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

点击翻译按钮获取中文摘要

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.