Predicting Daily Potable Water Savings by Using Rainwater Tanks at Urban Scale

1. Predicting Daily Potable Water Savings by Using Rainwater Tanks at Urban Scale Hui Xu College of Hydrology and Water Resources, Hohai University

2. Outline • Research question • Methodology • Rainwater tank model • Modelling scenarios and parameters • Measurement for Potable Water Savings • Results • Daily PWS of RWTs of variable scenario and uniform scenario • Differences between the daily PWSv(t) and PWSu(t) • Conclusions Urban Water Theme

3. Research question • Rainwater tanks are being promoted as an alternative supply in many cities • How do we accurately predict the daily performance from a cluster of RWTs at urban scale? • The impact of rainwater tanks at regional (i.e. whole-of-urban water system) scale is required for assessing supply/demand balance as well as storage levels, system shortfalls, system reliability and system yield Urban Water Theme

4. The two modules of the rainwater tank model Evaporation E t Rainfall-runoff module Rainfall Pt Depression storage capacity, Ci Runoff It Losses Lc Rainwater retained in the roof store, St Roof catchment Inflow (Rt) Spillage (Ot) Volume in tank (Vt) Yield (Yt) Collection tank Supply to house unit: mm Storage module Urban Water Theme

5. The two modules of the rainwater tank model Rainfall-runoff module It = maximum of (Pt + S(t-1) - Ci) and 0 St = maximum of (Pt + S(t-1)) - It – Et and 0 The roof runoff (Rt), in m3, that could be harvested during a time step (t) is determined by: Rt = A x (It /1000) x (1-Lc /100) Storage module Vt = Vt-1 + Rt - Ot - Yt The sequence of Yield After Supply can be summarized as: Yt = minimum of Dt or (Vt-1 + Rt – Ot) Vt = minimum of (Vt-1 + Rt – Ot -Yt) or (C-Yt) Dt is the requested demand for that time step and C is the capacity of the rainwater tank. Urban Water Theme

6. Input data Tank capacity Roof area Depression storage Continuing losses Rainfall and Evaporation data (filenames) Demand data End uses: Toilet; Garden Demands generated stochastically In-house values: Reflect occupancy level and appliance efficiency Garden demand: Based on daily maximum temperature data Parameters calibrated by using results of Melbourne Study Time step: 1-minute intervals and then aggregated Urban Water Theme Urban Water Theme

7. Templates Most of the input data can be provided as distributions rather than fixed values For example, roof area between 100 and 400 square metres Templates Template tells you how to define characteristics of the RWT Each template is “converted” to a house with fixed roof area, etc. Urban Water Theme Urban Water Theme

8. An example template Urban Water Theme

9. Two Scenarios Scenario 1—Variable scenario Tank and roof data statistical distribution information This allows multiple houses in a cluster with varying tank sizes, roof areas and roof losses The demand files were chosen at random from a library of demand input files Might represent a suburb, or a city • Scenario 2—Uniform scenario • A scenario where all the templates have been assigned specific values • A cluster of houses with average parameters • The demand files can also be specified as explicit files Urban Water Theme

10. Input parameters--Melbourne based data Table 1. Details of the input parameters in the rainwater tank model runs at daily time step (toilet and garden use) Urban Water Theme

11. Measurement for Potable Water Savings • The potable water savings was determined as a measure of how much potable water has been saved by rainwater supply in comparison to the overall demand, it can be expressed as: PWSt =100  St is the volume of water supplied by the rainwater tank in time step t (kL); Dt is the household water demand in time step t (kL); T is the total number of time steps; t is the simulation time step. Urban Water Theme

12. Results The simulated daily PWS of both variable and uniform RWT systems (PWSv) is a time series corresponding to 18263 days. Table 2. The statistical characteristics of the daily PWS time series in variable and uniform scenarios with different cluster size. The average absolute difference between the daily PWSv and PWSu is 3.0%, and the proportional difference is 19.0%. Urban Water Theme

13. Cluster size effect • Table 3. The mean, minimum and maximum standard deviation of daily PWS among all replicates for different cluster sizes. It was found that the minimum, mean and maximum SD of daily PWSv and PWSu time series decreased as the cluster size increased, indicating that larger cluster size tends to generate more stable results among duplicated simulation runs in both scenarios. Urban Water Theme

14. Dry year- 326mm/a 12% 10% Urban Water Theme

15. Wet year- 867mm/a 23% 20% Urban Water Theme Urban Water Theme

16. Normal – 644mm/a 18% 15% Urban Water Theme Urban Water Theme

17. Differences between the two scenarios Table 4. The statistical characteristics of the daily PWSv and PWSu and their differences for cluster size 1000. (%) • Among the total 18263 days, the uniform scenario produced overestimation in about 62% days, underestimation in 34% days. • The daily differences vary from -19.5% to 39.1% with a 5.6% standard deviation and 1.9% coefficient of variation . Urban Water Theme

18. Conclusions • By running the RWT model, the average daily PWSv is 15.8%, ranging from 0 to 70% for the variable scenario; while for the uniform scenario the average daily PWSu is 18.8%, ranging from 0 to 80%. • The uniform scenario produces a biased estimation for the daily potable water savings. • The behaviour of the daily PWS time series is quite complex and the variations between the two scenarios show both under-estimation and over-estimation. • Further research to develop a method providing a computational method which avoids the disadvantages of average input parameters on PWSv is suggested. Urban Water Theme

19. Thank you Urban Water Theme