Prediction of time-evolving sand ripples in shelf seas
Prediction of time-evolving sand ripples in shelf seas
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DOI：
10.1016/j.csr.2012.02.016
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发表时间：
2012-04-15
影响因子：
2.3
通讯作者：
Marten, K. V.
中科院分区：
文献类型：
--
作者：
Soulsby, R. L.;Whitehouse, R. J. S.;Marten, K. V.
预测海床上小尺度沙纹的存在和物理特性的能力对于确定海流和波浪感受到的海床粗糙度、沉积物输运应用和声学应用都很重要。本文提出了一个全时变沙纹模型，用于预测由水流、波浪或两者共同作用所产生的沙纹的高度、长度和方向，该模型包括了沙纹运动的阈值、涟漪冲刷和生物降解等过程。我们认为，这是目前唯一的预测功能，所有这些属性，因此更广泛地适用于陆架海比其他更复杂，但不太全面的方法。测试对一个大的数据集的意见，使用声学涟漪剖面仪在英格兰东海岸的一个网站，涟漪产生的潮流和波浪，表明预测捕捉到的主要功能观察。一个修改后的标准波/流的优势，在预测方程改善了某些方面的协议。电流产生的波纹的属性预测准确，但波长，并在较小程度上的高度，波浪产生的波纹显示一些预测不足。涟漪的方向是模拟在一个近似的方式，但所观察到的方向的主要特征被捕获。预测因子的生物降解特征无法用该数据集进行检验。皇冠版权所有（C）2012由爱思唯尔有限公司出版。保留所有权利。
The ability to predict the presence and physical properties of small-scale sand ripples on the sea bed is important for determining the bed roughness felt by currents and waves, for sediment transport applications, and for acoustic applications. A fully time-evolving model is presented for predicting the height, length and orientation of sand ripples generated by currents, waves or both, which includes processes dealing with threshold of motion, ripple wash-out and biological degradation. We believe that this is currently the only predictor to feature all these properties, and is thus more widely applicable to shelf seas than other more sophisticated, but less comprehensive, methods. Tests against a large data-set of observations using an Acoustic Ripple Profiler at a site on the east coast of England, where ripples are generated by both tidal currents and waves, showed that the predictor captures most of the main features observed. A modified criterion for the wave/current dominance in the prediction equations improved some aspects of the agreement. The properties of current-generated ripples are predicted accurately, but the wavelength, and to a lesser extent the height, of wave-generated ripples show some under-prediction. Ripple orientations are modelled in an approximate way, but the main features of the observed orientations are captured. The bio-degradation feature of the predictor could not be tested with this data-set. Crown Copyright (C) 2012 Published by Elsevier Ltd. All rights reserved.