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 $20\text{cm}{\text{century}}^{‐1}$. Our results are summarized in maps with estimated extreme sea level trends and 50‐year return levels.

中文翻译：

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具有变化点检测的极端沿海海平面长期趋势分析

海平面上升会通过增加洪水风险或引发更强的风暴潮而给沿海地区的人们带来灾难性的后果。通过应用极值方法，我们研究了每月最大海平面中的长期线性趋势。在长达169年的时间里，每月最大海平面是从世界各地沿海地区的多个潮汐仪中提取的。由于仪器的变化，位置的变化，地震，土地开垦，疏ging等，海平面数据可能包含其均值的不均匀变化，如果忽略这些变化会严重影响趋势估计。为了严格量化每月最大海平面数据的长期线性趋势和回报水平，我们使用一种遗传算法来估计数据中变化点的数量和时间。由于强烈的周期性和时间相关性与数据相关，因此使用自举技术来获得对估计趋势和收益水平更现实的置信区间。我们发现更改点的考虑使89个潮位计的估计线性趋势（考虑的潮位计的大约30％）改变了超过$20\text{厘米}{\text{世纪}}^{‐\mathrm{1个}}$。我们的结果被汇总在地图中，并带有估计的极端海平面趋势和50年的回波水平。