A full-scale fluvial flood modelling framework based on a high-performance integrated hydrodynamic modelling system (HiPIMS)
A full-scale fluvial flood modelling framework based on a high-performance integrated hydrodynamic modelling system (HiPIMS)
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DOI：
10.1016/j.advwatres.2019.103392
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发表时间：
2019-08
影响因子：
4.7
通讯作者：
X. Xia;Q. Liang;X. Ming
中科院分区：
文献类型：
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作者：
X. Xia;Q. Liang;X. Ming
传统上，大型集水区的全尺度河流洪水建模是使用耦合水文和水力/水动力模型进行的。这种传统的模拟方法不适合于模拟强降雨引发的特大洪水，这通常是一个高度瞬态和动态的径流和洪水过程。这项工作的目的是开发和展示一个建模框架，预测从源头（降雨）到影响（淹没）的河流洪水在一个大的集水区使用一个单一的高性能水动力学模型驱动的降雨输入的全尺度过程。建模框架被应用于重现洪水事件造成的2015年风暴德斯蒙德在2500 km 2伊甸园流域在5米的分辨率。没有密集的模型校准，预测结果比较以及与现场观测的淹没范围和整个流域的水位。敏感性试验表明，高分辨率的网格是必不可少的精确模拟河流洪水事件使用的二维水动力学模型。通过多个现代GPU的加速，模拟速度比真实的时间快2.5倍以上，尽管它涉及计算域内的1亿个计算单元。这项工作提供了一个新的和有前途的方法来评估和预测在真实的时间的极端河流洪水的风险，从强降雨。
Full-scale fluvial flood modelling over large catchments has traditionally been carried out using coupled hydrological and hydraulic/hydrodynamic models. Such a traditional modelling approach is not well suited for the simulation of extreme floods induced by intense rainfall, which is usually featured with highly transient and dynamic rainfall-runoff and flooding process. This work aims to develop and demonstrate a modelling framework to predict the full-scale process of fluvial flooding from the source (rainfall) to impact (inundation) over a large catchment using a single high-performance hydrodynamic model driven by rainfall inputs. The modelling framework is applied to reproduce the flood event caused by the 2015 Storm Desmond in the 2500 km2Eden Catchment at 5 m resolution. Without intensive model calibration, the predicted results compare well with field observations in terms of inundation extent and gauged water levels across the catchment. Sensitivity tests reveal that high-resolution grid is essential for accurate simulation of fluvial flood events using a 2D hydrodynamic model. Accelerated by multiple modern GPUs, the simulation is more than 2.5 times faster than real time although it involves 100 million computational cells inside the computational domain. This work provides a novel and promising approach to assess and forecast at real time the risk of extreme fluvial floods from intense rainfall.