Real-time flood forecasting computational frameworks that can dynamically integrate oceanic, coastal and estuarine processes are becoming essential to provide accurate and timely information for emergency response and planning in largely populated estuaries during extreme events. This study presents a newly developed real-time total water flood guidance system that is fully automated based on the coupled surge-wave (ADCIRC + SWAN) model and provides water level forecasts in the Chesapeake Bay for a lead-time of 84 h twice a day displayed on a web-based public interface. This system improved the current total water level predictions in the Bay (RMSE < 0.12 m) when compared to the existing operational forecasting systems over the period of 6 months (Jan’19-Jun’19). Furthermore, we demonstrated that a bias correction scheme and a multi-member ensemble forecast improve the overall flood prediction. Results suggests that this framework can improve our current capacity to predict total water levels in large estuaries.

能够动态整合海洋，沿海和河口过程的实时洪水预报计算框架，对于为极端事件中人口众多的河口的应急响应和计划提供准确及时的信息，变得至关重要。这项研究提出了一种新开发的实时总水淹指导系统，该系统基于耦合波（ADCIRC + SWAN）模型是全自动的，并提供了切萨皮克湾的水位预报，提前时间为84小时，两次是在基于Web的公共界面上显示的日期。与现有的六个月期间的运行预报系统（Jan'19-Jun'19）相比，该系统改进了海湾当前的总水位预测（RMSE <0.12 m）。此外，我们证明了偏差校正方案和多成员总体预报可以改善总体洪水预报。结果表明，该框架可以提高我们目前预测大型河口总水位的能力。