A hybrid beach morphology model applied to a high energy sandy beach
A hybrid beach morphology model applied to a high energy sandy beach
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DOI：
10.1007/s10236-015-0884-0
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发表时间：
2015-09
期刊：
影响因子：
2.3
通讯作者：
H. Karunarathna;R. Ranasinghe;D. Reeve
中科院分区：
文献类型：
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作者：
H. Karunarathna;R. Ranasinghe;D. Reeve
本文通过对一个高能桑迪海滩的海滩变化的5年预测，探讨了混合海岸动力学模型在海滩年际变化预测中的应用。建模方法结合了“减少物理”配方与数据驱动的方法，通过逆技术，形成混合沿海形态动力学模型。考虑用于模型演示的海滩是Narrabeen海滩，它是位于澳大利亚新南威尔士州的动态沙滩海滩。尽管它的简单性，我们发现，该模型是能够捕获海滩的变化在Narrabeen海滩在年际的时间尺度与均方根误差之间的测量和计算的海滩配置文件平均小于0.4米。即使该模型是用来预测年际海滩变化在这项研究中，其预测海滩变化的能力不限于该时间尺度，但取决于历史海滩剖面测量的频率，以确定关键的未知参数的模型。此外，剖面预测的长度在很大程度上取决于可用的历史测量的长度，其中较长的数据集允许在观测中包含的海滩行为范围内进行较长的预测。该模型能够可靠地预测跨年度和潜在的其他时间尺度的海岸变化，其高效率，使其有可能被用于提供所需的概率海岸变化预测，这将是非常有用的海岸管理目的的多个模拟。
In this paper, the application of a hybrid coastal morphodynamic model to forecast inter-annual beach change is discussed through the prediction of beach change in a high energy sandy beach over a period of 5 years. The modelling approach combines a ‘reduced-physics’ formulation with a data-driven approach through an inverse technique to form the hybrid coastal morphodynamic model. The beach considered for the demonstration of the model is the Narrabeen Beach, which is a dynamic sand beach located in New South Wales, Australia. Despite its simplicity, we find that the model is able to capture beach change at Narrabeen Beach at inter-annual timescales with root mean square error between measured and computed beach profiles less than 0.4 m on average. Even though the model is used to forecast inter-annual beach change in this study, its ability to predict beach change is not limited to that timescale but depends on the frequency of historic beach profile measurements available to determine key unknown parameters of the model. Also, the length of profile forecasts largely depends on the length of available historic measurements where longer data sets allow longer predictions within a range of beach behaviour contained in the observations. The ability of the model to reliably forecast coastal change at inter-annual and potentially at other timescales, and its high efficiency make it possible to be used in providing multiple simulations required for probabilistic coastal change forecasts which will be very useful for coastal management purposes.