Projecting Flood Frequency Curves Under a Changing Climate Using Spatial Extreme Value Analysis

使用空间极值分析预测气候变化下的洪水频率曲线

• 批准号: 2152887
• 负责人: Brian Reich
• 金额: $ 30万
• 依托单位: North Carolina State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2152887&HistoricalAwards=false
• 关键词: Projecting; Flood; Frequency; Curves; Under

Climate change is often described in terms of the mean, but it will be felt most acutely in terms of extreme events. In particular, the International Panel of Climate Change’s recent Sixth Assessment warns of an increase in the likelihood and magnitude of extreme flooding events in upcoming decades. Understanding the spatiotemporal variability of these changes is critical to mitigating their impact. However, current methods for spatial extreme value analysis are limited in their modeling flexibility and computational capabilities, and thus methodological work is required to analyze extreme events across the United States. Therefore, in this project, the investigators will develop new methodological and computational tools for spatial extreme value analysis and apply them to forecasting flood risk under a changing climate. The project team is comprised of an interdisciplinary group of statisticians and hydrologists to accomplish these ambitious objectives and ensure that the results are disseminated to the appropriate communities. The analysis combines fifty years of annual maximum streamflow observations at hundreds of gauges provided by the United States Geological Survey with CMIP6 climate model output produced under different climate scenarios. This analysis will provide high-resolution maps of anticipated change in flood risk and local flood frequency curves to inform water infrastructure projects. A highlight of the project is a workshop that will foster synergy between statisticians and hydrologists by encouraging the sharing of ideas, approaches and solutions to flood risk prediction, and aid in the formulation of a common language shared by statisticians and hydrologists for successful transfer of knowledge across disciplines. The overall objective is to improve resiliency to extreme flooding events in the United States.This project will result in major advances in both spatial extreme value analysis and hydrology. The investigators will pursue two methods that exploit recent developments in distributed computing, machine learning and artificial intelligence, respectively, to improve computation for spatial extreme value analysis. Computation for spatial extremes is challenging because the most common model is the max-stable process, and this model gives an intractable likelihood function and is thus not conducive to direct application of maximum likelihood or Bayesian analysis. To overcome this difficulty, this project will develop a divide-and-conquer method that analyzes data separately by subregion and then combines the results using generalized method of moments techniques. It is shown that this procedure has desirable theoretical properties and gives substantial performance gain over state-of-the-art methods. The project also develops a new method under the Bayesian framework that is preferred for uncertainty quantification. The new method decomposes the intractable likelihood function into a sequence of simpler functions, and uses deep-learning distribution regression to approximate these simpler functions. This approximation can be arbitrarily precise with computational requirements that scale linearly with the number of spatial locations, facilitating analysis of large datasets. The project culminates with the analysis of flood-frequency curves across the US. Compared to current methods, by using spatial extreme value analysis the analysis borrows information across space to improve estimation of small probabilities and estimate the probability of multiple locations simultaneously experiencing an extreme event. This project will produce new software for extreme value analysis and also train two graduate students in theoretical, computational and applied extreme value analysis in hydrology with a strong emphasis on interdisciplinary collaboration.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.

气候变化通常被描述为平均值，但在极端事件方面感受最强烈。特别是，国际气候变化专门委员会最近的第六次评估警告说，在未来几十年中，极端洪水事件的可能性和规模都将增加。了解这些变化的时空变异性对于减轻其影响至关重要。然而，目前的空间极值分析方法在建模灵活性和计算能力方面受到限制，因此需要进行方法学工作来分析美国各地的极端事件。因此，在本项目中，研究人员将开发新的空间极值分析方法和计算工具，并将其应用于预测气候变化下的洪水风险。该项目小组由一个由统计人员和水文学家组成的跨学科小组组成，以实现这些雄心勃勃的目标，并确保将成果传播给适当的社区。该分析结合了美国地质调查局提供的50年的年最大流量观测数据，以及不同气候情景下产生的CMIP 6气候模式输出。该分析将提供洪水风险预期变化的高分辨率地图和当地洪水频率曲线，为水利基础设施项目提供信息。该项目的一个亮点是举办一个讲习班，通过鼓励分享洪水风险预测的想法、方法和解决方案，促进统计人员和水文学家之间的协同增效作用，并帮助制定统计人员和水文学家共享的共同语言，以便跨学科成功转让知识。总体目标是提高美国对极端洪水事件的复原力，该项目将在空间极值分析和水文学方面取得重大进展。研究人员将采用两种方法，分别利用分布式计算，机器学习和人工智能的最新发展，以改善空间极值分析的计算。空间极值的计算是具有挑战性的，因为最常见的模型是最大稳定的过程，这种模型给出了一个棘手的似然函数，因此不利于直接应用最大似然或贝叶斯分析。为了克服这一困难，本项目将开发一种分而治之的方法，按次区域分别分析数据，然后使用广义矩法技术合并结果。结果表明，该过程具有理想的理论特性，并给出了大量的性能增益超过国家的最先进的方法。该项目还在贝叶斯框架下开发了一种新方法，该方法是不确定性量化的首选。新方法将难以处理的似然函数分解为一系列更简单的函数，并使用深度学习分布回归来近似这些更简单的函数。这种近似可以是任意精确的，其计算要求随空间位置的数量线性缩放，便于分析大型数据集。该项目的高潮是分析美国各地的洪水频率曲线。与目前的方法相比，通过使用空间极值分析，分析借用跨空间的信息，以改善小概率的估计，并估计多个位置同时经历极端事件的概率。该项目将开发新的极值分析软件，并培养两名水文学理论、计算和应用极值分析的研究生，重点是跨学科合作。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。