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Characterizing Trends, Variability, and Statistical Drivers of Multisectoral Water Withdrawals for Statewide Planning

机译:表征州计划范围内多部门取水的趋势,变化和统计驱动因素

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摘要

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.
机译:可持续的水管理需要了解在多个时间尺度,空间尺度和使用类型上影响用水的因素。但是,现有的用水实证研究主要是在市政或国家范围内进行的研究,在对规划很重要的中间规模(例如流域,州和流域层面)上留下了相当大的差距。这项工作通过使用弗吉尼亚州每月取水量的混合效应面板回归来评估统计建模方法可以很好地描述和解​​释多部门取水量的方法,从而解决了这一差距。所有部门的模型拟合度都很高,这表明统计模型在这些规模上都是有效的,只要以考虑撤出量和时间趋势显着差异的方式制定统计模型即可。在所评估的所有部门中,发现多个气候和经济变量与撤军量显着相关。这些关系表明,潮湿地区的抽水对干旱地区表现出相似的敏感性,干旱地区是更多研究的重点,并且纳入经济因素对于估算能源和工业用水特别重要。

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  • 来源
    《Journal of Water Resources Planning and Management》 |2020年第3期|04020002.1-04020002.13|共13页
  • 作者单位

    Virginia Tech Dept Biol Syst Engn Blacksburg VA 24060 USA;

    North Carolina State Univ Dept Civil Construct & Environm Engn Raleigh NC 27695 USA;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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