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Characterizing Trends, Variability, and Statistical Drivers of Multisectoral Water Withdrawals for Statewide Planning
Sustainable water management requires understanding the factors that influence water use across multiple time scales, spatial scales, and types of use. However, existing empirical research on water use largely consists of studies at either the municipal or national scale, leaving a sizeable gap at intermediate scales important for planning (e.g., watershed, state, and basin level). This work addresses this gap by using a mixed-effect panel regression of monthly water withdrawals in Virginia to evaluate how well statistical modeling approaches can characterize and explain multisectoral withdrawals. Model fit is high across all sectors, suggesting that statistical models can be effective at these scales as long as they are formulated in a manner that accounts for significant variance in withdrawal volumes and temporal trends. Multiple climatic and economic variables are found to be significantly associated with withdrawals in all sectors evaluated. These relationships suggest that withdrawals in humid regions exhibit similar sensitivities to arid regions that have been the focus of more research and that incorporating economic factors is particularly important for estimating energy and industrial water withdrawals.
Characterizing Trends, Variability, and Statistical Drivers of Multisectoral Water Withdrawals for Statewide Planning
Sustainable water management requires understanding the factors that influence water use across multiple time scales, spatial scales, and types of use. However, existing empirical research on water use largely consists of studies at either the municipal or national scale, leaving a sizeable gap at intermediate scales important for planning (e.g., watershed, state, and basin level). This work addresses this gap by using a mixed-effect panel regression of monthly water withdrawals in Virginia to evaluate how well statistical modeling approaches can characterize and explain multisectoral withdrawals. Model fit is high across all sectors, suggesting that statistical models can be effective at these scales as long as they are formulated in a manner that accounts for significant variance in withdrawal volumes and temporal trends. Multiple climatic and economic variables are found to be significantly associated with withdrawals in all sectors evaluated. These relationships suggest that withdrawals in humid regions exhibit similar sensitivities to arid regions that have been the focus of more research and that incorporating economic factors is particularly important for estimating energy and industrial water withdrawals.
Characterizing Trends, Variability, and Statistical Drivers of Multisectoral Water Withdrawals for Statewide Planning
Shortridge, Julie (Autor:in) / DiCarlo, Morgan Faye (Autor:in)
07.01.2020
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
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