1. MODELING SPATIOTEMPORAL NONSTATIONARITY IN URBAN WATER DEMAND UNDER CLIMATE CHANGE September 2011 Betsy Breyer, Heejun Chang, and Hossein Parandvash

2. Background  Adaptation to climate change requires an understanding how current behavior depends on climate variation  For urban water providers, water demand analysis is key to formulating an adaptation strategy  Neighborhood-level conservation planning can help reduce risk of municipal water shortages

3. Research questions 1. How does climate affect residential water demand?

4. Research questions 1. How does climate affect residential water demand? 2. How is the relationship between climate and water nonstationary? How does it vary over space?

5. Research questions 1. How does climate affect residential water demand? 2. How is the relationship between climate and water nonstationary? How does it vary over space? 3. How does the relationship between climate and water use vary during wet/dry years?

6. Research questions 1. How does climate affect residential water demand? 2. How is the relationship between climate and water nonstationary? How does it vary over space? 3. How does the relationship between climate and water use vary during wet/dry years? 4. How can we use the above information to forecast future water use under climate change?

7. Study Area: Portland, OR 2002-2009

8. Historical water use 2002 2003 2004 2005 2006 2007 2008 2009 Water Use Max. Temp. Base use

9. Mean Household Base Use

10. Mean Household Seasonal Use

11. Categorical variable: Outdoor space

12. Independent Variable: Max. temp. Monthly household water use (KL) Mean max. temp. (C)

13. Conceptual workflow Summarize variables by census block group Regressions of historical max. temp. on water use Compare variation in coefficients across space (density) and time (wet/dry years) Extrapolate from functional forms using climate simulation AB1

14. Mean building size: 241 m Mean building age: 12.3 years Mean outdoor space: 5077 m 2 2

15. Mean building size: 241 m Mean building age: 12.3 years Mean outdoor space: 5077 m 2 2

16. Mean building size: 197 m Mean building age: 97.6 years Mean outdoor space: 961 m 2 2

17. Mean building size: 197 m Mean building age: 97.6 years Mean outdoor space: 961 m 2 2

18. SDF Low vs. high density: temperature response coefficient Low Density High Density

19. Variation in temperature coefficient depends on density LowMed-LowMed-HighHigh Density Max. temp coefficient Global mean = 0.443

20. Mean Summertime Temperature Coefficients Mean Values Wet Year ‘05, ‘07 Normal Year ‘02, ‘04, ‘08, ‘09 Dry Year ‘03, ‘06 Low Density0.7610.7630.825 Medium-low Density0.442 0.459 Medium-high Density 0.3530.3580.371 High Density0.3320.322

21. SDF Coefficient Distribution: July 2002-09 Check labels H MH ML L DRY H MH ML L NORMAL H MH ML L WET 3.0 2.5 2.0 1.5 1.0 0.5 0.0

22. SDF July 2003, drought year

23. July 2004, normal year SDF

24. July 2005, wet year SDF

25. Key findings  Generally, a 1 ⁰ C 0.443 KL increase monthly per household  Local regression: water use in lower density areas tends to be more climate-sensitive  Some medium density areas as sensitive to temp. increases as low-density, esp. during dry years

26. SDF Future climate change scenario AB1 Monthly household water use (KL)

27. Next steps  Run spatial-temporal weighted regression model to improve model of localized variations in water use  Establish threshold temperatures to better capture nonlinear variations during peak use in summer  Link climate change scenarios with regional growth/land cover change scenarios through 2050

28. Household water use and max. temp. Monthly household water use (KL) Mean max. temp. (C)

29. Conclusions  Countervailing trends: - declining household-level base use - climate-driven increases in seasonal use  Portland is most vulnerable to climate-induced water stress along its urban fringe in normal years  High density areas show the greatest resilience to climate variation in drought years

30. Discussion