Temporal Analysis of Shoreline Recession and Accretion

Abstract

The precision with which estimates of shoreline rates-of-change reflect actual changes and predict future changes is dependent on: (1) the accuracy achieved in collecting shoreline position data, (2) the temporal variability of the shoreline movement, (3) the number of measurements used in the computation, (4) the proximity of the observations to actual changes in the trend (sampling bias), (5) the period of time between measurements, (6) the total time span of the record, and (7) the method used to calculate the rate. Over 75% of the data we have assembled in a comprehensive United States shoreline information system (CEIS) were computed using the end point rate (EPR) method. The EPR utilizes only two shoreline positions to calculate rate-of-change values. Methods used less frequently include the average of rates (AOR), linear regression (LR), and jackknife (JK) methods. All of these computation methods fit a linear model to shoreline response. For coastal areas with constant rates of shoreline change through time, the results of all the methods are identical. For a coastline with a non-linear response, a linear estimation method can only approximate the average rate-of-change. As the response of the coastline becomes more non-linear, the differences among the rate-of-change estimates given by the various methods increase. Using data from a 65 km long section of the Outer Banks of North Carolina, we demonstrate the differences in computational methods for estimating shoreline changes and we show how the potential sources of error can bias the final statistics.