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Social and Behavioral Sciences Commons™
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Articles 1 - 2 of 2
Full-Text Articles in Social and Behavioral Sciences
Uncovering The Influence Of Household Sociodemographic And Behavioral Characteristics On Summer Water Consumption In The Portland Metropolitan Area, Chang-Yu Hong, Heejun Chang
Uncovering The Influence Of Household Sociodemographic And Behavioral Characteristics On Summer Water Consumption In The Portland Metropolitan Area, Chang-Yu Hong, Heejun Chang
International Journal of Geospatial and Environmental Research
As urban areas continue to expand, sustainable urban water resource management has become an important issue in green and sustainable city planning. Using single-family residential (SFR) household survey, we identified the determinants of household summer daily water consumption from 2000 to 2005 in Portland, Oregon. The multiple regression results show that approximately 41% of variations in SFR water consumption is explained by average building size, household attitude to water conservation, community engagement of household, and presence of native plants in the garden. The multi-level modeling results show that household attitude to water conservation is an important predictor of SFR water …
Sensitivity Of Urban Water Consumption To Weather And Climate Variability At Multiple Temporal Scales: The Case Of Portland, Oregon, Heejun Chang, Sarah Praskievicz, Hossein Parandvash
Sensitivity Of Urban Water Consumption To Weather And Climate Variability At Multiple Temporal Scales: The Case Of Portland, Oregon, Heejun Chang, Sarah Praskievicz, Hossein Parandvash
International Journal of Geospatial and Environmental Research
The sensitivity of municipal water consumption to climate and weather variability is investigated for Portland’s water provider service area between 1960 and 2013. The relationship between detrended seasonal urban water use (the difference between total water use and base use) and weather and climate variables (precipitation, maximum temperature) is examined at daily, monthly, and seasonal scales using stepwise multiple regression and autoregressive integrated moving average (ARIMA) models. At a seasonal and a monthly timescales, interannual variation in maximum temperature is the most important predictor of seasonal water consumption per capita, explaining up to 48% of the variation in seasonal monthly …