CAREER: A Dynamic-Stochastic Approach to Rainfall and Flood Frequency Analysis Across Scales

职业生涯：跨尺度降雨和洪水频率分析的动态随机方法

• 批准号: 1749638
• 负责人: Daniel Wright
• 金额: $ 50.77万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-03-15 至 2025-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1749638&HistoricalAwards=false
• 关键词: CAREER; Dynamic; Stochastic; Approach; Rainfall

Floods arise from the interactions between rainfall and ground conditions such as soil moisture and river channel dynamics. Yet, floods can also be described as a collection of random processes using statistics in addition to physical modeling.The relationship between flood severity and likelihood ("flood frequency analysis") has become a centerpiece in flood risk management practice. Changes in land use due to urbanization and climate variability implies that existing data and methods may no longer be valid. Meanwhile, study of the links between dynamic and statistical behavior of floods has been hindered by the lack of long-term, high-resolution observations of rainfall, soil moisture, and streamflow. This leaves us with limited understanding of how factors such as soil moisture and river channel dynamics modulate extreme rainfall to produce long-term flood frequency. This research presents a hybrid approach that uses recent advances in hydrometeorological observations, data processing approaches, models, and theory to better understand floods in a changing world. It also proposes several innovative education and outreach initiatives that will reach K-12, undergraduate and graduate students, and instructors using a combination of physical-virtual active learning tools, interactive web-based "apps," and lesson modules to reinforce learning and assess outcomes.The research will advance understanding of the role of four-dimensional (surface rainfall rate, time, and x,y spatial coordinates) storm structure as a driver of rainfall and flood frequency. The first objective focuses on regional geospatial rainfall structure and its linkages to rainfall and flood frequencies and trends. The second objective focuses on the physical drivers of flood frequency including rainfall, soil moisture, and their interactions. The principal inveatigatgor will use stochastic-dynamic modeling to establish the cross-scale links between flood severity and frequency that have thus far proven elusive. The proposed work will advance understanding of flood variability at multiple, coupled scales and provides tools that leverage modern computing power, regional high-resolution rainfall observations, and distributed computational watershed models. Using a framework for physically-based flood frequency analysis, this research will provide better insights into underlying drivers and their links to nonstationary flood frequency and severity. This research has an education/outreach component to train a diverse and "data literate" generation of professionals able to create a flood-resilient nation. Activities and teaching modules will also improve data literacy and geospatial understanding of flood hydrometeorology and the water cycle among a range of audiences including UW-Madison students, K-12 science fair attendees, high school science teachers and students, water resources practitioners, and the public. These initiatives are motivated by recent research showing that diverse teaching approaches including group activities, technology, and real world examples can reinforce learning and attract underrepresented groups to STEM. The proposed work will involve graduate students, undergraduates, and high school educators directly in the development of education and outreach products.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.

洪水是由降雨和土壤湿度和河道动态等地面条件之间的相互作用引起的。然而，除了物理建模之外，洪水还可以被描述为使用统计数据的随机过程的集合。洪水严重程度和可能性之间的关系（“洪水频率分析”）已成为洪水风险管理实践的核心。城市化和气候变化导致的土地利用变化意味着现有数据和方法可能不再有效。与此同时，由于缺乏对降雨、土壤湿度和水流的长期、高分辨率观测，对洪水动态和统计行为之间联系的研究受到了阻碍。这使得我们对土壤湿度和河道动态等因素如何调节极端降雨以产生长期洪水频率的了解有限。这项研究提出了一种混合方法，利用水文气象观测、数据处理方法、模型和理论的最新进展，更好地了解不断变化的世界中的洪水。它还提出了几项创新教育和推广计划，将覆盖 K-12、本科生和研究生以及教师，使用实体与虚拟主动学习工具、交互式网络“应用程序”和课程模块的组合来强化学习和评估该研究将加深对四维（地表降雨率、时间和 x,y 空间坐标）风暴结构作为降雨和洪水频率驱动因素的作用的理解。第一个目标侧重于区域地理空间降雨结构及其与降雨和洪水频率和趋势的联系。第二个目标侧重于洪水频率的物理驱动因素，包括降雨、土壤湿度及其相互作用。主要研究将使用随机动态模型来建立洪水严重程度和频率之间迄今为止难以捉摸的跨尺度联系。拟议的工作将促进对多个耦合尺度的洪水变化的理解，并提供利用现代计算能力、区域高分辨率降雨观测和分布式计算流域模型的工具。使用基于物理的洪水频率分析框架，这项研究将更好地了解潜在驱动因素及其与非平稳洪水频率和严重程度的联系。这项研究包含教育/外展部分，旨在培训多样化且具有“数据素养”的一代专业人员，使他们能够创建一个具有抗洪能力的国家。活动和教学模块还将提高一系列受众对洪水水文气象学和水循环的数据素养和地理空间理解，包括威斯康星大学麦迪逊分校学生、K-12科学博览会参与者、高中科学教师和学生、水资源从业者以及民众。这些举措的动机是最近的研究表明，包括小组活动、技术和现实世界例子在内的多样化教学方法可以加强学习并吸引代表性不足的群体参与 STEM。拟议的工作将让研究生、本科生和高中教育工作者直接参与教育和推广产品的开发。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

U.S. Hydrologic Design Standards Insufficient Due to Large Increases in Frequency of Rainfall Extremes

由于极端降雨频率大幅增加，美国水文设计标准不足

• DOI: 10.1029/2019gl083235
• 发表时间: 2019
• 期刊: Geophysical Research Letters
• 影响因子: 5.2
• 作者: Wright, Daniel B.;Bosma, Christopher D.;Lopez‐Cantu, Tania
• 通讯作者: Lopez‐Cantu, Tania

Using Physically Based Synthetic Peak Flows to Assess Local and Regional Flood Frequency Analysis Methods

使用基于物理的合成峰值流量来评估当地和区域洪水频率分析方法

• DOI: 10.1029/2019wr024827
• 发表时间: 2019
• 期刊: Water Resources Research
• 影响因子: 5.4
• 作者: Perez, Gabriel;Mantilla, Ricardo;Krajewski, Witold F.;Wright, Daniel B.
• 通讯作者: Wright, Daniel B.

A storm-centered multivariate modeling of extreme precipitation frequency based on atmospheric water balance

基于大气水平衡的以风暴为中心的极端降水频率多元模型

• DOI: 10.5194/hess-26-5241-2022
• 发表时间: 2022
• 期刊: Hydrology and Earth System Sciences
• 影响因子: 6.3
• 作者: Liu, Yuan;Wright, Daniel B.
• 通讯作者: Wright, Daniel B.

Resilience to Extreme Rainfall Starts with Science

抵御极端降雨的能力始于科学

• DOI: 10.1175/bams-d-20-0267.1
• 发表时间: 2021
• 期刊: Bulletin of the American Meteorological Society
• 影响因子: 8
• 作者: Wright, Daniel B;Samaras, Constantine;Lopez-Cantu, Tania
• 通讯作者: Lopez-Cantu, Tania

Diverse Physical Processes Drive Upper‐Tail Flood Quantiles in the US Mountain West

不同的物理过程导致美国西部山区的上尾洪水分位数

• DOI: 10.1029/2022gl098855
• 发表时间: 2022
• 期刊: Geophysical Research Letters
• 影响因子: 5.2
• 作者: Yu, Guo;Wright, Daniel B.;Davenport, Frances V.
• 通讯作者: Davenport, Frances V.