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Entropy and Intermittency of River Bed Elevation Fluctuations
Journal of Geophysical Research: Earth Surface ( IF 3.9 ) Pub Date : 2020-06-24 , DOI: 10.1029/2019jf005499
Sevil Ranjbar 1 , Arvind Singh 1
Affiliation  

River beds evolve as a result of a complex interaction between strongly nonlinear processes such as near‐bed turbulence, particle‐particle interaction, and particle‐bed interaction. This interaction contributes to the initiation and evolution of extremely variable river bed elevation patterns, commonly known as bedforms that span across a range of spatiotemporal scales. In this paper, we employ a refined definition of entropy, that is, the multiscale entropy (MSE), to characterize the observed variability in the fluctuations of bed elevation time series under variable discharges obtained from a field‐scale laboratory flume. The MSE accounts for the sequence of data points in a series and quantifies the complexity and lack of information in a system. We show that the MSE of bed elevation fluctuations is higher for higher discharges. When compared with surrogates, which are the linearized series of a signal, the MSE provides across‐scale information about the underlying nonlinearity and linear correlation in a signal. The MSE difference between the original and surrogate series is due to the inherent nonlinearity that is higher for higher discharge at the smaller scales and peaks at intermediate scales. These results indicate the presence of a heterogeneous arrangement of extreme fluctuations that enhances the underlying complexity in bed elevation, rendering them less predictable at higher discharges. We further investigate the role of asymmetry of the bed elevation increments in observed complexity. Our results of asymmetry together with entropy suggest that characteristics of both small‐ and large‐scale features should be included for the accurate predictive modeling of sediment transport.

中文翻译:

河床标高波动的熵和间歇性

河床是由于强非线性过程(如近床湍流,颗粒与颗粒之间的相互作用以及颗粒与床之间的相互作用)之间复杂的相互作用而演变而来的。这种相互作用促进了河床高度变化模式的发生和演变,河床高度变化模式通常被称为跨各种时空尺度的河床形态。在本文中,我们采用对熵的精确定义,即多尺度熵(MSE),来表征在从现场规模的实验室水槽获得的可变流量下,床高程时间序列波动中观察到的变化。MSE考虑了一系列数据点的顺序,并量化了系统中信息的复杂性和不足。我们表明,床高高低的MSE对于较高的排放量较高。当与替代信号(信号的线性化序列)进行比较时,MSE提供有关信号中潜在的非线性和线性相关性的跨尺度信息。原始序列和替代序列之间的MSE差异是由于固有的非线性所致,对于较小的标度,较高的放电量较高,而在中间的标度,则较高。这些结果表明存在极端波动的异类排列,这会增加床层抬高的潜在复杂性,从而使它们在较高流量下难以预测。我们进一步调查床高增量的不对称性在观察到的复杂性中的作用。
更新日期:2020-08-22
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