Forecasting seasonal to multiyear shoreline change
Forecasting seasonal to multiyear shoreline change. Davidson, M.A.and Lewis, R. and Turner, I.L. Coastal Engineering , 57 (6). pp. 553630. (2010) Full text not available from this repository. Official URL:  http://www.sciencedirect.com/science/journal/03783839 

Abstract:  This contribution details a simple empirical model for forecasting shoreline positions at seasonal to interannual timescales. The onedimensional (1D) model is a simplification of a 2D behaviouraltemplate model proposed by Davidson and Turner (2009). The new model is calibrated and tested using fiveyears of weekly videoderived shoreline data from the Gold Coast, Australia. The modelling approach first utilises a leastsquares methodology to calibrate the empirical model coefficients using the first half of the dataset of observed shoreline movement in response to known forcing by waves. The model is then verified by comparison of hindcast shoreline positions to the second half of the observed shoreline dataset. One thousand synthetic timeseries of wave height and period are generated that encapsulate the statistical characteristics of the modelled wave field, retaining the observed seasonal variability and sequencing characteristics. The calibrated model is used in conjunction with the simulated wave timeseries to perform Monte Carlo forecasting of the resulting shoreline positions. The ensemblemean of the 1000 individual fiveyear shoreline simulations is compared to the unseen shoreline timeseries. A simple linear trend forecast of the shoreline position was used as a baseline for assessing the performance of the model. The model performance relative to this baseline prediction was quantified by several objective methods, including crosscorrelation (r), root mean square (RMS) error analysis and Brier Skill tests. Importantly, these tests involved no prior knowledge of either the wave forcing or shoreline response. The new forecast model was found to significantly improve shoreline predictions relative to the simple linear trend model, capturing well both the trend and seasonal shoreline variabilities observed at this site. Brier Skill Scores (BSS) indicate that the model forecasts based on unseen data were rated as ‘excellent’ (BSS = 0.83), and root mean square errors were less than 7 m (≈ 14% of the observed variability). The standard deviations of the 1000 individual simulations from ensembleaveraged ‘mean’ forecast were found to provide a useful means of predicting the higherfrequency (individual storm) shoreline variability, with 98% of the observed shoreline data falling within two standard deviations of the forecast position.


Item Type:  Article 

Subjects:  Coasts > Coastline changes Coasts > Beach management 

ID Code:  405 

Deposited On:  21 Apr 2010 17:56 

Last Modified:  28 Feb 2017 12:44 

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