Long-term Trend Analysis of Extreme Coastal Sea Levels with Changepoint Detection
利用极值方法分析全球多个验潮站长达169年的月最高海平面数据，通过遗传算法检测数据中的变点，发现考虑变点后约30%的站点线性趋势变化超过20厘米/世纪，并绘制了极端海平面趋势和50年重现期地图。
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.