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Analysis and modeling of field data on coastal morphological evolution over yearly and decadal time scales. Part 1: Background and linear techniques

Larson, Magnus LU ; Capobianco, M; Jansen, H; Rozynski, G; Southgate, HN; Stive, M; Wijnberg, KM and Hulscher, S (2003) In Journal of Coastal Research 19(4). p.760-775
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... (More)
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. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
canonical correlation analysis, empirical orthogonal functions, principal component analysis, bulk statistics, data analysis, modeling, principal oscillation patterns, long-term morphological evolution
in
Journal of Coastal Research
volume
19
issue
4
pages
760 - 775
publisher
Coastal Education and Research Foundation
external identifiers
  • wos:000186579700003
  • scopus:0345258522
ISSN
0749-0208
language
English
LU publication?
yes
id
dab46a6e-d1ee-4c0c-989d-e6645a50570d (old id 294988)
date added to LUP
2007-08-03 12:41:59
date last changed
2018-01-07 08:55:22
@article{dab46a6e-d1ee-4c0c-989d-e6645a50570d,
  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.},
  author       = {Larson, Magnus and Capobianco, M and Jansen, H and Rozynski, G and Southgate, HN and Stive, M and Wijnberg, KM and Hulscher, S},
  issn         = {0749-0208},
  keyword      = {canonical correlation analysis,empirical orthogonal functions,principal component analysis,bulk statistics,data analysis,modeling,principal oscillation patterns,long-term morphological evolution},
  language     = {eng},
  number       = {4},
  pages        = {760--775},
  publisher    = {Coastal Education and Research Foundation},
  series       = {Journal of Coastal Research},
  title        = {Analysis and modeling of field data on coastal morphological evolution over yearly and decadal time scales. Part 1: Background and linear techniques},
  volume       = {19},
  year         = {2003},
}