Samuel Adams, Austin Becker, Kyle McElroy, Noah Hallisey, P. Stempel, Isaac Ginis, Deborah Crowley
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引用次数: 0
摘要
由于全球气候变化和社会对关键基础设施(CI)的依赖性不断增加,预测沿海大风暴的后果变得越来越困难。过去的风暴不再是未来天气事件的可靠预测指标,而传统的脆弱性评估方法主要是以量化的方式呈现累积损失,缺乏地方应急管理人员制定有效计划和减灾战略所需的具体性。罗德岛海岸灾害建模与预测(RI-CHAMP)系统是一种基于地理信息系统(GIS)的建模工具,它将高分辨率风暴模拟与地理位置脆弱性数据相结合,根据当地对 CI 影响的关注来预测具体后果。本案例研究讨论了罗德岛州实施 RI-CHAMP 的情况,以预测飓风、热带风暴或东北风期间风和洪水对其 CI 的影响。本文介绍了脆弱性数据的收集和实地验证,以及 RI-CHAMP 将这些数据与风暴模型相结合的过程。在开发 RI-CHAMP 的 ArcGIS 在线仪表板的过程中,该项目深入参与了终端用户(应急管理人员、设施管理人员和其他利益相关者),以确保其提供具体、可操作的数据。在介绍真实和合成风暴模型结果的同时,还讨论了州和地方应急管理人员、设施所有者及其他人员如何使用这些模拟数据。
Ocean state rising: Storm simulation and vulnerability mapping to predict hurricane impacts for Rhode Island's critical infrastructure.
Predicting the consequences of a major coastal storm is increasingly difficult as the result of global climate change and growing societal dependence on critical infrastructure (CI). Past storms are no longer a reliable predictor of future weather events, and the traditional approach to vulnerability assessment presents accumulated loss in largely quantitative terms that lack the specificity local emergency managers need to develop effective plans and mitigation strategies. The Rhode Island Coastal Hazards Modeling and Prediction (RI-CHAMP) system is a geographic information system (GIS)-based modeling tool that combines high-resolution storm simulations with geolocated vulnerability data to predict specific consequences based on local concerns about impacts to CI. This case study discusses implementing RI-CHAMP for the State of Rhode Island to predict impacts of wind and inundation on its CI during a hurricane, tropical storm, or nor'easter. This paper addresses the collection and field verification of vulnerability data, along with RI-CHAMP's process for integrating those data with storm models. The project deeply engaged end-users (emergency managers, facility managers, and other stakeholders) in developing RI-CHAMP's ArcGIS Online dashboard to ensure it provides specific, actionable data. The results of real and synthetic storm models are presented along with discussion of how the data in these simulations are being used by state and local emergency managers, facility owners, and others.