Spatiotemporal variations of extreme sea levels around the South China Sea: assessing the influence of tropical cyclones, monsoons and major climate modes

  • Dat T. PhamEmail author
  • Adam D. Switzer
  • Gabriel Huerta
  • Aron J. Meltzner
  • Huan M. Nguyen
  • Emma M. Hill
Original Paper


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 ~ 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.


Extreme sea level Tide gauge South China Sea Tropical cyclones Monsoons Dynamic linear model 



This 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.

Supplementary material

11069_2019_3596_MOESM1_ESM.pdf (11.8 mb)
Supplementary material 1 (PDF 12013 kb)


  1. Amiruddin AM (2017) Sea-level changes over the last six decades in the South China Sea. University of SouthamptonGoogle Scholar
  2. Amiruddin AM, Haigh ID, Tsimplis MN, Calafat FM, Dangendorf S (2015) The seasonal cycle and variability of sea level in the South China Sea. J Geophys Res Oceans 120:5490–5513. CrossRefGoogle Scholar
  3. Chen H, Tkalich P, Malanotte-Rizzoli P, Wei J (2012) The forced and free response of the South China Sea to the large-scale monsoon system. Ocean Dyn 62:377–393. CrossRefGoogle Scholar
  4. Church JA, White NJ (2006) A 20th century acceleration in global sea-level rise. Geophys Res Lett. Google Scholar
  5. Church JA, White NJ (2011) Sea-level rise from the late 19th to the early 21st century. Surv Geophys 32:585–602. CrossRefGoogle Scholar
  6. Codiga DL (2011) Unified tidal analysis and prediction using the UTide Matlab functions. Graduate School of Oceanography, University of Rhode Island, Narragansett, RI.
  7. Coles S (2001) An introduction to statistical modeling of extreme values. Springer series in statistics. Springer, LondonCrossRefGoogle Scholar
  8. Dangendorf S, Müller-Navarra S, Jensen J, Schenk F, Wahl T, Weisse R (2014) North sea storminess from a novel storm surge record since AD 1843. J Clim 27:3582–3595. CrossRefGoogle Scholar
  9. de Vries H et al (1995) A comparison of 2D storm surge models applied to three shallow European seas. Environ Softw 10:23–42. CrossRefGoogle Scholar
  10. Devlin AT, Jay DA, Talke SA, Zaron ED, Pan J, Lin H (2017) Coupling of sea level and tidal range changes, with implications for future water levels. Sci Rep 7:17021. CrossRefGoogle Scholar
  11. Ding X, Zheng D, Chen Y, Chao J, Li Z (2001) Sea level change in Hong Kong from tide gauge measurements of 1954–1999. J Geod 74:683–689. CrossRefGoogle Scholar
  12. Durbin J, Koopman SJ (2012) Time series analysis by state space methods. Oxford statistical science series: 38, 2nd edn. Oxford University Press, OxfordGoogle Scholar
  13. Durbin J, Watson GS (1950) Testing for serial correlation in least squares regression: I. Biometrika 37:409–428. Google Scholar
  14. Durbin J, Watson GS (1951) testing for serial correlation in least squares regression. II. Biometrika 38:159–177. CrossRefGoogle Scholar
  15. Durbin J, Watson GS (1971) Testing for serial correlation in least squares regression. III. Biometrika 58:1–19. Google Scholar
  16. Fang G, Chen H, Wei Z, Wang Y, Wang X, Li C (2006) Trends and interannual variability of the South China Sea surface winds, surface height, and surface temperature in the recent decade. J Geophys Res Oceans. Google Scholar
  17. Feng X, Tsimplis MN (2014) Sea level extremes at the coasts of China. J Geophys Res Oceans 119:1593–1608. CrossRefGoogle Scholar
  18. Firing YL, Merrifield MA (2004) Extreme sea level events at Hawaii: influence of mesoscale eddies. Geophys Res Lett. Google Scholar
  19. Gönnert G, Dube SK, Murty T, Siefert W (2001) Global storm surges: theory, observations and applications. German Coastal Engineering Council, vol 63, 623 ppGoogle Scholar
  20. Haigh ID, Eliot M, Pattiaratchi C (2011) Global influences of the 18.61 year nodal cycle and 8.85 year cycle of lunar perigee on high tidal levels. J Geophys Res. Google Scholar
  21. Haigh ID, MacPherson LR, Mason MS, Wijeratne EMS, Pattiaratchi CB, Crompton RP, George S (2014a) Estimating present day extreme water level exceedance probabilities around the coastline of Australia: tropical cyclone-induced storm surges. Clim Dyn 42:139–157. CrossRefGoogle Scholar
  22. Haigh ID, Wijeratne EMS, MacPherson LR, Pattiaratchi CB, Mason MS, Crompton RP, George S (2014b) Estimating present day extreme water level exceedance probabilities around the coastline of Australia: tides, extra-tropical storm surges and mean sea level. Clim Dyn 42:121–138. CrossRefGoogle Scholar
  23. Haigh ID et al (2015) A user-friendly database of coastal flooding in the United Kingdom from 1915–2014. Sci Data 2:150021. CrossRefGoogle Scholar
  24. Han G, Huang W (2008) Pacific decadal oscillation and sea level variability in the Bohai, Yellow, and East China Seas. J Phys Oceanogr 38:2772–2783. CrossRefGoogle Scholar
  25. Hong Kong Royal Observatory (1980) Meteorological results 1980. Part III—tropical cyclone summaries. Royal Observatory, Hong Kong.
  26. Horsburgh KJ, Wilson C (2007) Tide–surge interaction and its role in the distribution of surge residuals in the North Sea. J Geophys Res Oceans. Google Scholar
  27. Hu Z, Tan Y, Song X, Zhou L, Lian X, Huang L, He Y (2014) Influence of mesoscale eddies on primary production in the South China Sea during spring inter-monsoon period. Acta Oceanol Sin 33:118–128. CrossRefGoogle Scholar
  28. Huang NE et al (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc R Soc Lond A Math Phys Eng Sci 454:903–995. CrossRefGoogle Scholar
  29. Huerta G, Sansó B (2007) Time-varying models for extreme values. Environ Ecol Stat 14:285–299. CrossRefGoogle Scholar
  30. Huerta G, Stark GA (2013) Dynamic and spatial modelling of block maxima extremes. Oxford University Press, Oxford. CrossRefGoogle Scholar
  31. Intergovernmental Panel on Climate Change (2014) Climate change 2013—the physical science basis: working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge.
  32. Jevrejeva S, Grinsted A, Moore JC, Holgate S (2006) Nonlinear trends and multiyear cycles in sea level records. J Geophys Res 111:C09012. CrossRefGoogle Scholar
  33. Joint Typhoon Warning Center (2018) Western North Pacific Ocean Best Track Data.
  34. Lee HS (2013) Estimation of extreme sea levels along the Bangladesh coast due to storm surge and sea level rise using EEMD and EVA. J Geophys Res Oceans 118:4273–4285. CrossRefGoogle Scholar
  35. Leffler KE, Jay DA (2009) Enhancing tidal harmonic analysis: robust (hybrid) solutions. Cont Shelf Res 29:78–88. CrossRefGoogle Scholar
  36. Li J, Zeng Q (2002) A unified monsoon index. Geophys Res Lett 29:115-111–115-114. Google Scholar
  37. Lowe JA et al (2010) Past and future changes in extreme sea levels and waves. In: Understanding sea-level rise and variability. Wiley-Blackwell, pp 326–375.
  38. Lunn D, Spiegelhalter D, Thomas A, Best N (2009) The BUGS project: evolution, critique and future directions. Stat Med 28:3049–3067. CrossRefGoogle Scholar
  39. Luu QH, Tkalich P, Tay TW (2015) Sea level trend and variability around Peninsular Malaysia. Ocean Sci 11:617–628. CrossRefGoogle Scholar
  40. Mantua NJ, Hare SR, Zhang Y, Wallace JM, Francis RC (1997) A Pacific interdecadal climate oscillation with impacts on salmon production. Bull Am Meteorol Soc 78:1069–1080CrossRefGoogle Scholar
  41. Marcos M, Tsimplis MN, Shaw AGP (2009) Sea level extremes in southern Europe. J Geophys Res Oceans. Google Scholar
  42. Marcos M, Calafat FM, Berihuete Á, Dangendorf S (2015) Long-term variations in global sea level extremes. J Geophys Res Oceans 120:8115–8134. CrossRefGoogle Scholar
  43. Mawdsley R, Haigh ID (2016) Spatial and temporal variability and long-term trends in skew surges globally. Front Mar Sci 3:29. CrossRefGoogle Scholar
  44. Mawdsley RJ, Haigh ID, Wells NC (2015) Global secular changes in different tidal high water, low water and range levels. Earth’s Fut 3:66–81. CrossRefGoogle Scholar
  45. Menéndez M, Woodworth PL (2010) Changes in extreme high water levels based on a quasi-global tide-gauge data set. J Geophys Res 115:C10011. CrossRefGoogle Scholar
  46. Neumann B, Vafeidis AT, Zimmermann J, Nicholls RJ (2015) Future coastal population growth and exposure to sea-level rise and coastal flooding—a global assessment. PLoS ONE 10:1–34. Google Scholar
  47. Newey WK, West KD (1987) A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix. Econometrica 55:703–708. CrossRefGoogle Scholar
  48. Ozsoy O, Haigh ID, Wadey MP, Nicholls RJ, Wells NC (2016) High-frequency sea level variations and implications for coastal flooding: a case study of the Solent. UK Cont Shelf Res 122:1–13. CrossRefGoogle Scholar
  49. Pawlowicz R, Beardsley B, Lentz S (2002) Classical tidal harmonic analysis including error estimates in MATLAB using T_TIDE. Comput Geosci 28:929–937. CrossRefGoogle Scholar
  50. Pugh D, Woodworth PL (2014) Sea-level science: understanding tides, surges, tsunami and mean sea-level changes. Cambridge University Press, New YorkCrossRefGoogle Scholar
  51. Rong Z, Liu Y, Zong H, Cheng Y (2007) Interannual sea level variability in the South China Sea and its response to ENSO. Glob Planet Change 55:257–272. CrossRefGoogle Scholar
  52. Saji NH, Goswami BN, Vinayachandran PN, Yamagata T (1999) A dipole mode in the tropical Indian Ocean. Nature 401:360–363Google Scholar
  53. Slangen ABA, Adloff F, Jevrejeva S, Leclercq PW, Marzeion B, Wada Y, Winkelmann R (2016) A review of recent updates of sea-level projections at global and regional scales. Surv Geophys. Google Scholar
  54. Soumya M, Vethamony P, Tkalich P (2015) Inter-annual sea level variability in the southern South China Sea. Glob Planet Change 133:17–26. CrossRefGoogle Scholar
  55. Sturtz S, Ligges U, Gelman A (2005) R2WinBUGS: a package for running WinBUGS from R. J Stat Softw 12:1–16CrossRefGoogle Scholar
  56. Talke SA, Orton P, Jay DA (2014) Increasing storm tides in New York Harbor, 1844–2013. Geophys Res Lett 41:3149–3155. CrossRefGoogle Scholar
  57. Tan CK, Ishizaka J, Matsumura S, Yusoff FM, Mohamed MIH (2006) Seasonal variability of SeaWiFS chlorophyll a in the Malacca Straits in relation to Asian monsoon. Cont Shelf Res 26:168–178. CrossRefGoogle Scholar
  58. Tkalich P, Vethamony P, Babu MT, Malanotte-Rizzoli P (2013) Storm surges in the Singapore Strait due to winds in the South China Sea. Nat Hazards 66:1345–1362. CrossRefGoogle Scholar
  59. Torres RR, Tsimplis MN (2014) Sea level extremes in the Caribbean Sea. J Geophys Res Oceans 119:4714–4731. CrossRefGoogle Scholar
  60. Tsimplis MN, Woodworth PL (1994) The global distribution of the seasonal sea level cycle calculated from coastal tide gauge data. J Geophys Res 99:16031–16039. CrossRefGoogle Scholar
  61. Wahl T, Chambers DP (2015) Evidence for multidecadal variability in US extreme sea level records. J Geophys Res Oceans 120:1527–1544. CrossRefGoogle Scholar
  62. Wahl T, Chambers DP (2016) Climate controls multidecadal variability in U.S. extreme sea level records. J Geophys Res Oceans 121:1274–1290. CrossRefGoogle Scholar
  63. Wang L, Chen W (2013) An intensity index for the east Asian winter monsoon. J Climate 27:2361–2374. CrossRefGoogle Scholar
  64. Wang G, Su J, Ding Y, Chen D (2007) Tropical cyclone genesis over the south China sea. J Mar Syst 68:318–326. CrossRefGoogle Scholar
  65. Wang B, Huang F, Wu Z, Yang J, Fu X, Kikuchi K (2009) Multi-scale climate variability of the South China Sea monsoon: a review. Dyn Atmos Oceans 47:15–37. CrossRefGoogle Scholar
  66. West M, Harrison J (1997) Bayesian forecasting and dynamic models, 2nd edn. Springer series in statistics. Springer, New York. Google Scholar
  67. White NJ et al (2014) Australian sea levels-Trends, regional variability and influencing factors. Earth Sci Rev 136:155–174. CrossRefGoogle Scholar
  68. Wolter K, Timlin MS (1993) Monitoring ENSO in COADS with a seasonally adjusted principal component index. Paper presented at the 17th climate diagnostics workshop, Norman, OKGoogle Scholar
  69. Wolter K, Timlin MS (1998) Measuring the strength of ENSO events: how does 1997/98 rank? Weather 53:315–324. CrossRefGoogle Scholar
  70. Woodworth PL, Blackman DL (2004) Evidence for systematic changes in extreme high waters since the mid-1970s. J Climate 17:1190–1197CrossRefGoogle Scholar
  71. Wu Z, Huang NE (2009) Ensemble empirical mode decomposition: a noise-assisted data analysis method. Adv Adapt Data Anal 01:1–41. CrossRefGoogle Scholar
  72. Wyrtki K (1961) Physical oceanography of the Southeast Asian waters. Scripps Institution of Oceanography, UC San Diego.

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Department of Marine Science and TechnologyVietnam National UniversityHanoiVietnam
  2. 2.Asian School of the EnvironmentNanyang Technological UniversitySingaporeSingapore
  3. 3.Earth Observatory of SingaporeNanyang Technological UniversitySingaporeSingapore
  4. 4.Department of Mathematics and StatisticsUniversity of New MexicoAlbuquerqueUSA

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