Coastal flooding models are used to predict the timing and magnitude of inundation during storms, both for real-time forecasting and long-term design. However, there is a need for faster flooding predictions that also represent flow pathways and barriers at the scales of critical infrastructure. This need can be addressed via subgrid corrections, which use information at smaller scales to ‘correct’ the flow variables (water levels, current velocities) averaged over the mesh scale. Recent studies have shown a decrease in run time by 1 to 2 orders of magnitude, with the ability to decrease further if the model time step is also increased.

In this study, subgrid corrections are added to a widely used, finite-element-based, shallow water model to better understand how they can improve the accuracy and efficiency of inundation predictions. The performance of the model, with and without subgrid corrections, is evaluated on scenarios of tidal flooding in a synthetic domain and a small bay in Massachusetts, as well as a scenario with a real atmospheric forcing and storm surge in southwest Louisiana. In these tests we observed that the subgrid corrections can increase model speed by 10 to 50 times, while still representing flow through channels below the mesh scale to inland locations.

沿海洪水模型用于预测风暴期间淹没的时间和大小，用于实时预测和长期设计。然而，需要更快的洪水预测，以代表关键基础设施规模的流动路径和障碍。这种需求可以通过子网格校正来解决，它使用较小尺度的信息来“校正”在网格尺度上平均的流量变量（水位、流速）。最近的研究表明，运行时间减少了 1 到 2 个数量级，如果模型时间步长也增加，则有能力进一步减少。

在这项研究中，子网格校正被添加到广泛使用的、基于有限元的浅水模型中，以更好地了解它们如何提高淹没预测的准确性和效率。在模拟域和马萨诸塞州一个小海湾的潮汐洪水情景以及路易斯安那州西南部具有真实大气强迫和风暴潮的情景中，评估了带有和不带有子网格校正的模型的性能。在这些测试中，我们观察到子网格校正可以将模型速度提高 10 到 50 倍，同时仍然代表流经网格尺度以下的通道到内陆位置。