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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
Publication Date:
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

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.

Multivariate analysis of nonlinearity in sandbar behavior

Box, G. E P. and Jenkins, G M.: Time series analysis: Forecasting and control, Holden-Day, San Francisco, 1970.; Battjes, J A. and Stive, M. J F.: Calibration and verification of a dissipation model for random breaking waves, J. Geophys. Res., 90, 9159–9167, 1985.; Aarninkhof, S., Ruessink, B G., and Roelvink, J A.: Nearshore subtidal bathymetry from time-exposure video images, J. Geophys. Res., 110, C06011, doi:10.1029/2004JC002791, 2005.; Abraham, F D.: Nonlinear coherence in multivariate research: invariants and the reconstruction of attractors, Nonlinear Dynamics, Psychology and Life Sciences, 1, 7–33, 1997.; Battjes, J A. and Janssen, J. P. F M.: Energy loss and set-up due to breaking of random waves, in: Proceedings of the 16th Conference on Coastal Engineering, 569–587, ASCE, 1978.; Cao, L., Mees, A., and Judd, K.: Dynamics from multivariate time series, Physica D, 121, 75–88, 1998.; Casdagli, M.: A dynamical systems approach to modeling input-output systems, 265–281, Addison-Wesley, 1992.; Casdagli, M C. and Weigend, A S.: Exploring the continuum between deterministic and stochastic modeling, in: Time Series Prediction: Forecasting the Future and Understanding the Past, edited by Weigend, A S. and Gershenfeld, N A., 347–366, Addison-Wesley, Massachusetts, 1993.; Cleveland, W S. and Devlin, S J.: Locally weighted regression: an approach to regression analysis by local fitting, J. Am. Stat. Assoc., 83, 596–610, 1988.; Elgar, S.: Coastal profile at Duck, North Carolina: A cautionary note, J. Geophys. Res., 106, 4625–4627, 2001.; Gallagher, E L., Elgar, S., and Guza, R T.: Observations of sand bar evolution on a natural beach, J. Geophys. Res., 103, 3203–3215, 1998.; Garcia, S P. and Almeida, J S.: Multivariate phase space reconstruction by nearest neighbor embedding with different time delays, Phys. Rev. E, 72, 027205, doi: 10.1103/PhysRevE.72.027205, 2005.; Hoefel, F. and Elgar, S.: Wave-induced sediment transport and sandbar migration, Science, 299, 1885–1887, 2003.; Holland, K T., Holman, R A., Lippmann, T C., Stanley, J., and Plant, N G.: Practical use of video imagery in nearshore oceanographic field studies, J. Oceanic Engineering, 22, 81–92, 1997.; Holman, R A. and Stanley, J.: The history and technical capabilities of Argus, Coast. Eng., 54, 477–491, 2007.; Jaffe, B E. and Rubin, D M.: Using nonlinear forecasting to learn the magnitude and phasing of time-varying sediment suspension in the surf zone, J. Geophys. Res., 101, 14 283–14 296, 1996.; Kantz, H. and Schreiber, T.: Nonlinear time series analysis, Cambridge University Press, Cambridge, first Edn., 1997.; Kuriyama, Y.: Medium-term bar behavior and associated sediment transport at Hasaki, Japan, J. Geophys. Res., 107, 3132, doi:10.1029/2001JC000899, 2002.; Kuriyama, Y. and Yanagishima, S.: Medium-term variations of bar properties and their linkages with environmental factors at HORS, Tech. Rep 4, Kashima port and airport research institute, Kashima, 2006.; Lippmann, T C. and Holman, R A.: Quantification of sand bar morphology: a video technique based on wave dissipation, J. Geophys. Res., 94, 995–1011, 1989.; Pape, L., Ruessink, B G., Wiering, M A., and Turner, I L.: Recurrent neural network modeling of nearshore sandbar behavior, Neural Networks, 20, 509–518, 2007.; Plant, N G., Holman, R A., Freilich, M H., and Birkemeier, W A.: A simple model for interannual sandbar behavior, J. Geophys. Res., 104, 15 755–15 776, 1999.; Plant, N G., Freilich, M H., and Holman, R A.: Role of morphologic feedback in surf zone sandbar response, J. Geophys. Res., 106, 973–989, 2001.; Plant, N G., Holland, T., and Holman, R A.: A dynamical attractor governs beach response to storms, Geophys. Res. Lett., 33, L17607, doi:10.1029/2006GL027105, 2006.; Reniers, A. J. H M. and Battjes, J A.: A laboratory study of lo


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