Quantifying potable water savings and water quality implications of

decentralised water sourcing options at the SEQ regional scale

Shiroma MaheepalaPrincipal Research Scientist, CSIRO

5 December 2012

Urban Water Security Research Alliance

OUTLINE

• Outline– Background– Aim– Case study– Results– Conclusions

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

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

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

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

Application of the methodology to roof water harvesting

Observed statistical distributions of tank size, and roof area and losses derived from observed data

Focus areas

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

Probabilistic representation of water use• 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

• For Brisbane observed use: 32 to 283 with a mean of 130.4 litres/person/day (excluding 13.3 l/p/d of observed leaks)

• For Brisbane average household occupancy: 2.6 people

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

Brisbane Statistic 

Frequency (events per day)

Half flush 

Full flush 

Tap shower Bath Dishwasher Clothes washer 

irrigation 

Mean 4.87 4.21 58.70 2.13 0.13 0.55 0.71 0.12 

Standard Deviation 

3.97  2.68  33.42  1.99  0.28  0.68  0.56  0.19 

Skewness  1.67  1.29  1.13  5.11  2.12  1.94  2.93  1.94 

 

Diurnal pattern and end use statistics for Brisbane (data sourced from Beal and Stewart, 2012, UWSRA Technical Report No. 47)

Probabilistic representation of water use: results

Toilet Laundry

Total

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

Stochastic simulation: input probability distributions

0

50

100

150

200

250

300

1 2 3 4 5 6 7 8 9 101112131415161718192021222324252627282930

effective roof area (m^2)

Probability Density Function

Histogram Normal

x26024022020018016014012010080604020

f(x)

0.48

0.44

0.4

0.36

0.32

0.28

0.24

0.2

0.16

0.12

0.08

0.04

0

Fitting effective tank sizes to log-normal distribution

Fitting effective roof areas to normal distribution

Effective tank sizes vary from 2.6 to 30.5 KL

Effective roof areas vary from 25 to 260 square m

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

Tank yield for Moreton Bay: simulation 1962 - 2011

With spatial variability: 43.9 KL/household/year

Without spatial variability: 50.3 KL/household/year (14.6% overestimate)

Tank yield for Brisbane: simulation 1962 - 2011

With spatial variability: 43.4 KL/household/year

Without spatial variability: 50.0 KL/household/year (15% overestimate)

Results for all areas and SEQ

Variable 

Cases

Average 

Demand 

Requested 

(kL)

Average 

Annual Yield 

(kL)

Average 

Annual 

Overflow (kL)

Average 

Annual 

Rainfall (mm)

Average 

Tank (kL)

Avg. Roof 

Area (m2)

Brisbane 62.4 43.4 61.8 1129 4.4 85Moreton Bay 62.5 43.9 67.9 1313 5.5 110Sunshine Coast 64.8 50.3 93.2 1676 5.6 155Ipswich 48.3 34.5 39.2 866 6.7 155Gold Coast 54.7 44.4 77.9 1455 5.6 180SEQ average 58.5 43.3 68.0 1287.8 5.6 137.0

Average 

Cases

Average 

Demand 

Requested 

(kL)

Average 

Annual Yield 

(kL)

Average 

Annual 

Overflow (kL)

Yield 

difference*

Overflow 

difference**

Average 

Tank (kL)

Avg. Roof 

Area (m2)

Brisbane 61.7 50.0 55.1 15% ‐11% 5.6 115.1Moreton Bay  61.7 50.3 61.3 15% ‐10% 5.5 105.1Sunshine Coast 55.6 57.1 86.1 14% ‐8% 5.7 105.7Ipswich 48.2 42.2 31.4 22% ‐20% 7.9 105.1Gold Coast 55.4 49.0 73.1 10% ‐6% 5.7 101.8SEQ average 56.5 49.7 61.4 15% ‐11% 6.1 106.5

*Yield is over-estimated by average case**Overflow is under-estimated by average case

Variable case: stochastic simulation of 10,000 householdsAverage case: 1 household, uses only ‘average’ parameters

Long term expected tank yield vary from 34.5 KL/hh/year in Ipswich to 50.3 KL/hh/year in Sunshine Coast

Long term expected tank yield in Moreton Bay: 43.9 KL/hh/year

Long term expected tank yield in SEQ: 43.3 KL/hh/year

At the SEQ scale, use of average values over-estimated the yield by 15% and under-estimated the overflow by 11%

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

Stochastic simulation of TP, TN and TSS loads: for Brisbane data

TSS TP TNVariable case: overflow load, kg, per house, per year

1.027 0.63 1.101Average case: overflow load, kg, per house, per year

0.856 0.489 0.931Difference, compared to variable case ‐17% ‐22% ‐15%

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 (1980 – 2010) daily simulation of the regional supply system using eWater CRC’s Source Integrated Modelling System

Impact on the regional supply cont..

• 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 kth Nearest Neighbourhood algorithm (eWater CRC)

System Storage behaviour with and without rainwater tanks

without tanks

with tanks

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.

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

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

www.urbanwateralliance.org.au