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

许多自然灾害（例如，洪水、热浪等）在气候变化的情况下，预计会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造成的灾害本身的变化，例如热带气旋平均强度的增加，仍然存在相当大的不确定性。这种不确定性使政策制定者不确定要为什么情况做计划，如果未来比预期更极端，就有可能发生灾难。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，社会弱势群体和边缘化群体承受的与自然灾害有关的风险比例过高。该学院早期职业发展（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。虽然该项目期间开发的工具和方法将适用于多种自然灾害，但该项目的范围和动机是开发，验证，并将其应用于风暴潮、河流和洪积（即，路易斯安那州沿海地区的洪水。独特的数据集和最先进的建模能力将被用来更好地描述这些来源的洪水的联合风险，并预测灾害将如何随着时间的推移在不断变化的景观（例如，地面沉降、侵蚀、与盐水入侵有关的植被变化）和与气候变化有关的环境强迫（例如，海平面上升，热带气旋特征的变化）。一个多分辨率，多模型框架和人工智能将允许在未来的情况下，这将被应用到路易斯安那州的沿海总体规划中使用的现有结构级风险模型的大集合的复合洪水灾害的估计。适应性风险管理战略，平衡经济效率和公平，这是强大的不确定性和不同的运营定义的公平，将确定使用的方法在深度不确定性下的决策（DMDU）。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学生和从业人员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，并被认为值得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来支持。

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.