008 to October 2009, which contradicts with NRW figure in the same period (see Table 4-8).

ILI & NRW in P7

58.0

58.5

59.0

59.5

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60.5

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63.5

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ILI

0%

10%

20%

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NR

W %

ILI NRW %

Figure 10: ILI vs NRW in P7 By definition, ILI indicates the performance of the distribution system from the sense of real losses management. The lower the ILI value is the better the performance, which should also mean that the lower the leakage level is, relatively to the theoretical leakage value. A decreasing ILI value should always mean something good, that an

34 MSc thesis

improvement in the network infrastructure has been done and that level of leakage has been reduced. However, the following chart of ILI vs CARL shows differently:

ILI & CARL in P7

58.0

58.5

59.0

59.5

60.0

60.5

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61.5

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63.5

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ILI

0

500,000

1,000,000

1,500,000

2,000,000

2,500,000

3,000,000

CA

RL

(lpd)

ILI CARL

Figure 11: ILI vs CARL in P7 The chart shows that even though ILI indicates improvement in leakage management, the actual leakage level, represented by CARL, shows the opposite. Current Annual Real Loss appears to remain at almost similar level, if not increased slightly. However at the same time it seems that CARL corresponds more with NRW value, which is logical, as seen in the next chart. Again, how could this happen? It appears that while CARL increases, at the same time UARL also increases even more and this buffers the deteriorating leakage level to produce better ILI value. Analyzing the components of UARL, it can be seen that almost all the components increases during the period, but the highest increase is the contribution of the number of connections (Nc). The comparison as shown in the following chart between ILI and number of connections in P7 confirms this analysis. It is known from the previous discussion that PALYJA have been very aggressively connecting new customers, a program that is called Densification. The densification program aims to connect as many people as possible within the existing network in the service area. As the result, UARL increases significantly, CARL remains (or increases slightly), and ILI drops, without any necessity for real actions to tackle leakage at all.

Handy Salim 35

NRW & CARL in P7

0%

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20%

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40%

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60%

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500,000

1,000,000

1,500,000

2,000,000

2,500,000

3,000,000

CA

RL

(lpd)

NRW % CARL

Figure 12: NRW vs CARL in P7

ILI & Number of Connections in P7

58.0

58.5

59.0

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60.0

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3900

3950

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of C

onns

ILI Number of connections

Figure 13: ILI vs Number of Connections in P7

36 MSc thesis

4.3 New Water Losses Performance Indicator

Going through the process of calculating the water losses performance indicators, traditional ones and ILI, some issues and limitations can already be identified. The traditional PIs such as NRW that is expressed in many different expressions and units appear to be simple in the application, since all the data required for the calculation are available (or have been made available for such purpose since the start). The issues become apparent when the PIs are used for benchmarking purpose, where no single standard can be used for different areas. ILI on the other hand, provides a good standard in the definition of UARL, a value that can always be compared to by any water distribution system, although also appear to have its limit. However, the process of calculating ILI requires much more efforts to gather the necessary components, and furthermore accuracy of the result is still questionable. The trouble lies in the necessity to differentiate actual real loss from apparent loss to acquire CARL, something that is difficult to do, and if even possible which methods are proven to be limitedly applicable in a water utility in case of PALYJA. However, what if we can combine the practicality and applicability of traditional PIs with the comparability of ILI? This means not having to distinguish real loss from apparent loss, but still having the standard to compare with other system. A new water loss performance indicator that shows not only the performance of leakage management but also commercial loss management, as both aspects are important, a combination of ILI and ALI (Apparent Loss Index (Rizzo, et al., 2007)).

ALI = Apparent Loss / 5% of water sales

ILI = CARL / UARL

NRW = CARL + Apparent Loss This new PI can be called the Distribution Performance Index (DPI), which formula is as follows:

DPI = CARL + Apparent Loss / (UARL + 5% of water sales)

DPI = NRW / (UARL + 5% of water sales) NRW can be easily calculated with the available data, UARL is the same, using the given formula by IWA, and water sales are also available information in a water utility. This means high applicability and practicality, and simultaneously provides the standard for even comparison between water distribution systems, because it takes into account the size attributes (network length, number of connections), relation with system pressure and commercial aspect. Utilizing this indicator, PALYJA can now apply a standard indicator to benchmark between its Permanent Areas, an indicator that will provide a fair ranking system for prioritizing improvement actions under limited resources. The following table presents the result of DPI ranking, together with the result of other indicators for comparison.

Handy Salim 37

Table 4-10: PA Ranking System in PALYJA (as of September 2009)

Rank PA NRW (%) PA NRW (lpd) PA UARL

(lpd) PA DPI

1 B6/4 6% S9/2 71,167 S9/9 2,594 B6/4 1

2 S9/2 7% B9/2 165,846 S9/11 2,651 S9/2 1.4

3 S9/11 12% S9/11 178,465 S9/3 4,205 S9/11 2.7

4 B6/3 15% S9/8 203,960 S11 4,233 B6/3 3.1

5 B2 16% B13 210,274 S9/8 4,253 B2 3.5

6 B9/2 20% B8 242,402 S9/7 4,680 B9/2 3.6

7 B6/2 21% B6/2 327,274 S9/2 6,811 B6/2 4.2

8 B8 23% B6/4 338,688 B13 7,043 S1 4.9

9 B13 26% B2 359,824 B8 7,553 B8 5

10 B14 30% B6/3 394,096 S9/5 10,656 B13 5.7

11 S9/8 30% S14 543,462 B2 11,334 S14 6.8

12 S1 34% B10 564,868 B9/2 13,014 B14 7.1

13 P4 35% S11 597,561 B10 14,419 S9/8 7.3

14 S14 36% S1 629,005 B6/2 15,119 P4 8.9

15 B10 38% S9/3 808,598 S4 16,382 B10 9.2

16 P10 39% S4 1,181,123 B6/3 20,392 P10 10.7

17 S12 40% S9/7 1,461,377 S13 21,330 S12 12.4

18 P7 44% S9/5 1,523,609 P6 21,744 P7 13.1

19 P5 45% S9/9 1,559,441 S1 25,550 P5 15.1

20 S9/9 47% B14 2,367,360 S12 25,679 P6 15.9

21 P6 47% S13 2,831,405 S14 30,273 S4 16.5

22 S8 51% P7 3,243,194 P5 34,792 S9/9 16.9

23 S11 51% P10 3,497,613 S8 37,891 S8 17.3

24 S4 52% P6 3,880,347 B6/4 38,085 S11 18

25 S9/5 56% S12 4,077,990 P7 43,607 S9/5 21.5

26 P3 56% S8 4,300,700 P10 52,430 P3 22.8

27 B15 60% P4 5,936,660 B14 52,994 S13 25.5

28 S13 61% P5 6,229,734 B15 77,304 B15 26.4

29 S9/3 69% B15 18,844,149 P3 82,860 S9/3 35.9

30 S9/7 75% P3 19,319,264 P4 110,456 S9/7 49.8

Very interestingly, the result of DPI ranking shows a very close resemblance (see the rows in darker colour) of the result of NRW that is expressed in the very traditional way in % to system supply, an indicator that is often said not sufficient for benchmarking. This raises questions, ‘would that mean that NRW (%) is not as misleading as it seems? Would it not mean that NRW (%) only appear misleading when apparent loss is taken out of the equation?’. The following table provides the calculation of DPI and NRW based on available data on September 2009. Table 4-11: Distribution Performance Index Data and Calculation – September 2009

NRW NRW Sales UARL PA

(lpd) (%)

Length of mains (km) (lpd)

Number of connections

Pressure (m)

(lpd) DPI

S1 629,005 34% 63 2,080,047 2,406 7.4 25,550 4.9

S4 1,181,123 52% 77 1,102,742 2,041 4.9 16,382 16.5

38 MSc thesis

NRW NRW Sales UARL PA (lpd) (%)

Length of mains (km) (lpd)

Number of connections

Pressure (m)

(lpd) DPI

S8 4,300,700 51% 139 4,207,433 6,350 4.4 37,891 17.3

S9/2 71,167 7% 16 896,043 602 7.8 6,811 1.4

S9/3 808,598 69% 10 366,553 272 9.7 4,205 35.9

S9/5 1,523,609 56% 24 1,205,110 533 11.3 10,656 21.5

S9/7 1,461,377 75% 21 493,693 639 4.7 4,680 49.8

S9/8 203,960 30% 16 474,206 746 4.2 4,253 7.3

S9/9 1,559,441 47% 26 1,788,238 630 2.4 2,594 17.0

S9/11 178,465 12% 18 1,287,386 621 2.9 2,651 2.7

S11 597,561 51% 42 580,200 1,218 2.2 4,232 18.0

S12 4,077,990 40% 293 6,074,000 13,582 1.4 25,679 12.4

S13 2,831,405 61% 75 1,795,867 3,124 4.9 21,330 25.5

S14 543,462 36% 46 986,033 1,709 12.2 30,273 6.8

B2 359,824 16% 41 1,836,000 3,286 2.9 11,334 3.5

B6/2 327,274 21% 38 1,264,032 1,824 6.2 15,119 4.2

B6/3 394,096 15% 14 2,155,680 3,586 5.5 20,392 3.1

B6/4 338,688 6% 47 5,704,992 5,822 5.9 38,085 1.1

B8 242,402 23% 12 825,984 918 6.9 7,553 5.0

B9/2 165,846 20% 17 658,368 967 10.5 13,014 3.6

B10 564,868 38% 40 940,032 1,879 5.7 14,419 9.2

B13 210,274 26% 28 592,704 915 5.1 7,043 5.7

B14 2,367,360 30% 127 5,628,096 13,354 3.5 52,994 7.1

B15 18,844,149 60% 182 12,708,576 20,930 3.3 77,304 26.4

P3 19,319,264 56% 192 15,265,792 20,319 3.6 82,860 22.8

P4 5,936,660 35% 138 11,205,964 14,555 6.7 110,456 8.9

P5 6,229,734 45% 113 7,579,578 8,525 3.4 34,792 15.1

P6 3,880,347 47% 49 4,444,293 3,608 5 21,744 15.9

P7 3,243,194 44% 36 4,089,574 4,206 9.3 43,607 13.1

P10 3,497,613 39% 110 5,462,931 9,526 4.7 52,429 10.7

While it is clear that this research does not intend to enter into the debate over the reasonable balance between real and apparent losses in the developing countries, a deeper analysis reveals that the component of the theoretical minimum apparent losses (5% of water sales) appears to be dominant over the UARL in the calculation of DPI, as seen in the following table showing the comparison between apparent losses and real losses target. Therefore, an experiment was done by substituting the 5% figure with lower figure (down to 1% of sales) in order to see the impact to the calculation and at the same time to test the theoretical apparent losses target proposed by Rizzo, et al. Table 4-12: Apparent Losses Target vs Real Losses Target

PA 5% of sales / UARL

4% of sales / UARL

3% of sales / UARL

2% of sales / UARL

1% of sales / UARL

S1 4 3 2 2 1 S4 3 3 2 1 1 S8 6 4 3 2 1

S9/2 7 5 4 3 1

Handy Salim 39

PA 5% of sales / UARL

4% of sales / UARL

3% of sales / UARL

2% of sales / UARL

1% of sales / UARL

S9/3 4 3 3 2 1 S9/5 6 5 3 2 1 S9/7 5 4 3 2 1 S9/8 6 4 3 2 1 S9/9 34 28 21 14 7

S9/11 24 19 15 10 5 S11 7 5 4 3 1 S12 12 9 7 5 2 S13 4 3 3 2 1 S14 2 1 1 1 0 B2 8 6 5 3 2

B6/2 4 3 3 2 1 B6/3 5 4 3 2 1 B6/4 7 6 4 3 1 B8 5 4 3 2 1

B9/2 3 2 2 1 1 B10 3 3 2 1 1 B13 4 3 3 2 1 B14 5 4 3 2 1 B15 8 7 5 3 2 P3 9 7 6 4 2 P4 5 4 3 2 1 P5 11 9 7 4 2 P6 10 8 6 4 2 P7 5 4 3 2 1 P10 5 4 3 2 1

As seen, when the figure of 5% as suggested by Rizzo, et al. is applied in PALYJA case, the apparent losses target dominates over UARL by a factor of 2 to 34, thus suggesting that the target for apparent losses outweighs one for the real losses. It is somewhat surprising when most of the actual water balance figures made in many utilities, including the ones mentioned in Chapter 2, show that real losses usually are higher than the apparent losses. This imbalance stresses that there is a need for an adjustment, either by reducing the target for apparent losses (to lower than 5% of sales) or increasing the target for real losses – UARL, when applied in a real case scenario. The next table shows the results when the figure 1% is applied to the ranking system, replacing 5%, showing that still the two systems, while less than before, reflect each other closely. The best PAs are still the best; the worst PAs are still the worst. Table 4-13: NRW in % and Modified DPI

DPI Rank PA NRW % PA (NRW / UARL+

1%sales)

1 B6/4 6% B6/4 3.6

2 S9/2 7% S9/2 4.5

3 S9/11 12% B9/2 8.5

40 MSc thesis

DPI Rank PA NRW % PA (NRW / UARL+

1%sales)

4 B6/3 15% B6/3 9.4

5 B2 16% S9/11 11.5

6 B9/2 20% B6/2 11.8

7 B6/2 21% B2 12.1

8 B8 23% S14 13.5

9 B13 26% S1 13.6

10 B14 30% B8 15.3

11 S9/8 30% B13 16.2

12 S1 34% B14 21.7

13 P4 35% S9/8 22.7

14 S14 36% B10 23.7

15 B10 38% P4 26.7

16 P10 39% P10 32.7

17 S12 40% P7 38.4

18 P7 44% S4 43.1

19 P5 45% S12 47.2

20 S9/9 47% S8 53.8

21 P6 47% P5 56.3

22 S8 51% P6 58.6

23 S11 51% S11 59.6

24 S4 52% S9/5 67.1

25 S9/5 56% S13 72.1

26 P3 56% S9/9 76.2

27 B15 60% P3 82.0

28 S13 61% B15 92.2

29 S9/3 69% S9/3 102.7

30 S9/7 75% S9/7 152.0

This explains that the dominance of sales over UARL in the formula yields the close resemblance between the NRW % system and the DPI, because it is clear that sales have a very close relationship to total supply. In other words, the higher the component of sales used in the formula of DPI, the closer the result would resemble NRW (%) result, as seen in the evolution shown in the following figures.

Handy Salim 41

NRW % and DPI (using 0% of sales)

0%

10%

20%

30%

40%

50%

60%

70%

80%

B6/4 S9/2

S9/11

B6/3 B2B9/2 B6/2 B8

B13 B14S9/8 S1 P4

S14 B10 P10 S12 P7 P5S9/9 P6 S8

S11 S4S9/5 P3

B15 S13S9/3 S9/7

Permanent Areas

NR

W %

0

100

200

300

400

500

600

700

DP

I

NRW % DPI 0%

Figure 14: NRW % and DPI not using water sales component

NRW % and DPI (using 5% of sales)

0%

10%

20%

30%

40%

50%

60%

70%

80%

B6/4 S9/2

S9/11

B6/3 B2B9/2 B6/2 B8

B13 B14S9/8 S1 P4

S14 B10 P10 S12 P7 P5S9/9 P6 S8

S11 S4S9/5 P3

B15 S13S9/3 S9/7

Permanent Areas

NR

W %

0

10

20

30

40

50

60

DP

I

NRW % DPI 5%

Figure 15: NRW % and DPI using 5% of water sales

42 MSc thesis

NRW % and DPI (using 100% of sales)

0%

10%

20%

30%

40%

50%

60%

70%

80%

B6/4 S9/2

S9/11

B6/3 B2B9/2 B6/2 B8

B13 B14S9/8 S1 P4

S14 B10 P10 S12 P7 P5S9/9 P6 S8

S11 S4S9/5 P3

B15 S13S9/3 S9/7

Permanent Areas

NR

W %

0

0.5

1

1.5

2

2.5

3

3.5

DP

I

NRW % DPI 100%

Figure 16: NRW % and DPI using 100% of water sales The first result that does not incorporate sales component shows very high variation between NRW% and DPI, while the higher the water sales component is incorporated, the lower the variation and the higher the resemblance between the two systems as seen when the whole 100% of water sales is taken for the calculation of the last result. This concludes that the 5% of water sales figure proposed by Rizzo, et al. is somewhat too high and dominant, and therefore should be replaced with a lower figure – or by increasing UARL, which depends on the local situation, for the application of a combined indicator. Nevertheless, the DPI provides a tool for the water utility to prioritise actions in conjunction with the integrated program undertaken by the company to overcome water losses. Once an area is identified as the ‘worst’, the integrated action team can be immediately dispatched into the area to make improvements, something that to date is not yet done accordingly. The application of DPI itself can be widen by combining the value with other indicators that are considered important for investment decision making to produce some kind of a Scoring Tool. Together with DPI, variables such as network rehabilitation cost, project payback period, potential water sales, etc. can be incorporated in to the scoring tool with some weighing system, for example: Project Rank = 0.4 DPI rank + 0.1 project cost rank + 0.3 project payback period rank

+ 0.2 potential water sales rank The result of this multi criteria analysis formula should provide a new ranking that is useful for the management team to make a decision for a prioritized action in an area. Furthermore, results of improvement actions can be evaluated from the value of the DPI after actions, something that NRW (%) system is unable to provide for its close relation to supply regime.

Handy Salim 43

5 Discussion & Conclusions

This chapter discusses the process and the results of the assessment of the different water losses indicators, together with the conclusions that are derived.

5.1 Traditional PIs

5.1.1 Percentage to System Supply

The use of the % to total supply method for expressing the relative amount of water losses (NRW) in most water utilities in the world is not for no reasons. Indeed, application of this traditional indicator is very simple, only two sets of basic information are needed: supply data and billed consumption data, which require only proper metering to acquire. It is simple, but one always can argue that the simplicity and apparent applicability come only from the system that has been designed to provide such data since the beginning, a %-NRW-data-providing system. However, in the evolution of a utility, in order to pursue continuous improvements, there is also the need to detail more the assessment of % water losses system by assessing the value by district metered area (DMA). This is where more works are required, as in the case study of PALYJA in establishments of the Permanent Areas. Networks need to be isolated; sub meters need to be installed along with other monitoring equipments, all these require considerable investments of time, efforts, and capital. True that for more advance application, things might get more complicated. Furthermore, when the percentage system is used for benchmarking between water utilities, it has become clear from the results of the assessment that the arguments by many international water losses practitioners are indeed reasonable, the % system can be misleading. However, not fully being in the line with those arguments, the bias does not directly come from using the sole value of water loss to compare at one selected period, but lies within the use of the value to evaluate progress or improvement level. It is always fair to say that one utility having 10% losses is better than another losing 50%, no misleading in that. But it is not fair to say that a water utility that managed to reduce 10% of the existing water losses is better than a water utility that reduced only 2% in the same period. Factors such as the size of the area, including network length and number of customers, and service level have very important roles to play in such assessment, which is what the % system is missing.

5.1.2 Volume / Time / Size of Area

The volume/time/network length and/or number of connections are upgrades to the traditional % system in order to incorporate the attributes of area size in the account. It has been proven in the results presented in the previous chapter that these indicators provide more objective view of the performance of a water utility, therefore reducing the bias.

44 MSc thesis

In terms of applicability, these indicators are almost in no way below the % system, since information of network length and number of connections should also be basic, while it is true that some water utilities might still struggle for having such information as network length when network map is not even available. This however is an extreme case. Nevertheless, unlike the % system (and ILI eventually) the volume/time/network length and number of connections provide no standard to compare to as a buffer. The % system has the total supply as a standard value to compare to, therefore showing efficiency level. ILI has UARL as a standard value to compare to, therefore showing infrastructure condition. But what do the volume/time/length of network and number of connections have to compare to? The values would only be absolute values that post very few meaning but to say that such amount of water is lost in an area of such size. Is it good or is it bad? What should be the target? Furthermore, these indicators also miss an important property in a distribution system, which is the service level. They do not take into account the system pressure, as one of the most important indicators of service. Certainly it would not be difficult just to add one more variable into the formula to produce something like for instance volume/time/network length/number of connections/average pressure. However, the question would be how fair it is to assume a linier relationship between volume of losses and system pressure, as mirrored in the new formula, while recent studies confirm it is not.

5.2 Infrastructure Leakage Index (ILI)

ILI indeed appears to be a very promising water losses indicator, with the incorporation of almost all the important attributes of a water utility: size and service level. Moreover, the introduction of Unavoidable Annual Real Losses(UARL) as a standard value to compare to, makes ILI seem to be perfect for a benchmarking tool. However, including some limitations which have been reported in some literatures regarding the UARL standard formula and the accuracy, this indicator also holds some problem regarding its applicability. The first issue lies within the definition itself which highlights the leakage portion over the total losses. In fact, ILI is a leakage (or real loss, or physical loss) related indicator for benchmarking purpose. This inevitably gives impression that somehow leakage is a more important benchmarking object than the apparent (or commercial) loss. It is understood that the concept was developed in the more advanced world where the level of commercial loss might not be significant or can even be neglected. But unfortunately, the other part of the world where this indicator should be of more use does not have the same luxury; apparent losses are still high in most parts of the developing world and are as important as the real losses. Second issue is related to the actual application in reality. The Current Annual Real Losses (CARL) should be a very useful figure, if it ever exists. It was found to be very difficult to find a single literature that connects the theoretical calculation of ILI with the method to calculate CARL that follows the correct definition. In fact, the examples given in most of the literatures presented the misleading estimation method by assuming the total loss (NRW) as only leakage, therefore neglecting apparent loss which in many cases in the developing world can be significant.

Handy Salim 45

While some leakage level estimation techniques do exist, it has been proven in this research that application of these techniques in a water utility with high losses are also difficult and are satiated with limitations and assumptions, therefore the accuracy is questionable. The same question as was posted previously remains, ‘how can a water utility that is facing high water loss tell accurately how much water is lost to leakage and how much is to commercial loss?’ Once such distinction is recognised there is nothing that would prevent the water utility to find and fix them, apart from the issues that would never be solved either by applying the water indicator anyway, and therefore the water losses would not be as high any more, and therefore the indicator would not be very useful anymore. Furthermore, even if the CARL figure does exist, which is eventually used in this research as the result of an experiment, the third issue comes to surface. ILI gives so much attention to the leakage level that it also inescapably gives the impression that the indicator shows how good a water utility manages the leakage level. However, this research has proven that even without making any improvement in the leakage management, a water utility might appear positive before ILI, simply by increasing the connection density, due to the fact that this increases significantly the standard value of UARL without much impact to the current leakage level. In other words, the ILI favours the system with high connection density. The same thing applies to the length of pipe in the same logic, and therefore ILI does not really indicate the leakage management performance of a water utility. Finally, the decision to use ILI as a tool for making decisions for donors, funding agencies and loan providers, i.e. the World Bank, should therefore be reconsidered. The findings from this research have made it apparent that there are still some gaps to be filled for the ILI to become a water losses indicator that can be used to ‘judge’ or to make investment decisions.

5.3 Distribution Performance Index (DPI) – Final Words

For a water utility that is facing high losses problem in the developing country, there is nothing more important than to overcome the issue, deal with the losses. Benchmarking the performance might be a way to promote competition (Anwandter & Ozuna, 2002; Berg & Lin, 2008; De Witte & Marques, 2007) in order to enhance the motivation to deal with the problem. However, what if there is the case where motivation is not an issue? It is clear in PALYJA, with the operation of a special Non Revenue Water division, that the water utility is taking very seriously the water losses issue. In such case, the main objective of using a water losses indicator would be for internal purposes, to aid in the improvement strategies. It is clear from this research that while ILI provides very good standard and is fairly complete, it is not easily applicable, while the ones fairly applicable, such as NRW (%) system, are not sufficient for a standard. The Distribution Performance Index combines the advantages of ILI and the applicability of the traditional PIs. It is fairly easy to calculate. It takes into account the size of the service area and the level of service. It incorporates both types of losses, real and apparent losses. It sets standard for a water losses management performance. And furthermore, it suits the need of the water utility for an indicator that can be used not only for benchmarking but for internal purposes of

46 MSc thesis

directing the course of actions. DPI is a very good tool to prioritize areas to deploy investments in a limited resources situation. Nevertheless, it is still far from being perfect. DPI as it utilises the components used in ILI, still holds the limitation that exists within the formulation of UARL and in the 5% of sales standard, as discussed in the previous section. There is a need to make adjustments based on real situation to the formula of UARL and/or apparent losses target, either by increasing the UARL figure (i.e. to multiply by a factor) or by reducing the 5% of water sales to a lower figure, or a combination of both, which suggests further studies. It has the risk to favour areas with higher connection density and network length, and therefore tend to mislead when not appropriately interpreted. But what in life is not misleading when is not appropriately interpreted? And finally, comparing the DPI with the result of the % system shows a very intriguing result. Applying a theoretical formula alone yields a result that resembles very closely the result from the traditional NRW (%) system. While it was proven that the theoretical formula might have overestimated minimum apparent losses, it still requires further studies, tailor made, in order to apply it in a real case, based on situations. Coming this far, there are still some more questions that occurred during the research process that remain unanswered, simply because they are beyond the scope of this research. Some of them are: ‘Would a water utility reduce more of its water losses if it did not expand the network and connect more customers?’ ‘What is the necessity to benchmark the water losses performance of water utilities if the benchmarking tool (i.e. indicators) is not even applicable in the same water utilities that need benchmarking the most?’ Answering these questions is of no doubt an opportunity for further interesting research and study topics in the future to complement the findings and conclusions that are made so far.

Handy Salim 47

References

Alegre, H. (2006). Performance indicators for water supply services (2nd ed.). London: IWA Publishing.

Anwandter, L., & Ozuna, T. (2002). Can public sector reforms improve the efficiency of public water utilities? Environment and Development Economics, 7(04), 687-700.

Balkaran, C., & Wyke, G. (2003). Managing Water Loss: Strategies for the Assessment, Reduction and Control of Non-Revenue Water (NRW) in Trinidad and Tobago. Paper presented at the First OOCUR Annual Conference: The challenge for utility regulators in the caribean, Port of Spain, Trinidad & Tobago.

Berg, S., & Lin, C. (2008). Consistency in performance rankings: the Peru water sector. Applied Economics, 40(4-6), 793.

De Witte, K., & Marques, R. (2007). Designing incentives in local public utilities, an international comparison of the drinking water sector. KU Leuven.

Farley, M., & Trow, S. (2003). Losses in water distribution networks: IWA Publishing. Fidel, R. (2008). Are we there yet?: Mixed methods research in library and information

science. Library & information science research, 30(4), 265-272. Garcia, V., Cabrera, E., Garcia-Serra, J., Arregui, F., & Almandoz, J. (2003). Stochastic

prediction of the minimum night flow demand in a District Metered Area. Paper presented at the International Conference on Computing and Control for the Water Industry, London, UK.

Guibentif, H., Rufenacht, H., Rapillard, P., & Rüetschi, M. (2006). Acceptable level of water losses in geneva. Methodology, 9(2003), 60.

Halim, R. D. (2009). Effectiveness and efficiency of NRW reduction strategies case study PALYJA, jakarta. Unpublished MSc. Thesis, UNESCO-IHE, Delft.

Hardeman, S. (2008). A cost-benefit analysis of leak detection and the potential of real water savings for new mexico water systems. Unpublished Professional project, The University of New Mexico, Albuquerque, New Mexico.

Hunaidi, O., Brothers, K. (2007). Night flow analysis of pilot DMAs in Ottawa. Paper presented at the Water Loss Specialists Conference, IWA.

IBNet (2010). The international benchmarking network for water and sanitation utilities. Indicator report Retrieved March 2, 2010, from http://www.ib-net.org/IBNetProduction/IndicatorSearch.aspx

Ismail, Z., & Puad, W. (2007). Non-Revenue Water Losses: A Case Study. Asian Journal of Water, Environment and Pollution, 4(2), 113-117.

Kingdom, B., Liemberger, R., Marin, P. (2006). The challenge of reducing non-revenue water (NRW) in developing countries. Paper presented at the Water Supply and Sanitation Sector Board Discussion Paper Series. from http://siteresources.worldbank.org/INTWSS/Resources/WSS8fin4.pdf

Kunkel, G. (2003). Comittee report: applying worldwide BMPs in water loss control. Journal AWWA, 95(8), 15.

Lambert, A. (2000a). A Realistic Basis for an International Comparison of Real Losses from Public Water-Supply Systems. Water and Environment Journal, 14(3), 186-192.

Lambert, A. (2000b, May). What do we know about pressure:leakage relationships in distribution systems? Paper presented at the IWA conference: system approach

48 MSc thesis

to leakage control and water distribution systems management, Brno, Czech Republic.

Lambert, A., Brown, T., Takizawa, M., & Weimer, D. (1999). Review of performance indicators for real losses from water supply systems. Journal of Water Services Research and Technology (Aqua), 48(6), 227-237.

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Lambert, A., & Hirner, W. (2002). Losses from water supply systems: standard terminology and recommended performance measures (Vol. 32): CEON/ CEES.

Liemberger, R. (2002). Do you know how misleading the use of wrong performance indicators can be. Paper presented at the IWA Managing Leakage Conference, Cyprus.

Liemberger, R. (2005). ILI: 'smarter' water loss indicator of IWA. Aqua press International 3 Retrieved 1 February, 2010, from http://www.aquamedia.at/templates/index.cfm/id/17959

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PALYJA. PALYJA (2009a). LTDB. Retrieved November 6, 2009, from PALYJA: PALYJA (2009b). PA services monthly database report 2009 - 11. Jakarta: PALYJA. PALYJA (2009c). PACS monthly report 0801 - 0910. Jakarta: PALYJA. PALYJA (2009d). Production & distribution report. Jakarta: PALYJA. PALYJA (2009e). PT PAM lyonnaise jaya volume of water delivered and water billed.

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Rizzo, A., Vermersch, M., St John, S., Micallef, G., & Pace, R. (2007). Apparent water loss control: the way forward”. IWA Water21 Journal, August 2007.

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Appendices

Appendix A Map of the Permanent Areas in PALYJA

B 14

B 20

B 19

B 18

B 17B 16 B 11

B 15

B 5

B 10

B 3

B 4

B 8

P 8

P 9

B 1

B 2

B 6

B 12 B 7

B 13

B 9

S 6S 9

S 8 S 13S 10 S 11

S 12

S 1S 2

S 3S 4

S 14

S 5 S 7

P 1

P 2P 7P 4

P 5

P 10

P 6

P 11 P 3

Integrated Actions

Technical Actions

Zero Deep Well

Non Priority PA

[Source: PA Services Presentation – Comprehensive Action in 2010 (PALYJA, 2010b)]

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Appendix B Night Flow Analysis in P7

Supply & Physical Loss Flows in P7

0.00

20.00

40.00

60.00

80.00

100.00

120.00

140.00

16:0

0

16:5

0

17:4

0

18:3

0

19:2

0

20:1

0

21:0

0

21:5

0

22:4

0

23:3

0

0:20

1:10

2:00

2:50

3:40

4:30

5:20

6:10

7:00

7:50

8:40

9:30

10:2

0

11:1

0

12:0

0

12:5

0

13:4

0

14:3

0

15:2

0

TIme

Flo

w (

L/s)

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

16.00

18.00

Pre

ssur

e (m

)

Supply Flows Physical Loss Flows Consumption Pressure

P7 NRW Breakdown

Physical Loss, 55.60 l/s, 55%

Billed Consumption, 43.05 l/s, 42%

Commercial Loss, -2.95 l/s, 3%

[Source: NRW Breakdown – Sample Breakdown P7 0604 (PALYJA, 2006)]

Handy Salim 53