Collaborative Research: CAS - Climate: Improving Nonstationary Intensity-Duration-Frequency Analysis of Extreme Precipitation by Advancing Knowledge on the Generating Mechanisms

合作研究：CAS - 气候：通过增进对生成机制的认识来改进极端降水的非平稳强度-持续时间-频率分析

• 批准号: 2221803
• 负责人: Giuseppe Mascaro
• 金额: $ 22.8万
• 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: 2022-12-01 至 2025-11-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2221803&HistoricalAwards=false
• 关键词: Collaborative; Research; CAS; Climate; Improving

2221803 (Mascaro) and 2221808 (Kunkel). This project will address fundamental questions at the intersection of climate dynamics and civil engineering aimed at improving the design of infrastructure systems under climate change. Current procedures to design infrastructure against extreme precipitation and associated flooding rely on intensity-duration-frequency (IDF) curves. These curves are generated through statistical analyses of historical rain gage records under the assumptions of stationarity ("the future is the same as the past") and existence of a homogeneous statistical population ("the statistical variability of precipitation caused by multiple generating mechanisms is explained by a single distribution"). These assumptions have been recently challenged by theoretical arguments and climate simulations, which suggest that extreme precipitation (EP) statistics are expected to change in a future warmer climate. The main goal of this project is to advance knowledge on changes in the generating mechanisms of sub-daily and daily EP and use this new knowledge to develop a novel physics-driven statistical framework to inform the development of improved nonstationary IDF curves. Two research hypotheses will be investigated: (1) The occurrence and/or thermodynamic and dynamic components of the generating mechanisms of EP are changing in time, leading to changes in IDF design values; and (2) improved nonstationary IDF curves can be developed through statistical models that incorporate information on changes in the generating mechanisms of EP simulated by climate models. The research hypotheses will be tested using hourly and daily rainfall records, atmospheric re-analyses, and climate simulations in multiple regions of the U.S. spanning a wide range of dominant mechanisms of EP, including fronts, extratropical cyclones, tropical cyclones, mesoscale convective systems, air mass convection, and North American monsoon. New knowledge on the physical mechanisms leading to the non-stationarity of EP statistics at sub-daily and daily durations in the U.S. will be acquired by applying deep learning techniques. Novel methods for detection, physical validation, and uncertainty quantification of regional trends in EP and in occurrence and characteristics of the generating mechanisms will be designed by combining statistical tests and historical climate simulations from the Coupled Model Intercomparison Project Phase 6. This knowledge will inform a nonstationary statistical model for IDF analysis of EP that accounts for mixed populations. Regionalization techniques will be designed to reduce uncertainty in parameter estimation and incorporate regional changes in the physical mechanisms affecting EP. A Bayesian framework will be adopted to account for uncertainty. The added value of the nonstationary statistical model compared to current stationary approaches will be assessed through cross-validation bootstrapping experiments. The statistical model will be then applied with changes in EP mechanisms simulated by selected general circulation models under different future scenarios and used to compute new design metrics accounting for nonstationary extreme regimes. The methodological advancements are intended to address critical limitations of current stationary IDF design methods. Most importantly, there are multiple generating mechanisms for EP whose frequency and intensity may change in different ways in response to anthropogenically-forced climate change. The research will develop a framework for the objective incorporation of this knowledge into IDF curves, providing more robust estimates of future changes. This project is targeted to have multiple broader impacts. It seeks to improve infrastructure design and management against EP under climate change, which would (i) reduce storm-related damages ($261.1 billion from 2001 to 2020 in the U.S.), thus benefitting society; and (ii) increase the resilience of critical infrastructure, thus improving national security. The project will develop an outreach program that involves practitioner education through ongoing collaborations of the investigators with engineering and construction firms, public agencies, and the American Society of Civil Engineers. Additionally, the project will develop new aspects of curricula at the investigators’ institutions in the areas of engineering, hydrology, and atmospheric sciences.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.

2221803(马斯卡罗)和2221808(昆克尔)。该项目将解决气候动力学和土木工程交叉的基本问题，旨在改善气候变化下基础设施系统的设计。目前针对极端降水和相关洪水的基础设施设计程序依赖于强度-持续时间-频率(IDF)曲线。这些曲线是在平稳性(“未来与过去相同”)和存在同质统计总体(“由多种生成机制引起的降水统计变异性用单一分布来解释”)假设下，通过对历史雨量计记录的统计分析而产生的。这些假设最近受到了理论论证和气候模拟的挑战，它们表明，极端降水(EP)统计数据预计将在未来气候变暖时发生变化。这个项目的主要目标是促进对亚日和每日EP产生机制变化的了解，并利用这一新知识开发一个新的物理驱动的统计框架，为改进的非平稳IDF曲线的发展提供信息。将研究两个假设：(1)EP产生机制的发生和/或热力学和动力成分随时间变化，导致IDF设计值的变化；(2)改进的非平稳IDF曲线可以通过包含气候模式模拟的EP产生机制变化信息的统计模型来开发。研究假设将使用美国多个地区的每小时和每日降雨量记录、大气再分析和气候模拟来检验，这些地区涵盖了EP的一系列主要机制，包括锋面、温带气旋、热带气旋、中尺度对流系统、气团对流和北美季风。通过应用深度学习技术，将获得有关导致美国次日和每日持续时间的EP统计数据非平稳的物理机制的新知识。将结合耦合模式比较项目第六阶段的统计试验和历史气候模拟，设计新的方法，用于探测、物理验证和不确定性量化EP的区域趋势以及产生机制的发生和特征。这些知识将为考虑混合人口的EP的IDF分析提供一个非平稳统计模型。将设计区域化技术，以减少参数估计的不确定性，并在影响环境保护的物理机制中纳入区域变化。将采用贝叶斯框架来解释不确定性。非平稳统计模型与当前平稳方法相比的附加值将通过交叉验证自举实验进行评估。然后，统计模型将与选定的大气环流模型在不同未来情景下模拟的EP机制的变化一起应用，并用于计算考虑非平稳极端区域的新设计指标。方法学上的进步旨在解决当前固定IDF设计方法的严重局限性。最重要的是，EP有多种发生机制，其频率和强度可能会以不同的方式变化，以响应人类强迫的气候变化。这项研究将制定一个框架，客观地将这一知识纳入IDF曲线，提供对未来变化的更可靠的估计。这个项目的目标是产生多个更广泛的影响。它寻求改善基础设施设计和管理，以应对气候变化下的环境保护，这将(I)减少与风暴相关的损失(2001年至2020年美国为2611亿美元)，从而造福社会；(Ii)提高关键基础设施的弹性，从而改善国家安全。该项目将开发一个外展计划，通过调查人员与工程和建筑公司、公共机构以及美国土木工程师协会的持续合作，涉及从业者教育。此外，该项目将在研究机构的工程、水文学和大气科学领域开发新的课程。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。