Numerical simulation of floods from multiple sources using an adaptive anisotropic unstructured mesh method
Numerical simulation of floods from multiple sources using an adaptive anisotropic unstructured mesh method
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DOI：
10.1016/j.advwatres.2018.11.011
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发表时间：
2019-01
影响因子：
4.7
通讯作者：
R. Hu;Fangxin Fang;P. Salinas;Christopher C. Pain;N. D. Sto.Domingo;Ole Mark
中科院分区：
文献类型：
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作者：
R. Hu;Fangxin Fang;P. Salinas;Christopher C. Pain;N. D. Sto.Domingo;Ole Mark
两个或两个以上极端事件（例如降水和风暴潮）的同时发生可能导致沿海城市发生严重洪水。必须开发强大的数值工具，以改进洪水预测（特别是在从几米到几公里的广泛空间尺度上）和评估极端事件的联合影响。已经开发了各种数值模型来进行城市地区的高分辨率洪水模拟。然而，跨整个计算域使用高分辨率网格可能导致高计算负担。最近，自适应各向同性非结构化网格技术已首次引入到城市洪水模拟和应用到一个简单的洪水事件观察到的结果，流量超过能力的涵洞在长时间或强降雨期间。优于现有的自适应网格细化方法（AMR，局部嵌套静态网格方法），这种自适应非结构化网格技术可以动态修改（粗化和细化网格）并调整网格以实现期望的精度，从而随着流动的发展更好地捕获瞬态和复杂的流动动态。上述基于二维浅水方程的自适应网格洪水模型（称为Floodity）通过引入（1）各向异性动态网格优化技术得到了进一步发展（2）多个洪水源（极端降雨和海平面事件）;以及（3）各向异性DMO和高分辨率数字地形模型（DTM）数据的独特组合。它已被应用到一个人口稠密的城市化地区内格雷夫，丹麦。MIKE 21 FM的结果被用来验证我们的模型。为了评估模型预测的不确定性，洪水结果对极端海平面、降雨量和网格分辨率的敏感性已经进行了评估。使用各向异性DMO使我们能够捕捉高分辨率的地形特征（建筑物，河流和街道）只有在需要的地方和时间，从而提供更准确的洪水预测，同时降低计算成本。它还使我们能够更好地捕捉不断变化的流动特征（干湿锋）。
The coincidence of two or more extreme events (precipitation and storm surge, for example) may lead to severe floods in coastal cities. It is important to develop powerful numerical tools for improved flooding predictions (especially over a wide range of spatial scales - metres to many kilometres) and assessment of joint influence of extreme events. Various numerical models have been developed to perform high-resolution flood simulations in urban areas. However, the use of high-resolution meshes across the whole computational domain may lead to a high computational burden. More recently, an adaptive isotropic unstructured mesh technique has been first introduced to urban flooding simulations and applied to a simple flooding event observed as a result of flow exceeding the capacity of the culvert during the period of prolonged or heavy rainfall. Over existing adaptive mesh refinement methods (AMR, locally nested static mesh methods), this adaptive unstructured mesh technique can dynamically modify (both, coarsening and refining the mesh) and adapt the mesh to achieve a desired precision, thus better capturing transient and complex flow dynamics as the flow evolves.In this work, the above adaptive mesh flooding model based on 2D shallow water equations (named as Floodity) has been further developed by introducing (1) an anisotropic dynamic mesh optimization technique (anisotropic-DMO); (2) multiple flooding sources (extreme rainfall and sea-level events); and (3) a unique combination of anisotropic-DMO and high-resolution Digital Terrain Model (DTM) data. It has been applied to a densely urbanized area within Greve, Denmark. Results from MIKE 21 FM are utilized to validate our model. To assess uncertainties in model predictions, sensitivity of flooding results to extreme sea levels, rainfall and mesh resolution has been undertaken. The use of anisotropic-DMO enables us to capture high resolution topographic features (buildings, rivers and streets) only where and when is needed, thus providing improved accurate flooding prediction while reducing the computational cost. It also allows us to better capture the evolving flow features (wetting-drying fronts).