This study presents a novel approach to determine the distribution of compound flood events composed of storm surge and static water levels along the Great Lakes shoreline. A mixture distribution of the bulk (Student-t) and tail (GPD-negative binomial) components of storm surge is estimated in a hierarchical Bayesian modeling framework. Parameters are modeled across the entire shoreline using Gaussian processes and hourly gauged data, with priors for spatial autocorrelation informed by numerical output from a lake hydrodynamic model. The distribution of total water levels is obtained through Monte Carlo sampling that combines the estimated distribution of surge with stochastic traces of static lake levels that account for water level management, seasonality, and plausible variability in water supplies. The approach can therefore support coastal flood risk assessments in cases when the distribution of static water levels changes are due to altered water level management or climate change. The model is applied in a case study on Lake Ontario. Results suggest that spatial variability in parameter estimates varies significantly by month and mixture distribution component. Evaluations of performance indicate the model is able to capture adequately storm surge behavior at gauges across the lakeshore, even under cross-validation. A frequency analysis of total water levels at two ungauged sites is presented, with specific attention given to the implications of model assumptions on uncertainty in design events. The paper concludes with a discussion of model limitations and avenues for future work.

本研究提出了一种确定由风暴潮和沿五大湖海岸线静水位组成的复合洪水事件分布的新方法。在分层贝叶斯建模框架中估计风暴潮的主体 (Student-t) 和尾部 (GPD 负二项式) 分量的混合分布。使用高斯过程和每小时测量的数据对整个海岸线的参数进行建模，空间自相关的先验由湖泊水动力模型的数值输出提供。总水位的分布是通过蒙特卡罗采样获得的，该采样将估计的潮汐分布与静态湖泊水位的随机轨迹相结合，这些轨迹说明了水位管理、季节性和供水的合理可变性。因此，当静态水位分布因水位管理改变或气候变化而发生变化时，该方法可以支持沿海洪水风险评估。该模型应用于安大略湖的案例研究。结果表明，参数估计的空间变异性因月份和混合分布分量而异。性能评估表明，即使在交叉验证下，该模型也能够在整个湖岸的仪表上充分捕捉风暴潮行为。介绍了两个未测量地点的总水位频率分析，特别关注模型假设对设计事件不确定性的影响。本文最后讨论了模型的局限性和未来工作的途径。