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

合作研究：飓风对自然和混合基础设施影响的综合数值模拟和现场观测

• 批准号: 2139882
• 负责人: Qin Chen
• 金额: $ 37.22万
• 依托单位: Northeastern 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: 2022-03-15 至 2025-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2139882&HistoricalAwards=false
• 关键词: Collaborative; Research; Integrated; Numerical; Modeling

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.

保护沿海社区、经济中心和基础设施网络对我们的国家安全和长期繁荣至关重要。自然基础设施包括障壁岛、沙丘、湿地、红树林和珊瑚礁，已得到推广，以减轻沿海洪水和侵蚀。然而，自然要素的集体有效性以及它们如何与硬基础设施一起作为混合系统提供洪水风险降低仍然是未知的。该项目利用从路易斯安那州切尼尔平原的劳拉飓风和德尔塔飓风（2020年）快速实地活动中收集的独特水动力、生态地貌和岩土工程数据来填补这一关键知识空白。该研究将沿海湿地的形态动力学建模与基于实地的水动力学，生态地貌和岩土工程测量相结合，以评估和预测自然和混合基础设施受飓风影响的性能。该项目将利用现有的外联方案和区域伙伴关系传播研究成果。研究团队将与沿海和岩土研究社区以及执业工程师和资源管理人员分享建模工具和新的理解。该项目提供了独特的机会，交叉培训本科生和研究生在两个机构的海岸和岩土工程领域，并开发现场观测和数值模拟skills.Effective海岸灾害缓解需要综合的现场观测和数值模拟，以表征动态海岸过程在适当的空间和时间尺度。该项目将沿海湿地的地表水流和形态动力学建模与水动力学，地貌，生态和岩土工程测量相结合，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。该项目利用在飓风期间收集的独特数据（i）推进对地表径流、风暴潮和波浪衰减的时空变化的理解（ii）评估数值模型在预测自然景观中海岸侵蚀和沉积物迁移方面的功效;及（iii）探讨岩土工程性质、地层学及植被生物力学性质在控制海岸线退缩及飓风导致植被连根拔起的程度方面所扮演的角色。全面的实地、实验室和遥感数据收集，加上数值建模，将识别和评估根系和土壤特性及其在连根拔起和侵蚀过程中的作用。新的现场测试将能够成功收集以前具有挑战性或无法量化的观察结果。预计综合方法和建模工具将适用于其他海岸线，用于评估自然和混合基础设施的性能，以提高海岸弹性。该奖项反映了NSF的法定使命，并被认为值得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。

项目成果

Field observations and long short-term memory modeling of spectral wave evolution at living shorelines in Chesapeake Bay, USA

• DOI: 10.1016/j.apor.2023.103782
• 发表时间: 2023-12
• 期刊: Applied Ocean Research
• 影响因子: 4.3
• 作者: Nan Wang;Qin Chen;Hongqing Wang;William D. Capurso;Lukasz Niemoczynski;Ling Zhu;G. Snedden

Towards a unified drag coefficient formula for quantifying wave energy reduction by salt marshes

建立统一的阻力系数公式来量化盐沼减少的波浪能量

• DOI: 10.1016/j.coastaleng.2022.104256
• 发表时间: 2023
• 期刊: Coastal Engineering
• 影响因子: 4.4
• 作者: Zhu, Ling;Chen, Qin;Ding, Yan;Jafari, Navid;Wang, Hongqing;Johnson, Bradley D.
• 通讯作者: Johnson, Bradley D.

Data-driven modeling of Bay-Ocean wave spectra at bridge-tunnel crossing of Chesapeake Bay, USA

• DOI: 10.1016/j.apor.2023.103537
• 发表时间: 2023-06
• 期刊: Applied Ocean Research
• 影响因子: 4.3
• 作者: Nan Wang;Q. Chen;Ling Zhu

Morphodynamic modeling of Fourleague Bay in Mississippi River Delta: Sediment fluxes across river-estuary-wetland boundaries

• DOI: 10.1016/j.coastaleng.2023.104399
• 发表时间: 2023-09
• 期刊: Coastal Engineering
• 影响因子: 4.4
• 作者: Nan Wang;Qin Chen;Kelin Hu;Kehui Xu;S. Bentley;Jiaze Wang

INTEGRATION OF MORPHODYNAMIC MODELING WITH FIELD OBSERVATIONS OF HURRICANE IMPACTS ON NATURAL AND HYBRID INFRASTRUCTURE

形态动力学建模与飓风对自然和混合基础设施影响的现场观测相结合

• DOI: 10.1142/9789811275135_0226
• 发表时间: 2023
• 期刊: WORLD SCIENTIFIC
• 作者: CHEN, QIN;JAFARI, NAVID;ZHU, LING;HUANG, JIE;CADIGAN, JACK;BEKKAYE, JASMINE;RAUBENHEIMER, BRITT
• 通讯作者: RAUBENHEIMER, BRITT