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Multivariate Analysis of Nonlinearity in Sandbar Behavior : Volume 15, Issue 1 (18/02/2008)

By Pape, L.

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Book Id: WPLBN0003988457
Format Type: PDF Article :
File Size: Pages 14
Reproduction Date: 2015

Title: Multivariate Analysis of Nonlinearity in Sandbar Behavior : Volume 15, Issue 1 (18/02/2008)  
Author: Pape, L.
Volume: Vol. 15, Issue 1
Language: English
Subject: Science, Nonlinear, Processes
Collections: Periodicals: Journal and Magazine Collection, Copernicus GmbH
Historic
Publication Date:
2008
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications

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Ruessink, B. G., & Pape, L. (2008). Multivariate Analysis of Nonlinearity in Sandbar Behavior : Volume 15, Issue 1 (18/02/2008). Retrieved from http://cn.ebooklibrary.org/


Description
Description: Department of Physical Geography, Faculty of Geosciences, IMAU, Utrecht University, P.O. Box 80.115, 3508 TC Utrecht, The Netherlands. Alongshore sandbars are often present in the nearshore zones of storm-dominated micro- to mesotidal coasts. Sandbar migration is the result of a large number of small-scale physical processes that are generated by the incoming waves and the interaction between the wave-generated processes and the morphology. The presence of nonlinearity in a sandbar system is an important factor determining its predictability. However, not all nonlinearities in the underlying system are equally expressed in the time-series of sandbar observations. Detecting the presence of nonlinearity in sandbar data is complicated by the dependence of sandbar migration on the external wave forcings. Here, a method for detecting nonlinearity in multivariate time-series data is introduced that can reveal the nonlinear nature of the dependencies between system state and forcing variables. First, this method is applied to four synthetic datasets to demonstrate its ability to qualify nonlinearity for all possible combinations of linear and nonlinear relations between two variables. Next, the method is applied to three sandbar datasets consisting of daily-observed cross-shore sandbar positions and hydrodynamic forcings, spanning between 5 and 9 years. Our analysis reveals the presence of nonlinearity in the time-series of sandbar and wave data, and the relative importance of nonlinearity for each variable. The relation between the results of each sandbar case and patterns in bar behavior are discussed, together with the effects of noise. The small effect of nonlinearity implies that long-term prediction of sandbar positions based on wave forcings might not require sophisticated nonlinear models.

Summary
Multivariate analysis of nonlinearity in sandbar behavior

Excerpt
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