CAREER: Risk-Based Methods for Robust, Adaptive, and Equitable Flood Risk Management in a Changing Climate

职业：在气候变化中实现稳健、适应性和公平的洪水风险管理的基于风险的方法

• 批准号: 2238060
• 负责人: David Johnson
• 金额: $ 50万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2028-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2238060&HistoricalAwards=false
• 关键词: CAREER; Risk; Based; Methods; Robust

Many natural hazards (e.g., floods, heat waves, etc.) are expected to become more frequent and severe under climate change. However, there is still considerable uncertainty about the rate and extent of contributing factors like sea level rise and the resulting changes in the hazards themselves, such as increases in the average intensity of tropical cyclones. This uncertainty leaves policymakers unsure of what conditions to plan for, leading to the possibility of catastrophes if the future turns out to be more extreme than expected. On the other hand, preparing for a worst-case scenario that never comes to pass may require overinvestment of scarce resources that could have been allocated to other societal and economic concerns. Research also shows that socially vulnerable and marginalized communities bear a disproportionate share of risks associated with natural hazards. The goal of this Faculty Early Career Development (CAREER) grant is to improve decision-makers' ability to manage risk from extreme events by (i) better quantifying natural hazards risks and (ii) identifying risk-informed, adaptive, and equitable management strategies.While the tools and methods developed during this project will be applicable to multiple natural hazards, the scope and motivation of the project are to develop, validate, and apply them in the context of storm surge, riverine, and pluvial (i.e., rainfall) flooding in coastal Louisiana. Unique datasets and state-of-the-art modeling capabilities will be leveraged to better characterize the joint risk of flooding from these sources and predict how the hazard will evolve over time under shifting landscapes (e.g., land subsidence, erosion, changes to vegetation associated with saltwater intrusion) and climate change-related environmental forcings (e.g., sea level rise, changes to tropical cyclone characteristics). A multi-resolution, multi-model framework and artificial intelligence will permit estimation of compound flood hazard in a large ensemble of future scenarios, which will then be applied to an existing structure-level risk model used in Louisiana’s Coastal Master Plan. Adaptive risk management strategies that balance economic efficiency and equity, and which are robust to uncertainties and varied operational definitions of equity, will be identified using methods for decision-making under deep uncertainty (DMDU). Educational components of the project will focus on increasing the adoption of DMDU methods and risk analysis, and helping STEM students and practitioners to translate natural hazards research into real-world policy impact.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.

在气候变化的影响下，许多自然灾害（例如洪水、热浪等）预计将变得更加频繁和严重。然而，海平面上升等影响因素的速度和程度以及由此产生的变化仍然存在很大的不确定性。灾害本身，例如热带气旋平均强度的增加，使政策制定者不确定要制定哪些计划，如果未来情况比预期更加极端，则可能会导致灾难。准备一个研究还表明，最糟糕的情况永远不会发生，这可能需要对稀缺资源进行过度投资，而这些资源本可以分配给其他社会和经济问题。该学院早期职业发展（CAREER）补助金旨在通过（i）更好地量化自然灾害风险和（ii）确定风险知情、适应性和公平的管理策略来提高决策者管理极端事件风险的能力。该项目期间开发的工具和方法将适用于多种自然灾害，该项目的范围和动机是在路易斯安那州沿海的风暴潮、河流和雨洪（即降雨）洪水的背景下开发、验证和应用它们独特的数据集和州。将利用最先进的建模功能来更好地描述这些来源洪水的联合风险，并预测在不断变化的景观（例如地面沉降、侵蚀、与咸水相关的植被变化）下，灾害将如何随时间演变。入侵）和与气候变化相关的环境强迫（例如，海平面上升、热带气旋特征的变化）。多分辨率、多模型框架和人工智能将允许在大量未来情景中估计复合洪水灾害，然后将其应用于路易斯安那州沿海总体规划中使用的现有结构级风险模型，该模型平衡经济效率和公平性，并且对不确定性和不同的公平性操作定义具有鲁棒性，将使用以下方法进行确定。该项目的教育部分将侧重于增加 DMDU 方法和风险分析的采用，并帮助 STEM 学生和从业者将自然灾害研究转化为现实世界的政策影响。该奖项反映了 NSF 的贡献。法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。