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Long‐term trend analysis of extreme coastal sea levels with changepoint detection

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  • Mintaek Lee
  • Jaechoul Lee

Abstract

Sea level rise can bring disastrous outcomes to people living in coastal regions by increasing flood risk or inducing stronger storm surges. We study long‐term linear trends in monthly maximum sea levels by applying extreme value methods. The monthly maximum sea levels are extracted from multiple tide gauges around the coastal regions of the world over a period of as long as 169 years. Due to instrument changes, location changes, earthquakes, land reclamation, dredging, etc., the sea level data could contain inhomogeneous shifts in their means, which can substantially impact trend estimates if ignored. To rigorously quantify the long‐term linear trends and return levels for the monthly maximum sea level data, we use a genetic algorithm to estimate the number and times of changepoints in the data. As strong periodicity and temporal correlation are pertinent to the data, bootstrap techniques are used to obtain more realistic confidence intervals to the estimated trends and return levels. We find that the consideration of changepoints changed the estimated linear trends of 89 tide gauges (approximately 30% of tide gauges considered) by more than 20cmcentury‐1. Our results are summarized in maps with estimated extreme sea level trends and 50‐year return levels.

Suggested Citation

  • Mintaek Lee & Jaechoul Lee, 2021. "Long‐term trend analysis of extreme coastal sea levels with changepoint detection," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(2), pages 434-458, March.
    • Handle: RePEc:bla:jorssc:v:70:y:2021:i:2:p:434-458
    DOI: 10.1111/rssc.12466
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    References listed on IDEAS

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    1. Lee Fawcett & David Walshaw, 2012. "Estimating return levels from serially dependent extremes," Environmetrics, John Wiley & Sons, Ltd., vol. 23(3), pages 272-283, May.
    2. Christopher S. Watson & Neil J. White & John A. Church & Matt A. King & Reed J. Burgette & Benoit Legresy, 2015. "Unabated global mean sea-level rise over the satellite altimeter era," Nature Climate Change, Nature, vol. 5(6), pages 565-568, June.
    3. Fryzlewicz, Piotr, 2014. "Wild binary segmentation for multiple change-point detection," LSE Research Online Documents on Economics 57146, London School of Economics and Political Science, LSE Library.
    4. David S. Matteson & Nicholas A. James, 2014. "A Nonparametric Approach for Multiple Change Point Analysis of Multivariate Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(505), pages 334-345, March.
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