Management implications of spatiotemporal non-stationarity in municipal water consumption patterns and drivers |
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| Institution: | 1. Grupo de Estudios Ambientales, IMASL – CONICET & Universidad Nacional de San Luis, Avenida Italia 1556, D5700HHW San Luis, Argentina;2. Department of Earth and Planetary Sciences, Rutgers University, Wright Laboratories, 610 Taylor Road, Piscataway, NJ 08854-8066, USA |
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| Abstract: | This study analyzed the individual and joint influences of social, urban, and physical drivers on patterns of county-scale municipal water consumption (MWC) the for the state of Texas using a cross-sectional research design on three distinct temporal slices (1990, 2000, and 2010). Global multiple linear regression models and measures of global and local spatial association were combined to determine which drivers significantly influenced county-scale per capita MWC, whether or not the statistically significant drivers varied over time, and to assess the degree to which the patterns and drivers of MWC exhibited spatial stationarity. Overall results suggested the social, urbanized, and physical environments contributed significantly to the patterns of per capita MWC to varying degrees in each year. The social and urbanized environments consistently exerted the strongest influences on per capita MWC, while the physical environment was generally less important. The social environment had the greatest cumulative influence in all three years, and the urbanized environment singly accounted for the majority of the variation in per capita MWC when the joint influences of the other significant drivers were considered. Spatial analysis of MWC patterns and drivers suggested that they both exhibited weak to moderate degrees of spatial non-stationarity in each year, as well as that MWC patterns and drivers may be sensitive to regional and climatic boundaries. Identification of temporally consistent MWC drivers merged with longitudinal and cross-sectional research designs can improve water management strategies by offering managers greater insight into the relationships between landscape change and water consumption patterns. |
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| Keywords: | Municipal water consumption (MWC) Municipal water management Spatiotemporal non-stationarity Spatial analysis Geographic information systems (GIS) Spatial autocorrelation |
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