|Title||Spatiotemporal variations of extreme sea levels around the South China Sea: assessing the influence of tropical cyclones, monsoons and major climate modes|
|Publication Type||Journal Article|
|Year of Publication||2019|
|Authors||Pham DT, Switzer AD, Heurta G, Meltzner AJ, Nguyen HM, Hill EM|
|Accession Number||WOS: 000487900800008|
With sea levels projected to rise as a result of climate change, it is imperative to understand not only long-term average trends, but also the spatial and temporal patterns of extreme sea level. In this study, we use a comprehensive set of 30 tide gauges spanning 1954-2014 to characterize the spatial and temporal variations of extreme sea level around the low-lying and densely populated margins of the South China Sea. We also explore the long-term evolution of extreme sea level by applying a dynamic linear model for the generalized extreme value distribution (DLM-GEV), which can be used for assessing the changes in extreme sea levels with time. Our results show that the sea-level maxima distributions range from similar to 90 to 400 cm and occur seasonally across the South China Sea. In general, the sea-level maxima at northern tide gauges are approximately 25-30% higher than those in the south and are highest in summer as tropical cyclone-induced surges dominate the northern signal. In contrast, the smaller signal in the south is dominated by monsoonal winds in the winter. The trends of extreme high percentiles of sea-level values are broadly consistent with the changes in mean sea level. The DLM-GEV model characterizes the interannual variability of extreme sea level, and hence, the 50-year return levels at most tide gauges. We find small but statistically significant correlations between extreme sea level and both the Pacific Decadal Oscillation and El Nino/Southern Oscillation. Our study provides new insight into the dynamic relationships between extreme sea level, mean sea level and the tidal cycle in the South China Sea, which can contribute to preparing for coastal risks at multi-decadal timescales.