Modeling, Variability and Predictability of North American Hydrologic Extremes

北美水文极端事件的建模、变化和可预测性

• 批准号: 0321948
• 负责人: Kelly Redmond
• 金额: --
• 依托单位: Nevada System of Higher Education, Desert Research Institute
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-04-01 至 2008-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0321948&HistoricalAwards=false
• 关键词: Modeling; Variability; Predictability; North; American

This research is intended to produce tools to assess potential regional effects of global anthropogenic climate change on the structure of North American daily hydrology in different seasons. The PI will apply climate prediction methodology to forecast the probabilities of hydrological weather extremes. A new comprehensive statistical framework will be developed for the description of daily precipitation and streamflow data. Using this framework, the PI will examine variability and predictability of daily hydrological extremes. He will apply statistical methods designed for data with heavy-tailed distributions (distributions with relatively large numbers of extreme events that can't be accounted for by exponentially decaying tails of traditional Probability Density Functions (PDFs). He will then examine the influence of climate forcing in space and time on the shape of seasonal distributions of daily hydrology. The parameters describing the PDF's shape will become predictands from which statistical information, e.g., frequencies of extreme events, can be derived. The PI will work with scientists at UCSD, SIO and the University of Nevada, Reno. This research has broader impacts of research results applied to the important societal issue of extreme hydrologic events in the context of a changing climate.

这项研究旨在制作工具，以评估全球人为气候变化对北美不同季节每日水文学结构的潜在区域影响。PI将应用气候预测方法来预测发生极端水文天气的可能性。将开发一个新的综合统计框架，用于描述日降水量和径流数据。利用这一框架，PI将检查每日水文极端情况的可变性和可预测性。他将应用为具有重尾分布(具有相对较大数量的极端事件的分布)的数据设计的统计方法，这些分布无法用传统概率密度函数(PDF)的指数衰减尾部来解释。然后，他将研究空间和时间上的气候强迫对每日水文季节分布形状的影响。描述PDF形状的参数将成为预测值，从中可以得出统计信息，例如极端事件的频率。PI将与加州大学圣地亚哥分校、SIO和内华达大学里诺分校的科学家合作。这项研究对气候变化背景下极端水文事件这一重要社会问题的研究成果具有更广泛的影响。