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Dataset for: Is barrier island morphology a function of tidal and wave regime? Public Deposited

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Classification of barrier island morphology stems from the seminal work of M. O. Hayes and others, which linked island shape to tidal range and wave height and defined coastal energy regimes (i.e., wave-dominated, mixed energy, tide-dominated). If true, this general relationship represents a process-based framework to link modern and ancient systems, and is key for determining paleomorphodynamic relationships. Here we present a new semi-global database of barrier islands and spits (n = 702). Shape parameters (aspect, circularity, and roundness) are used to quantify island boundary shape, and assess potential correlation with coastal energy regime using global wave and tide models. In adopting the original energy classification as originally put forth (i.e., wave dominated, wave-influenced mixed, tide-influenced mixed, tide dominated), results show that wave-dominated islands have statistically different mean shape values from those in the mixed energy fields, but the two mixed energy designations are not distinct from each other. Furthermore, each energy regime field contains a wide range of island shapes, with no clear trends present. Linear regression modeling shows that tidal range and wave height account for < 10% of the documented variance in island shape, a strong indication that other controls must be considered. Therefore, while energy regime distinctions can be used descriptively, their utility in predicting and constraining island shape is limited: barrier island shape is not indicative of coastal energy regime, and vice versa. Our analysis also demonstrates empirical scaling relationships among modern barrier islands for the first time, with implications for subsurface prediction.

This is the dataset of the Modern Barrier Island Database published in Mulhern et al., 2017 Marine Geology paper titled "Is Barrier Island Morphology a Function of Wave and Tide Regime?" with the DOI  https://doi.org/10.1016/j.margeo.2017.02.016. If using this dataset please cite both the dataset and the paper.

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  • 10/14/2021
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