Collaborative Research: CAS-Climate: Nonstationarity of Compound Coastal Floods in the Anthropocene

人类活动，例如水文规章和改变的土地覆盖土地利用（即城市化和砍伐森林）直接调节了低沿海地区的淡水状况。大规模的人为活动也会导致全球变暖，从而推动海平面上升（SLR）并加剧自然灾害（即飓风）的强度。这些陆地和沿海灾害驱动因素通过非线性相互作用协同产生复合洪水，产生的风险水平是每个驱动因素单独无法预期的。然而，这些非平稳过程的时空变异性及其相关风险尚未得到很好的理解。复合洪水模式导致了最近的许多水文气候灾害（如飓风哈维、玛丽亚和艾达），并且预计将在人为影响下演变。该项目有助于为水文学家和沿海规划者提供一个坚实的业务框架，可用于量化沿海水文压力源对沿海社区和生态系统的预期影响，从而在SLR和快速城市化面前促进有效的资源分配，以减轻风险。该项目将支持一系列教育和宣传活动，这些活动将利用和加强阿拉巴马大学现有的项目，特别是那些招收来自不同背景的学生的项目，这些学生在STEM领域没有很好的代表。该项目建立了一个基本的知识体系，了解各种复合洪水驱动因素（如河流流量、降雨和沿海海平面）之间不断变化的物理和统计依赖关系，并加深了我们对这种非平稳依赖关系如何影响洪水动态和相关风险的理解。该团队将开发一个基于混合统计过程的建模框架，该框架集成了用于非平稳多变量数据分析的统计工具、机器学习算法和用于概率洪水淹没测绘的耦合水文-水动力模型。该项目由地球科学理事会和先进网络基础设施办公室共同资助，以支持地球科学领域的人工智能/机器学习和开放科学活动。该奖项反映了美国国家科学基金会的法定使命，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。

Human activities, such as hydrologic regulations and altered land-cover land-use (i.e., urbanization and deforestation) directly modulate freshwater regime to lower coastal regions. Large-scale anthropogenic activities also contribute to global warming, which drives sea level rise (SLR) and intensifies the magnitude of natural hazards (i.e., hurricanes). These terrestrial and coastal hazard drivers synergize to produce compound floods through nonlinear interactions that yield in a level of risk not expected from each driver in isolation. Yet, the spatiotemporal variability of these nonstationary processes and the associated risks are not well understood. Compounding flood patterns are responsible for many of the recent hydroclimate disasters (i.e. hurricanes Harvey, Maria, and Ida), and are expected to evolve under anthropogenic effects. This project helps provide a solid operational framework for hydrologists and coastal planners that can be used for quantifying the expected impacts of coastal hydrology stressors on coastal communities and ecosystems, thereby facilitating efficient resource allocation for risk mitigation in the face of SLR and rapid urbanization. This project will support a suite of educational and awareness activities, which will utilize and enhance programs already in existence at The University of Alabama, particularly those to recruit students from diverse backgrounds that are not well represented in STEM fields. This project creates a fundamental body of knowledge on the ever-changing physical and statistical dependence among various compound flooding drivers over time (e.g., river flow, rainfall and coastal sea level) and advances our understanding on how such a nonstationary dependence affects the flooding dynamics and the associated risks. The team will develop a Hybrid Statistical-Process Based modeling framework that integrates statistical tools for nonstationary multivariate data analysis, machine learning algorithms and coupled hydrologic-hydrodynamic models for probabilistic flood inundation mapping. This project is co-funded by a collaboration between the Directorate for Geosciences and the Office of Advanced Cyberinfrastructure to support AI/ML and open science activities in the geosciences.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.