Collaborative Research: Modeling, Variability and Predictability of North American Hydrologic Extremes

合作研究：北美水文极端事件的建模、变化和可预测性

• 批准号: 0236898
• 负责人: Alexander Gershunov
• 金额: $ 32.79万
• 依托单位: University of California-San Diego Scripps Inst of Oceanography
• 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=0236898&HistoricalAwards=false
• 关键词: Collaborative; Research; Modeling; Variability; Predictability

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 PIs 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 PIs will examine variability and predictability of daily hydrological extremes. They 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). They 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.This research has broader impacts including graduate student education and research results applied to the important societal issue of extreme hydrologic events in the context of a changing climate.

这项研究的目的是提供工具，以评估全球人为气候变化对北美不同季节每日水文结构的潜在区域影响。 研究所将采用气候预测方法，预测极端水文天气的可能性。 将制定一个新的综合统计框架，用于描述每日降水量和流量数据。 利用这一框架，PI将研究每日水文极端情况的可变性和可预测性。 他们将应用为具有重尾分布的数据设计的统计方法（具有相对大量极端事件的分布，这些极端事件无法通过传统概率密度函数（PDF）的指数衰减尾部来解释。 然后，他们将研究气候强迫在空间和时间上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，统计信息，例如，这项研究具有更广泛的影响，包括研究生教育和研究成果应用于气候变化背景下极端水文事件的重要社会问题。