Collaborative Climate Change Study - · PDF fileThe Collaborative climate change study was...
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Collaborative Climate Change Study Theme 4 – Demand Side Impacts – Final Report
Transcript of Collaborative Climate Change Study - · PDF fileThe Collaborative climate change study was...
Collaborative Climate Change Study Theme 4 – Demand Side Impacts – Final Report
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Executive summary
Background The Collaborative climate change study was undertaken by a number of government departments, universities and research organisations to investigate the impact of climate change in Sydney Water’s area of operations. The project was organised into five themes. This report details the work undertake in Theme 4.
The focus of Theme 4 was to determine the demand side impacts of potential climate change. The research team was made up of staff from Sydney Water and CSIRO. The objectives of the research were to:
1. better understand the relationship between potential changes in climate (temperature, rainfall and evaporation) and water demand in urban areas
2. determine likely water demand given the implementation of the Metropolitan Water Plan under a number of different future climate scenarios
3. understand the potential impact of demand hardening on future drought response initiatives
4. inform water managers of any resulting vulnerability of elements of the water supply system
5. provide a tool to analyse the demand response and resilience of future Metropolitan Water Plan options under different climate change scenarios.
Framework for scenario modelling The modeling framework we used included inputs such as population and dwelling projections, savings from water conservation programs, climate conditions from various greenhouse gas emission scenarios and the relationships between demand and climate variables developed using regression modeling.
Relationship between demand and climate We developed the relationship between demand and climate for six customer sectors in fourteen water supply systems. The time step for the model was limited by the frequency of customer meter reading so we used a monthly time step.
The calibration process produced reasonable fits of modeled demand to observed demand for most sectors in most zones. In general, the models for the ‘single residential’ sector showed the best fit. The validation exercise was challenging as the validation datasets included periods of water restrictions – this issue is difficult to overcome due to the high incidence of water restrictions over the past 15 to 20 years.
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Results and findings We used the climate demand model established in this study to examine the following scenarios:
•••• Current climate based on the global climate model (GCM) results
•••• Current climate based on the historic data reanalysis
•••• Low greenhouse gas emission - scenario B1
•••• Medium greenhouse gas emission - scenario A1B
•••• High greenhouse gas emission - scenario A2
The estimated future demand for Sydney under each scenario is shown in Figure 1.
0
200
400
600
800
2030 2070Year
Wate
r D
em
an
d (
GL
/year)
GCM Reanalysis B1 A1B A2
Figure 1 Average annual demands for different scenarios
Table 1 Average annual demand increases due to climate changes for different scenarios
2030 2070 Estimated demand increase
B1 A1B A2 B1 A1B A2
GL/year increase from current demand 4.6 1.5 6.2 9.1 22.3 25.1
% increase from current demand 0.8% 0.3% 1.1% 1.4% 3.5% 3.9%
The estimated increase in demand due to climate changes for various scenarios are given in Table 1. The results show that the climate change will only have a minor impact on total water demand.
Climate change will also increase the annual demand variability. The difference between maximum and minimum annual demand due to climate variability could increase from 50 GL/year under current climate conditions to up to 75 GL/year under future climate conditions.
The highest increase in average annual demand due to climate change (from the current climate demand of 639GL/year) is about 25 GL/year in 2070, under the A2 emission scenario. This is much less than the estimated range for the variability in annual demand (52 GL/year in 2030 and 73 GL/year in 2070). That is, the increase in water demand for Sydney will be influenced more by natural climate variability than human induced climate change impacts.
Given that the impact of climate change on total demand is around four percent, or 25 GL/year, in 2070, it is difficult to estimate any significant impact on demand hardening1, or the impact of drought restrictions on water use. Importantly, peoples’ attitude to water use, including both their indoor and outdoor water use, will directly impact on the reduction in demand achieved when drought restrictions are implemented. It will therefore be important to monitor peoples’ attitude to water use and drought restrictions.
Climate change will also result in a slight increase in the savings from water conservation programs targeting outdoor use. This would partly offset the increase in water demand due to climate change. It is difficult to quantify this effect since there is no data available to establish the relationship between the savings and key climate variables. However, as the increase is very small it should be interpreted that climate change impacts would not significantly affect the savings achieved by demand management programs.
1 Demand hardening – the reduction in effectiveness of measures designed to reduce water consumption in drought
periods, due to the uptake overtime of programs/appliances/fittings to improve water efficiency and substitute mains
water with alternatives, such as rainwater tanks.
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Contents
Executive summary ...........................................................................................................ii
Background................................................................................................................................... ii
Framework for scenario modelling.............................................................................................. ii
Relationship between demand and climate ................................................................................ ii
Results and findings.....................................................................................................................iii
1. Background and purpose ..........................................................................................5
1.1. Background ......................................................................................................................... 5
1.2. Aims .................................................................................................................................... 5
1.3. Approach............................................................................................................................. 5
1.4. Work program ..................................................................................................................... 6
2. Methodology ...............................................................................................................7
2.1. Modelling framework ........................................................................................................... 7
2.2. Developing a relationship between demand and key climate variables.............................. 14
2.3. Calibration ......................................................................................................................... 17
2.4. Validation .......................................................................................................................... 18
2.5. Comparison between metered data and model prediction ................................................. 21
2.6. Limitations of the methodology .......................................................................................... 22
3. Results.......................................................................................................................23
3.1. Breakdown by water supply systems and sectors.............................................................. 24
3.2. Demand variability............................................................................................................. 26
3.3. Impacts of climate change on savings from water conservation programs......................... 29
3.4. Demand hardening............................................................................................................ 31
4. Discussion ................................................................................................................32
5. Conclusion ................................................................................................................33
6. References ................................................................................................................34
Appendix A: Calibration statistics for all sectors and water delivery systems..........35
Appendix B: Relationships between demand and key climate variables for all sectors and water delivery systems ..............................................................................38
Appendix C: Calibration plots for all sectors and water delivery systems.................42
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Tables
Table 1 Average annual demand increases due to climate changes for different scenarios ............ 3
Table 2 Weather stations for each climate variable in each supply zone....................................... 10
Table 3 Relationships between non-residential properties and residential dwellings*.................... 12
Table 4: Calibration statistics for Ryde sectors.............................................................................. 18
Table 5: Validation statistics for Ryde sectors ............................................................................... 20
Table 6 Unaccounted for water in 2006-07.................................................................................... 22
Table 7 Average annual demands for different scenarios.............................................................. 24
Table 8 Average annual demand increases due to climate changes for different scenarios .......... 24
Table 9 Annual demand increases (GL/year) due to climate change for Scenario A2 in 2030....... 25
Table 10 Annual demand increases (GL/year) due to climate change for A2 Scenario in 2070..... 26
Table 11 Annual demand variability for various scenarios............................................................. 29
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Figures
Figure 1 Average annual demands for different scenarios .............................................................. 3
Figure 2 The modelling framework for Theme 4.............................................................................. 8
Figure 3 Water supply systems, average rainfall and available weather stations ............................ 9
Figure 4 Population, dwellings and non-residential properties from 1995 to 2070 ......................... 11
Figure 5 Relationship between non-residential properties and residential dwellings for Ryde water supply zone..................................................................................................................... 12
Figure 6: Comparison of monthly supply zone (bulk release) data with aggregated customer meter readings for Ryde............................................................................................................ 16
Figure 7: Calibration for Ryde (month versus consumption in litres per property per day) ............. 18
Figure 8: Validation of models against 1988-1996 data for Ryde (month versus consumption in litres per property per day) .............................................................................................. 19
Figure 9: Validation of models against January 2003 to January 2004 data for Ryde (month versus consumption in litres per property per day)...................................................................... 20
Figure 10 Comparison of metered data and model prediction ....................................................... 21
Figure 11 Average annual demands for different scenarios .......................................................... 23
Figure 12 Monthly demand variability for various scenarios in 2030.............................................. 27
Figure 13 Monthly demand variability for various scenarios in 2070.............................................. 27
Figure 14 Annual demand distribution over simulation period for various scenarios in 2030 ......... 28
Figure 15 Annual demand distribution over simulation period for various scenarios in 2070 ......... 28
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Acronyms
CSIRO Australian Commonwealth Scientific and Research Organization
SCA Sydney Catchment Authority
WATHNET WATHNET is a suite of programs for generalised water supply simulation using network linear programming.
UNSW University of New South Wales
SWC Sydney Water
GCM Global climate model
BASIX Building Sustainability Index
EUM Sydney Water’s End Use Model
ABS Australian Bureau of Statistics
DoP The New South Wales Department of Planning
SLA Statistical local area
LGA Local government area
DSP Development Servicing Plan
API Antecedent Precipitation Index
UFW Unaccounted for water
IPCC Intergovernmental Panel on Climate Change
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1. Background and purpose
1.1. Background The Collaborative climate change study was undertaken by a number of government departments, universities and research organisations. The project was organised into five themes:
•••• Theme 1 – Characterisation and attribution of current climate
•••• Theme 2 – High-resolution climate projections and impacts
•••• Theme 3 – Supply side impacts
•••• Theme 4 – Demand side impacts
•••• Theme 5 - Communication and coordination.
This report outlines the work undertaken in Theme 4 of the study.
1.2. Aims There were five main aims for the research:
1. To better understand the relationship between potential changes in climate (temperature, rainfall and evaporation) and water demand in urban areas including behavioural response and predicted future response.
2. To determine likely water demand given the implementation of the Metropolitan Water Plan under a number of different future climate scenarios.
3. To understand the potential impact of demand hardening on future drought response initiatives.
4. To inform water managers of any resulting vulnerability of elements of the water supply system.
5. To provide a tool to analyse the demand response and resilience of future Metropolitan Water Plan options under different climate change scenarios.
1.3. Approach We carried out the research in eight steps:
•••• STEP 1 - Assess the existing climate information for the area serviced by Sydney Water for:
•••• spatial availability
•••• format
•••• length of record.
•••• STEP 2 - Assess the demand data for this area for:
•••• availability
•••• format
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•••• length of record.
•••• STEP 3 - Determine the relationships between demand and key climate variables.
•••• STEP 4 - Liaise with the Sydney Catchment Authority to ensure:
•••• consistency in approach
•••• scope
•••• use of data
•••• modelling output.
•••• STEP 5 - Confirm the demand and supply side scenarios to be considered including:
•••• rainwater tanks
•••• water efficient appliances
•••• recycled water
•••• impact on various sectors.
•••• STEP 6 - Establish a framework for scenario modelling and develop appropriate tools.
•••• STEP 7 - Determine the impact on demand for a range of climate scenarios including the role of demand hardening.
•••• STEP 8 - Report on findings and document all assumptions.
1.4. Work program This theme was broken into three projects:
1. We set the scope of the study based on available data and synergies with other themes, for example, the supply side impacts.
2. We analysed historical data to find causal links between weather and behavioural demand for water. We used this analysis to establish a modelling framework to analyse scenarios.
3. We used the modelling framework to predict water demand given the climate scenarios that were developed in Themes 1 and 2.
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2. Methodology
2.1. Modelling framework
2.1.1. Overview
We developed the climate demand model to determine the changes in demand due to climate change. Currently Sydney Water uses an End Use Model (EUM) to predict the impact of water saving initiatives on current and future water demand. The climate demand model is different from the EUM in a number of ways:
•••• The EUM predicts an average annual demand for average weather conditions. The climate demand model can predict demand for a range of future weather scenarios.
•••• The EUM predicts the total demand in Sydney Water’s area of operations and does not consider the variation between different parts of this area.
•••• In the End Use Model, residential outdoor demand is calculated by subtracting an estimate of indoor demand from metered household demand. There is no independent measurement to confirm the validity of this calculation. The climate demand model quantifies the relationship between demand and key climate variables on a geographically disaggregated basis.
It would be ideal for the climate demand model to be built in conjunction with the EUM. Unfortunately there are no time series of individual end use components available. Regularly metered data is only available at the household level. There have been a number of end use studies but these have relatively small sample sizes and only capture water use over short periods of time. We therefore built the climate demand model at a customer sector level.
It has long been understood that different customer sectors have different responses to weather conditions. As such, six customer sectors across Sydney’s demand catchments have been modelled to capture their different characteristics related to weather conditions. These sectors are:
•••• Single residential: Outdoor consumption in this sector is responsive to climate. This sector has the largest water use and accounts for about 50 percent of the total demand.
•••• Multi-residential: This sector accounts for more than 20 percent of the total demand, however it does not respond to climate as strongly as the single residential sector as it has a smaller outdoor component.
•••• Industrial: Many industries consume water irrespective of climate conditions. The consumption pattern of this sector is dominated by a few large users.
•••• Commercial: Cooling tower and outdoor usage is the main component responsive to climate conditions.
•••• Government and other: This sector has similar characteristics to the commercial sector.
•••• Primary producer: This sector has a strong response to climate, however it accounts for less than one percent of the total consumption. This means that the overall impact of this sector on total consumption is minimal.
To encompass the weather variability across Sydney, the demand catchments were divided into 14 water supply zones (Figure 3):
•••• Cascade
•••• Illawarra
•••• Macarthur
•••• Nepean
•••• North Richmond
•••• Orchard Hills
•••• Potts Hill (excluding Sutherland)
•••• Prospect South
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•••• Prospect East
•••• Prospect North
•••• Ryde
•••• Warragamba
•••• Woronora
•••• Sutherland
Sutherland supply zone is treated as a separate zone because it can switch between the Potts Hill and Woronora delivery systems. Therefore, this area could not be assigned uniquely to a single delivery system and so was kept separate in the model.
Figure 2 shows the relationship between various components of the model in terms of inputs and outputs. Each component is described in more detail in the following sections.
Population
14 water systems
Population
14 water systems
Climate DemandModel
Statistical results: Demand at 2030, 2070
100 replicates over 20 years monthly demand at 14 water systems
Dwellings
14 water systems6 sectors
Dwellings
14 water systems6 sectors
DM Programs
14 water systems6 sectors + UFW
DM Programs
14 water systems6 sectors + UFW
Dwellings & Consumption
14 water systems6 sectors
Dwellings & Consumption
14 water systems6 sectors
Historic Climate Data
•Rainfall•Evaporation•Temperature
Historic Climate DataHistoric Climate Data
•Rainfall•Evaporation•Temperature
Regression Model 14 water systems
6 sectors
Demand and ClimateRelationships
14 water systems6 sectors
Demand and ClimateRelationships
SCA WATHNETModel
Downscaling output100 replicatesover 20 years
•Rainfall•Evaporation•Temperature
Emission Scenarios
Downscaling output100 replicatesover 20 years
•Rainfall•Evaporation•Temperature
Emission Scenarios
Work undertaken by:
UNSW
CSIRO
SCA
SWC
Work undertaken by:
UNSW
CSIRO
SCA
SWC
Figure 2 The modelling framework for Theme 4
2.1.2. Geographical division and selection of weather stations
Figure 3 shows the 30-year average annual rainfall over the 14 water supply systems. The red dots are all the weather stations that have more than 40 years of data, which were required for statistical downscaling in Theme 2.
We broke up the study area to capture the variability of both weather conditions and the demand response. This also made it easier to input the predicted water demand into the WATHNET model used in Theme 3. In the WATHNET model, each water filtration plant represents a water supply node.
Each water supply zone has a dedicated water filtration plant with the exception of the Sutherland zone. Either Prospect or Woronora can supply this zone. From time to time, the Sutherland zone switches between Potts Hill delivery system supplied by Prospect water filtration plant and Woronora delivery system supplied by Woronora water filtration plant. We therefore modeled Sutherland as a separate zone.
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Figure 3 Water supply systems, average rainfall2 and available weather stations
Of the nine stations with long-term data:
•••• all have rainfall data
•••• 8 stations have temperature data
•••• only 4 stations have pan evaporation data.
For each system we selected a weather station based on:
•••• distance between the station and the system
•••• similarity of topographic conditions (such as coastal or inland).
Table 2 shows which stations we selected for rainfall, temperature and evaporation data for each system.
2 Australian Bureau of Meteorology, 30-year average between 1961 and 1990.
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Table 2 Weather stations for each climate variable in each supply zone
Weather Station Water Supply
System Rainfall Maximum
Temperature Pan Evaporation
Cascade Katoomba Katoomba Warragamba
Illawarra Dapto Bowling Club Wollongong Uni. Sydney Airport
Macarthur Camden Camden Prospect Dam
Nepean Picton Council Depot/Camden
Camden Nepean Dam
North Richmond
Richmond UWS Hawkesbury
Richmond UWS Hawkesbury
Richmond UWS Hawkesbury
Orchard Hills Richmond UWS
Hawkesbury Richmond UWS
Hawkesbury Richmond UWS
Hawkesbury
Potts Hill Sydney Airport Sydney Airport Sydney Airport
Prospect South
Prospect Dam Prospect Dam Prospect Dam
Prospect East Prospect Dam Prospect Dam Prospect Dam
Prospect North
Prospect Dam Prospect Dam Prospect Dam
Ryde Riverview
Observatory Riverview
Observatory Sydney Airport
Warragamba Warragamba Met.
Station Camden Warragamba
Woronora Sydney Airport Sydney Airport Sydney Airport
Sutherland Sydney Airport Sydney Airport Sydney Airport
2.1.3. Population and Dwelling Forecasts
Following each census the Australian Bureau of Statistics (ABS) projects the population of various areas of Australia for the next fifty years. The latest census was in 2006. The Department of Planning (DoP) converts these figures to Local Government Areas (LGAs). A composite team of various government representatives, including Sydney Water (SWC) meets no less than twice a year to provide input into these figures. This team, called the Population Projection Group:
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•••• advises DoP in the production of official State and sub-State population projections based on agreed inputs and modelling processes
•••• monitors state population trends and, if requested, provides assistance to the ABS in their review and revision of the NSW estimated resident population
•••• assists DoP to produce high quality projections, no less than twice per inter-censual period, of:
•••• NSW population by age and sex
•••• regional populations by age and sex
•••• statistical local area (SLA) populations by age and sex
•••• local government area (LGA) populations by age and sex
•••• household and dwelling projections for metropolitan regions to 30 years from the last census
•••• assists DoP in obtaining approval for a regular and fixed timetable for the production and release of projections
•••• makes available appropriate input data to assist the DoP to produce robust, high quality and timely projections.
The development forecasting team in Sydney Water takes the thirty-year projections by LGA prepared by DoP, and adjusts them to better align with Sydney Water’s area of operations. Sydney Water produces dwelling and population projections for network and delivery systems and Development Servicing Plans (DSPs) for the next thirty years.
The population forecasts used in this study are based on the preferred series from the New South Wales Population Projections released by the DoP in 2005 (DoP 2005). The current DoP population figures are forecast up to 2031 (revised population figures to 2036 are due to be released from the DoP in the near future). Beyond 2031, Sydney Water has used a five-year moving average method for each system up to 2070. This method calculates the central tendency over time. The population in Sydney Water area of operations is forecast to increase by about 2 million from 2007 to 2070 as shown in Figure 4.
Figure 4 Population, dwellings and non-residential properties from 1995 to 2070
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The detailed residential dwelling (single and multi-residential dwellings) forecasts are up to 2031. Similarly Sydney Water has used a five-year moving average method for each system to calculate central tendency over time and produce a residential dwelling forecast up to 2070.
Figure 5 Relationship between non-residential properties and residential dwellings for Ryde water supply zone
To forecast non-residential properties we established a simple relationship between the total number of residential dwellings and the numbers of commercial and industrial properties. We used the five years from 2002 to 2007 for each water supply system. The relationship for the Ryde system is shown in Figure 5. The relationships for all water supply zones are given in Table 3.
Table 3 Relationships between non-residential properties and residential dwellings*
Water Supply System Commercial Dwellings Industrial Dwellings
Cascade 2041.1 x ln(RD*) - 19733.2 107.6 x ln(RD) - 984.6
North Richmond 2097.1 x ln(RD) - 19666.5 1611.5 x ln(RD) - 15252.4
Orchard Hills 2140.2 x ln(RD) - 22475.4 4531.3 x ln(RD) - 49674.2
Prospect South 4051.7 x ln(RD) - 45507.2 5154.5 x ln(RD) - 57887.5
Prospect North 6731.1 x ln(RD) - 78726.5 4047.1 x ln(RD) - 46858.3
Prospect East 7470.1 x ln(RD) - 79827.9 2692.9 x ln(RD) - 27065.8
Ryde 46598.8 x ln(RD) - 571417.4 5155.4 x ln(RD) - 62144.6
Potts Hill (Excluding Sutherland) 57373.5 x ln(RD) - 728584.1 2143.9 x ln(RD) - 21052.2
Sutherland 2117.6 x ln(RD) - 21696.1 6172.4 x ln(RD) - 65594.6
Warragamba 2.2 x ln(RD) + 25.3 48.4 x ln(RD) - 350.0
Nepean 158.1 x ln(RD) - 1205.2 218.0 x ln(RD) - 1901.8
Macarthur 3131.7 x ln(RD) - 33814.6 6572.2 x ln(RD) - 72658.0
Illawarra 1670.9 x ln(RD) - 16469.9 1590.0 x ln(RD) - 17316.5
Woronora 1092.1 x ln(RD) - 10699.1 1544.6 x ln(RD) - 15734.1
* Total number of residential dwellings in each water supply system
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The primary producer sector accounts for less than 1% of total consumption and the number of dwellings is unlikely change significantly. Therefore, we have assumed that the number of properties in this sector will remain constant after 2006/07.
The number of properties in the government and other sector has shown a slight decline over the last five years. This decline is mainly due to one-off factors; in particular changes to the way properties are classified in Sydney Water’s billing database. It is unlikely that this trend will continue in the future. For this study, we have assumed that the number of properties in the government and other sector will remain constant from 2006/07. This may result in an overestimate of the consumption of the government and other sector, but it is unlikely to significantly affect the results
The number of dwellings in Sydney Water serviced area is forecast to increase by about 1.1 million from 2007 to 2070 as shown in Figure 4.
2.1.4. Water conservation programs
The model includes savings from all water conservation programs included in Sydney Water’s 2007/08 Water Conservation & Recycling Implementation Report (SWC 2008) and 2008 Metropolitan Water Plan Progress Report (NSW Government 2009). A full list is given below.
Residential
•••• WaterFix (residential)
•••• DIY water saving kits
•••• Washing Machine Rebates
•••• Rainwater Tank Rebate
•••• Love Your Garden
•••• Water Wise Rules
•••• Toilet Replacement Program
Business
•••• Business Programs (includes NSW Government
Water Efficiency, Pilot Water Savings Fund, Water Savings Fund
and Climate Change Fund)
•••• Every Drop Counts in Schools
•••• Rainwater Tanks in Schools
•••• Monitoring Top 100 Businesses
•••• Smart Rinse
••••
BizFix (Includes Business Amenity Retrofit)
Leak reduction
•••• Leak reduction
•••• Active Leak Detection
•••• Pressure Management
•••• Improved leak/break response times
Recycled water
•••• Recycled water
•••• Operational schemes
•••• Existing STP reuse and minor recycling
•••• Rouse Hill – stage 1 (releases 1 and 2)
•••• Wollongong – stage 1
•••• Botany - Orica (non Sydney Water scheme)
•••• SOPA (non Sydney Water scheme)
Schemes under development
•••• Schemes under development
•••• Rouse Hill – stage 2 (release 3 and 4)
•••• Hoxton Park stage 1 & 2
•••• Ropes Crossing
•••• Wollongong – stage 2+3
•••• Quakers Hill – stage 1+2
•••• Camellia
•••• STP Onsite (Bondi and Malabar STP)
•••• Colebee Golf Course (non Sydney Water scheme)
Other industrial and residential
•••• Other industrial and residential
•••• Kurnell (non Sydney Water scheme)
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Regulatory measures
•••• WELS
•••• BASIX
Pilots
•••• Commercial Apartment High Rise Tower
•••• SMWU-Council program
•••• Spray Gun Retrofit Pilot
We allocated water savings up to 2007-08 from each program to the appropriate customer categories in each of the fourteen water supply systems. We based this allocation on the data available in Sydney Water’s database.
For future contributions, we allocated all savings from recycled water projects according to their locations. For residential and business programs, we used the same allocation as the existing program or the distribution of the population forecast over the water supply systems. BASIX and WELS programs are the only two programs that are carried through all simulation periods up to 2070. Savings for BASIX were allocated according to the dwelling forecasts for each system.
We converted the forecasts of annual savings to monthly savings assuming no seasonal pattern and independence of weather conditions. For those programs targeting outdoor water uses, such as the Rainwater Tank Rebate and Love Your Garden program savings are likely to be seasonal and vary from year to year depending on weather conditions. However, as mentioned before, we did not have sufficient data to establish the relationship between particular end uses and key climate variables. We discuss how this issue affects results in Section 3.
2.2. Developing a relationship between demand and key climate variables
The critical task for Theme 4 was to establish a relationship between water demand and key climate variables. The general problem can be described as “based on historical time series, identify a relationship linking covariates, such as climate variables, with the dependent variable which is water usage”. In this part of the study, only climate dependent variability in demand is considered while long terms changes in demographics or appliance stocks, or those relating to restrictions and the price of water, are ignored.
In this project we considered the climate variables of rainfall, evaporation and temperature. We evaluated supply zone as well as customer sector water demand datasets to determine which variables were the most relevant for this study.
There are a number of approaches to establish relationships between climate and demand. In this section of the report we review existing approaches and detail the methodology that was developed and utilised in this project.
2.2.1. Review of existing models for water use estimation
Most water use prediction models draw conclusions from historical observation using statistical methods. We have also used a methodology that is statistical in nature. Maheepala et al (2002) provided a review of different water use estimation models, of which a number have influenced the approach used in this report (Maidment 1985, 1986; Zhou 2001, 2002; Gato 2007; Babel 2007).
In the work by Maidment et al (1985, 1986) and Zhou et al (2001, 2002) a statistical time series model for predicting hourly water consumption was applied to water use in Melbourne, Australia. This method was also described in a case study (Gato 2007). Due to the daily and hourly time step, this model is suitable for supporting the operational management of the urban water system. In case studies this has accounted for about 80-85% of the variance in daily water consumption and about 65% of the variance in the peak hourly flows. This type of model uses a range of covariates. It particularly shows the usefulness of including the Antecedent Precipitation Index
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(API) and evaporation. However, for policy analysis, such a model can be argued to be impractical because of its operational focus and its inherent auto-regressive components.
Similarly, Babel (2007) applied a multivariate econometric approach to yearly domestic water demand in Katmandu, Nepal. After trying a range of demand functions a Log-Log function was applied where covariates were the:
•••• number of connections
•••• water tariff
•••• education level (described as the ratio between population and number of students)
•••• total annual rainfall.
These accounted for 96.5% of the variation. The yearly time step reduces the size of the data set considerably and makes it difficult to link water use and climate variables.
2.2.2. Method used in this study
We developed regression models for each sector in each of the supply zones. As discussed in the previous section, we grouped customers in six sectors and divided the Sydney’s demand catchments into 14 supply zones.
To convert the quarterly meter readings for these sectors into daily values, we divided each meter reading evenly between each day in the quarter for that property. In other words, it is assumed that a property uses water consistently each day over a three-month period until the next meter reading is taken. Although this assumption is false the consequential error is somewhat mitigated because all properties do not have their meters read on the same day. Meter reading is carried out on a rolling basis across Sydney. Therefore, when the daily meter read consumption is summed, there is some variation from day to day (Figure 6).
Despite the variation of the data set there is a ‘smoothing’ effect at play. On any given day, the summed data set will contain influences from meter reads 3 months previous and 3 months following. The variation from day to day will therefore not be as great as ‘true’ variation. The bulk supply release data set will show greater variation, because it is more accurately measuring daily consumption (Figure 6).3
We chose a monthly rather than daily time step for regression modeling because of the limitations of the data. We developed the models on a consumption per property basis to reduce the influence of properties coming on-line and off-line during the analysis period. We could still easily convert the outputs to total consumption for a sector by simply multiplying by the number of properties in the sector.
We chose 1997-2002 for the regression model because it is more representative of future ‘baseline’ conditions than any other time sequence in the past. This is because baseline refers to demand without the influence of restrictions or any other demand mitigation measure. Since 2003, Sydney has been subject to water restrictions due to an ongoing drought. If we developed a model over the period 2003-2007, it would be representative of demand during restriction periods, which was not the purpose of the exercise. We did not use time sequences earlier than 1997 because it is best to use the most recent period. This is because consumer behavior, economic conditions, appliance type and property mix all influence consumption. These are more similar today to 1997-2002 than any other prior period.
3 Note that the difference between the two is not indicative of an error in the meter read total. Bulk releases
include unaccounted for water (UFW) whereas the meter read total, which is based on customers’ meters rather than bulk water releases, does not.
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Figure 6: Comparison of monthly supply zone (bulk release) data with aggregated customer meter readings for Ryde
Demand hardening is the effect over time of a reduction in discretionary water use through implementation of demand management measures. This results in a reduction in the savings that can be achieved by temporary water restrictions in times of drought. The 1997-2002 period was generally free of significant trends in consumption. This means the models should also have limited trends. This is important because the effects of demand hardening will not be significant. The models can therefore be used to forecast into the future without the possibility of demand hardening affecting the results.
Daily climate data (temperature, rainfall and evaporation) were provided by the SCA. They derived this data from gauging stations in the Sydney region. Climate stations were matched to bulk supply zones by considering proximity, elevation and prevailing weather patterns. Table 2 contains the list of supply systems and matched stations. We trailed the models using different climate stations than those shown in Table 2. These trials showed the models were highly sensitive to the choice of weather station. It was therefore important to use the most representative climate station possible.
We grouped the daily climate data to monthly for the regression model. A wide variety of parameters were developed to allow the regression model to identify those that provided the best fit. These parameters included
•••• total evaporation
•••• average maximum temperature
•••• total rainfall
•••• rain days
•••• median evaporation
•••• antecedent precipitation index (API).
API is calculated by the formula:
jjj PAPIkAPI +⋅=−1
where Pj is the rainfall in day j. Differing values of k were used to see which provided the best fit to each model.
In this model, API acts as a measure of soil moisture or the consumer’s memory of previous rainfall as these both have an influence on water consumption.
17
We then weighted each of the climate parameters by the same method we used to aggregate the meter read data set. As the consumption is ‘smoothed’ due to quarterly readings, consumption in any month could be due to climate influences from the previous 3 months or following 3 months. The following formula was therefore adopted to represent the influences of climate on any given value of the meter read data set:
Xtw = 1/24*Xt-3+3/24*Xt-2+5/24*Xt-1+6/24*Xt+5/24*Xt+1+3/24*Xt+2+1/24*Xt+3
Where Xtw is the weighted climate parameter of interest in the month t.
We used the generalized linear model for our regression model. We chose a linear model because they are simple and provide the best fit. We tested both the weighted and non-weighted climate parameters to see which would provide the best fit. In general, the weighted parameters were best.
Once the models had been calibrated, they were validated against two data sets, 1988-1996 and January 2003 – January 2004.
2.3. Calibration We obtained a reasonable fit for the models to historical data for most sectors in most zones. In general, the ‘single residential’ sector had the best fit. Most of the other sectors had reasonable fits with R2 values generally well above 0.5 and correlation values generally above 0.7. The exceptions to this were the industrial model for almost every zone and a few models for other sectors in a few systems, such as commercial in North Richmond and multi-residential in Nepean, which had only a small number of properties. The results for Ryde are shown in Figure 7 and Table 4. Appendices A, B and C contain the results and equations for all systems and sectors.
Single residential had the best fit because consumption in this sector is responsive to climate and the data sets are generally large. Multi-residential data sets are also large, however this sector does not respond to climate as readily as it has a smaller outdoor component. Consequently, the fit was not as good as single residential.
Primary producer has a strong response to climate, however the data sets are often very small. This means the consumption of individual users is likely to have a greater impact on total consumption than for sectors with larger data sets. Consequently, ‘primary producer’ had very good fits in some zones (e.g. R2 of 0.87 in Macarthur), but poorer for others (e.g. R2 of 0.32 in Woronora).
‘Industrial’ was the most difficult sector to fit models to. This is unsurprising because many industries consume water irrespective of climate conditions and there are often individual users who consume very large volumes. The less predictable nature of industrial demand is shown in Figure 7, where in comparison to other sectors, consumption is less predictable, not as periodic (i.e. less seasonal) and there is less of a discernable pattern between demand and climate.
18
Figure 7: Calibration for Ryde (month versus consumption in litres per property per day)
Table 4: Calibration statistics for Ryde sectors
Customer Sector R2 Variance of Error Variance Correlation
Single Residential 0.91 1371 9645 0.96
Multi-Residential 0.87 70 269 0.93
Commercial 0.49 11863 21138 0.70
Industrial 0.25 68859 18250 0.50
Government 0.85 64938 245931 0.92
Primary Producer 0.83 330119 1207440 0.91
2.4. Validation The validation exercise demonstrated that the models, in general, could forecast demand with a reasonable degree of accuracy however it also highlighted their limitations. Each of the data sets used for validation have different underlying demand structures to the 1997-2002 calibration period. During 1988-1996, restrictions were in place from July 1994 to September 1996. The
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behavior of companies and individuals with respect to water consumption is also likely to be different to the 1997-2002 period due to the time elapsed and differing economic conditions and social attitudes. The 2003-2004 validation period was affected by water restrictions. The validation exercise is therefore expected to show some differences between recorded and ‘predicted’ consumption. This can be seen in Figure 8 and Figure 9, which show the validation results for Ryde. There are obvious differences due to trends in consumption, which the climate-based regression modeling is unable to account for. More encouraging was the response of the models to climate. Generally, as recorded consumption increases and decreases, so does predicted consumption. This generalisation is more likely to hold true in sectors with a greater response of consumption to climate but does not hold true for the industrial sector.
The equivalent of R2 for evaluation of models outside the calibration period is the Reduction of Error (RE) that indicates the prediction skill of a particular model. Perfect prediction skill is indicated by an RE value of 1, whilst a RE value below zero indicates no skill in prediction.
The single residential sector models generally had the best validation results. RE values were in the range of -0.91 to 0.45, and correlation values 0.48 to 0.75. Once again, industrial performed the worst, with RE values ranging from -14.25 to -0.05 and correlation values from 0.15 to 0.56. The validation statistics for sectors in Ryde is shown in Table 5. The conclusion is that the models are suitable for most sector, with some adjustment for long-term changes, but that all the fitted climate models are poor for the industrial sector, which shows only very little climate dependent demand.
Figure 8: Validation of models against 1988-1996 data for Ryde (month versus consumption in litres per property per day)
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Figure 9: Validation of models against January 2003 to January 2004 data for Ryde (month versus consumption in litres per property per day)
Table 5: Validation statistics for Ryde sectors
RE Correlation Customer Sector
1988-96 2003-04 1988-96 2003-04
Single Residential 0.14 -0.91 0.60 0.66
Multi-Residential -4.63 -3.54 0.56 0.39
Commercial -1.21 -3.48 0.51 0.73
Industrial -2.51 -14.25 0.34 0.45
Government -0.17 -0.03 0.26 0.60
Primary Producer 0.45 0.25 0.83 0.89
Despite poor RE values, the validation exercise demonstrates the sector level models can be used to predict a ‘baseline’ demand. In this instance, ‘baseline’ demand is referring to consumption patterns in the future similar to the period 1997-2002. The poor RE validation values are most likely due to fundamental differences in the consumption patterns in the validation periods (i.e. 1988-1996 and 2003-2004) compared to the calibration period (i.e. 1997-2002). The correlation values suggest that the models are responding to climate parameters in relative synchronisation with recorded consumption but the magnitude of the consumption is different.
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2.5. Comparison between metered data and model prediction
In the previous sections, a comparison was made between measured data and predicted results for individual sectors and water supply systems. As an overall comparison, the aggregate metered consumption (dark blue line in Figure 10) from the regression models for individual sectors and water supply systems was compared with the total metered data (aqua line). The green line shows the difference between metered and predicted consumption. It is noted that the model can reproduce the measured data very well during the calibration period from 1997 to 2002. The average monthly error is about 2%.
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Figure 10 Comparison of metered data and model prediction
The pink line indicates the bulk water consumption. The difference between bulk water consumption and metered consumption is unaccounted for water (UFW), which includes un-metered consumption and leakage.
UFW sector is not climate dependent, so it was modeled as an annual averaged value and evenly distributed over each month. The UFW estimates for each system were estimated based on 2006-07 data and are shown in Table 6. The listed values include the savings of 22.7 GL/Yr achieved by 2006-07 by Sydney Water’s leakage reduction programs. In this study, the un-metered consumption is assumed to be zero after 2030 and UFW is assumed to be 9% of the total consumption of each water supply system.
22
Table 6 Unaccounted for water in 2006-07
Water Supply System UFW (ML/Yr) UFW (%)
Cascade 412,316 9.8%
North Richmond 1,201,650 19.4%
Orchard Hills 1,963,293 9.8%
Prospect South 4,494,876 11.4%
Prospect North 7,697,337 11.1%
Prospect East 3,658,276 11.9%
Ryde 8,575,025 11.9%
Potts Hill (Exclude Sutherland) 20,881,775 11.9%
Sutherland 1,840,352 11.9%
Warragamba 409,530 34.8%
Nepean 892,228 23.7%
Macarthur 3,495,095 13.2%
Illawarra 2,641,645 6.7%
Woronora 1,086,997 11.9%
2.6. Limitations of the methodology The key assumption of this study is that water consumption will respond to climate parameters similarly in future to the past. This assumption may not be true but is required because we cannot accurately predict the future changes in response of water consumption to climate parameters. This assumption may lead to errors in the model’s prediction but it is impossible to quantify these errors. Changes in water use patterns may also be caused by a new climate regime.
Future climate regimes are likely to be different from historic one. There may be longer dry periods, higher temperature and evaporation and higher intensity rainfall events. Response of water consumption to climate parameters is therefore likely to be different in the future than the past, simply because the climate has changed. The magnitude of this effect is difficult to gauge it is unlikely to cause significant model error as the latest Global Climate Model (GCG) indicated that climate change in Sydney region is relatively mild. This could be tested when future water consumption and climate data is available.
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3. Results The climate demand model has been used to estimate demand by 2030 and 2070 for the following scenarios:
1. Current climate based on the global climate model (GCM) results;
2. Current climate based on the historic data reanalysis;
3. Low greenhouse gas emission scenario B1;
4. Medium greenhouse gas emission scenario A1B; and
5. High greenhouse gas emission scenario A2.
The statistical downscaling data for the current climate (GCM and reanalysis) contains 100 replicates of 43 years (1960-2002) of continuous daily rainfall, maximum temperature and pan evaporation data. In the future climate scenarios (B1, A1B and A2) we used 100 replicates of 20 years of continuous daily rainfall, maximum temperature and pan evaporation data cut and centered at 2030 and 2070.
We carried out the analyses for both baseline and estimated demands. Baseline demand is the demand one would expect without any water conservation activities. The estimated demands are the forecast demands with these water conservation activities. These are shown in Figure 11 and detailed in Table 7.
The difference between GCM and reanalysis is less than 1 GL and negligible. For all three future scenarios, the average annual demands will increase slightly. The significant increase in annual demand from 2030 to 2070 is mainly due to the growth of population and dwellings. The difference between baseline and estimated demands is the demand management program savings.
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Figure 11 Average annual demands for different scenarios
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Table 7 Average annual demands for different scenarios
2030 2070
Estimated demand
GCM Re-
analysis B1 A1B A2 GCM
Re-analysis
B1 A1B A2
Baseline Demand
(GL/year) 786 787 791 788 793 945 946 955 969 972
Estimated Demand
(GL/year) 567 568 571 568 573 639 640 648 661 664
By subtracting the average annual estimated demand under the GCM scenario from those under the future climate scenarios, we obtained the increase in annual average demand by 2030 and 2070 that can be attributed to climate changes. These are shown in Table 8. In general, the increase in average demand due to climate change is relatively small, ranging from 0.3% to 1.1% by 2030 and 1.4% and 3.9% by 2070. As expected, for all three future scenarios, the impacts of climate change in 2070 are more severe than in 2030.
In 2030 the low emission scenario results in a larger increase in demand than the medium emission scenario. By 2070 the medium scenario results in a larger increase than the low emission scenario. This is consistent with the finding in Theme 2 that the global warming from the B1 (low) scenario is higher than in the A1B (medium) scenario in early years but is surpassed by A1B in later years. The impacts of climate change for all scenarios are more severe in 2070 than in 2030. Demand depends on temperature, evaporation and rainfall, and although the rainfall in 2070 does not change much compared with that in 2030, both evaporation and temperature increase.
Table 8 Average annual demand increases due to climate changes for different scenarios
2030 2070 Estimated demand increase
B1 A1B A2 B1 A1B A2
GL/year increase from current demand 4.6 1.5 6.2 9.1 22.3 25.1
% increase from current demand 0.8% 0.3% 1.1% 1.4% 3.5% 3.9%
3.1. Breakdown by water supply systems and sectors
The annual demand increases for the most severe scenario for each water supply systems and sector are listed in Table 9 and Table 10. The unaccounted for water (UFW) sector was calculated based on the assumption that by 2030 UFW will be about 9% of the total demand in each water supply system. The small increases in UFW in Table 9 and Table 10 do not signify that UFW is climate dependent but rather that it will increase in proportion to the total demand.
The majority of the increase is from the residential and commercial sectors as expected. Within the residential sector, single residential dwellings show the largest increase since the climate impact is mainly from outdoor water uses. Temperature and evaporation increases also impact the use of air conditioners and outdoor use in the commercial sector.
In general, annual demand increases due to climate change are more severe in 2070 than in 2030 in all sectors. In systems such as Orchard Hills, Prospect North and Macarthur, the annual demand
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increase in the residential sector is highest for the single residential sector. This is because most of the dwelling growth in these systems is single residential dwellings in the green field areas in these systems. On the other hand, in Potts Hill water supply system, the increase is higher for the multi-residential sector as the majority of dwelling growth in this system are multi-dwellings.
Table 9 Annual demand increases (GL/year) due to climate change for Scenario A2 in 2030
Sector
System
Sin
gle
Resid
en
tial
Mu
lti-
Resid
en
tial
Co
mm
erc
ial
Ind
ustr
ial
Go
vern
men
t &
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er
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mary
Pro
du
cer
UF
W
To
tal
Cascade 0.04 0.00 0.00 0.00 0.00 0.00 0.01 0.05
North Richmond
0.07 0.01 0.00 0.01 0.01 0.00 0.00 0.09
Orchard Hills 0.29 0.01 0.02 0.03 0.01 0.00 0.04 0.39
Prospect South
0.25 0.07 0.05 0.00 0.05 0.02 0.01 0.46
Prospect North
0.84 0.06 0.11 0.10 0.04 0.03 0.12 1.30
Prospect East
0.08 0.06 0.06 0.05 0.02 0.00 0.00 0.27
Ryde 0.26 0.22 0.21 0.03 0.04 0.00 0.03 0.79
Potts Hill 0.36 0.49 0.55 0.13 0.32 0.00 0.00 1.86
Sutherland 0.07 0.03 0.02 0.00 0.01 0.00 0.00 0.12
Warragamba 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.01
Nepean 0.05 0.00 0.00 0.00 0.00 0.00 0.00 0.06
Macarthur 0.33 0.02 0.05 0.02 0.06 0.02 0.05 0.54
Illawarra 0.11 0.01 0.05 0.04 0.01 0.00 0.00 0.22
Woronora 0.05 0.00 0.00 0.00 0.01 0.00 0.00 0.05
Sydney 2.80 0.99 1.12 0.41 0.59 0.08 0.25 6.23
26
Table 10 Annual demand increases (GL/year) due to climate change for A2 Scenario in 2070
Sector
System
Sin
gle
Resid
en
tial
Mu
lti-
Resid
en
tial
Co
mm
erc
ial
Ind
ustr
ial
Go
vern
men
t &
Oth
er
Pri
mary
Pro
du
cer
UF
W
To
tal
Cascade 0.15 0.01 0.01 0.00 0.01 0.00 0.02 0.20
North Richmond
0.28 0.06 0.00 0.04 0.03 0.01 0.00 0.42
Orchard Hills 3.08 0.06 0.10 0.14 0.05 0.01 0.34 3.80
Prospect South
0.93 0.21 0.13 0.00 0.13 0.08 0.07 1.54
Prospect North
4.57 0.46 0.35 0.32 0.18 0.11 0.59 6.59
Prospect East
0.29 0.22 0.19 0.13 0.06 0.00 0.00 0.88
Ryde 1.04 0.63 0.62 0.08 0.14 0.01 0.25 2.76
Potts Hill 1.14 1.51 1.53 0.33 0.79 0.00 0.06 5.36
Sutherland 0.21 0.09 0.04 0.00 0.02 0.00 0.00 0.36
Warragamba 0.04 0.00 0.00 0.00 0.00 0.01 0.00 0.05
Nepean 0.24 0.01 0.00 0.00 0.01 0.01 0.00 0.27
Macarthur 1.28 0.09 0.11 0.10 0.15 0.07 0.01 1.82
Illawarra 0.54 0.05 0.14 0.14 0.05 0.01 0.00 0.92
Woronora 0.18 0.01 0.00 0.00 0.02 0.00 0.00 0.21
Sydney 13.96 3.40 3.21 1.29 1.65 0.31 1.34 25.15
3.2. Demand variability The monthly demand variability for various scenarios is shown in Figure 12 and Figure 13. In these figures, the blue line indicates the mean daily demand value in megalitres per day (millions of litres per day). The shaded area denotes the minimum and maximum range over the simulation periods. Although the mean monthly demands in the future scenarios are slightly higher than those in the
27
current climate conditions, the variability around the mean monthly demands (the shaded area) is quite similar.
1,331
1,401
1,488
1,594
1,707
1,7691,7721,714
1,627
1,516
1,387
1,327
1,000
1,100
1,200
1,300
1,400
1,500
1,600
1,700
1,800
1,900
2,000
Jul-2
9
Aug
-29
Sep
-29
Oct
-29
Nov
-29
Dec
-29
Jan-
30
Feb-3
0
Mar
-30
Apr
-30
May
-30
Jun-
30
De
ma
nd
(M
L/D
ay
)
1,335
1,404
1,495
1,592
1,706
1,7711,7691,717
1,634
1,523
1,395
1,338
1,000
1,100
1,200
1,300
1,400
1,500
1,600
1,700
1,800
1,900
2,000
Jul-2
9
Aug
-29
Sep
-29
Oct
-29
Nov
-29
Dec
-29
Jan-
30
Feb-3
0
Mar
-30
Apr
-30
May
-30
Jun-
30
De
ma
nd
(M
L/D
ay
)
1,338
1,412
1,501
1,605
1,727
1,7901,786
1,729
1,636
1,527
1,397
1,337
Jul-2
9
Aug
-29
Sep
-29
Oct
-29
Nov
-29
Dec
-29
Jan-
30
Feb-3
0
Mar
-30
Apr
-30
May
-30
Jun-
30
1,343
1,406
1,500
1,606
1,731
1,7881,7931,738
1,650
1,537
1,404
1,341
Jul-2
9
Aug
-29
Sep
-29
Oct
-29
Nov
-29
Dec
-29
Jan-
30
Feb-3
0
Mar
-30
Apr
-30
May
-30
Jun-
30
(a) Current
(c) A1B
(b) B1
(d) A2
Figure 12 Monthly demand variability for various scenarios in 2030
1,501
1,599
1,719
1,863
2,023
2,1052,1082,035
1,913
1,768
1,594
1,513
1,200
1,400
1,600
1,800
2,000
2,200
2,400
Jul-6
9
Aug
-69
Sep
-69
Oct
-69
Nov
-69
Dec
-69
Jan-
70
Feb-7
0
Mar
-70
Apr
-70
May
-70
Jun-
70
De
ma
nd
(M
L/D
ay
)
1,479
1,564
1,669
1,798
1,937
2,0142,0191,950
1,845
1,710
1,550
1,477
1,200
1,400
1,600
1,800
2,000
2,200
2,400
Jul-6
9
Aug
-69
Sep
-69
Oct
-69
Nov
-69
Dec
-69
Jan-
70
Feb-7
0
Mar
-70
Apr
-70
May
-70
Jun-
70
De
ma
nd
(M
L/D
ay
)
1,494
1,579
1,686
1,809
1,966
2,0472,050
1,983
1,881
1,745
1,574
1,497
Jul-6
9
Aug
-69
Sep
-69
Oct
-69
Nov
-69
Dec
-69
Jan-
70
Feb-7
0
Mar-
70
Apr-7
0
May
-70
Jun-7
0
1,506
1,604
1,729
1,858
2,023
2,1192,1192,052
1,931
1,779
1,602
1,516
Jul-6
9
Aug
-69
Sep
-69
Oct
-69
Nov
-69
Dec
-69
Jan-
70
Feb-7
0
Mar
-70
Apr
-70
May
-70
Jun-
70
(a) Current
(c) A1B
(b) B1
(d) A2
Figure 13 Monthly demand variability for various scenarios in 2070
Figure 14 and Figure 15 show annual demand variability over the simulation period for various scenarios. In general, the curves for future scenarios are shifted to the right. This indicates that annual demand increases will be more significant in later years. The curves for future scenarios are a little bit flatter. This denotes annual demand variability under the future climate conditions is slightly higher than for the current climate.
28
540,
000
550,0
00
560,
000
570,
000
580,0
00
590,
000
600,0
00
Demand (ML/Yr)
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
540,
000
550,
000
560,
000
570,
000
580,
000
590,
000
600,
000
Demand (ML/Yr)
Pe
rce
nti
le
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
540,
000
550,
000
560,
000
570,
000
580,
000
590,
000
600,
000
Demand (ML/Yr)
Pe
rce
nti
le
540,
000
550,
000
560,
000
570,
000
580,
000
590,
000
600,0
00
Demand (ML/Yr)
(a) Current
(c) A1B
(b) B1
(d) A2
Figure 14 Annual demand distribution over simulation period for various scenarios in 2030
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
600,
000
610,
000
620,
000
630,
000
640,
000
650,
000
660,
000
670,
000
680,
000
690,
000
700,
000
Demand (ML/Yr)
Pe
rce
nti
le
600,
000
610,
000
620,
000
630,
000
640,
000
650,
000
660,0
00
670,
000
680,0
00
690,0
00
700,
000
Demand (ML/Yr)
600,
000
610,
000
620,
000
630,
000
640,
000
650,
000
660,0
00
670,
000
680,0
00
690,0
00
700,
000
Demand (ML/Yr)
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
600,
000
610,
000
620,
000
630,
000
640,
000
650,
000
660,
000
670,
000
680,
000
690,
000
700,
000
Demand (ML/Yr)
Pe
rce
nti
le
(a) Current
(c) A1B
(b) B1
(d) A2
Figure 15 Annual demand distribution over simulation period for various scenarios in 2070
The median values and the range of annual demands for various scenarios are given in Table 11. The maximum difference of annual demand is defined as the difference between maximum and minimum annual demand due to climate variability. The median values (i.e. 50th percentile values) are similar to the average annual demand given in Table 7.
29
Table 11 Annual demand variability for various scenarios
Estimated demand (GL/Yr)
2030 2070 Climate
scenario Median
(Range)
Maximum
difference
Median
(Range)
Maximum
difference
Current 567
(545-595) 50
639
(611-675) 64
B1 571
(550-597) 47
648
(618-689) 71
A1B 568
(542-606) 65
661
(626-700) 75
A2 573
(551-603) 52
665
(626-699) 73
The following observations can be made from these analyses:
• Climate change will not only cause average annual demand to increase, but will also increase the variability in annual demands with the exception of scenario B1 in 2030.
• The highest increase in average annual demand due to climate change is about 25 GL/Yr. This is much less than the estimated range for the variability in annual demand (47 GL by 2030 for B1 to 75 GL for A1B). This means that, in the Sydney region, the year-to-year variability in demand due to year-to-year variability in weather is larger than the increase in average annual demand due to climate change.
3.3. Impacts of climate change on savings from water conservation programs
In this study, the relationships between water demand and key climate variables have been established for baseline demand (demand before subtracting savings from water conservation programs) only. This is because the historical data that was used to establish the relationship between demand and climate variables was largely unaffected by the programs listed in section 2.1.4. This raises the question of which water conservation programs will be affected by climate change and how significant the effect will be on these savings and thereby on the forecast demand with climate change and water conservation.
To answer these questions, both qualitative and quantitative methods have been adopted. Climate change mostly impacts on outdoor water usage. Therefore, all water conservation programs targeting outdoor water use are potential candidates for further analysis. These programs include:
30
•••• Rainwater tank rebate
•••• Love your garden
•••• Water Wise Rules
•••• BASIX
•••• any recycling projects that provide recycled water for outdoor use (eg dual reticulation projects or displacement of potable water use by irrigators).
3.3.1. Rainwater Tank Rebate
The Rainwater Tank Rebate Program offers customers a rebate for installing and connecting a new rainwater tank. Properties that have to install a tank to comply with BASIX are excluded. We used Sydney Water’s rainwater tank model to analyse savings from rainwater tanks using the rainfall under the current and future climate conditions in 2070 as per the A2 scenario. The savings are estimated to be 3,133 ML/Yr and 3,375 ML/Yr, respectively, for current and future climate conditions. The yields of rainwater tanks increase in future scenarios as the amount of rainfall and total number of wet days is predicted to increase in summer and autumn and slightly decrease in winter and spring. However, the saving increase is relatively small at only 7.7%.
3.3.2. Love Your Garden
The Love Your Garden Program provides customers tailored advice about their garden’s specific watering needs. If customers keep to the recommended watering regime for the current conditions, for these customers the predicted increase in demand due to climate change would be avoided. So, relative to the new baseline demand, this program may achieve additional savings.
3.3.3. Water Wise Rules
The most significant measure of this program is to restrict watering between 10AM and 4PM. This will avoid the high evaporation loss during the daytime. Savings from this program are likely to increase due to the increased baseline demand due to climate change.
3.3.4. Recycling Projects
For recycling projects, the availability of recycled water depends on the capacity of the recycled water treatment plant, so it is unlikely that additional savings can be achieved even if demand for recycled water will increase due to climate change. Additional demand is usually met by potable water top up at the recycled water treatment plant.
3.3.5. BASIX
The New South Wales Government introduced the Building Sustainability Index (BASIX) in 2004. It is designed to reduce drinking water use and energy consumption by new residential dwellings. There are a range of options to meet the BASIX reduction target, including using recycled water, rainwater tanks, water efficient devices and water efficient appliances. Those dwellings using rainwater tanks as an option could achieve additional savings.
3.3.6. Summary
Overall, climate change not only causes the baseline demand to increase but it may also increase the savings from those programs targeting outdoor water uses. These additional savings are likely to be minor or negligible and are therefore unlikely to significantly affect the estimated increases in demand as estimated in this study.
31
3.4. Demand hardening Demand hardening is defined as a reduction in discretionary demand due to behavioral change and the implementation of long-term water conservation measures. This results in water restrictions becoming less effective in the future.
Mandatory water restrictions have been in place since October 2003. Given the long period of water restrictions some of the forced behavioral changes during restrictions may be permanent. However, the exact extent of such a residual behavioral effect will only be revealed some time after water restrictions are lifted. It may or may not exceed the savings from Water Wise Rules already included in the forecast. We have not included this impact in the present analysis because of the difficulty to quantify it.
The second source of demand hardening is from water conservation programs which target outdoor water uses (as restrictions generally relate to outdoor uses). These programs include:
•••• Rainwater tank rebate
•••• Love your garden
•••• Water Wise Rules
•••• BASIX
•••• any recycling projects for outdoor residential and other irrigation use.
We estimated demand hardening as the portion of savings from the above listed water conservation programs that were not included in the estimated savings from the current drought water restrictions. This resulted in an estimate of about 22 GL/Yr at 2007-08. Water Wise Rules account for 19 GL/Yr of this 22 GL/Yr.
As savings from water conservation programs such as BASIX continue to grow and the number of residential dwellings increases, the potential savings from water restrictions as well as demand hardening will change over time and will further depend on the exact measures included in any future water restrictions. A detailed analysis of demand hardening is beyond the scope of this study and it should be addressed as part of the drought management plan.
Climate change will push unrestricted demand up. When drought water restrictions are introduced, the potential savings from the water restrictions will also increase proportional to the demand increase. This means that relative to the baseline demand under the current climate conditions, climate change should tend to ease demand hardening. However, given that the impact of climate change on the current demand is around four percent (or around 25 GL/year) in 2070 under the A2 emission scenario, it is difficult to estimate any significant impact on demand hardening, or the impact of drought restrictions on water use. Importantly, peoples’ attitude to water use, including both their indoor and outdoor water use, will directly impact on the reduction in demand achieved when drought restrictions are implemented. It will therefore be important to continue to monitor peoples’ attitude to water use and drought restrictions.
32
4. Discussion The model established in this study is adequate to predict the impacts of climate change on water demand. However, the demand forecast for future scenarios strongly relies on the following factors:
•••• future climate conditions
•••• dwellings and population forecasts
•••• saving estimates of water conservation programs
•••• relationship between demand and key climate variables.
We investigated three future greenhouse gas emission scenarios for this study. The uncertainties of the future climate conditions still remain. Any limitations from the downscaling model will carry through and affect the results in this report.
Non-residential dwelling forecasts are not available and a simple regression equation was used to estimate the future projections in this study. A better approach could improve the forecast of non-residential dwellings and produce a more robust estimate of the total impact from these customer sectors.
There is no data available for the relationship between savings from water conservation programs and key climate parameters. Further study is needed to quantify the impacts of climate change on the savings from water conservation programs if climate change significantly affects demand.
Sector level regression modeling offers some advantages in forecasting demand, compared to supply zone regression modeling although there are significant limitations. Sectors that are most responsive to climate (in this case, ‘single residential’ and ‘primary producer’) have more meaningful models. Fitting models to sectors that have less response to climate is more difficult.
Step changes in consumption, due to fundamental changes in the underlying structure of demand, cannot be predicted using the approach of this study. The only aspect of demand that can be adequately predicted is that which is influenced by climate. Any other influences, such as behavior, appliances or users coming on-line / off-line are not accounted for. This means sectors which are highly dependant on these influences, such as industrial and commercial, have models that poorly represent consumption. The industrial sector is likely to be influenced by major users coming on-line or going off-line. Similarly, the commercial sector is likely to be influenced by economic conditions. These factors have not been included in the regression model.
To improve sector models, step changes and underlying demand trends need to be accounted for. One possibility is combining the approach of bottom-up models such as the EUM with the regression modeling approach outlined in this report. Bottom-up models seek to forecast demand by accounting for each end use, for example, toilets, taps, sports grounds, showers, or gardens. Bottom-up models are very good at accounting for trends due to property stock and appliance stock. They are much better equipped at predicting step-changes and trends, however some of these will always be impossible to predict.
The data collection method for sector consumption is a limitation to the sector models. Models can only ever be as good as the data used to develop them. In this case, there is a smoothing effect from the input data due to averaging the consumption of each property across a quarter. This means that the model is a smoothed model. In other words, the model does not predict monthly demand. Rather, it predicts the smoothed collated meter data set. Actual monthly demand for each sector will be even more variable. This limitation can only be overcome once improved metering technologies have been installed which will provide more accurate information on the variability of demand and how it responds to climate.
33
5. Conclusion This study has shown that climate change will have a minor impact on water demand in Sydney Water’s area of operation. By 2030 the increase in average annual demand would be:
•••• 4.6 GL/year or 0.8% for scenario B1
•••• 1.5 GL/year or 0.3% for scenario A1B
•••• 6.2 GL/year or 1.1% for scenario A2.
By 2070 the increase in average annual demand would be:
•••• 9.1 GL/year or 1.4% for scenario B1
•••• 22.3 GL/year or 3.5% for scenario A1B
•••• 25.1 GL/year or 3.9% for scenario A2.GL/Yr.
Climate change will not only increase the average annual demand, but also increase the annual demand variability. The maximum difference of annual demand, which is defined as the difference between maximum and minimum annual demand due to climate variability, could increase from 50 GL/Yr under current climate conditions to up to 75 GL/Yr under future climate conditions.
Climate change also causes a slight increase in the savings from some water conservation programs. The programs that are affected are all those targeting outdoor water use except for recycling projects. This will partly offset the impacts of climate change on water demand. It is difficult to quantify the impact on water savings because there is no data available to establish the relationship between the savings and key climate variables. We used Sydney Water’s rainwater tank model to show that the future climate conditions under scenario A2 could bring a 7.7% increase of savings for the rainwater tank rebate program. However, as the increase is very small it should be interpreted that climate change impacts would not significantly affect the savings achieved by demand management programs.
Given that the impact of climate change on the current demand is around four percent (or around 25 GL/year) in 2070 under the A2 emission scenario, it is difficult to estimate any significant impact on ‘demand hardening’, or the impact of drought restrictions on water use. Importantly, peoples’ attitude to water use, including both their indoor and outdoor water use, will directly impact on the reduction in demand achieved when drought restrictions are implemented. It will therefore be important to continue to monitor peoples’ attitude to water use and drought restrictions.
The model established in this study is adequate to predict the impacts of climate change on water demand, but there are several areas that need to be improved in order to produce a reliable and robust demand forecast. The highest priority is the need for improved forecasting for non-residential dwellings. This could be included as part of the Department of Planning’s forecasts. Further research and data collection are also required to establish the relationship between key climate variables and the savings of water conservation programs.
34
6. References Babel M.S., Das Gupta A. and P. Pradhan (2007) A multivariate econometric approach for domestic water demand modeling: An application to Kathmandu, Nepal. Water Resources Management 21, pp. 573-589. Department of Planning (2005) “New South Wales State and Regional Population Projections 2001-2051”, 2005 release. Gato S., Jayasuriya N. and P. Roberts (2007) ‘Forecasting Residential Water Demand: Case Study’. Journal of Water Resources Planning and Management 133, pp. 309-319. Maheepala S., Mitchell G., Ramasamy S. and P. Whetton (2002) 'Development of a Weather Adjustment Process for Urban Water Use.' CSIRO, Melbourne, Australia. Maidment D.R., Miaou S.P., and M.M. Crawford (1985) Transfer function models of daily urban water use. Water Resources Research 21, pp. 425-432. Maidment D.R. and S.P. Miaou (1986) Daily water use in nine cities. Water Resources Research 22, pp. 845-851. New South Wales Government (2009) “2008 Progress Report – Metropolitan Water Plan”, March 2009. Rixon A., Moglia M., and S. Burn (2007) “Exploring Water Conservation Behaviour through Participatory Agent-Based Modelling”. In 'Topics on System Analysis and Integrated Water Resource Management'. (Eds A Castelletti and R Soncini Sessa) pp. 73-96. (Elsevier) Sydney Water (2008) “Water Conservation and Recycling Implementation Report 2007-08”, September 2008. Syme G. and B. Nancarrow (2006) “Social psychological considerations in the acceptance of reclaimed water for horticultural irrigation”. In 'Growing crops with reclaimed wastewater'. (Eds D Stevens, J Kelly, M McLaughlin and M Unkovich) pp. 189-196. (CSIRO Publishing: Collingwood, Australia) Troy P. and B. Randolph (2006) “Water Consumption and the Built Environment: A Social and Behavioural Analysis” City Futures Research Centre, Sydney. Zhou S.L., McMahon T.A. and Q.J. Wang (2001) “Frequency analysis of water consumption for metropolitan area of Melbourne”. Journal of Hydrology 247, pp. 72-84.
Zhou S.L., McMahon T.A., Walton A. and J. Lewis (2002) “Forecasting operational demand for an urban water supply zone”. Journal of Hydrology 259, pp. 189-202.
35
Appendix A: Calibration statistics for all sectors and water delivery systems
Table A.1 Calibration Statistics
Supply Zone Sector R2 Correlation
Cascade Single Residential 0.79 0.89
Multi-Residential 0.56 0.75
Commercial 0.38 0.61
Industrial 0.26 0.51
Government & Others 0.62 0.79
Primary Producer 0.74 0.86
North Richmond Single Residential 0.91 0.95
Multi-Residential 0.81 0.90
Commercial 0.14 0.37
Industrial 0.52 0.72
Government & Others 0.77 0.87
Primary Producer 0.75 0.87
Orchard Hills Single Residential 0.89 0.95
Multi-Residential 0.61 0.78
Commercial 0.37 0.60
Industrial 0.26 0.51
Government & Others 0.80 0.90
Primary Producer 0.74 0.86
Prospect South Single Residential 0.87 0.93
Multi-Residential 0.69 0.83
Commercial 0.71 0.84
Industrial N/A N/A
Government & Others 0.62 0.78
Primary Producer 0.86 0.93
Prospect North Single Residential 0.86 0.93
Multi-Residential 0.78 0.88
Commercial 0.78 0.88
Industrial 0.52 0.72
36
Supply Zone Sector R2 Correlation
Government & Others 0.76 0.87
Primary Producer 0.84 0.91
Prospect East Single Residential 0.91 0.95
Multi-Residential 0.37 0.61
Commercial 0.65 0.81
Industrial 0.23 0.48
Government & Others 0.67 0.82
Primary Producer 0.44 0.66
Ryde Single Residential 0.91 0.96
Multi-Residential 0.87 0.93
Commercial 0.49 0.70
Industrial 0.25 0.50
Government & Others 0.85 0.92
Primary Producer 0.83 0.91
Potts Hill (Exclude Sutherland) Single Residential 0.87 0.94
Multi-Residential 0.42 0.65
Commercial 0.65 0.81
Industrial 0.12 0.35
Government & Others 0.74 0.86
Primary Producer 0.78 0.88
Sutherland Single Residential 0.80 0.90
Multi-Residential 0.30 0.54
Commercial 0.71 0.84
Industrial N/A N/A
Government & Others 0.82 0.90
Primary Producer 0.59 0.77
Warragamba Single Residential 0.83 0.91
Multi-Residential 0.65 0.81
Commercial 0.36 0.60
Industrial 0.21 0.46
Government & Others 0.34 0.58
Primary Producer 0.70 0.84
Nepean Single Residential 0.77 0.88
Multi-Residential 0.13 0.35
Commercial 0.40 0.63
Industrial N/A N/A
37
Supply Zone Sector R2 Correlation
Government & Others 0.49 0.70
Primary Producer 0.80 0.89
Macarthur Single Residential 0.90 0.95
Multi-Residential 0.90 0.95
Commercial 0.83 0.91
Industrial 0.15 0.44
Government & Others 0.46 0.68
Primary Producer 0.87 0.93
Illawarra Single Residential 0.86 0.93
Multi-Residential 0.76 0.87
Commercial 0.86 0.93
Industrial 0.17 0.41
Government & Others 0.51 0.71
Primary Producer 0.63 0.79
Woronora Single Residential 0.81 0.90
Multi-Residential 0.79 0.89
Commercial 0.30 0.55
Industrial N/A N/A
Government & Others 0.22 0.83
Primary Producer 0.32 0.57
38
Appendix B: Relationships between demand and key climate variables for all sectors and water delivery systems
Table B.1 Relationships between demand (L/h/d) and key climate variables
Supply Zone Sector Regression Equation
Cascade Single Residential 369.276+(2.8476*TotalEvapW)-0.8686*MinAPI90+1.8123*Month
Multi-Residential 289.136+24.802*EvapMedW-0.2882*MinAPI90
Commercial 2479.2176+1.9935*TotalRainfallW+2.597*Month-1.5439*MinAPI90
Industrial 933.217-13.997*RainDaysW+0.1055*TotalEvapW
Government & Others 3137.463+22.575*TotalEvapW-4.522*MinAPI90
Primary Producer 261.14+585.41*EvapMedW-0.6106*TotalRainfall
North Richmond Single Residential 483.559+144.057*EvapMedW-2.961*MinAPI90W
Multi-Residential 359.483+41.886*EvapMedW-0.4971*MinAPI90
Commercial 2837.174194
Industrial 1108.779+48.373*TempMaxW-3.022*MinAPI90
Government & Others 4675.6+854.2*EvapMedW-20.349*MinAPI90W
Primary Producer 1903.8+482.1*EvapMedW-0.4894*TotalRainfall
Orchard Hills Single Residential 510.3475+3.6076*TotalEvapW-2.1011*MinAPI90+2.6221*Month
Multi-Residential 388.07088+0.77346*TotalEvapW
Commercial 2332.414+7.123*TotalEvapW
Industrial 2433.88+31.323*TempMaxW
Government & Others 6853.29+42.37*TotalEvapW-23.948*MinAPI90
Primary Producer 1644.701+42.643*Total EvapW-20.9*MinAPI90+26.46*Month
Prospect South Single Residential 617.126+90.856*EvapMedW-0.9774*MinAPI90
Multi-Residential 376.1825+8.8292*TempMaxW
Commercial 1558.22+80.252*TempMaxW
Industrial 5316.461539
39
Supply Zone Sector Regression Equation
Government & Others 4438.8+404.13*TempMaxW-24.47*MinAPI90+32.47*Month
Primary Producer 2772.071+63.213*TotalEvapW-1.9071*TotalRainfall
Prospect North Single Residential 491.3006+4.807*TotalEvapW)-2.1485*MinAPI90+2.753*Month
Multi-Residential 420.85586+1.05345*TotalEvap-0.3611*MinAPI90
Commercial 1187.338+61.366*TempMaxW-2.637*MinAPI90W
Industrial 3703.395+85.903*TempMaxW
Government & Others 3716.3+887.22*EvapMedW-18.529*MinAPI90
Primary Producer 368.9+1645.32*EvapMedW-19.425*MinAPI90
Prospect East Single Residential 575.99077+2.34553*TotalEvapW-0.6121*MinAPI90
Multi-Residential 421.8607+5.1334*TempMaxW
Commercial 1711.915+43.426*TempMaxW
Industrial 7179.48+51.28*TempMax
Government & Others 7598.595+43.257*TotalEvapW-15.89*MinAPI90
Primary Producer -1676.28+426.14* TempMax
Ryde Single Residential 530.8089+4.3471*TotalEvapW-1.1145*MinAPI90
Multi-Residential 268.828+7.2224*TempMaxW-0.1558*MinAPI90W+0.3442*Month
Commercial 1157.913+57.785*TempMaxW
Industrial 2811.93+47.08*TempMaxW
Government & Others 4255.55+991.33*EvapMedW-6.782*MinAPI90
Primary Producer 675.156+52.413*TotalEvapW-1.354*TotalRainfall
Potts Hill (Exclude Sutherland) Single Residential 555.13+55.32*EvapMedW-0.6657*MinAPI90
Multi-Residential 370.454+5.4734*TempMaxW-0.2312*MinAPI90
Commercial 1742.431+59.421*TempMaxW
Industrial 5447.9+77.59*TempMaxW
Government & Others 2979.13+312.76*TempMaxW-0.6596*TotalRainfall
Primary Producer 116.659+22.131*TotalEvapW-1.0493*AvAPI95W
Sutherland Single Residential 490.768+90.111*EvapMedW-0.9576*MinAPI90W
Multi-Residential 285.104+6.057*TempMaxW
Commercial 1041.01+48.56*TempMaxW
Industrial 11743.71749
Government & Others 3505.749+30.22*TotalEvapW-11.21*MinAPI90
Primary Producer 197.596+97.008*TotalEvapW
Warragamba Single Residential 468.47+199.5*EvapMedW-
40
Supply Zone Sector Regression Equation
2.486*MinAPI90+3.182*Month
Multi-Residential 313.5939+6.0068*TotalEvapW-1.763*MinAPI90
Commercial 459.35+72.51*TempMaxW-0.8475*AvAPI95W
Industrial 1356.075236
Government & Others 3698.34+29.05*TotalEvapW-11.16*MinAPI90
Primary Producer 2006.042+51.385*TotalEvapW-16.95*MinAPI90
Nepean Single Residential 457.8+155.44*EvapMedW-2.895*MinAPI90
Multi-Residential 345.905+30.063*EvapMedW
Commercial 1137.6493+3.7157*TotalEvap
Industrial 45462.88981
Government & Others 3612.957+38.814*TotalEvapW-34.36*MinAPI90
Primary Producer 1519.41+590.57*EvapMedW-7.686*MinAPI90
Macarthur Single Residential 544.2964+4.3439*TotalEvapW-1.9423*MinAPI90
Multi-Residential 369.45833+1.59648*TotalEvapW-0.4498*MinAPI90
Commercial 1315.604+63.637*TempMaxW
Industrial 4322.5+254.6*EvapMedW-16.56*MinAPI90W
Government & Others -1113.74+565.62*TempMaxW-73.43*MinAPI90
Primary Producer 861.911+45.851*TotalEvapW-20.446*MinAPI90
Illawarra Single Residential 489.35703+1.55252*TotalEvapW-0.9073*MinAPI90
Multi-Residential 332.2966+10.5267*EvapMedW-0.256*MinAPI90W
Commercial 440.14+79.96*TempMaxW-1.925*MinAPI90
Industrial 39363.3+643*MinAPI90W
Government & Others 5161.43+20.765*TotalEvapW-1.9639*TotalRainfall+29.36*Month
Primary Producer -349.78+186.65*TempMaxW-0.3782*TotalRainfall+7.69*Month
Woronora Single Residential 490.3747+2.8484*TotalEvapW-1.674*MinAPI90+2.7705*Month
Multi-Residential 372.817+11.758*EvapMedW-0.32765*MinAPI90
Commercial 1844.469-27.025*MinAPI90W
Industrial 1880.284971
Government & Others 4791.7+689*EvapMedW-12.19*MinAPI90+15.775*Month
Primary Producer 2189.281+12.034*TotalEvapW+7.825*Month-6.807*MinAPI90
41
Table B.2 Key Climate Variables
Variable Description
TempMax Average maximum temperature
TempMed Medium maximum temperature
TotalEvap Total evaporation
EvapMed Medium evaporation
TotalRainfall Total rainfall
RainDays Number of rain days
AvAPI95 Average API with k=0.95
AvAPI90 Average API with k=0.90
MinAPI90 Minimum API with k=0.90
TempMaxW Weighted average maximum temperature
TempMedW Weighted medium maximum temperature
TotalEvapW Weighted total evaporation
EvapMedW Weighted medium evaporation
TotalRainfallW Weighted total rainfall
RainDaysW Weighted number of rain days
AvAPI95W Weighted average API with k=0.95
AvAPI90W Weighted average API with k=0.90
MinAPI90W Weighted minimum API with k=0.90
Month Month factor from 1 to 12
42
Appendix C: Calibration plots for all sectors and water delivery systems
Figure A.1 Calibration for Cascade (month versus consumption in litres per property per day)
Cascade Multi-Residential
0
50
100
150
200
250
300
350
400
450
Jan-
97
Jul-9
7
Jan-9
8
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
nsu
mp
tio
n (
L/P
rop
ert
y/D
ay)
Metered Data
Fitted Model
Cascade Single Residential
0
100
200
300
400
500
600
700
800
900
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
ns
um
pti
on
(L
/Pro
pe
rty
/Da
y)
Metered Data
Fitted Model
Cascade Commercial
0
500
1000
1500
2000
2500
3000
3500
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
ns
um
pti
on
(L
/Pro
pe
rty
/Da
y)
Metered Data
Fitted Model
Cascade Industrial
0
100
200
300
400
500
600
700
800
900
1000
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
ns
um
pti
on
(L
/Pro
pe
rty
/Da
y)
Metered Data
Fitted Model
Cascade Government & Others
0
1000
2000
3000
4000
5000
6000
7000
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
ns
um
pti
on
(L
/Pro
pe
rty
/Da
y)
Metered Data
Fitted Model
Cascade Primary Producer
0
500
1000
1500
2000
2500
3000
3500
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
ns
um
pti
on
(L
/Pro
pe
rty
/Da
y)
Metered Data
Fitted Model
43
Figure A.2 Calibration for North Richmond (month versus consumption in litres per property per day)
North Richmond Single Residential
0
200
400
600
800
1000
1200
1400
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
nsu
mp
tio
n (
L/P
rop
ert
y/D
ay)
Metered Data
Fitted Model
North Richmond Multi-Residential
0
100
200
300
400
500
600
700
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
nsu
mp
tio
n (
L/P
rop
ert
y/D
ay)
Metered Data
Fitted Model
North Richmond Commercial
0
1000
2000
3000
4000
5000
6000
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
nsu
mp
tio
n (
L/P
rop
ert
y/D
ay)
Metered Data
Fitted Model
North Richmond Industrial
0
500
1000
1500
2000
2500
3000
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
nsu
mp
tio
n (
L/P
rop
ert
y/D
ay)
Metered Data
Fitted Model
North Richmond Government & Others
0
2000
4000
6000
8000
10000
12000
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
nsu
mp
tio
n (
L/P
rop
ert
y/D
ay)
Metered Data
Fitted Model
North Richmond Primary Producer
0
500
1000
1500
2000
2500
3000
3500
4000
4500
5000
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
nsu
mp
tio
n (
L/P
rop
ert
y/D
ay)
Metered Data
Fitted Model
44
Figure A.3 Calibration for Orchard Hills (month versus consumption in litres per property per day)
Orchard Hills Single Residential
0
200
400
600
800
1000
1200
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
nsu
mp
tio
n (
L/P
rop
ert
y/D
ay)
Metered Data
Fitted Model
Orchard Hills Multi-Residential
0
100
200
300
400
500
600
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
nsu
mp
tio
n (
L/P
rop
ert
y/D
ay)
Metered Data
Fitted Model
Orchard Hills Commercial
0
500
1000
1500
2000
2500
3000
3500
4000
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
nsu
mp
tio
n (
L/P
rop
ert
y/D
ay)
Metered Data
Fitted Model
Calibration for Orchard Hills Industrial
0
500
1000
1500
2000
2500
3000
3500
4000
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
nsu
mp
tio
n (
L/P
rop
ert
y/D
ay)
Metered Data
Fitted Model
Orchard Hills Government & Others
0
2000
4000
6000
8000
10000
12000
14000
16000
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
nsu
mp
tio
n (
L/P
rop
ert
y/D
ay)
Metered Data
Fitted Model
Orchard Hills Primary Producer
0
2000
4000
6000
8000
10000
12000
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
nsu
mp
tio
n (
L/P
rop
ert
y/D
ay)
Metered Data
Fitted Model
45
Figure A.4 Calibration for Prospect South (month versus consumption in litres per property per day)
Prospect South Single Residential
0
200
400
600
800
1000
1200
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
ns
um
pti
on
(L
/Pro
pe
rty
/Da
y)
Metered Data
Fitted Model
Prospect South Multi-Residential
0
100
200
300
400
500
600
700
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
ns
um
pti
on
(L
/Pro
pe
rty
/Da
y)
Metered Data
Fitted Model
Prospect South Commercial
0
500
1000
1500
2000
2500
3000
3500
4000
4500
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
ns
um
pti
on
(L
/Pro
pe
rty
/Da
y)
Metered Data
Fitted Model
Prospect South Industrial
0
1000
2000
3000
4000
5000
6000
7000
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
ns
um
pti
on
(L
/Pro
pe
rty
/Da
y)
Metered Data
Fitted Model
Prospect South Government & Others
0
2000
4000
6000
8000
10000
12000
14000
16000
18000
20000
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
ns
um
pti
on
(L
/Pro
pe
rty
/Da
y)
Metered Data
Fitted Model
Prospect South Primary Producer
0
2000
4000
6000
8000
10000
12000
14000
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
ns
um
pti
on
(L
/Pro
pe
rty
/Da
y)
Metered Data
Fitted Model
46
Figure A.5 Calibration for Prospect North (month versus consumption in litres per property per day)
Prospect North Single Residential
0
200
400
600
800
1000
1200
1400
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
ns
um
pti
on
(L
/Pro
pe
rty
/Da
y)
Metered Data
Fitted Model
Prospect North Multi-Residential
0
100
200
300
400
500
600
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
ns
um
pti
on
(L
/Pro
pe
rty
/Da
y)
Metered Data
Fitted Model
Prospect North Commercial
0
500
1000
1500
2000
2500
3000
3500
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
ns
um
pti
on
(L
/Pro
pe
rty
/Da
y)
Metered Data
Fitted Model
Prospect North Industrial
0
1000
2000
3000
4000
5000
6000
7000
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
ns
um
pti
on
(L
/Pro
pe
rty
/Da
y)
Metered Data
Fitted Model
Prospect North Government & Others
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
ns
um
pti
on
(L
/Pro
pe
rty
/Da
y)
Metered Data
Fitted Model
Prospect North Primary Producer
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
ns
um
pti
on
(L
/Pro
pe
rty
/Da
y)
Metered Data
Fitted Model
47
Figure A.6 Calibration for Prospect East (month versus consumption in litres per property per day)
Prospect East Single Residential
0
100
200
300
400
500
600
700
800
900
1000
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
nsu
mp
tio
n (
L/P
rop
ert
y/D
ay)
Metered Data
Fitted Model
Prospect East Multi-Residential
440
460
480
500
520
540
560
580
600
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
nsu
mp
tio
n (
L/P
rop
ert
y/D
ay)
Metered Data
Fitted Model
Prospect East Commercial
0
500
1000
1500
2000
2500
3000
3500
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
nsu
mp
tio
n (
L/P
rop
ert
y/D
ay)
Metered Data
Fitted Model
Prospect East Industrial
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-0
0
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
nsu
mp
tio
n (
L/P
rop
ert
y/D
ay)
Metered Data
Fitted Model
Prospect East Government & Others
0
2000
4000
6000
8000
10000
12000
14000
16000
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-9
9
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
nsu
mp
tio
n (
L/P
rop
ert
y/D
ay)
Metered Data
Fitted Model
Prospect East Primary Producer
0
2000
4000
6000
8000
10000
12000
14000
16000
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-9
9
Jul-9
9
Jan-
00
Jul-0
0
Jan-0
1
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
nsu
mp
tio
n (
L/P
rop
ert
y/D
ay)
Metered Data
Fitted Model
48
Figure A.7 Calibration for Ryde (month versus consumption in litres per property per day)
Ryde Single Residential
0
200
400
600
800
1000
1200
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
nsu
mp
tio
n (
L/P
rop
ert
y/D
ay)
Metered Data
Fitted Model
Ryde Multi-Residential
340
360
380
400
420
440
460
480
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
nsu
mp
tio
n (
L/P
rop
ert
y/D
ay)
Metered Data
Fitted Model
Ryde Commercial
0
500
1000
1500
2000
2500
3000
3500
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
nsu
mp
tio
n (
L/P
rop
ert
y/D
ay)
Metered Data
Fitted Model
Ryde Government & Others
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
nsu
mp
tio
n (
L/P
rop
ert
y/D
ay)
Metered Data
Fitted Model
Ryde Primary Producer
0
1000
2000
3000
4000
5000
6000
7000
8000
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
nsu
mp
tio
n (
L/P
rop
ert
y/D
ay)
Metered Data
Fitted Model
Ryde Industrial
0
500
1000
1500
2000
2500
3000
3500
4000
4500
5000
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
nsu
mp
tio
n (
L/P
rop
ert
y/D
ay)
Metered Data
Fitted Model
49
Figure A.8 Calibration for Potts Hill (month versus consumption in litres per property per day)
Potts Hill (Exclude Sutherland) Single Residential
0
100
200
300
400
500
600
700
800
900
1000
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
nsu
mp
tio
n (
L/P
rop
ert
y/D
ay)
Metered Data
Fitted Model
Potts Hill (Exclude Sutherland) Multi-Residential
400
420
440
460
480
500
520
540
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
nsu
mp
tio
n (
L/P
rop
ert
y/D
ay)
Metered Data
Fitted Model
Potts Hill (Exclude Sutherland) Commercial
0
500
1000
1500
2000
2500
3000
3500
4000
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
nsu
mp
tio
n (
L/P
rop
ert
y/D
ay)
Metered Data
Fitted Model
Potts Hill (Exclude Sutherland) Industrial
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
nsu
mp
tio
n (
L/P
rop
ert
y/D
ay)
Metered Data
Fitted Model
Potts Hill (Exclude Sutherland) Government & Others
0
2000
4000
6000
8000
10000
12000
14000
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
nsu
mp
tio
n (
L/P
rop
ert
y/D
ay)
Metered Data
Fitted Model
Potts Hill (Exclude Sutherland) Primary Producer
0
1000
2000
3000
4000
5000
6000
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
nsu
mp
tio
n (
L/P
rop
ert
y/D
ay)
Metered Data
Fitted Model
50
Figure A.9 Calibration for Sutherland (month versus consumption in litres per property per day)
Sutherland Single Residential
0
200
400
600
800
1000
1200
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
nsu
mp
tio
n (
L/P
rop
ert
y/D
ay)
Metered Data
Fitted Model
Sutherland Multi-Residential
0
100
200
300
400
500
600
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
nsu
mp
tio
n (
L/P
rop
ert
y/D
ay)
Metered Data
Fitted Model
Sutherland Commercial
0
500
1000
1500
2000
2500
3000
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
nsu
mp
tio
n (
L/P
rop
ert
y/D
ay)
Metered Data
Fitted Model
Sutherland Industrial
0
2000
4000
6000
8000
10000
12000
14000
16000
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
nsu
mp
tio
n (
L/P
rop
ert
y/D
ay)
Metered Data
Fitted Model
Sutherland Government & Others
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
nsu
mp
tio
n (
L/P
rop
ert
y/D
ay)
Metered Data
Fitted Model
Sutherland Primary Producer
0
5000
10000
15000
20000
25000
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
nsu
mp
tio
n (
L/P
rop
ert
y/D
ay)
Metered Data
Fitted Model
51
Figure A.10 Calibration for Warragamba (month versus consumption in litres per property per day)
Warragamba Single Residential
0
200
400
600
800
1000
1200
1400
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
nsu
mp
tio
n (
L/P
rop
ert
y/D
ay)
Metered Data
Fitted Model
Warragamba Multi-Residential
0
200
400
600
800
1000
1200
1400
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
nsu
mp
tio
n (
L/P
rop
ert
y/D
ay)
Metered Data
Fitted Model
Warragamba Commercial
0
500
1000
1500
2000
2500
3000
3500
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
nsu
mp
tio
n (
L/P
rop
ert
y/D
ay)
Metered Data
Fitted Model
Warragamba Industrial
0
500
1000
1500
2000
2500
3000
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
nsu
mp
tio
n (
L/P
rop
ert
y/D
ay)
Metered Data
Fitted Model
Warragamba Government & Others
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
nsu
mp
tio
n (
L/P
rop
ert
y/D
ay)
Metered Data
Fitted Model
Warragamba Primary Producer
0
2000
4000
6000
8000
10000
12000
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
nsu
mp
tio
n (
L/P
rop
ert
y/D
ay)
Metered Data
Fitted Model
52
Figure A.11 Calibration for Nepean (month versus consumption in litres per property per day)
Nepean Single Residential
0
200
400
600
800
1000
1200
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
nsu
mp
tio
n (
L/P
rop
ert
y/D
ay)
Metered Data
Fitted Model
Nepean Multi-Residential
0
100
200
300
400
500
600
700
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
nsu
mp
tio
n (
L/P
rop
ert
y/D
ay)
Metered Data
Fitted Model
Nepean Commercial
0
200
400
600
800
1000
1200
1400
1600
1800
2000
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
nsu
mp
tio
n (
L/P
rop
ert
y/D
ay)
Metered Data
Fitted Model
Nepean Industrial
0
10000
20000
30000
40000
50000
60000
70000
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
nsu
mp
tio
n (
L/P
rop
ert
y/D
ay)
Metered Data
Fitted Model
Nepean Government & Others
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
nsu
mp
tio
n (
L/P
rop
ert
y/D
ay)
Metered Data
Fitted Model
Nepean Primary Producer
0
500
1000
1500
2000
2500
3000
3500
4000
4500
5000
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
nsu
mp
tio
n (
L/P
rop
ert
y/D
ay)
Metered Data
Fitted Model
53
Figure A.12 Calibration for Macarthur (month versus consumption in litres per property per day)
Macarthur Single Residential
0
200
400
600
800
1000
1200
1400
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
nsu
mp
tio
n (
L/P
rop
ert
y/D
ay)
Metered Data
Fitted Model
Macarthur Multi-Residential
0
100
200
300
400
500
600
700
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
nsu
mp
tio
n (
L/P
rop
ert
y/D
ay)
Metered Data
Fitted Model
Macarthur Commercial
0
500
1000
1500
2000
2500
3000
3500
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
nsu
mp
tio
n (
L/P
rop
ert
y/D
ay)
Metered Data
Fitted Model
Macarthur Industrial
0
1000
2000
3000
4000
5000
6000
7000
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
nsu
mp
tio
n (
L/P
rop
ert
y/D
ay)
Metered Data
Fitted Model
Macarthur Government & Others
0
5000
10000
15000
20000
25000
30000
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
nsu
mp
tio
n (
L/P
rop
ert
y/D
ay)
Metered Data
Fitted Model
Macarthur Primary Producer
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
nsu
mp
tio
n (
L/P
rop
ert
y/D
ay)
Metered Data
Fitted Model
54
Figure A.13 Calibration for Illawarra (month versus consumption in litres per property per day)
Illawarra Single Residential
0
100
200
300
400
500
600
700
800
900
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
nsu
mp
tio
n (
L/P
rop
ert
y/D
ay)
Metered Data
Fitted Model
Illawarra Multi-Residential
310
320
330
340
350
360
370
380
390
400
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
nsu
mp
tio
n (
L/P
rop
ert
y/D
ay)
Metered Data
Fitted Model
Illawarra Commercial
0
500
1000
1500
2000
2500
3000
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
nsu
mp
tio
n (
L/P
rop
ert
y/D
ay)
Metered Data
Fitted Model
Illawarra Industrial
0
10000
20000
30000
40000
50000
60000
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
nsu
mp
tio
n (
L/P
rop
ert
y/D
ay)
Metered Data
Fitted Model
Illawarra Government & Others
0
2000
4000
6000
8000
10000
12000
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
nsu
mp
tio
n (
L/P
rop
ert
y/D
ay)
Metered Data
Fitted Model
Illawarra Primary Producer
0
1000
2000
3000
4000
5000
6000
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
nsu
mp
tio
n (
L/P
rop
ert
y/D
ay)
Metered Data
Fitted Model
55
Figure A.14 Calibration for Woronora (month versus consumption in litres per property per day)
Woronora Single Residential
0
200
400
600
800
1000
1200
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
nsu
mp
tio
n (
L/P
rop
ert
y/D
ay)
Metered Data
Fitted Model
Woronora Multi-Residential
340
360
380
400
420
440
460
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
nsu
mp
tio
n (
L/P
rop
ert
y/D
ay)
Metered Data
Fitted Model
Woronora Commercial
0
500
1000
1500
2000
2500
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
nsu
mp
tio
n (
L/P
rop
ert
y/D
ay)
Metered Data
Fitted Model
Woronora Industrial
0
500
1000
1500
2000
2500
3000
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
nsu
mp
tio
n (
L/P
rop
ert
y/D
ay)
Metered Data
Fitted Model
Woronora Government & Others
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
nsu
mp
tio
n (
L/P
rop
ert
y/D
ay)
Metered Data
Fitted Model
Woronora Primary Producer
0
1000
2000
3000
4000
5000
6000
Jan-
97
Jul-9
7
Jan-
98
Jul-9
8
Jan-
99
Jul-9
9
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Co
nsu
mp
tio
n (
L/P
rop
ert
y/D
ay)
Metered Data
Fitted Model
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