Search

Van der Weerd, A.J. and Wijnberg, K.M., 2016. Aeolian sediment flux derived from a natural sand trap. In: Vila-Concejo, A.; Bruce, E.; Kennedy, D.M., and McCarroll, R.J. (eds.), Proceedings of the 14th International Coastal Symposium (Sydney, Australia). Journal of Coastal Research, Special Issue, No. 75, pp. 338-342. Coconut Creek (Florida), ISSN 0749-0208. In 2011, a mega-nourishment (the 'Sand Motor') was constructed along the Dutch Coast. Since it is a pilot project, its evolution is closely monitored. This paper presents first results on the temporal variation in aeolian sediment transport across the nourishment, based on (a) the rate of infill over a 4 year period of a small lake in the nourishment, (b) one year of semi-hourly collected video imagery and (c) four year of hourly-averaged wind data. It appeared that, apart from approximately the first half year, the infill occurred quite linearly over time at an average rate of about 1.9·104 m3/yr. The rate of infill in the first half year period was equivalent to an annual rate of 8.4·104 m3/yr. From the combination of video image data and wind data, it was derived that aeolian sand transport (by saltation) was only observed at hourly averaged wind speeds of at least 7 m/s. The monthly frequency of occurrence of above 7 m/s wind speed, was reasonably well correlated with monthly frequency of occurrence of aeolian transport (r=0.79). Nevertheless, when hourly wind speed exceeded 7 m/s, transport was only observed about 23% of the time, indicating the importance of supply limiting conditions for aeolian transport from the Sand Motor. [ABSTRACT FROM AUTHOR]

A number of techniques for non-linear analysis of time series data have been developed in recent years and applied in many environmental sciences. In this paper, some of these techniques are reviewed and their usefulness for coastal morphological data assessed, with examples in coastal morphology and related fields where these are available. The methods reviewed are time-delay embedding techniques, singular spectrum analysis, forecasting signatures, fractal analysis and neural networks. It is expected that readers from diverse backgrounds such as statistics, environmental modeling, and data measurement would be interested in this subject. Accordingly, introductions have been provided for some concepts and background material. These include the general purposes of data analysis, the approaches traditionally used by statisticians and physical scientists, the general nature of coastal morphological data, and the distinction between linear and non-linear analysis methods. It is concluded that the information potentially provided by these techniques is essential in understanding the generic behavior of coastal morphology on yearly and decadal timescales, and hence in constructing models and making forecasts of future morphological evolution. However, for these purposes, non-linear data analysis techniques usually require longer data series, and higher spatial and temporal resolution, than is available in present coastal morphological data sets. Data from remote sensing sources have the potential to meet these requirements. [ABSTRACT FROM AUTHOR]

A number of statistical techniques to analyze and model coastal morphological evolution over yearly and decadal (i.e., long-term) time scales based on field data are presented. After a general introduction to long-term morphological modeling, mainly linear methods are discussed, whereas nonlinear methods are treated in a companion paper (SOUTHGATE et al., 2001). The theoretical background to the methods introduced is summarized and examples of field applications are given to illustrate each method. High-quality field data sets from different sites in the world, including Germany, The Netherlands, and United States, were employed in these examples. The analysis and modeling techniques used encompassed bulk statistics (mean, standard deviation, correlation etc), random sine functions, empirical orthogonal functions, canonical correlation analysis, and principal oscillation pattern analysis. Besides an evaluation of how suitable respective technique is for analyzing and modeling long-term morphological evolution, some general observations are presented regarding scales of morphological response as derived from the field applications. Data describing the evolution of both natural and anthropogenically affected coastal systems were studied. All methods investigated proved their usefulness for extracting characteristics of long-term morphological evolution, as well as for modeling this evolution, when applied under the right circumstances. However, more sophisticated techniques rely on more data in time and space, which is typically the limiting factor in the application of statistical methods as those presented here. [ABSTRACT FROM AUTHOR]