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

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

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

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?

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?

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?

Study Area: Portland, OR

Historical water use Water Use Max. Temp. Base use

Mean Household Base Use

Mean Household Seasonal Use

Categorical variable: Outdoor space

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

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

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

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

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

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

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

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

Mean Summertime Temperature Coefficients Mean Values Wet Year ‘05, ‘07 Normal Year ‘02, ‘04, ‘08, ‘09 Dry Year ‘03, ‘06 Low Density Medium-low Density Medium-high Density High Density

SDF Coefficient Distribution: July Check labels H MH ML L DRY H MH ML L NORMAL H MH ML L WET

SDF July 2003, drought year

July 2004, normal year SDF

July 2005, wet year SDF

Key findings  Generally, a 1 ⁰ C 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

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

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

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

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

Discussion