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 (Formula presented.). Our results are summarized in maps with estimated extreme sea level trends and 50-year return levels.
Original language | English (US) |
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Pages (from-to) | 434-458 |
Number of pages | 25 |
Journal | Journal of the Royal Statistical Society. Series C: Applied Statistics |
Volume | 70 |
Issue number | 2 |
DOIs | |
State | Published - Mar 2021 |
Externally published | Yes |
Keywords
- bootstrap confidence interval
- changepoints
- extreme sea levels
- generalized extreme value distribution
- genetic algorithm
- temporal correlation
ASJC Scopus subject areas
- Statistics and Probability
- Statistics, Probability and Uncertainty