1. Diurnal Water Use & Implications for Master Planning Michigan Section AWWA Annual Conference August 13, 2010 Janice Skadsen Michigan Section AWWA Annual Conference August 13, 2010 Janice Skadsen

2. Co-Authors Molly Wade, City of Ann Arbor Pete Perala, City of Ann Arbor (retired) Stan Plante, CDM Henry Fan, CDM Mark TenBroek, CDM

3. Goals of the Master Plan IMPROVE city’s capacity to predict flow and pressure in existing distribution system. DETERMINE system improvements needed to meet current and projected water demands PRIORITIZE capital improvement projects that will sustain reliable water distribution into the future

4. Water Master Plan Project Data Collected –Collect detailed diurnal and seasonal water use patterns for different types of customers Data Purpose –Use patterns in hydraulic model (InfoWater) to provide more realistic water demands

5. City of Ann Arbor Statistics Service area about 50 square miles Population about 115,000 5 pressure districts About 27,000 meters All pipe InfoWater hydraulic model

6. Automatic Data Readers (AMR) Installed in 2004 to: –Reduce FTEs for manual meter reading –Reduce workman’s comp claims –Improve data information and timeliness –Improve customer service Provides real-time detailed data –Collect data twice per day Cost $6.9M for approximately 27,000 meters

7. AMR Pattern Approach Reprogrammed 100 meters: –Used 30 minute data collection intervals –Meters selected to represent a range of user types –Data collection between February, 2009 and April, 2009 –Data collection completed September, 2009 Processed data: –Develop weekly patterns

10. AMR Data Patterns Residential patterns: –Consistent –Outdoor Waterers –Irrigation only meter –Snowbird (not sampled) Small commercial patterns Large user patterns Irrigation & outdoor waterer patterns

11. Sample size = 22 SundaySaturday

12. Sample size = 6 More peaks than consistent user Pattern is average over May-August Apply this pattern to summer months Use constant pattern for winter months SundaySaturday

14. Additional Residential Patterns Snowbird: –No samples showing reduced winter use –Recommend consistent residential

15. AMR Data Patterns Residential patterns Small commercial patterns Large user patterns Irrigation & outdoor waterer pattern Pattern comparisons

16. Sample size = 4

17. Sample size = 8

18. Sample size = 5

19. Sample size = 4

20. AMR Data Patterns Residential patterns Small commercial patterns Large user patterns Irrigation & outdoor waterer pattern Pattern comparisons

21. Demand Distribution - Largest 200 Users 39% of total system demand 27% from Top 50 users

22. Large Users User types: –12 Campus (Univ. of Michigan, community college) –12 Medical (2 major hospitals) –7 Student Housing –3 Hotels –2 U of M Power Plant connections –2 Wholesale Customers –1 Retirement Home –1 Office –1 Unique (mixed commercial /residential)

24. Sample size = 11

26. Sample size = 12

28. Sample size = 7

30. Sample size = 3

31. Sample size = 1

32. Retirement Homes Approach Assume two types of use: –Assisted living: Recommend using monitored pattern –Retirement community: Recommend using multi-family pattern

34. Scio Approach Monitored pattern reflects tank operation Composite demands unknown Recommend using Ann Arbor’s composite pattern

36. AMR Data Patterns Residential patterns Small commercial patterns Large user patterns Irrigation & outdoor waterer pattern Pattern comparisons

37. Seasonal Patterns Criteria: –Consider all AMR data for 2 years –Standard deviation > 40% of monthly averages –Summer use (May – August) > rest of year (Sept, Oct, Mar & Apr)

38. Seasonal Water wUsers 20% of residential 22% of small commercial 0% of large users Irrigation only meters (746 accounts, 4 large user) – develop generated pattern due to lack of data

39. AMR Data Patterns Residential patterns Small commercial patterns Large user patterns Irrigation & outdoor waterer pattern Pattern comparisons –Average day –Non-summer day –Summer max day –Max day Existing diurnal pattern

40. 1.5 ~2.0

41. 14.8

42. 13.3

43. 20.8

44. 30.3

48. Benefits Higher peaks and lower minimums observed versus typical assumptions Improved understanding of water use, particularly local conveyance Effort minimal to reprogram and collect data, but some effort to analyze Data collection limited by volunteer participation & battery life

49. Recommendations Consider developing residential user classes –Consistent year-round use –Summer waterer with increased summer peaks Use large user flows and patterns directly where available Consider a variety of commercial and small industrial patterns where possible