Urban Water Security Research Alliance

Transcription

1 Urban Water Security Research Alliance Quantifying potable water savings and water quality implications of decentralised water sourcing options at the SEQ regional scale Shiroma Maheepala Principal Research Scientist, CSIRO 5 December 2012

2 OUTLINE Outline Background Aim Case study Results Conclusions

3 Total water cycle planning and decentralised water sourcing options Examining the effectiveness of decentralised water sourcing options is an important aspect of LGA scale TWCM planning This is to identify the most sustainable way to achieve QDC s 70 kl/year mandatory water savings target Maximise grid water savings Minimise environmental impacts Decentralised water servicing options Roof water harvesting Local stormwater harvesting Local wastewater recycling

4 Challenge: decentralised sourcing options To evaluate the effectiveness of decentralised sourcing options, it is fundamental to understand Contributions to grid water savings Amount of pollutant removal at the catchment scale Grid water savings and pollutant removal rates of decentralised sourcing options can vary spatially depending on: Storage capacity, inflow and demand placed on the source Effectiveness of each scheme should be evaluated individually, but this is not practical Hence, spatial variability is often ignored, but this will introduce errors

5 Research Questions How do we account for the spatial variability of decentralised sourcing options, when quantifying grid water savings and catchment scale pollutant removal? How do we assess supply security of the Grid in the presence of decentralised sourcing options? Initial focus: roof water harvesting

6 Methodology Stochastic simulation of the storage behaviour of decentralised sourcing options over a 50-year period of climate Instead of using average values, use all probable values for storage, catchment inflow, losses and demand Use probability distributions derived from observed data to generate probable values

7 Application of the methodology to roof water harvesting Observed statistical distributions of tank size, and roof area and losses derived from observed data

8 Focus areas Average annual rainfall: Ipswich 866 mm; Brisbane 1129 mm; Moreton Bay 1313 mm; Gold Coast 1455 mm; Sunshine Coast 1676 mm

9 Observed data: 2010 Winter SEQ water consumption data from South East Queensland Residential End Use Study, Beal and Stewart (2011), UWSRA Technical Report No. 31 Probabilistic representation of water use For Brisbane observed use: 32 to 283 with a mean of litres/person/day (excluding 13.3 l/p/d of observed leaks) For Brisbane average household occupancy: 2.6 people

10 Probabilistic representation of water use cont.. For each end use, generated 10,000 probable demand time series: Probability for triggering the event derived from the observed diurnal pattern Probability distributions for volume, frequency of use, flow rate and duration, derived from the observed data Diurnal pattern and end use statistics for Brisbane (data sourced from Beal and Stewart, 2012, UWSRA Technical Report No. 47) Brisbane Statistic Frequency (events per day) Half flush Full flush Tap shower Bath Dishwasher Clothes washer irrigation Mean Standard Deviation Skewness

11 Probabilistic representation of water use: results Total Toilet Laundry

12 Stochastic simulation of storage behaviour: data Tank sizes sourced from: Measured data in SEQ: Biermann et al. (2012): UWSRA Technical Report No. 66 Home and garden Waterwise Rebate Scheme (HWRS), provided by Mark Askins of QWC Connected roof areas sourced from: Measured data in SEQ: Biermann et al. (2012): UWSRA Technical Report No. 66 Tank Losses: literature based values

13 Stochastic simulation: input probability distributions Effective tank sizes vary from 2.6 to 30.5 KL effective roof area (m^2) Effective roof areas vary from 25 to 260 square m Probability Density Function Fitting effective roof areas to normal distribution Fitting effective tank sizes to log-normal distribution f(x) x Histogram Normal

14 Stochastic simulation: time step and number of iterations Daily simulation overestimates annual yield by 3% and annual overflow by 30% compared to hourly simulation 10,000 iterations is adequate

15 Tank yield for Moreton Bay: simulation Without spatial variability: 50.3 KL/household/year (14.6% overestimate) With spatial variability: 43.9 KL/household/year

16 Tank yield for Brisbane: simulation Without spatial variability: 50.0 KL/household/year (15% overestimate) With spatial variability: 43.4 KL/household/year

17 Variable Cases Results for all areas and SEQ Variable case: stochastic simulation of 10,000 households Average case: 1 household, uses only average parameters Average Demand Requested (kl) Average Annual Yield (kl) Average Annual Overflow (kl) Average Annual Rainfall (mm) Average Tank (kl) Avg. Roof Area (m2) Long term expected tank yield vary from 34.5 KL/hh/year in Ipswich to 50.3 KL/hh/year in Sunshine Coast Brisbane Moreton Bay Sunshine Coast Ipswich Long term expected tank yield 39.2 in Moreton Bay: Gold Coast SEQ KL/hh/year average Average Cases Average Demand Requested (kl) Average Annual Yield (kl) Average Annual Overflow (kl) Yield difference* Overflow difference** Average Tank (kl) Long term expected tank yield in SEQ: 43.3 KL/hh/year Avg. Roof Area (m2) Brisbane % 11% Moreton At the Bay SEQ scale, 61.7 use 50.3 of average 61.3 values 15% over-estimated 10% Sunshine Coast % 8% the yield by 15% and under-estimated the overflow by 11% Ipswich % 20% Gold Coast % 6% SEQ average % 11% *Yield is over-estimated by average case **Overflow is under-estimated by average case

18 Comparison of our results with other rainwater tank yield studies in SEQ Beal et al. (2012) study based on 2008 water consumption data 20 kl/hh/y to 95 kl/hh/y with a mean of 50 kl/hh/y Chong et al. (2011) study based on 2008 and 2010 consumption data 25 kl/hh/y to 89 kl/hh/y with a mean of 58 kl/hh/y Umapathi et al. (2012) study based detailed monitoring of rainwater use in 20 homes 40 kl/hh/y QWC analysis based on 2011 Brisbane consumption data 37 kl/hh/y Moreton Bay TWCM Plan study, 57 kl/hh/y

19 Stochastic simulation of TP, TN and TSS loads: for Brisbane data Variable case: overflow load, kg, per house, per year TSS TP TN Average case: overflow load, kg, per house, per year Difference, compared to variable case 17% 22% 15%

20 Assessing regional supply system behaviour in the presence of small-scale sources Hypothetical representation of the SEQ supply system to develop the method 30 year ( ) daily simulation of the regional supply system using ewater CRC s Source Integrated Modelling System

21 Impact on the regional supply cont.. System Storage behaviour with and without rainwater tanks Need to analyse the regional supply system for many different but plausible climate patterns - to account for climate variability and change Upscale the time series of supply obtained with stochastic simulation, using k th Nearest Neighbourhood algorithm (ewater CRC) with tanks without tanks

22 Conclusions For small-scale sources, storage capacity, inflow and losses can vary spatially. The demand placed on small-scale sources can also vary spatially. Observed data in SEQ supports this view. We examined the effect of not considering the spatial variability for roof water harvesting in SEQ. Results indicated that the use of average values can over-estimate the yield by 15%; under-estimate the overflow by 11%; under-estimate TSS, TP and TN loads from the tank by 17%, 22% and 15% respectively. Hence, we recommend the use of stochastic simulation to quantify potable water savings and pollutant removal potential of decentralised sources.

23 Conclusions cont.. Stochastic simulation applies to other small-scale sources, if there are many small-scale schemes. Stochastic simulation/statistical up-scaling/source IMS has the potential to quantify the grid water supply and catchment pollutant removal potential of small-scale sources. Further work is needed to demonstrate this capability. Further work is continuing in Adelaide to examine the optimal mix of water sources for metropolitan Adelaide. A project funded by the Goyder Research Institute

24 Urban Water Security Research Alliance Acknowledgement Co-authors: Esther Coultas and Luis Newmann Data providers: Cara Beal, Rodney Stewart, Ashok Sharma and Sharon Biermann Mark Askins, Phillip Chan, Tad Bagdon and Patricia Hurikino of the Queensland Water Commission for providing access to their study, tank data and their valuable advice