TY - JOUR
T1 - Space and time dynamics of urban water demand in Portland, Oregon and Phoenix, Arizona
AU - Lee, Seung Jae
AU - Chang, Heejun
AU - Gober, Patricia
N1 - Funding Information:
Financial assistance for this Sector Applications Research Program (SARP) project was provided by the Climate Program Office of the U.S. Department of Commerce, National Oceanic and Atmospheric Administration (NOAA) pursuant to NOAA Award No. NA09OAR4310140. Additional financial support was provided by the National Science Foundation through the Decision Center for a Desert City (SES-0345945) and the James F. and Marion L. Miller Foundation sustainability Grant. The statements, findings, conclusions, and recommendations expressed in this material are those of the research team and do not necessarily reflect the views of NOAA, US Department of Commerce, the National Science Foundation, or the US Government. The authors also wish to thank Adam Q. Miller, Water Resources Planner at the City of Phoenix, for help with the City of Phoenix water data. We also appreciate Sally Wittlinger who created the base map of the study area.
Publisher Copyright:
© 2015, Springer-Verlag Berlin Heidelberg.
PY - 2015/5/1
Y1 - 2015/5/1
N2 - Critical to effective urban climate adaptation is a clearer understanding of the sensitivities of resource demand to changing climatic conditions and land cover situations. We used Bayesian Maximum Entropy (BME) stochastic procedures to estimate temperature and precipitation at the very small scale of urban Census Block Groups (CBGs) in Phoenix, Arizona and Portland, Oregon, and then compared average household water use patterns by climate conditions and land cover characteristics between and within the two cities. Summer household water use was positively related to maximum temperatures and dense vegetation cover in terms of grass cover and trees and shrubs; it was negatively related to precipitation amounts in both cities. Water use was more sensitive to maximum temperature, precipitation levels, and vegetation cover in Phoenix than in Portland. There was substantial intra-city variation with greater sensitivity in urban water use associated with higher densities of trees and shrubs in both cities, but in Phoenix, the highest sensitivities to maximum temperatures occurred in CBGs with the most grass cover while in Portland, high sensitivity was associated with CBGs with the least grass cover. Many of the latter are in highly built-up downtown areas of Portland where artificial irrigation is required to maintain landscapes during the hot summer season. Take-home messages are: (1) BME space/time statistics provide efficient estimates of missing precipitation and temperature data to create continuous high resolution meteorological data that improve water demand analysis and (2) use of landscaping for urban climate adaptation will have differing impacts on water use, depending on local climate conditions, urban layout, and the type of vegetation cover.
AB - Critical to effective urban climate adaptation is a clearer understanding of the sensitivities of resource demand to changing climatic conditions and land cover situations. We used Bayesian Maximum Entropy (BME) stochastic procedures to estimate temperature and precipitation at the very small scale of urban Census Block Groups (CBGs) in Phoenix, Arizona and Portland, Oregon, and then compared average household water use patterns by climate conditions and land cover characteristics between and within the two cities. Summer household water use was positively related to maximum temperatures and dense vegetation cover in terms of grass cover and trees and shrubs; it was negatively related to precipitation amounts in both cities. Water use was more sensitive to maximum temperature, precipitation levels, and vegetation cover in Phoenix than in Portland. There was substantial intra-city variation with greater sensitivity in urban water use associated with higher densities of trees and shrubs in both cities, but in Phoenix, the highest sensitivities to maximum temperatures occurred in CBGs with the most grass cover while in Portland, high sensitivity was associated with CBGs with the least grass cover. Many of the latter are in highly built-up downtown areas of Portland where artificial irrigation is required to maintain landscapes during the hot summer season. Take-home messages are: (1) BME space/time statistics provide efficient estimates of missing precipitation and temperature data to create continuous high resolution meteorological data that improve water demand analysis and (2) use of landscaping for urban climate adaptation will have differing impacts on water use, depending on local climate conditions, urban layout, and the type of vegetation cover.
KW - Bayesian maximum entropy
KW - Climate adaptation
KW - Maximum temperature
KW - Urban water use
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U2 - 10.1007/s00477-014-1015-z
DO - 10.1007/s00477-014-1015-z
M3 - Article
AN - SCOPUS:84939938570
SN - 1436-3240
VL - 29
SP - 1135
EP - 1147
JO - Stochastic Environmental Research and Risk Assessment
JF - Stochastic Environmental Research and Risk Assessment
IS - 4
ER -