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The Application of GIS and CARE-W on Water Distribution

Networks in Skärholmen Pressure Zone, Stockholm, Sweden Tao Zhang Royal Institute of Technology (KTH) Sweden

Abstract

Cities all over Europe spend in total about 1 billion Euro per year for urban network rehabilitation. This amount is supposed to increase in the coming years. Urban infrastructure management is becoming more and more important for cities around the world. This paper presents the integration of Geographic Information System (GIS) and Computer Aided Rehabilitation on Water Networks (CARE-W) in management of water distribution networks and the rehabilitation planning. An advanced idea is to pay more attention on pro- active approaches and to use predictive analysis to achieve long- term economic efficiency. The necessary maps for analysis were collected from Stockholm Water Company. A spatial database was designed and created using related database analysis approaches for this project. A number of GIS operation and analysis and CARE-W tools have been used to calculate International Water Association Performance Indicators and do the failure forecasting. Results obtained were displayed in GIS maps, tables, and graphics. It has been proved that GIS is a competent and effective tool for managing networks. Moreover CARE-W, designed as a professional tools for water network planning and rehabilitation, has been proven to be useful. The project has been carried out in Skärholmen, Stockholm, Sweden.

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1. Introduction Water distribution networks co-exist with human society for around two- millionaire years. They serve people�s daily water consumption quietly after being laid underground. They are necessary and important but seldom noticed by the public, except when they are under construction or maintenance. Then people notice the inconvenience when these networks are under �unhealthy conditions�. Accidents caused by poor quality water distribution networks can be found in every country. In the U.S., 24% of the waterborne disease outbreaks over the past decade were caused by contamination entering the water distribution system. Recently the news announced that filthy water leaked out of a pipe and poured into a section of the subway line that is under construction in Beijing, P. R. China. This water burst caused the nearby road to sink into a huge hole, 20 meters long, 10 meters wide and 10 meters deep, in a busy section of one of the ring roads encircling the city. Moreover, in most Western European countries, water infrastructures are up to, and in most cases over, 100 years old. Due to deterioration, infrastructures of this age are likely to suffer from problems such as internal tuberculation and corrosion, cracking and leakage, which can result in several operational problems. Principally rehabilitation and relining are reactive methods after failure happens. However there is one saying that �prevention is better than cure�. So ideally it is better to build up strategies to find problems before they happen, although the benefits from prevention are commonly overlooked. Meanwhile, the water distribution network problems are usually very difficult to define, so until now management planning methods for network rehabilitation are still poorly developed when compared with financial and technological investment. The rapid growth of computer technology, such as Geographic Information System (GIS), has been widely used in various fields since it was being born in year 1989. A GIS is a powerful configuration of computer hardware and software used for compiling, storing, managing, manipulating, analyzing, and mapping (displaying) spatially-referenced information. (Haestad Methods, 2003) Computer Aided Rehabilitation on Water Network (CARE-W) is a research project supported by the European Commission under the Fifth Framework Programme of the European Union and contributing to the implementation of the Key Action "Sustainable Management and Quality of Water" within the Energy, Environment and Sustainable development program. The ultimate goal is to provide the most cost-efficient system of maintenance and repair of water distribution networks, with the aim to guarantee the security of water supply that meets social, health, economic and environmental requirements. CARE-W includes modules for rehabilitation assessment and reporting. 2. Objective Integrating GIS and CARE-W, in order to provide utility managers reliable and scientific support on water distribution network management and rehabilitation. Three aims are explained below: ! Apply GIS and CARE-W for failure forecasting in order to give guidance on network

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rehabilitation planning; ! Apply GIS and CARE-W to implement Performance Indicators and compare the results

with IWA- PI guidance ranges; ! Use CARE-W to test water distribution network reliability. 3. Test Area

Skärholmen is a suburb in the southern part of Stockholm (Figure 1, the red colour area). It is primarily made up of Skärholmen congregation. The districts that make up the suburb are Bredäng, Skärholmen, Sätra and Vårberg. The population as of 2004 is 31,125 on an area of 8.86 km². One water reservoir and three pump stations are located in this area. The total length of water distribution mains is 221.3 km and total service connections are 8225. The reason that Skärholmen was selected as the test area is because corrosions and burst problems happen

frequently in this area. Meanwhile, Stockholm Water Company has already built up a comprehensive geodatabase in this area, which provides abundant information for this project. 4. Methodology Figure 2 shows maps used for the project. They were collected from Stockholm Water Company�s geodatabase. Environmental information was surveyed, which includes rainfall, temperature, and PH value. Meanwhile, a user- requirement survey was firstly carried out to determine what the end users need. Based on the feedback, a design of database is carefully taken in order to avoid unnecessary data collection, because data collection cost is usually high and the processes are time- consuming. Figure 2: Analysis Maps

Figure 1: Skärholmen Map

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Figure 3: Experiment Methodology The methodology is shown in Figure 3. It gives the general overview of how to fulfil this project. Data preparation for CARE-W models occupied 80% time. ! CARE-W PI Tool provides users codes for

calculating performance indicators. The input data for PI Tool is depending on data from GIS analysis. The objects together with their spatial relationships were carefully identified and analysed.

! CARE-W Failure Model is a sensitive model,

and currently users and the operators have to hold the responsibility for data quality control. Many correction works have to be done in GIS and by manual methods.

! CARE-W Reliability Model needs an

EPANET hydraulic model for network reliability test. It is the easiest part in this project, but conversely, there is an obstacle to link results back to GIS platform.

Tools, which have been used in this project, are: ! ArcToolBox ! ArcMap ! ArcCataLog ! AutoCAD ! CARE-W Failure Model ! CARE-W Reliability Model ! CARE-W PI Tool and ! EPANET The full experiment records can be read from the following report: Tao Zhang, (2006), Application of GIS and CARE-W Systems on Water Distribution Networks, Skärholmen, Stockholm, Sweden, (Unpublished, available on request from the author taozkth@gmail.com)

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5. Results and Results Analysis 5.1 Failure Forecasting A. Material Distribution Figure 4: Material Distribution Map Table 1: Material Distribution

Material Distribution

52.49

0.14 0.00 1.78

8.07

0.10

12.04

25.39

0.00

10.00

20.00

30.00

40.00

50.00

60.00

CGI

GST

ASB

Copper PE PV

C

Steel

DCI

Material

Percentage

Percentage %

B. Diameter Distribution Figure 5: Diameter Distribution Map Table 2: Diameter Distribution

Diameter Distribution

21.62

64.06

14.31

0.0020.0040.0060.0080.00

D ≤

100/110

100/110

< D ≤

300/315

D >

300/315

Diameter

Percentage

Percentage %

C. Installation Year Distribution Figure 6: Installation Year Distribution Map Table 3: Installation Year Distribution

Installation Year Distribution

0.00

5.00

10.00

15.00

20.00

25.00

30.00

Before 1909

1909-1910

1921-1925

1926-1930

1931-1935

1936-1940

1941-1945

1946-1950

1951-1955

1956-1960

1961-1965

1966-1970

1971-1975

1976-1980

1981-1985

1986-1990

1991-1995

1996-2000

Year

Per

centa

ge

Percentage %

D. Current Failure Rate Figure 7: Failure Rate Map Table 4: Failure Rate

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Failure rate by material group

0.050.12

0.34

0.02

0

0.1

0.2

0.3

0.4

Ductile

Iron PE

Material

Failure Rate

(No./km/yr)

Failure rate by material group

It has been proven that the difficulties of accurately predicting pipe failures are high, because there are many factors that affect failures and influence maintenance decisions. In this project, only four main factors are considered, which are material, diameter, pipe age (from Installation Year), current failures, which are presented above as map A to D. They have been used to generate Failure Forecasting map by considering different �Weights� and mathematic methodology. The final failure forecasting map is showed below as Figure 8. Figure 8: Failure Forecasting Map

5.2 Synergy between map A and C If we look at the �Installation Year Distribution�, it is obvious that the 1960s is the construction rush period for the water networks in Skärholmen. Meanwhile, by historical records, gray cast iron is the major material for water pipes before 1970. After year 1976, when ductile iron started to play the main role in pipe material market, the construction speed had been slowed down a lot. These facts explained why gray cast iron is still the major material group, although it has already been replaced from market by other pipe materials. 5.3 Performance Indicators Results The experiment with International Water Association Performance Indicators ended with 12 PIs successfully calculated. To achieve this, 21 Utility Indicators have been analyzed by GIS analysis. Utility managers can use the results to analyse the evolution from year to year, or compare different results, and compare these results with Performance Indicators� guidance ranges.

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4 PIs were picked up for further discussion (See Table 5). A �DataSet Comparison Graph� for each of them is presented for a better understanding. They only contain statistics from year 1996 to year 2000. Table 5: Four PIs Op15 Op26

Mains rehabilitation Mains failures

Qs23 Qs26

Pressure complaints Interruptions complaints

Op15- Mains Rehabilitation: 100% * (Length of transmission and distribution mains rehabilitated during the year/ total length) Figure 9: DataSet Comparison Graph for Op15

The light grey-green area is guidance range scale. Guidance range is a normal range for an event based on globe statistics support, but it has been pointed out in CARE-W WP1 report that the proposed guidance ranges do not correspond to the statistics of the test made. The sample is not sufficiently big and representative to provide valid values. (Helena Alegre, 2004) In �Mains Rehabilitation� guidance range, the value should be from 1.0% to 2.0% per year. The result from Skärholmen shows that the mains

rehabilitated is below this guidance rage in every of these five years. The blue cycle, found at the right side of this graph, is the average value for mains rehabilitation from these 5 years, which is about 0.5%. This number is just reaching the half of the lowest guidance range line. Op26- Mains failures:100*(Number of mains failures during the year, including failures of pipes, valves, fittings and service connection insertion point failures/ total mains length) Figure 10: DataSet Comparison Graph for Op26

The �Mains Failures� guidance range is from No. 0 to 30 per 100km per year. The statistics in year 1996, 1997, 1998 and 2000 are within this range. But the mains failures are over this range and reach 42/100km/year in year 1999. However the average for these five years is 28/100km/year. If we consider a relationship between these two results, a net graph based on these two

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can be assumed (See Figure 11). Figure 11: Trend Graphic for Op15 and Op26

The �Mains Rehabilitation� has been timed 100 to get a better view for finding the trend because although values have been changed by timing a number, the trend is unchanged. By comparing values from year 1996 to year 2000, �Mains Rehabilitation� percentage goes up and down with the same trend from �Mains Failures�.

Qs23- Pressure Complaints: 100* (Number of pressure complaints during the year/ number of service connections) Figure 12: DataSet Comparison Graph for Qs 23

The �Pressure Complaints� guidance range requires the value should be 0 %. However it is difficult to reach in reality. The average value for pressure complaints is around 8% in Skärholmen from year 1996 to 2000. And in year 1996, it reached the maximum value with 14.8 % and in year 1999, it reached the lowest value 2.6 %.

Qs26- Interruptions complaints: 100*(Number of complaints due to supply interruptions during the year/ number of service complaints during the year) Figure 13: DataSet Comparison Graph for Qs 26

The �Interruptions Complaints� guidance range for this PI is still 0 %, but in Skärholmen, it has an average of 4.5% in these five years. In year 1996, the interruptions complaints were 0% but it reached 10% in year 2000.

Trend Graph for Op15 and Op26

0

100

200

2000 1999 1998 1997 1996

year

Value

Op15 Mains Rehabilitation

Op26 Mains Failures

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5.4 Network Reliability The test on network reliability needs a combined application between EPANET and RelNet. The aim is to assess hydraulic reliability of each junction, the total hydraulic reliability of the network and hydraulic critical index (HCI) for each pipe segment. A higher value of HCI means a higher impact of the discarded link on the total network reliability. If the result HCI is equal to 1, it means no demand is satisfied in all nodes of the network. The more HCI values close to 0, the more demand is satisfied at the required pressure. If HCI is equal to 0, the demand is fully satisfied. The HCI results in Skärholmen for all pipes are nearly zero. 5.5 Other Results In this experiment, it has been found out there are some records missing in Stockholm Water Company�s database. Moreover zincification pipes still exist in this network with rare quantity, which should have been replaced long time ago. 6. Conclusion and Recommendation This 6 month experiment succeeded with 90 percent of the aims and objective. They give decision makers suggestions on where the failures are and they help utility managers understand the whole situations by comparing with PIs� guidance ranges. The failure forecasting map was shown in different ranks. With this result, utility managers will be able to know the spatial relationships that exist among these places with high potential failure dangers. Such knowledge will help decision makers know the sections of the network, to which they should pay more attention. Performance Indicator studies report decision makers important PIs and enable them to analyze trends from one year to the other, to compare different results. The study shows there are still gaps between CARE-W system and GIS. Not all results from CARE-W can be linked back to GIS platform and be presented as GIS maps. Moreover as it has been mentioned in COST 236/03, there are still knowledge gaps between the current know- how and an effective global assessment of areas of involving several � sometimes conflicting- viewpoints. To achieve a reasonable and sound plan for water distribution network management and rehabilitation planning is far important to make integrated analysis of various factors and know how they related with surrounding environmental issues. Conversely there is a major obstacle to innovation. Several researchers have demonstrated that water industries are not very oriented to change approaches, analysis tools and software that are very well established in their management process. So questions according to implications coming from the integration of existing database and also puzzles on using GIS in enterprises with cost- efficiency need answers.

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7. Reference CARE-W Website, Computer Aided Rehabilitation of Water Networks, http://care-w.unife.it/ China, Digital Times, (2006). Beijing is coming a part at the seams-Clifford Coonan, http://chinadigitaltimes.net/2006/01/beijing_is_coming_apart_at_the_seams_clifford_coonan.php, Access: 2006-03-13 Clark, R.M., (1998). �Urban Drinking Water Distribution Systems: A U.S. Perspective.� Proceedings of the Conference entitled Regional Water System Management: Water Conservation, Water Supply and System Integration, held in Valencia, Spain ESRI Website, GIS and Mapping Software, http://www.esri.com/, Access: 2006-01-17 European Cooperation in the Field of Scientific and Technical Research � COST-, (2003), Draft Memorandum of Understanding for the implementation of a European Concerted Research Action Designated as COST Action C18 �Performance assessment of urban infrastructure services: the case of water supply, wastewater and solid waste� Haestad Methods, 2003, Advanced Water Distribution Modelling and Management, ISBN: 0-9714141-2-2, HAESTAD PRESS, Waterbury, CT USA

Helena Alegre, 2004, WP1 - Construction of a control panel of performance indicators for rehabilitation, http://care-w.unife.it/intro.html, Access: 2005-10-26

Tao Zhang, (2006), Application of GIS and CARE-W Systems on Water Distribution Networks, Skärholmen, Stockholm, Sweden (Unpublished, available on request from the author taozkth@gmail.com)