LEAPS-MPS: Computational Modeling to Characterize and Attribute Uncertainty in Future Coastal Risk

LEAPS-MPS：计算模型来表征和归因未来沿海风险的不确定性

• 批准号: 2213432
• 负责人: Anthony Wong
• 金额: $ 18万
• 依托单位: Rochester Institute of Tech
• 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: 2022-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2213432&HistoricalAwards=false
• 关键词: LEAPS; MPS; Computational; Modeling; Characterize

The project aims to examine how coastal areas can mitigate damages from sea-level change and coastal flooding. Rising global sea levels and intensifying storms cause risks for people and property in coastal areas. Strategies to manage these risks include protective measures like building seawalls, elevating existing structures, and relocation away from the coast. However, uncertainty is inherent in the geophysical processes, the mathematical models, and the observational data used to calibrate those models. These modeling uncertainties lead to uncertainty in the optimal strategy to defend against coastal hazards. This research will assess how different geophysical and socioeconomic factors lead to uncertainty in the decisions and costs to effectively protect coastal areas, and uncertainty in the estimated damages from potentially under-protecting coastal assets. The project will provide training opportunities for students to develop software and conduct computer model experiments. These activities will support the representation and persistence of students from underrepresented minority groups by enhancing students’ sense of science identity through engaging in projects. Further, the research will be conducted at a Carnegie R2 university, where the resources made available through this project will have positive impacts. The research will investigate models for sea-level change and coastal impacts by exploiting mathematical structures for coastal adaptation decision-making and gridded global climate data. Machine learning and statistical tools will be integrated with existing geophysical and socioeconomic models, bridging coastal adaptation decisions to uncertainties in geophysical processes, in climate and socioeconomic models, and in observational data. This coupled modeling framework will serve as a laboratory to characterize uncertainty in future coastal adaptation costs and decisions. The uncertainty will be decomposed and attributed to geophysical drivers of risk and socioeconomic uncertainties using supervised machine learning and global sensitivity analysis methods. The research will assess the extent to which different uncertainties affect the optimal strategies for adapting coastal areas to the risks posed by sea-level change. As optimality is typically defined by minimizing expected loss, additional decision-making criteria will be implemented in the models employed, thereby enabling an exploration of the role of risk aversion and imperfect information. A standard modeling framework for integrated assessment will be used, which will facilitate future efforts to expand this work to examine the larger role of sea-level hazards in integrated assessments of climate risks.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.

该项目旨在研究沿海地区如何减轻海平面变化和沿海洪水的损害。全球海平面上升并加剧风暴会给沿海地区的人和财产带来风险。管理这些风险的策略包括受保护的措施，例如建造海堤，提高现有结构，并搬离海岸。但是，在地球物理过程，数学模型和用于校准这些模型的观察数据中遗传了不确定性。这些建模不确定性导致了防御沿海危害的最佳战略的不确定性。这项研究将评估不同的地球物理和社会经济因素如何导致决策和成本的不确定性，以有效保护沿海地区，以及估计的损害损害损害的不确定性可能会导致潜在的较低的沿海资产。该项目将为学生提供开发软件并进行计算机模型实验的培训机会。这些活动将通过从事项目来增强学生的科学认同感，从而支持来自代表性不足的少数群体的学生的代表性和持久性。此外，该研究将在卡内基R2大学进行，在该大学中，通过该项目提供的资源将产生积极的影响。该研究将通过利用数学结构进行沿海适应决策和扎根全球气候数据来调查海平面变化和沿海影响的模型。机器学习和统计工具将与现有的地球物理和社会经济模型集成在一起，将沿海适应性决定与地球物理过程，气候和社会经济模型以及观察数据中的不确定性进行弥合。这个耦合的建模框架将成为一个实验室，以表征未来沿海适应成本和决策的不确定性。不确定性将被分解，并归因于使用监督的机器学习和全球敏感性分析方法的地球物理驱动因素和社会经济不确定性。该研究将评估不同的不确定性影响在多大程度上影响将沿海地区适应海平面变化带来的风险的最佳策略。由于通常通过最大程度地减少预期损失来定义最佳性，因此在模型所采用的模型中将实施其他决策标准，从而探索风险规避和不完善的信息的作用。将使用一个综合评估的标准建模框架，这将促进未来的努力扩大这项工作，以研究海平面危害在气候风险综合评估中的更大作用。这项奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子和更广泛的影响审查标准来通过评估来获得的珍贵支持。