| 1. |
- Hanson, Hans, et al.
(författare)
-
Modelling of coastal evolution on yearly to decadal time scales
- 2003
-
Ingår i: Journal of Coastal Research. - 0749-0208. ; 19:4, s. 790-811
-
Tidskriftsartikel (refereegranskat)abstract
- There is still no universal model for analysing and predicting coastal evolution and its governing processes on yearly to decadal time scales. Instead, depending on the nature of the problem and project objectives, there is a wide range of models available, each focusing on the problem complex from a specific standpoint. The present paper gives an overview of available numerical model types. A differentiation is made between equilibrium and non-equilibrium model types as well as between longshore uniform and non-longshore uniform model types. These models are discussed in terms of their general assumptions, approaches, and applicability. Most of the model descriptions are supplemented by an illustrative example. In addition, generic issues, such as level of knowledge on different scales, selection of model type on the basis of the nature of the application, the concept of equilibrium, model validation and utilisation are discussed.
|
|
| 2. |
- Larson, Magnus, et al.
(författare)
-
Analysis and modeling of field data on coastal morphological evolution over yearly and decadal time scales. Part 1: Background and linear techniques
- 2003
-
Ingår i: Journal of Coastal Research. - 0749-0208. ; 19:4, s. 760-775
-
Tidskriftsartikel (refereegranskat)abstract
- 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.
|
|
| 3. |
- Southgate, HN, et al.
(författare)
-
Analysis of field data of coastal morphological evolution over yearly and decadal timescales. Part 2: Non-linear techniques
- 2003
-
Ingår i: Journal of Coastal Research. - 0749-0208. ; 19:4, s. 776-789
-
Tidskriftsartikel (refereegranskat)abstract
- 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.
|
|
