Considerations for Shoreline Position Prediction

Bruce C. Douglas, Mark Crowell, Stephen P. Leatherman

Abstract


CROWELL et al. (1997), using series of sparsely sampled sea-level values as surrogate data for shoreline change evaluated several well-known shoreline position prediction algorithms. They concluded that in the absence of physical changes such as opening of inlets or shore engineering, linear regression over the longest possible period was the most reliable predictor of shoreline trends for extended intervals (30+ years). They also noted that shorelines, like sea-level, have unpredictable interannual and longer quasi-periodic fluctuations that can mask an underlying trend for many years. Thus an effective prediction algorithm for predicting shoreline position at all temporal scales must reflect persistence of these variations while at the same time correctly accounting for the underlying long-term trend. Successful interpretation of shoreline behavior and prediction of future position requires knowledge of the nature and impact of past erosional events, particularly due to major storms. A simple mathematical model that mimics many of the characteristics of shoreline position variation and real shoreline position data from Delaware between 1845 -1993 are employed to illustrate the difficulties of the prediction problem. The northeaster of March 1962, the largest in this century, provides a revealing case study of the response of a shoreline to a severe storm event. The effect of this storm, which lasted through five high tides, was to "overshoot" the long-term trend of erosion by a very large amount, with subsequent accretion taking place for a decade or longer back toward the position predicted by the underlying long-term (~150 year) trend. Thus for a long time, the beach appeared to be accreting rather than eroding. Long-term planning, such as for 30 or 60 year building setbacks, requires the most careful attention to the long-term erosion trend and the historical record of storms, including their impacts on the shoreline position and beach recovery.


Keywords


Coastal erosion; erosion rates; erosion; forecasting; development setbacks

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