Scaling Analysis of Tide Gauge Data from the Atlantic, Gulf of Mexico, and Pacific Coasts of the United States

Christopher C. Barton, J. R. Smigelski, S. F. Tebbens

Research output: Contribution to conferencePresentation

Abstract

Most coastal regions are subject to inundation due to many periodic and non-periodic inputs including for example: diurnal and semi diurnal tides, storms, tsunamis, and global sea level change. Tide gauge data provide a frequently sampled long term record of fluctuations in water level. A power-spectral-density analysis of tidal gauge data is used to quantify persistence (degree of internal correlation over various time intervals) in terms of the scaling exponent β and to identify temporal changes in persistence. The stations are located at different proximity to the open ocean, including bays, harbors, and channels. The datasets are from the NOAA CO-OPS Verified Hourly Station Datum. The length of the data sets ranges from 3 years to 101 years. The hourly data sets are decimated to one record every four hours. All data sets analyzed show three distinct regions of persistence with two inflection points at approximately one day and five days. For times less than one day, the scaling exponent ranges between 0 < β < 2.6. For the time interval 1 to 5 days, the scaling exponent ranges between 1.1 < β < 2.1. For times greater than 5 days, the scaling exponent ranges between 0.4 < β < 0.9. Persistence generally decreases as period increases but is stable between the inflection points. At Duck, NC, long term persistence in the tide gauge signal is 0.6 as compared to 0.9 for the biweekly shoreline position signal over twenty years, suggesting a strong correlation between the two and the possibility of using tide gauge data to quantify nearby shoreline mobility over similar time intervals.

Original languageAmerican English
StatePublished - Jan 1 2008
EventEos, Transactions -
Duration: Jan 1 2011 → …

Conference

ConferenceEos, Transactions
Period1/1/11 → …

Disciplines

  • Earth Sciences
  • Environmental Sciences
  • Physical Sciences and Mathematics

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