Collaborative Research: Integrated Numerical Modeling and Field Observations of Hurricane Impacts to Natural and Hybrid Infrastructure

保护沿海社区、经济枢纽和基础设施网络对我们的国家安全和长期繁荣至关重要。推广了由堰塞岛、沙丘、湿地、红树林和珊瑚礁组成的自然基础设施，以减轻沿海洪水和侵蚀。然而，自然因素的集体有效性以及它们如何与硬基础设施作为一个混合系统来提供减少洪水风险的作用仍不清楚。该项目利用从路易斯安那州切尼尔平原上的飓风劳拉和三角洲(2020)的快速现场活动中收集的独特的水动力、生态地貌和岩土数据，以填补这一关键知识空白。这项研究将沿海湿地的地貌动力学建模与基于现场的水动力、生态地貌和岩土测量相结合，以评估和预测自然和混合基础设施在飓风影响下的性能。该项目将利用现有的外展计划和区域伙伴关系来传播研究成果。研究团队将与沿海和岩土研究社区以及实习工程师和资源经理分享建模工具和新的理解。该项目提供了独特的机会，在两个机构交叉培训海岸和岩土工程领域的本科生和研究生，并发展现场观测和数值模拟技能。有效的沿海灾害缓解需要综合的现场观测和数值模拟，以在适当的空间和时间尺度上描述动态的海岸过程。该项目将滨海湿地的地表水流和地貌动力学建模与水动力、地貌、生态和土工措施相结合，以改变我们对受飓风影响的自然和混合基础设施的响应和恢复的理解。该项目利用飓风期间收集的独特数据，以(I)促进对混合基础设施提供的陆上水流、风暴潮和波浪衰减(洪水保护)的时空变化的了解；(Ii)评估数值模型预测海岸侵蚀和自然景观中泥沙输送的有效性；以及(Iii)探索岩土特性、地层和植被生物力学特性在控制飓风造成的海岸线后退和植被连根拔起方面的作用。综合的野外、实验室和遥感数据收集，辅之以数值模拟，将确定和评估根系和土壤的性质及其在连根拔起和侵蚀过程中的作用。这种新颖的实地测试将使以前具有挑战性或无法量化的观测数据得以成功收集。预计综合方法和建模工具将适用于其他海岸线，以评估自然和混合基础设施在海岸恢复能力方面的性能。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。

The protection of coastal communities, economic hubs, and infrastructure networks is critical to our national security and long-term prosperity. Natural infrastructure consisting of barrier islands, dunes, wetlands, mangroves, and reefs has been promoted to mitigate coastal flooding and erosion. However, the collective effectiveness of natural elements and how they work together with hard infrastructure as a hybrid system to provide flood risk reduction remain unknown. This project harnesses the unique hydrodynamic, eco-geomorphic, and geotechnical data collected from rapid field campaigns of Hurricanes Laura and Delta (2020) on the Louisiana Chenier Plain to fill this key knowledge gap. The research integrates morphodynamic modeling of coastal wetlands with field-based hydrodynamic, eco-geomorphic, and geotechnical measurements to assess and predict the performance of natural and hybrid infrastructure subject to hurricane impacts. The project will leverage existing outreach programs and regional partnerships to disseminate research results. The research team will share the modeling tools and new understanding developed with the coastal and geotechnical research communities, as well as practicing engineers and resource managers. The project provides unique opportunities to cross-train undergraduate and graduate students in both coastal and geotechnical engineering areas at two institutions and develop both field observation and numerical modeling skills.Effective coastal hazard mitigation requires integrative field observations and numerical modeling to characterize dynamic coastal processes at appropriate space and time scales. This project integrates overland flow and morphodynamic modeling of coastal wetlands with hydrodynamic, geomorphic, ecological, and geotechnical measurements to transform our understanding of the response and recovery of natural and hybrid infrastructure impacted by hurricanes. The project harnesses the unique data collected during hurricanes to (i) advance the understanding of the spatiotemporal variation in overland flow, storm surge, and wave attenuation (flood protection) provided by hybrid infrastructure; (ii) evaluate the efficacy of numerical models to predict coastal erosion and sediment transport across a natural landscape; and (iii) explore the role of geotechnical properties, stratigraphy, and vegetation biomechanical properties in controlling the magnitude of shoreline retreat and vegetation uprooting by hurricanes. The comprehensive field, laboratory, and remote-sensing data collection, complemented by numerical modeling, will identify and evaluate root and soil properties and their roles in uprooting and erosion processes. The novel field testing will enable successful collection of observations that were previously challenging or impossible to quantify. It is anticipated that the integrative approach and modeling tools will be applicable to other coastlines for evaluating natural and hybrid infrastructure performance for coastal resilience.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.