1. The value and challenges of micro- component domestic water consumption datasets Jo Parker Working as part of the ESPRC - ARCC water project with the support of Anglian Water Services (AWS)

2. Study aim Examine the sensitivity of long-term water demand micro- components to climate variability and change. Jo Parker

3. What are micro-components? Source: Ofwat

4. Estimating/forecasting household water demand? Traditionally water into supply. Complexity of household water demand. Micro-component data provides us with the ability to investigate water use at the household scale. Jo Parker

5. The ‘Golden 100’ Micro-componentsSocio-economic variablesMeteorological variablesOther variables BathOccupancy rateMinimum temperature ( o C)Day of week ShowerRegionMaximum temperature ( o C)Month of year BasinBilling typeRainfall (mm)Bank holiday WCACORN classificationSunshine (hours per day) Kitchen sinkRateable value Washing machine Dishwasher External tap More than 22million data points. Too large to handle in excel. 100 households.

6. The ‘Golden 100’

7. Error checking algorithm 1.Basic error checks. 2.Remove large outliers  percentile approach. 3.Stratification. 4.Second screening. 5.Apply transformation. 6.Regression analysis.

8. 1. Basic error checks Remove gross errors. Completeness checks. Dummy variables. Remove 0l/d PCC. Sunday000000 Monday100000 Tuesday010000 Wednesday001000 Thursday000100 Friday000010 Saturday000001

9. 2. Percentile approach Remove PCC outliers (0.05% threshold determined via sensitivity testing). e.g., one rogue entry purported 98,020 litres/day for a single occupancy household.

10. 3. Stratification

11. 4. Second Screening User defined threshold. e.g., secondary screening (250l/d threshold) removed values such as 131218l/d in bath usage for a 3 occupancy household. Excluding external usage.

12. 5. Transformation The Kolmogorov-Smirnov normality test. Box-Cox transformation.

13. 6. Regression – One approach doesn’t fit all Jo Parker Metered households, East region, single occupancy. BasinBath

14. Bath (non-zero) Jo Parker Metered households, East region, single occupancy.

15. 6. Regression Analyse the frequency of usage and non- usage (Logistic regression) Is this weather, bank holiday, day of the week etc. sensitive? Analyse the volume used (Multiple linear regression) Is this weather, bank holiday, day of the week etc. sensitive? Jo Parker

16. Variables modelled Observed data input (subpopulation) Micro-components modelled Explanatory variables used MeteredBathMean temperature ( o C) UnmeteredShowerTemperature range ( o C) BasinSunshine (hr) WCRainfall (mm) Kitchen sink7 day rainfall (mm) Washing machine Regional soil moisture deficit index (mm) DishwasherDay of week External tapMonth of year Year Bank holiday Occupancy rate ACORN category

17. Basin water usage vs. Daily mean Temp. Relatively insensitive to Mean T What is causing striations? Understand peak users (>40l/d)?

18. Bath water usage vs. Daily mean Temp. Relatively insensitive to Mean T What is causing striations between 20- 60l/d? Understand peak users (>80l/d)?

19. Dishwasher water usage vs. Daily mean Temp. Metered Relatively insensitive to Mean T Understand peak users (2 uses per day)? Unmetered Slight negative correlation with Mean T Metered households Unmetered households

20. Shower water usage vs. Daily Mean Temp. If we look at peak cluster  positive correlation with Mean T.

21. External water usage vs. Mean Temp. Non-linear sensitivity to Mean T Where is the tipping point? Metered households Unmetered households

22. Thank you Jo Parker