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Importance of data for urban water projects

Tomas MetelkaZdenek Svitak

Ivanka Yordanova Prunet

BULAQUA 2015

RDBMS

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1. Current situation in urban water data

2. Importance of proper data management

3. Case study – Western region Bulgaria project

4. Conclusions

Content

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1. Project data available at distinct organisations

2. Project data available in distinct formats (CAD, GIS, XLS,…)

3. Some data digital, some other paper harcopies

4. Some data free of charge, some other not

5. Some data easily accessible, some other not

6. Data quantity and quality varies from good to bad

7. Data utilization sometimese usfull, sometimes useless

Current situation in urban water data

Time, resource and budget consuming “repetitive” process to collect the data for current urban water projects

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1. To avoid duplication of data (what data source is correct ?)

2. To avoid misunderstanding in data interpretation and errors in projects

3. To reduce repetitive data collection works

4. To reduce resources, time and budget for data collection

5. To allow for easy access to data

6. To allow for clear understanding of data content

7. To simplify data integration and use

8. To allow for long term data utilization

Need for data managment

Reduction of time, resource and budget in data collection for current and future urban water projects

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1. Clearly defined data structures (data model)

2. Clearly defined coordinate systems

3. Clearly defined links to other data sources (administrative data)

4. Documented and published data management guidelines

5. Use of primarily digital data wherever possible

6. Easily accessible data

Sustainable data managment

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Case study – Western region projectPREPARATION OF REGIONAL WATER AND WASTEWATER MASTER PLANS FOR

WESTERN REGION - MIDP-MP-QCBS3 – 2012-2013

• 19 regions (out of 52)

• Settlements above 2000 PE: 118

• Total population: 1 166 000 inhabs.

• Total area size: 40 000 km2

• Client: MRDPW • Beneficients: Municipalities + WU• Duration: 18 months• DHI, NIRAS, RAMBOLL, SCG, AQP• DHI: Full water supply RMP

Data model definition

City

Municipality

District

Region Region 1

District 1.1

Municipality 1.1.1

City 1.1.1.1 Village 1.1.1.2

District 1.2

Municipality 1.2.1

WSS 1.1.1

Water sources

Reservoirs

Pumping stations

Network

Supply zone

Comunities

ViK 1.2.1

Administrative data Water supply system data ViK data

Data structure description

1. Coordinate systems WGS84

2. GIS x CAD data files

3. GIS Layersa) Cadaster

b) Settlements (EKATE)

c) JAICA (rivers, regions, etc.)

d) Street maps (or alternative)

e) Natura 2000

f) Model results !!!

g) …

4. Connection to Investmentsa) Automatic data updates

Use of spatial (GIS) data

MIKE URBAN

Investment

WSS EKATTE

Investment_ID

Description_Group

Parameters

Priority

Natura2000_dist

Natura2000_code

Natura2000_name

WSS_Bansko

0267

6

Bansko2

Integrated water project Bansko Rehabilitation of water pipes, Phase 2(2025);

DN 150; L= 10000 m

Medium_Priority

1

100 BG0000209

Pirin

WSS_Bansko

0267

6

Bansko3

Integrated water project Bansko Rehabilitation of water pipes , Phase 3(2034);

DN 150; L= 10000 m

Long_Priority1

100 BG0000209

Pirin

WSS_Gotse Delchev

1739

5

Gotse Delchev2

Integrated water project Gotse Delchev. Rehabilitation of water pipes, Phase 2 (2025)

DN 150; L= 10000 m

Medium_Priority

1

860 BG0002076

Mesta

WSS_Gotse Delchev

1739

5

Gotse Delchev3

Integrated water project Gotse Delchev. Rehabilitation of water pipes, Phase 3 (2034)

DN 150; L= 10000 m

Long_Priority1

860 BG0002076

Mesta

1. Digital processing of all data from A to Z

2. GIS based data structure

3. Core Water dataa) Asset data

b) Model data

c) Administrative data

d) Investment data

Basic project data structure and storageWSS Asset data

Administrative data(Population and water demand projection)

Model dataAnalysis, Summarization

Maps, Graphs

Investment data Development options

Optimized Investment

MODELgeodatabase

Investment Geodatabase

CADASTER Geodatabase

WSS Asset data

Administrative data(Population and water demand projection)

Model dataAnalysis, Summarization

Maps, Graphs

Investment data Development options

Optimized Investment

1. STRUCTURAL DATAa. pipes (X, Y, DN, Z, material)b. pumps (X, Y, Z, Q/H curve, )c. Reservoirs (X,Y,Z, dimensions)d. Intakes (X,Y,Z, capacity)e. Valves (X,Y,Z, operation rules)f. topological connections

2. FUNCTIONAL DATAa. Q,P, time seriesb. Q/H graphsc. Water demand time series

3. OTHER DATAa. Cadaster, b. DTM, c. surveys, …

Data Collection activities

2 data digitization groups (6-8 persons)

5 data collection teams (15 persons)

2 monitoring teams (6-8 persons)

Data collection period: 6 months

approx. 50-60 manmonths

RESULTS ?

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Water supply digital data

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Water supply digital data – detail 1

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Water supply digital data – detail 2

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Water supply data analysis (material)

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Water supply data analysis (material)

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City Water supply data

Municipal Water supply data

Regional Water supply data

Water supply data for Bulgaria

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1. 1/3 of Bulgaria water supply data was collected – proof of concept

2. Data has monetary value

3. Well structured information helps in a course of project

4. Need for right data structure definition for urban water projects

5. GIS based water asset database at ViK would be appreciated

Conclusions

Thank you for your attentionTomas Metelka, DHI a.s.t.metelka@dhigroup.com

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