Key Drivers of Households Water Use in South Australia: Practical Implications

Transcription

1 Key Drivers of Households Water Use in South Australia: Practical Implications Goyder Institute for Water Research Project: Optimal Water Resource Mix for Metropolitan Adelaide Mark Thyer (UoA), Nicole Arbon, (UoA), Martin Lambert (UoA), Darla Hatton McDonald (CSIRO), Steve Kotz (SA Water)

2 Future water use is difficult to predict Future Water Use? Rebound? Static? Increase efficiency? Source: ABS 2

3 Knowledge of water end use essential for IUWM Mains Water Water Use Outdoor Shower Washing Machine Toilet Taps Greywater Re-use: Shower Washing Machine Wastewater Greywater Rainwater Alternative supply: Outdoor Toilet

4 What is impact of changes in household water use on larger water supply systems? regional (city) Interested in impacts at larger scale Spatial scale cluster household Interventions occur at small scales shower rose mins Time scale years

5 Objective: Evaluate key behavioural drivers of household water use Smart meters measure high resolution water use (0.014L/pulse) at 10 sec intervals Data downloaded via laptop Selected representative 150 study households Predictive model of household water use behaviour appliances etc. Household survey of attitudes/behaviour 5

6 150 Study Households represent 65% Metro. Adelaide households 65% representation based on: Income Family Composition Appliance proportion Occupancy Dwelling Structure Under-represented: Single parent families Renters, units etc. Newer housing stock Owner occupied separate houses 6

7 Significant variability in end-use between different households Mean daily indoor usage (L/person/day) Toilet Washing Machine Shower Dishwasher Tap Bath Avg: 135 L/p/day 29 (21%) 48 (36%) 25 (18%) 28 (21%) 7

8 Households cannot predict their own indoor end use Perceived % Washing machine end use Actual % Households need greater information (e.g. monitoring) to identify cost-effective water saving opportunities 8

9 Front loading washing machines offer biggest water saving potential Showers (<9L/min) Toilet (6/3L) Washing machine (front loader) Total Current % 43% 35% 55% - Potential Savings (L/p/day) (15% indoor) No behaviour difference (freq/duration) with water efficient appliances (e.g. longer showers) Schemes that encourage uptake of efficient washing machines are encouraged 9

10 Distinct household usage types require target demand management Mean daily usage (L/person/day) Adults 55+ only High income family households High With income All children family households Very shower high use, but lower washing machine and toilet use Less likely to think they are water conservers (longer showers) Indoor use low, due to efficient washing machines and lower toilet frequency Water saving potential should target shower behaviour (e.g. shower timers) Toilet Washing Machine Shower Dishwasher Tap Bath 10

11 Distinct household usage types require targeted demand management Mean daily usage (L/person/day) Adults 55+ only Adults 55+ only households High With income All children households family Lower shower use, but higher washing machine and toilet use Likely to think they are water conservers (shorter showers) Indoor use high, due to inefficient washing machines and higher toilet use frequency Water saving is from efficient washing machines Likely to be growing household usage type as population ages Toilet Washing Machine Shower Dishwasher Tap Bath

12 Strong Seasonal Impact on Water Use Mean daily usage (L/person/day) Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Winter: 153 L/p/day, Summer: 500 L/p/day Approx. 40% of total household water use 12

13 Households cannot predict their own seasonal end use Perceived % Seasonal end use (outdoor, evap air conditioners etc. ) Estimated % Households need greater information (e.g. monitoring) to identify cost-effective water saving opportunities 13

14 Strong variability in seasonal water use between households Larger property area increases use (25%) Lower income decreases use (20%) Adults 55+ only increases use (12%) Targeted approach to design and management of water use systems is required 14

15 Peak Daily Water Use Occurs in Summer 15 Jan 13 Feb 13 Mar 13 Apr 13 May 13 Jun 13 Jul 13 Aug 13 Sep 13 Oct 13 Nov 13 Total daily usage / households (kl/household/day) Dec 13 Jan 14 Feb 14 Daily Mean Day, Peaking factor = 1 Peak Day, Peaking factor = 2.8 Dates

16 Small proportion of households contribute to peak demand % contribution of each household Feb Jan Jan Feb Jan Dec Jan Jan Jan Mar 13 Contribution of each household to peak daily demand % of households 20% of households contribute to 50% of volume on peak demand days Significant opportunity to reduce peak demands and infrastructure costs by targeting high peak households 16

17 Objective: Provide reliable predictions of household end-uses BESS: Behavioural End-Use Stochastic Simulator A Urban Water Use Framework

18 BESS provided reliable end-use predictions with local data Ave. Indoor Usage (L/person/day) Show er & Bath Washing Machine % Error in Average % Error in Ave. Ave. Usage (L/person/day) BESS provides reliable end-use predictions for households with similar characteristics Further development needed improve transferability of end-use predictions (include household usage types). 18

19 Drivers of decrease in demand during drought Mean daily usage (L/household/day) pre ( ) height ( ) post ( ) During drought, 15% decrease in water use BESS estimates 50% dec. due to uptake efficient appliances, 50% decrease due to decrease in outdoor Will reduced post-drought demand continue? 19

20 Predictions of Future Demand Future Water Use? Demand Management (uptake of water efficient appliances) reduce household demand by 7%, wastewater volumes by 11% Future reductions are lower demand hardening 100% uptake Does not include behavioural change lower limit 20

21 Summary Identified key drivers of household water use in Adelaide Smart metering, analysis and surveys Identified practical opportunities for more targeted approaches for water system management and design Reduce water use, reduce infrastructure costs Future research Longer term monitoring, seasonal water use 40%, but highly variable due to climate Improved modelling of end-use Include under-represented households 21