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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

Residential water use in Austin and Sunset Valley, Texas : can our use be predicted from economic and climatic factors?

Kennedy, Brian Joseph 04 December 2013 (has links)
This paper discusses residential water demand in Central Texas, specifically the Cities of Austin and Sunset Valley. Predicting and managing residential water demand is a much researched topic that has gained importance as water has been recognized as a finite resource whose conservation and efficient use becomes more important as population grows and development patterns sprawl. Using monthly water use data from both cities, a statistical analysis was conducted of usage numbers and patterns. Several variables were considered in the modeling process including: monthly precipitation and average temperature, house size (sq. ft.), lot size (sq. ft.), appraised value of homestead, type of landscaping and presence of pool. For the City of Austin, aggregate monthly water distributed to single family residences and climate data that corresponded to each month were used in a linear regression for the fiscal years 2003-07. The results indicate that there is a significant relationship between water use among single family residential Austin Water Utility customers and precipitation and temperature (R² = .456). A more thorough examination of water use in Sunset Valley revealed a somewhat inconclusive relationship between residential water use and the aforementioned independent variables. Both a "fixed effects" panel data model and a simple linear regression model reported extremely low R² results (both .097). Several reasons are proposed in an attempt to explain the results, which differ from previous studies but no clear reason is identifiable. / text

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