With a service area population exceeding four million people and with close to 90 % of the water supply being imported from sources outside the city, the Los Angeles water system is subject to multiple stressors, including climate change and population growth. The influence of various factors on water demand in Los Angeles was evaluated through development and application of multiple linear regression models for residential, commercial, industrial, and governmental water demand categories from 1970 to 2014 in the service area of the Los Angeles Department of Water and Power. Performance of the models in describing historical water demand was compared using the coefficient of determination, mean average percent error, and normalized root mean square error. Overall, the results of the linear regression models demonstrated that each water demand category is affected by different parameters. However, price and population were found to have the most significant impact on all categories. The seasonality of residential water demand was well described with the model based on monthly data, with precipitation and temperature being highly significant factors. Fitting of the residential data furthermore revealed that price and conservation have significantly counteracted the impact of population growth on water demand.
Urban water demand Water management Water conservation Regression analysis
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The authors would like to thank Simon Hsu, Chris Repp, and Priscilla Gonzalez of the Los Angeles Department of Water and Power for providing information used in this study. Support for this work was provided by a Carnegie Mellon University College of Engineering Dean’s Fellowship and a Steinbrenner Institute U.S. Environmental Sustainability Fellowship to Negin Ashoori. The Fellowship was supported by a grant from the Colcom Foundation, and by the Steinbrenner Institute for Environmental Education and Research. The work was also supported by the Hamerschlag University Professorship of David Dzombak, and the H. John Heinz Professorship of Mitchell Small.
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