How much data is enough? The importance of morphological sampling interval and duration for calibration of empirical shoreline models |
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| Institution: | 1. Water Research Laboratory, School of Civil and Environmental Engineering, University of New South Wales, 110 King St, Manly Vale, NSW 2093, Australia;2. School of Marine Science and Engineering, University of Plymouth, Drake Circus, Plymouth, Devon PL4 8AA, UK;1. U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center, 600 4th St South, St Petersburg, FL 33701, United States;2. U.S. Geological Survey, Woods Hole Coastal and Marine Science Center, 384 Woods Hole Rd, Woods Hole, MA 02543, United States |
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| Abstract: | The ability to robustly predict future shoreline position under the influence of changing waves and sea-level rise is a key challenge to scientists and engineers alike. While extrapolating a linear trend out in time is a common baseline approach, the recent development of a number of empirical shoreline models allows the prediction of storm and annual-scale variability as well. The largest constraint in applying these models is the availability of high quality, adequate duration data sets in order to calibrate model free parameters. This contribution outlines several such models and discusses the monitoring programs required to calibrate and hindcast shoreline change from 1 to 10 years at two distinct beach types: a storm-dominated site and the second exhibiting a large seasonal variability. The seasonally-dominated site required longer data sets but was less sensitive to sampling interval, while the storm-dominated site converged on shorter, more frequently sampled data sets. In general, calibration based on a single year of observed shorelines resulted in a large range of model skill and was not considered robust. Monitoring programs of at least two years, with shorelines sampled at dt ≤ 30 days were sufficient to determine initial estimates of calibration coefficients and hindcast short-term (1–5 years) shoreline variability. In the presence of unresolved model processes and noise, hindcasting longer (5 + years) data sets required longer (5 + years) calibration data sets, particularly when sampling intervals exceeded 60 days. |
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