Shoreline variability via empirical orthogonal function analysis: Part I temporal and spatial characteristics

Miller, Jon K.; Dean, Robert G.

doi:10.1016/j.coastaleng.2006.08.013

Cited by 103 publications

(77 citation statements)

References 28 publications

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“…1984-Sep 1986, Dec 1986-Feb 1987, 1989-1990, 1992-1997. This alongshore difference in the morphology of northerly and southerly profiles has also been observed by Miller and Dean (2007a) We appreciate the suggestion made by the reviewer. A discussion of the YIC and its interpretation are summarised in the revised manuscript on pages 8 and 9.…”

Section: C1 (B) " …We Do Not Know At This Point What the Form Of Thementioning

confidence: 81%

“…10 1984 -Sep 1986, Dec 1986 -Feb 1987, 1989-1990, 1992 -1997). This alongshore difference in the morphology of northerly and southerly profiles has also been observed by Miller and Dean (2007a) via the analysis of shoreline data at Duck. In the revised manuscript, we also clarify that the estimated SDP gains associated with Profiles 58 and 188 are normalised such that their summation at each k th time-step is 1 while representing weights for relating the volumes of these two profiles to those of Profile 62.…”

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confidence: 83%

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Nonlinear transfer function modelling of beach morphology at Duck, North Carolina

Gunawardena

Ilic

Pinkerton

et al. 2009

Coastal Engineering

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This paper presents a simple nonlinear data-based modelling approach for predicting the beach profile volume at Duck, North Carolina, USA. The state-dependent parameter form of the general transfer function (SDP TF) model is used to describe nonlinearity influencing these morphological data in two case examples. Case 1 investigates the nonlinearity associated with the dependency of wave forcing on the preceding beach volume. Case 2 investigates the ability to model the variables within the well known diffusion equation for beach volume using this data-based approach. The results of this study show that the SDP TF approach can be used successfully to develop statistically robust models for describing nonlinearity in beach morphological systems. Furthermore, these models are shown to predict the beach volumes over both short (1 month ahead) and long (2 years ahead) time periods, and thus show great potential for practical applications in coastal zone management and engineering. We thank you and the two reviewers for their constructive comments and suggestions.We have fully taken on board all suggested revisions, we believe these will further improve the manuscript. The following addresses all comments made by the two reviewers in detail. Please note that the revised figures are enclosed within the manuscript as these were generated using Matlab TM , and hence could not be saved separately as image files. Best regards yohamaComments from "Reviewer 1" Specific Comments C1. "Clarify position of profiles along coast (including position of Pier)."We welcome the suggestion of the reviewer. This has been clarified by including a location map (Figure 1) of the beach profiles (Profiles 58, 62, 188 and 190) and the FRF pier at Duck. C2. "Clear explanation of the 'volume' discussed in the paper"We agree with the reviewer and apologise for the confusion. We have clarified that the beach profile volume per unit metre of shoreline were computed by integrating the beach profile data between 75 m and 700 m, which extend well beyond the average position of the depth of closure at Duck (latter is at the 4 m depth at ~410 m crossshore) to the 7 m depth (page 10). C3. "Applying Eq.(6) and a straight and parallel coastline is present, no interaction between the profiles should occur. That calls for equal 'volumes' in the 3 profiles; but the 'volume' depends [see also ii)] e.g. on the position of the reference line. Please clarify this point."Detailed Response to Reviewers 2 Here, is our explanation for this comment, which is summarised in the revised paper (page 10). Although the bathymetric contours at Duck are parallel at times, this is not always the case (Miller et al., 1983;Plant et al., 1996;Miller and Dean, 2007) because of longshore differences in the morphology of profiles on either side of the pier. As seen in Figure 2 of the revised manuscript, alongshore volume differences between northerly and southerly profiles occur, particularly post 1991. Reversed migration directions of the nearshore sand bars on either si...

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Section: C1 (B) " …We Do Not Know At This Point What the Form Of Thementioning

confidence: 81%

mentioning

confidence: 83%

Nonlinear transfer function modelling of beach morphology at Duck, North Carolina

Gunawardena

Ilic

Pinkerton

et al. 2009

Coastal Engineering

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“…In coastal morphology, it can extract the significant components of shoreline data sets. It has been applied widely in coastal morphology to study either cross-, 2007b;Miller and Dean, 2007a;Kang and Winant et al, 1975). In the study area, this technique has been applied by Pradjoko the Nanakita River mouth based on data…”

Section: Coastal Engineering 2014mentioning

confidence: 99%

Analysis of Shoreline Behavior on Sendai Coast Before and After the 2011 Tsunami

Vo¹,

Mitobe

Tanaka

2014

Int. Conf. Coastal. Eng.

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The earthquake and tsunami in 2011 caused serious damages on Sendai Coast, Miyagi Prefecture, Japan. Shoreline change characteristics for periods both before and after the tsunami are extracted from processing of aerial photographs together with conducting of Empirical Orthogonal Function (EOF) analysis. The evolution of shoreline at the Nanakita River mouth and the Natori River mouth areas both before and after the tsunami has been discussed. Shoreline change rates at both the river mouths reveal the recovery of morphology but in different aspect, the fast recovery at the Nanakita River mouth while very slow recovery at the Natori River mouth. The results of EOF analysis show that the dominant components of shoreline changes before and after the tsunami are different each other. The dominant component after the tsunami at the two river mouths is also different. The total contribution of the first and the second component is very high in the total contribution of all the components. The contribution of the first component after the tsunami is larger than the first component before the tsunami. The results also show the significant effect of the river mouth on the recovery of the morphology around.

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“…The EOF analysis to measure the variability of shoreline along shore has been done before by MunozPerez et al [12]. Miller and Dean [13] also analyzed the variability of shoreline along shore in several locations in the United States and Australia. In addition, Rithpring and Tanaka [14] analyze topographic changes at Natori River estuary due to construction of the Yuriage port and around the Sendai port in Japan.…”

Section: Introductionmentioning

confidence: 99%

Application of Empirical Orthogonal Function Models to Analyze Shoreline Change at Bangkalan Madura

Arkwright¹,

Suntoyo²

2013

MST

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Bangkalan's shoreline, especially on the opposite side of Surabaya, has been evaluated to determine the morphological changes due to wave attack, near-shore current, long-shore sediment transport and coastal configuration. This research aims to determine the dominant patterns of variation of Bangkalan's shoreline change, expressed by Eigen-function in empirical orthogonal function (EOF) models. That was started with data collection such as oceanographic data (wave and tidal), bathymetry and topographic map and sediment data. All data was used for forecasting two-monthly shoreline. Coordinate of two-monthly shoreline was used as input of EOF model. The first Eigen mode is a profile of shoreline equilibrium. The second Eigen mode shows pivot point that separates the different behaviors, which indicates a positive balance of shoreline from the direction of the dominant force. The models execution based on 1986's shoreline show the shoreline change significantly at some cells e.g. around Suramadu bridge (cell 1-40), Batuporon (cell 70-100), Jungdima (cell 142-170) and at Kamal port (cell 230-250). The model of shoreline change using EOF was validated with the One-line model and data of 1995's map's shoreline. The E.O.F. value of model RMSE, 0.02, is less than the root mean square error (RMSE) value of One-line model, 0.04, which shows that the EOF model performance better than One-line models.

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Shoreline variability via empirical orthogonal function analysis: Part I temporal and spatial characteristics

Cited by 103 publications

References 28 publications

Nonlinear transfer function modelling of beach morphology at Duck, North Carolina

Nonlinear transfer function modelling of beach morphology at Duck, North Carolina

Analysis of Shoreline Behavior on Sendai Coast Before and After the 2011 Tsunami

Application of Empirical Orthogonal Function Models to Analyze Shoreline Change at Bangkalan Madura

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