CAS-Climate: A Novel Process-Driven Method for Flood Frequency Analysis Based on Mixed Distributions

CAS-Climate：一种基于混合分布的过程驱动洪水频率分析新方法

• 批准号: 2212702
• 负责人: Giuseppe Mascaro
• 金额: $ 35.61万
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-15 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2212702&HistoricalAwards=false
• 关键词: CAS; Climate; Novel; Process; Driven

Floods are among the most common and impactful natural hazards. In the U.S., these extreme events caused a total of $144.4 billion in damages and 2550 fatalities from 1991 to 2020. A crucial task to limit the impacts of flooding, design infrastructure, and manage water resources is to increase the accuracy of flood frequency estimates. These are currently generated through statistical analyses of annual peak flows under the implicit assumption that flood events at a given site are caused by the same physical mechanism. This assumption has been challenged by observational evidence and demonstrated to lead to inaccurate estimates. This project will address key limitations of current flood frequency methods by designing a novel approach that incorporates the effect of multiple physical mechanisms leading to flood generation into a statistical model. The approach will be tested at more than 1000 stream gages covering a large range of climatic conditions in the U.S. The knowledge generated by the project will (1) contribute to improving national guidelines for flood estimation, (2) be disseminated to regional stakeholders involved in flood management through training activities, and (3) innovate curricula at Arizona State University. An undergraduate and graduate students will be directly involved in the project activities.The main research hypothesis of this project is that the accuracy of flood frequency analysis is improved by using mixed probability distributions of peak-over-threshold (POT) flood series associated with a set of dominant atmospheric and hydrologic processes. To investigate this hypothesis, the dominant large-scale meteorological patterns (LSMPs) causing flood events will be first identified from atmospheric reanalyses. Key hydrologic processes and conditions occurring in the basins under different flood-producing LSMPs will be obtained from the recent retrospective hydrologic simulations of the National Water Model. Machine learning will be applied to variables characterizing LSMPs and hydrologic processes to group flood events in each basin into a set of dominant flood-generating mechanisms. The hypothesis that the corresponding flood sub-samples are drawn from statistically heterogeneous populations will be tested with a new regional framework based on statistical tests, Monte Carlo simulations, and physical considerations. These physical and statistical insights will be incorporated into a novel method for flood frequency analysis based on mixed distributions of POT series. Performance and uncertainty of the mixed POT model will be quantified and compared with those of homogenous distributions fitted to annual peak flows, as in current approaches, and to POT series.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

洪水是最常见和最具影响力的自然灾害之一。在美国，从1991年到2020年，这些极端事件共造成1444亿美元的损失和2550人死亡。限制洪水影响、设计基础设施和管理水资源的一项关键任务是提高洪水频率估计的准确性。目前，这些是通过对年洪峰流量的统计分析产生的，隐含的假设是，给定地点的洪水事件是由相同的物理机制引起的。这一假设受到了观测证据的挑战，并被证明会导致不准确的估计。该项目将通过设计一种新的方法，将导致洪水生成的多种物理机制的影响纳入统计模型，解决当前洪水频率方法的主要局限性。该方法将在美国1000多个流量计上进行测试，涵盖了美国大范围的气候条件。该项目产生的知识将（1）有助于改善洪水估计的国家指导方针，（2）通过培训活动传播给参与洪水管理的区域利益相关者，以及（3）亚利桑那州立大学的创新课程。本项目的主要研究假设是，利用与一组主导大气和水文过程相关的超阈值洪峰（POT）洪水序列的混合概率分布，提高洪水频率分析的精度。为了研究这一假设，将首先从大气再分析中确定导致洪水事件的主要大尺度气象模式（LSMPs）。根据国家水模型最近的回顾性水文模拟，将获得不同洪水产生LSMP下流域发生的关键水文过程和条件。机器学习将应用于表征LSMPs和水文过程的变量，以将每个流域的洪水事件分组为一组主要的洪水生成机制。假设相应的洪水子样本来自统计上异质的人口将进行测试，一个新的区域框架的基础上统计测试，蒙特卡罗模拟和物理考虑。这些物理和统计的见解将被纳入一个新的方法，洪水频率分析的基础上混合分布的POT系列。混合POT模型的性能和不确定性将被量化，并与适用于年峰值流量的均匀分布（如当前方法）和POT系列的性能和不确定性进行比较。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。