The ability to quickly and accurately forecast flooding is increasingly important as extreme weather events become more common. This work focuses on designing a cloud-based real-time modeling system for supporting decision makers in assessing flood risk. The system, built using Amazon Web Services (AWS), automates access and pre-processing of forecast data, execution of a computationally expensive and high-resolution 2D hydrodynamic model, Two-dimensional Unsteady Flow (TUFLOW), and map-based visualization of model outputs. A graphical processing unit (GPU) version of TUFLOW was used, resulting in an 80x execution time speed-up compared to the central processing unit (CPU) version. The system is designed to run automatically to produce near real-time results and consume minimal computational resources until triggered by an extreme weather event. The system is demonstrated for a case study in the coastal plain of Virginia to forecast flooding vulnerability of transportation infrastructure during extreme weather events.

随着极端天气事件变得越来越普遍，快速准确地预测洪水的能力变得越来越重要。这项工作专注于设计基于云的实时建模系统，以支持决策者评估洪水风险。该系统使用Amazon Web Services（AWS）构建，可自动执行对预测数据的访问和预处理，执行计算上昂贵且高分辨率的2D流体力学模型，二维非稳态流（TUFLOW）以及基于地图的可视化可视化模型输出。使用了TUFLOW的图形处理单元（GPU）版本，与中央处理单元（CPU）版本相比，执行时间加快了80倍。该系统旨在自动运行以产生接近实时的结果，并消耗最少的计算资源，直到被极端天气事件触发为止。