@article{10902/20764,
year = {2021},
month = {1},
url = {http://hdl.handle.net/10902/20764},
abstract = {ABSTRACT: Shorelines respond to a number of "drivers" operating on a variety of time-scales. For some time-scales (e.g., seasonal), the driver-shoreline relationship is often evident; however, at longer timescales (e.g., multiannual), the shoreline changes may be superimposed on changes at shorter time-scales and thus are diffcult to identify. Here, we predict shoreline evolution from storm events to decadal timescales, using a novel approach based on the Complete Ensemble Empirical Mode Decomposition. This approach identifies and links the primary time-scales in the model drivers (large-scale sea level pressure [SLP] and/or waves) with the same time-scales in the shoreline position. The multiscale approach reproduced shoreline changes at two beaches more skillfully than a common shoreline model when SLP and wave information were used in combination. In addition, the analysis can be applied to climate indices, providing the opportunity to link longer time-scales with climate patterns (e.g., El Niño Southern Oscillation).},
publisher = {American Geophysical Union},
publisher = {Geophysical Research Letters Volume 48, Issue 1 16 January 2021  e2020GL089263},
title = {A Multiscale Approach to Shoreline Prediction},
author = {Montaño, Jennifer and Coco, Giovanni and Cagigal Gil, Laura and Méndez Incera, Fernando Javier and Rueda Zamora, Ana Cristina and Bryan, Karin R. and Harley, Mitchell D.},
}