The accuracy of two-dimensional hydrodynamic models (2D models) is improved when high-resolution Digital Elevation Models (DEMs) are used. However, the entailed high spatial discretisation results in excessive computational expenses, thus prohibiting their implementation in real-time forecasting especially at a large scale. This paper presents a sub-model approach that adapts 1D static models to tailor high-resolution 2D model grids relevant to specified targets, such that the tailor-made 2D hydrodynamic sub-models yield fast processing without significant loss of accuracy via a GIS-based multi-scale simulation framework. To validate the proposed approach, model experiments were first designed to separately test the impact of two outcomes (i.e., the reduced computational domains and the optimised boundary conditions) towards final 2D prediction results. Then, the robustness of the sub-model approach was evaluated by selecting four focus areas with distinct catchment terrain morphologies as well as distinct rainfall return periods of 1–100 years. The sub-model approach resulted in a 45–553 times faster processing with a 99% reduction in the number of computational cells for all four cases; the goodness of fit regarding predicted flood extents was above 0.88 of F², flood depths yield Root Mean Square Errors (RMSE) below 1.5 cm and the discrepancies of u- and v-directional velocities at selected points were less than 0.015 ms^-1. As such, this approach reduces the 2D models’ computing expenses significantly, thus paving the way for large-scale high-resolution 2D real-time forecasting.

中文翻译：

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快速大规模大规模高分辨率2D城市洪水建模的多目标特定子模型方法

当使用高分辨率数字高程模型（DEM）时，二维水动力模型（2D模型）的精度会提高。但是，随之而来的高空间离散化会导致过多的计算费用，从而特别是在大规模情况下，禁止在实时预测中实施它们。本文提出了一种子模型方法，该方法适用于1D静态模型以定制与指定目标相关的高分辨率2D模型网格，从而使定制的2D流体动力学子模型可通过基于GIS的方法快速处理而不会显着降低精度多尺度仿真框架。为了验证所提出的方法，首先设计了模型实验来分别测试两个结果的影响（即，减少的计算域和优化的边界条件），最终得出2D预测结果。然后，通过选择四个具有不同集水区地形形态以及不同降雨返回期（1-100年）的重点区域来评估子模型方法的鲁棒性。子模型方法使所有四种情况的处理速度提高了45-553倍，计算单元数量减少了99％。关于预测洪水范围的拟合优度高于F的0.88 子模型方法使处理速度提高了45-553倍，在所有四种情况下计算单元数量均减少了99％。关于预测洪水范围的拟合优度高于F的0.88 子模型方法使所有四种情况的处理速度提高了45-553倍，计算单元数量减少了99％。关于预测洪水范围的拟合优度高于F的0.88²，洪水深度产生低于1.5 cm的均方根误差（RMSE），且选定点的u和v方向速度差异小于0.015 ms ^-1。这样，该方法可显着减少2D模型的计算费用，从而为大规模高分辨率2D实时预测铺平了道路。