Coastal compound floods (CCFs) are triggered by the interaction of multiple mechanisms, such as storm surges, storm rainfall, tides, and river flow. These events can bring significant damage to communities, and there is an increasing demand for accurate and efficient probabilistic analyses of CCFs to support risk assessments and decision-making. In this study, a multi-tiered Bayesian network (BN) CCF analysis framework is established. In this framework, multiple tiers of BN models with different complexities are designed for application with varying levels of data availability and computational resources. A case study is conducted in New Orleans, LA, to demonstrate this framework. In the Tier-1 BN model, storm surges and river flow are incorporated based on hydrodynamic simulations. A seasonality node is used to capture the dependence between concurrent river flow and tropical cyclone (TC) parameters. In the Tier-2 BN model, joint distribution models of TC parameters are built for separate TC intensity categories. TC-induced rainfall is modeled as input to hydraulic simulations. In the Tier-3 BN model, potential variations of meteorological conditions are incorporated by quantifying their effects on TC activity and coastal water level. Flood antecedent conditions are also incorporated to more completely represent the conditions contributing to flood severity. In this case study, a series of joint distribution, numerical, machine learning, and experimental models are used to compute conditional probability tables needed for BNs. A series of probabilistic analyses is performed based on these BN models, including CCF hazard curve construction and CCF deaggregation. The results of the analysis demonstrate the promise of this framework in performing CCF hazard analysis under varying levels of resource availability.

翻译：海岸复合洪水由风暴潮、风暴降雨、潮汐与河道径流等多种机制的相互作用引发。这类事件可能对社区造成重大损害，因此对海岸复合洪水进行准确高效的概率分析以支持风险评估与决策制定的需求日益增长。本研究建立了一个多层级贝叶斯网络海岸复合洪水分析框架。该框架设计了具有不同复杂度的多个层级贝叶斯网络模型，以适应不同数据可获取性与计算资源水平下的应用。研究以路易斯安那州新奥尔良市为例进行了案例验证。在 Tier-1 贝叶斯网络模型中，基于水动力模拟纳入了风暴潮与河道径流，并采用季节性节点来捕捉同期河道径流与热带气旋参数间的依赖关系。在 Tier-2 贝叶斯网络模型中，针对不同的热带气旋强度类别分别构建了热带气旋参数的联合分布模型，并将热带气旋引发的降雨建模为水力学模拟的输入。在 Tier-3 贝叶斯网络模型中，通过量化气象条件变化对热带气旋活动及沿海水位的影响，纳入了潜在的气象条件变异。此外，还引入了洪水前期条件，以更完整地表征影响洪水严重程度的各类条件。本案例研究综合运用了一系列联合分布模型、数值模型、机器学习模型及实验模型，以计算贝叶斯网络所需的条件概率表。基于这些贝叶斯网络模型，研究进行了一系列概率分析，包括海岸复合洪水灾害曲线构建与海岸复合洪水分解分析。分析结果表明，该框架在不同资源可获取性条件下进行海岸复合洪水灾害分析具有良好前景。