LIST OF SYMBOLS
Chapter 7: Summary, Conclusions and Recommendations Summary and conclusions drawn from the research
2.2.1. Historical Average or Pattern Based Approach
The historical average or pattern based approach is used by the water utilities as the primary method for estimating water demand (Institute for Sustainable Futures, 2011). In this approach, the historical average or water use patterns are determined based on different ways, these are:
1) Per capita based: average per capita per day (PCPD) water use is first calculated based on historical bulk water use data. The PCPD water use value is then multiply with the projected population. This approach is applied by Snelling et al (2005) in the three water utilities (i.e. City West Water, South East Water and Yarra Valley Water) in Melbourne, Australia to estimate the PCPD residential water demand;
Table 2.1 Summary of urban water demand modelling approaches
Modelling Approach
Reference Explanatory Variable/
Data Used
Time Scale Sector Location of Study Purpose of Study
Historical Average or Pattern Based (Section 2.2.1) Snelling et al (2005) Snelling et al (2005) Alvisi et al (2007)
Past water use, Population
Past water use, Population
Past water use
Daily
Annual
Daily, weekly
Residential
Non-residential
Total urban area
Melbourne, Australia
Melbourne, Australia
Castelfranco Emilia, Italy
To estimate the per capita per day residential water demand. To estimate per property non- residential water demand. To estimate total urban water demand. Climate Correction (Section 2.2.2) Maheepala and Roberts (2006) Perera et al (2009)
Past water use, Climate variables, Population Past water use, Climate variables, Population
Daily
Daily
Total urban area
Total urban area
Yarra Valley Water, Melbourne
Barwon Water, Geelong
To estimate total urban water demand.
To estimate total urban water demand
Trend Analysis (Section 2.2.3)
DEUS (2002)
Billings and Jones (2008)
Past water use
Past water use
Daily
Daily
Total urban area
Total urban area
NSW, Australia
All states, USA
To understand the climate effects in total urban water demands. To detect the trend in total urban water demand.
Analysis of Base and Seasonal Use (Section 2.2.4)
Maidment et al (1985)
Maidment and Miaou (1986)
Past water use, Temperature
Past water use, Temperature
Daily
Daily
Total urban area
Total urban area
Austin at Texas, USA
Florida, Pennsylvania and Texas, USA
To model the daily base and seasonal water use.
To model the daily base and seasonal water use.
Zhou et al (2000)
Gato et al (2005)
Gato et al (2007b and 2007c)
Miaou (1990a)
Past water use, Temperature, Evaporation
Rainfall, Temperature
Rainfall, Temperature, Evaporation, Socioeconomic factors (e.g., Population, Household income and Water price)
Rainfall, Temperature, Evaporation, Socioeconomic factors (e.g., Population, Household income and Water price)
Daily
Daily
Daily
Daily, Monthly
Total urban area
Total urban area
Residential
Total urban area
Melbourne, Australia
East Doncaster, Australia East Doncaster, Australia
Austin at Texas, USA
To model the daily base and seasonal water use.
To model the daily base and seasonal water use.
To model the daily base and seasonal water use.
To model the daily base and seasonal water use.
Regression Modelling (Section 2.2.5)
Froukh (2001)
Babel et al (2007)
Past water use, Household income, Household occupancy rate, Household composition (i.e., number of adults in relation to children), Water price, Climatic conditions (i.e., Rainfall and Temperature) Population, Ratio of the total population to the university student, household size, Number of households, Income, Water price, Educational level, Temperature, Rainfall Daily Daily Residential Residential Swindon, UK Kathmandu Valley, Nepal
To estimate daily household consumptions.
To estimate daily household consumptions.
Polebitski and Palmer (2010)
Williams and Suh (1986), Schneider and Whitlach (1991) Malla and Gopalakrishnan (1999) Miaou (1990b) Berke et al (2002)
Population, household size, Lot size, Number of households, Income, Water price,
Temperature, Rainfall, Policy Customer size, Different price measures (e.g., Marginal price, Average revenue, etc.), Average temperature during summer months
Water Price, Number of Employee
Water price, Household income and population, Total annual precipitation, Total annual precipitation in the summer months, Average yearly temperature, Average yearly temperature in the summer months
Water Price, Temperature, Rainfall Bi-monthly Annual Monthly Annual Monthly Residential Non-residential Non-residential Residential Residential Seattle, Washington, USA USA Honolulu Oklahoma City, Tulsa and Tucson area, USA
Washington, USA
To model single family residential water demands within individual census tracts.
To model annual water demand for commercial and industrial sectors.
To model monthly water demand for commercial and industrial sectors.
To modelling annual residential water demand.
To model monthly single-family residential water demand.
End-Use Modelling (Section 2.2.6)
Mayer et al (1999) Census data such as the number of people per household and their ages, The frequency of use, Duration and flow per water-use event, Occurrence over the day for different end-uses such as flushing the toilet, doing the laundry, washing hands, etc.
Daily Residential Several states in USA To analyse water use patterns at
end-use level and to estimate daily household water use.
Loh and Coghlan (2003)
Roberts (2005)
Heinrich (2007)
Willis et al (2009)
Jacobs and Haarhoff (2004a; 2004b)
Blokker et al (2010)
Gato et al (2007a)
Blokker et al (2011)
Household size, Frequency and duration of occurrence, flow per event.
Household size, Frequency and duration of occurrence, flow per event
Household size, Frequency and duration of occurrence, flow per event
Household size, Frequency and duration of occurrence, flow per event
Household size, Frequency and duration of occurrence, flow per event
Household size, Frequency and duration of occurrence, flow per event
Household size, Frequency and duration of occurrence, flow per event
Household size, Frequency and duration of occurrence, flow per event Daily Daily Daily Daily Monthly Second time interval 5 Second time interval Second time interval Residential Residential Residential Residential Residential Residential Residential Non-residential Perth, Australia Melbourne, Australia
Kapiti coast, New Zealand
Gold Coast, Australia
South Africa
Netherlands
Greater Melbourne, Australia
Netherlands
To analyse water use patterns at end-use level and to estimate daily household water use. To analyse water use patterns at end-use level and to estimate daily household water use. To analyse water use patterns at end-use level and to estimate daily household water use. To analyse water use patterns at end-use level and to estimate daily household water use. To estimate monthly average water demand for a number of indoor (e.g., bath, dishwasher, shower, etc.) and outdoor (e.g., pool evaporation, garden vegetation, etc.) activities. To predict water demand pattern at one second time scale.
To model GIS-based end water use.
To predict water demand pattern in office buildings, hotels and nursing homes.
Pieterse-Quirijns et al, (2010)
Household size, Frequency and duration of occurrence, flow per event
Second time interval
Non-residential Netherlands To drive design rule through end-
use demand modelling.
Agent Based Modelling (Section 2.2.7) Athanasiadis et al (2005) Rixon et al (2007) Perugini et al (2008) Galán et al (2009)
Simulator agent, Meteorological agent, Water supplier agents, Consumer agents
Artificial data
Household type, Price
Past water use, socioeconomic and geo-referenced data, Urban development plan, Census data
Monthly
Quarterly
Yearly
Quarterly
Residential
Total urban area
Residential Residential Thessaloniki, Greece Australia Australia Valladolide, Spain
To simulate the residential water demand and supply chain.
To explore the effects of tariff structure depletion on urban water demand.
To analyse the impact of urban water trading on households. To evaluate the impacts of interactions between water consumption, urban dynamics, technological and opinion diffusion. Artificial Intelligence Methods (Section 2.2.8) Jain et al (2001) Bougadis et al (2005) Zhang et al (2006) Adamowski (2008)
Past water use, Temperature, Rainfall
Past water use, Temperature, Rainfall
Past water use, Temperature, Rainfall
Past water use, Temperature,
Weekly Weekly Weekly Daily
Total urban area
Total urban area
Total urban area
Total urban area
Kanpur, India
Ottawa, Canada
Louisville, USA
Ottawa, Canada
To predict weekly urban water demand.
To predict weekly urban water demand.
To predict weekly urban water demand.
Firat et al (2009; 2010)
Herrera et al (2010)
Ghiassi et al (2008)
Yurdusev and Firat (2009)
Rainfall, Population
Past water use, Temperature, Rainfall, Past water use Past water use, Temperature, Rainfall, Past water use Past water use, Temperature, Rainfall, Past water use
Average monthly water bill, Population, Number of households, Gross national product, Monthly average temperature, Monthly total rainfall, Monthly average humidity, Inflation rate
Monthly Hourly Monthly, Weekly, Daily, Hourly Monthly
Total urban area
Total urban area
Total urban area
Total urban area
City of Izmir, Turkey
South-eastern Spain
San Jose, California, USA
Izmir, Turkey
water demand.
To forecast monthly urban water demand.
To forecast hourly urban water demand.
To forecast monthly, weekly, daily and hourly urban water demand.
To forecast monthly urban water use.
2) sector based: average water use per sector such as residential (single and multi-residential properties), non-residential (commercial, industrial, institutional, etc. sectors) and non-revenue (real and apparent losses) is first calculated. This is then projected based on population growth or sector- specific base units (e.g. number of properties or utility accounts, employment, floor space, etc.). Snelling et al (2005) also used this approach to estimate non-residential water demand per property in the three water retailers (i.e. City West Water, South East Water and Yarra Valley Water) in Melbourne, Australia;
3) pattern-based: average water use is calculated at different time steps such as daily or weekly for the Julian day or specific week of the year to get the water use pattern at different time scale. This approach is used by Alvisi et al (2007) in the municipality of Castelfranco Emilia, Italy to estimate the total urban water demand.
The historical average or pattern based approach is a simple water use estimation method and easy to use by water utilities. However, this approach relies on a historical average to estimate future water use and has limited capability to adequately account for changes in demand caused by external factors such as climate, structural (e.g. growth in use of more water efficient appliance such as dual flush toilets and low flow shower heads) and other changes to the urban water system (e.g., increased dependency on water source, such as rainwater and major reuse) (Institute for Sustainable Futures, 2011). Hence, it is necessary to complement this primary forecasting method with other analytical techniques that adequately account for the impacts of the above factors.