Collaborative Research: Probabilistic Debris Modeling in Coastal Storm Events: A Case of Complex Coupling Between Human-Built-Natural Systems

合作研究：沿海风暴事件中的概率碎片建模：人造自然系统之间复杂耦合的案例

基本信息

批准号：
2002522
负责人：
Jamie Padgett
金额：
$ 27.61万
依托单位：
William Marsh Rice University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-08-15 至 2024-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2002522&HistoricalAwards=false
关键词：
Collaborative Research Probabilistic Debris Modeling

项目摘要

Natural disasters, like hurricanes, tropical storms, and flood events, can generate massive amounts of debris, posing significant challenges to coastal communities. Financial and logistical burdens related to debris removal account for nearly a third of reimbursed recovery costs, and threats to public safety range from impaired emergency response to health hazards. There is need for more accurate models to estimate debris presence and quantities after, and preferably prior to a storm, so communities can better prepare for and manage debris burdens. Knowledge about what drives debris patterns is limited, so current predictive models are not accurate enough to support planning or response, particularly for multi-hazard storm events. In addition, coastal landscapes are constantly changing, and as development patterns shift so do prospective debris volumes and locations. This project addresses the need for integrative models that reflect the complex coupling between human-built-natural systems that underpin debris generation in storm events and the cascading impacts on coastal communities. This project examines the drivers of debris patterns and also develops models that couple those insights with infrastructure, socio-demographic, and human health impacts, among others. Resulting insights can inform policy decisions, coastal planning and risk mitigation, and debris management. Synthesis of model outcomes and policy implications will be shared via web hosted interactive story mapping.This project will transform our understanding of debris generation from coastal storms and its cascading consequences on communities by deriving coupled models of the interactions between human-built-natural systems. New methods will be developed for probabilistic modeling of debris presence and volumes, harnessing and fusing empirical data from past events, storm simulations, and physics-based estimates of fragility. A significant departure from traditional debris prediction models will be afforded by introducing nested statistical surrogate models informed by multi-resolution land use/land cover characteristics, physical vulnerability of structures or vegetation, and multi-hazard storm intensities. As a result, coupled models of development and debris potential are enabled for the first time to explore current and future storm risks and policy scenarios. By integrating models of the cascading consequences of debris with socio-demographic characteristics, this project will also shed new light on relationships between regional debris effects and social vulnerability that can inform future planning, management and mitigation efforts. Knowledge co-production and the practical viability of research methods will be advanced by working with stakeholders in testbed communities, to increase the likelihood that resulting models will be useful for future plans and decisions.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.

飓风，热带风暴和洪水事件等自然灾害会产生大量碎片，对沿海社区面临重大挑战。与碎片清除有关的财务和后勤负担占报销恢复成本的近三分之一，以及对公共安全范围的威胁，从紧急响应受损对健康危害。之后，最好是在暴风雨前估算碎屑的存在和数量，因此需要更准确的模型，因此社区可以更好地为碎屑负担做好准备和管理。有关驱动碎屑模式的知识是有限的，因此当前的预测模型不足以支持计划或响应，尤其是对于多危险风暴事件。此外，沿海景观在不断变化，并且随着开发方式的变化，前瞻性碎片量和位置也会发生变化。该项目解决了对反映人类自然系统之间复杂耦合的综合模型的需求，这些系统是在暴风雨事件中产生碎片和层叠影响沿海社区的杂物的复杂耦合。该项目检查了碎片模式的驱动因素，还开发了这些模型，这些模型将这些见解与基础设施，社会人口统计学和人类健康影响等等。产生的见解可以为政策决策，沿海计划和风险减轻和碎片管理提供信息。模型成果的综合和政策含义将通过网络托管互动故事映射共享。该项目将通过得出人类建筑 - 自然系统之间的相互作用的耦合模型来改变我们对沿海风暴及其对社区的层叠后果的理解。将开发新的方法，用于对碎片存在和体积的概率建模，利用和融合过去事件，风暴模拟以及基于物理学的脆弱性估计的经验数据。通过引入多分辨率土地使用/土地覆盖特征，结构或植被的物理脆弱性以及多危险的风暴强度，通过引入嵌套的统计替代模型以及多危险的风暴强度来实现与传统碎片预测模型的重大不同。结果，首次启用了开发和碎屑潜力的模型，以探索当前和未来的风险风险和政策情况。通过将碎片级联后果的模型与社会人口统计学特征整合在一起，该项目还将为区域碎片效应和社会脆弱性之间的关系提供新的启示，这些关系可以为未来的计划，管理和缓解工作提供依据。知识共同生产和研究方法的实际生存能力将通过与测试床社区的利益相关者合作，以增加结果模型对未来的计划和决策有用的可能性。该奖项反映了NSF的法定任务，并认为通过使用该基金会的知识分子功能和广泛影响来评估CRITERIA CRITERIA CRITERIA CRITERIA。

项目成果

期刊论文数量（5）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Performance-Based Coastal Engineering Framework

基于性能的海岸工程框架

DOI：
10.3389/fbuil.2021.690715
发表时间：
2021
期刊：
Frontiers in Built Environment
影响因子：
3
作者：
González-Dueñas, Catalina;Padgett, Jamie E.
通讯作者：
Padgett, Jamie E.

Knowledge-Informed Data-Driven Modeling of Coupled Human-Built–Natural Systems: The Case of Hurricane-Induced Debris

耦合人造自然系统的知识知情数据驱动建模：飓风引发的碎片案例

DOI：
10.1061/nhrefo.nheng-1705
发表时间：
2023
期刊：
Natural Hazards Review
影响因子：
2.7
作者：
González-Dueñas, Catalina;Meads, Mitchell M.;Padgett, Jamie E.;Highfield, Wesley E.
通讯作者：
Highfield, Wesley E.

A Data-Driven Approach to Hurricane Debris Modeling

数据驱动的飓风碎片建模方法