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

许多自然灾害(如洪水、热浪等)预计在气候变化下将变得更加频繁和严重。然而，对于海平面上升等促成因素的速度和程度以及由此引起的灾害本身的变化，如热带气旋平均强度的增加，仍然存在相当大的不确定性。这种不确定性让政策制定者不确定要为什么情况做计划，如果未来被证明比预期的更极端，可能会发生灾难。另一方面，为永远不会发生的最坏情况做准备，可能需要过度投资本可分配给其他社会和经济问题的稀缺资源。研究还表明，社会弱势和边缘化社区承担了与自然灾害有关的过大比例的风险。学院早期职业发展(Career)补助金的目标是通过(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。虽然在此项目期间开发的工具和方法将适用于多种自然灾害，但项目的范围和动机是在路易斯安那州沿海的风暴潮、河流和暴雨(即降雨)洪水的背景下开发、验证和应用它们。将利用独特的数据集和最先进的建模能力，更好地描述来自这些来源的洪水的联合风险，并预测灾害将如何在不断变化的地貌(例如，地面沉降、侵蚀、与咸水入侵相关的植被变化)和与气候变化有关的环境压力(例如，海平面上升、热带气旋特征的变化)下随时间演变。多分辨率、多模型框架和人工智能将允许在未来的大量情景中估计复合洪水风险，然后将其应用于路易斯安那州海岸总体计划中使用的现有结构级风险模型。将使用深度不确定性下的决策方法(DMDU)确定平衡经济效率和公平的适应性风险管理战略，这些战略对不确定因素和不同的股权操作定义具有很强的稳健性。该项目的教育部分将侧重于更多地采用DMDU方法和风险分析，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的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.