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dc.contributor.authorPham, Dat Tienen_US
dc.contributor.authorSwitzer, Adam D.en_US
dc.contributor.authorHuerta, Gabrielen_US
dc.contributor.authorMeltzner, Aron J.en_US
dc.contributor.authorNguyen, Huan M.en_US
dc.contributor.authorHill, Emma M.en_US
dc.identifier.citationPham, D. T., Switzer, A. D., Huerta, G., Meltzner, A. J., Nguyen, H. M., & Hill, E. M. (2019). Spatiotemporal variations of extreme sea levels around the South China Sea : assessing the influence of tropical cyclones, monsoons and major climate modes. Natural Hazards, 98(3), 969-1001. doi:10.1007/s11069-019-03596-2en_US
dc.description.abstractWith 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 ~ 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 Niño/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.en_US
dc.description.sponsorshipMinistry of Education (MOE)en_US
dc.description.sponsorshipNational Research Foundation (NRF)en_US
dc.relation.ispartofNatural Hazardsen_US
dc.rights© 2019 Springer Nature B.V. All rights reserved.en_US
dc.titleSpatiotemporal variations of extreme sea levels around the South China Sea : assessing the influence of tropical cyclones, monsoons and major climate modesen_US
dc.typeJournal Articleen
dc.contributor.schoolAsian School of the Environmenten_US
dc.contributor.researchEarth Observatory of Singaporeen_US
dc.subject.keywordsExtreme Sea Levelen_US
dc.subject.keywordsTide Gaugeen_US
dc.description.acknowledgementThis research was supported by grants from National Research Foundation Singapore (National Research Fellow Award No. NRF-RF2010-04) and the Singapore Ministry of Education under the Research Centres of Excellence initiative Academic Research Fund (AcRF) Complexity Tier 1 Project RGC4/14 “Preparing Asian mega cities for changing climate and the potential Increase in extreme sea levels and storm surges”. E. Hill was supported by NRF Award No. NRF-NRFF2010-064. H. Nguyen was supported by QGTD 13.09/2014 project (Vietnam National University). We acknowledge the University of Hawaii Sea Level Center (UHSLC) for hourly tide-gauge data at most sites; Hoang Trung Thanh (Vietnam Marine Hydrometeorological Centre) for providing hourly data for Hon Dau, Da Nang, Quy Nhon and Vung Tau tide gauges. Support for the Twentieth Century Reanalysis Project data set is provided by the US Department of Energy, Office of Science Innovative and Novel Computational Impact on Theory and Experiment (DOE INCITE) program, and Office of Biological and Environmental Research (BER), and by the National Oceanic and Atmospheric Administration Climate Program Office. We appreciate Francisco M. Calafat for sharing the training data set so that we could test our model; Wang Lin for his East Asian Winter Monsoon index data; Xiangbo Feng, Marcos Marta and Robert Mawdsley for discussions on methods and data processing. We would like to express our thanks to Pavel Adamek and Constance Chua for their linguistic advice that significantly improved this manuscript. This work comprises Earth Observatory of Singapore contribution 152.en_US
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