Shoreline response to variations in waves and water levels: An engineering scale approach

A simple new shoreline change model has been developed, calibrated and evaluated with several sets of high quality field data. The model is based upon previous research, which indicates that the shoreline will approach an equilibrium position exponentially with time, when subjected to constant forcing in the form of waves and water levels. The engineering scale approach used here simulates the shoreline response to these cross-shore processes in an extremely efficient and practical manner, while requiring only readily obtainable wave and water level data as input. The equilibrium shoreline is defined by using a conservation of volume argument and equilibrium beach profile theory to derive an expression for the equilibrium shoreline change due to a combination of local tide, storm surge, and wave induced setup. The rate at which the equilibrium condition is approached is governed by a rate coefficient that can either be taken as a constant, or parameterized in terms of the local wave and sediment properties. A total of eight physically based rate parameters are evaluated, where the erosion and accretion are parameterized separately. According to the results, the most effective parameterization of the accretion rate is obtained using a surf zone Froude number, while the erosion rate is best parameterized by either the surf similarity parameter or the breaking wave height cubed. Three calibration coefficients representing a baseline for converting the equilibrium shoreline changes into equilibrium shoreline positions, and separate erosion and accretion constants, are evaluated by minimizing the error between model hindcasts and historical shoreline data. The extensive set of shoreline data used to calibrate and evaluate the model was compiled from a variety of sources, and consists of shoreline measurements from a total of thirteen sites within the United States and Australia. Overall the model successfully simulates the shoreline changes at 11 of the 13 study sites with an average normalized mean square error of 0.643. Other tools designed to help evaluate the model, such as a categorical assessment procedure and a model performance index, also indicate a similar high degree of success.