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Ocean Dynamics

, Volume 65, Issue 11, pp 1383–1397 | Cite as

Extreme water level analysis at three stations on the coast of the Northwestern Pacific Ocean

  • Jianlong Feng
  • Wensheng JiangEmail author
Article

Abstract

In this study, the data from three long-term observation stations, Aburatsu, Xiamen, and Hong Kong, which are located on the northwest Pacific Ocean coast, were analyzed to estimate the 100-year annual maximum water levels. The performances of four common frequency analysis methods, namely the Gumbel, Weibull, GEV, and GPD distributions, were evaluated. It is found that the GEV model performs best among these four distribution models in Hong Kong and Aburatsu, whereas the Gumbel distribution is the best at the Xiamen station. It is also found that the GEV model generally performs better than the Gumbel model in regard to the mean high correlation coefficient and the mean minimum root-mean-square error. Moreover, in this study, the r-largest value model was used to study temporal trends in the 50-year annual maximum water levels on the northwest Pacific coast over the past fifty years using the observation data of Hong Kong, Xiamen, and Aburatsu. The results show that there are two temporal features in the 50-year return levels at all three stations, with the first being an overall increasing trend over the whole period and the other being an oscillatory trend over the period of observation. The relationships between the temporal trends and the Pacific Decadal Oscillation (PDO), sea level rise, and change of typhoons were also analyzed in this paper. It is found that when the PDO index is shifted to be 4 years in advance, a significantly negative correlation will occur between the PDO index and the 50-year return levels. However, sea level rise and changes of typhoons cause the overall increase over the entire period.

Keywords

Extreme water level Climate change Annual maximum water level Sea level rise Pacific Decadal Oscillation (PDO) 

Notes

Acknowledgments

This work is supported by Public Science and Technology Research Funds Projects of Ocean (201305020–4) and NSFC-Shandong Joint Fund for Marine Science Research Centers (grant no. U1406401). We acknowledge the comments of two anonymous reviewers and appreciate the suggestions of the associate editor.

References

  1. Anderson PL, Meerschaert MM (1998) Modeling river flows with heavy tails. Water Resour Res 34:2271–2280. doi: 10.1029/98WR01449 CrossRefGoogle Scholar
  2. Bengtsson L, Hodges KI, Esch M, Keenlyside N, Kornblueh L, Luo JJ, Yamagata T (2007) How may tropical cyclones change in a warmer climate? Tellus Ser A Dyn Meteorol Oceanogr 59A:539–561. doi: 10.1111/j.1600-0870.2007.00251.x CrossRefGoogle Scholar
  3. Brown J, Souza A, Wolf J (2010) Surge modelling in the Eastern Irish Sea: present and future storm impact. Ocean Dyn 60:227–236. doi: 10.1007/s10236-009-0248-8 CrossRefGoogle Scholar
  4. Butler A, Heffernan JE, Tawn JA, Flather RA, Horsburgh KJ (2007) Extreme value is of decadal variations in storm surge elevations. J Mar Syst 67:189–200. doi: 10.1016/j.jmarsys.2006.10.006 CrossRefGoogle Scholar
  5. Chen J (1997) The impact of sea level rise on China’s coastal areas and its disaster hazard evaluation. J Coast Res 13:925–930Google Scholar
  6. Chow VT, Maidment DR, Mays LW (1988) Applied Hydrology. McGraw-Hill, New YorkGoogle Scholar
  7. Coles SG (2001) An Introduction to statistical modelling of extreme values. Springer, LondonCrossRefGoogle Scholar
  8. Cunnane C (1978) Unbiased plotting positions—a review. J Hydrol 37:205–222. doi: 10.1016/0022-1694(78)90017-3 CrossRefGoogle Scholar
  9. Dasgupta S, Laplante B, Murray S, Wheeler D (2009) Climate change and the future impact of storm-surge disasters in developing countries. Center for Global Development 182Google Scholar
  10. Davison AC, Ramesh NI (2000) Local likelihood smoothing sample extremes. J R Stat Soc Ser B 92:191–208CrossRefGoogle Scholar
  11. De Michele C, Salvadori G (2005) Some hydrological applications of small sample estimators of generalized Pareto and extreme value distributions. J Hydrol 301:37–53. doi: 10.1016/j.jhydrol.2004.06.015 CrossRefGoogle Scholar
  12. Dong JX, Zhang TY, Fu X, Wu W, Zhao LD, Wu SH, Yu FJ (2008) Calculation of the storm surges in the Shacheng Bay in Fujian Province in 100 years return periods. Mar Sci Bull 27:9–16, in Chinese, English abstract Google Scholar
  13. Eastoe EF, Tawn JA (2010) Statistical models for overdispersion in the frequency of peaks over threshold data for a flow series. Water Resour Res 46:W02510. doi: 10.1029/2009WR007757 CrossRefGoogle Scholar
  14. Elsner JB, Kossin JP, Jagger TH (2008) The increasing intensity of the strongest tropical cyclones. Nature 455:92–95. doi: 10.1038/nature07234 CrossRefGoogle Scholar
  15. Emanuel KA (1987) The dependence of hurricane intensity on climate. nature 326:483–485. doi: 10.1038/326483a0 CrossRefGoogle Scholar
  16. Esteban M, Webersik C, Shibayama T (2009) Effect of a global warming-induced increase in typhoon intensity on urban productivity in taiwan. Sustain Sci 4:151–163. doi: 10.1007/s11625-009-0089-x CrossRefGoogle Scholar
  17. Fan J, Farmen M, Gijbels I (1998) Local maximum likelihood estimation and inference. J R Stat Soc Ser B (Stat Methodol) 60:591–608CrossRefGoogle Scholar
  18. FEMA (Federal Emergency Management Agency of the United States), (2005) Final draft guidelines for coastal flood hazard analysis and mapping for the Pacific Coast of the United States. FEMA Study Contractor, Northwest Hydraulic Consultants. (https://www.fema.gov/national-flood-insurance-program-flood-hazard-mapping/coastal-flood-hazard-mapping-requirements)
  19. Haigh ID, Nicholls R, Wells N (2010) A comparison of the main methods for estimating probabilities of extreme still water levels. Coast Eng 57:839–849. doi: 10.1016/j.coastaleng.2010.04.002 CrossRefGoogle Scholar
  20. Hall P, Tajvidi N (2000) Nonparametric analysis of temporal trend when fitting parametric models to extreme-value data. Stat Sci, 153–167. doi:  10.1214/ss/1009212755
  21. Hosking JRM, Wallis JR (1987) Parameter and quantile estimation for the generalized pareto distribution. Technometrics 29:339–349CrossRefGoogle Scholar
  22. Hou JM, Yu FJ, Yuan Ye FX (2011) Spatial and temporal distribution of red tropical storm surge disasters in China. Mar Sci Bull 30:535–539 (in Chinese with English abstract)Google Scholar
  23. Houghton JT, Ding Y, Griggs DJ, Noguer M, Van Der Linden PJX, Dai KM, Johnson CA (2001) Climate Change 2001: the scientific basis. Contribution of Working Group 1 to the Third Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University PressGoogle Scholar
  24. Howard T, Lowe J, Horsburgh K (2010) Interpreting century-scale changes in North Sea storm surge climate derived from coupled model simulations. J Clim 23:6234–6247. doi: 10.1175/2010JCLI3520.1 CrossRefGoogle Scholar
  25. Huang W, Xu S, Nnaji S (2008) Evaluation of GEV model for frequency analysis of annual maximum water levels in the coast of United States. Ocean Eng 35:1132–1147. doi: 10.1016/j.oceaneng.2008.04.010 CrossRefGoogle Scholar
  26. IPCC (2007) Climate Change 2007: The physical science basis. Cambridge University Press, C. N. Y., ed.Google Scholar
  27. IPCC (2013) Climate change 2014: The physical science basis. Cambridge University Press, C. N. Y., ed.Google Scholar
  28. Irish JL, Resio DT, Ratcliff JJ (2008) The influence of storm size on hurricane surge. J Phys Oceanogr 38:2003–2013. doi: 10.1175/2008JPO3727.1 CrossRefGoogle Scholar
  29. Jia GD, Chen FJ, Peng P (2008) Sea surface temperature differences between the Western Equatorial Pacific and Northern South China Sea since the Pliocene and their paleoclimatic implications. Geophys Res Lett 35:L18609. doi: 10.1029/2008GL034792 CrossRefGoogle Scholar
  30. Katz RW, Parlange MB, Naveau P (2002) Statistics of extremes in hydrology. Adv Water Resour 25:1287–1304. doi: 10.1016/S0309-1708(02)00056-8 CrossRefGoogle Scholar
  31. Kottegoda N, Kottegoda NT (1997) Probability, statistics, and reliability for civil and environmental engineers. McGraw-Hill CompaniesGoogle Scholar
  32. Leadbetter MR (1983) Extremes and local dependence in stationary sequences. Z Wahrsch Verw Gebiete 65:291–306CrossRefGoogle Scholar
  33. Letetrel C, Marcos M, Martín MB, Woppelmann G (2010) Sea level extremes in Marseille (NW Mediterranean) during 1885–2008. Cont Shelf Res 30:1267–1274. doi: 10.1016/j.csr.2010.04.003 CrossRefGoogle Scholar
  34. Li FL, Jiao ML, 2012 Decadal variability of SSTA in the South China Sea and its relationship with PDO. Marine Science Bulletin (In Chinese with English abstract) 31Google Scholar
  35. Lin N, Emanuel KA, Smith JA, Vanmarcke E (2010) Risk assessment of hurricane storm surge for New York City. J Geophys Res-Atmos 115:D18121. doi: 10.1029/2009JD013630 CrossRefGoogle Scholar
  36. Lin C, Ho C, Zheng Q, Huang S, Kuo N (2011) Variability of sea surface temperature and warm pool area in the South China Sea and its relationship to the Western Pacific warm pool. J Oceanogr 67:719–724. doi: 10.1007/s10872-011-0072-x CrossRefGoogle Scholar
  37. Lombard A, Cazenave A, Dominh K, Cabanes C, Nerem RS (2005) Thermosteric sea level rise for the past 50 years; comparison with tide gauges and inference on water mass contribution. Glob Planet Chang 48:303–312. doi: 10.1016/j.gloplacha.2005.02.007 CrossRefGoogle Scholar
  38. 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 78:1069–1079. doi: 10.1175/1520-0477(1997)078<1069:APICOW>2.0.CO;2 CrossRefGoogle Scholar
  39. Marcos M, Jordà G, Gomis D, Pérez B (2011) Changes in storm surges in Southern Europe from a regional model under climate change scenarios. Glob Planet Chang 77:116–128. doi: 10.1016/j.gloplacha.2011.04.002 CrossRefGoogle Scholar
  40. Mcinnes KL, Walsh KJE, Hubbert GD, Beer T (2003) Impact of sea-level rise and storm surges on a coastal community. Nat Hazards 30:187–207CrossRefGoogle Scholar
  41. Meehl GA, Hu A, Santer BD (2009) The mid-1970s climate shift in the pacific and the relative roles of forced versus inherent decadal variability. J Clim 22:780–792. doi: 10.1175/2008JCLI2552.1 CrossRefGoogle Scholar
  42. Méndez FJ, Menéndez M, Luceño A, Losada IJ (2007) Analyzing monthly extreme sea levels with a time-dependent GEV model. J Atmos Ocean Technol 24:894–911CrossRefGoogle Scholar
  43. 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. doi: 10.1029/2009JC005997 CrossRefGoogle Scholar
  44. Menéndez M, Méndez FJ, Losada IJ, Graham NE (2008) Variability of extreme wave heights in the northeast Pacific Ocean based on buoy measurements. Geophys Res Lett 35:L22607. doi: 10.1029/2008GL035394 CrossRefGoogle Scholar
  45. Miller L, Douglas BC (2004) Mass and volume contributions to twentieth-century global sea level rise. Nature 428:406–409. doi: 10.1038/nature02309 CrossRefGoogle Scholar
  46. Mochizuki T, Ishii M, Kimoto M, Chikamoto Y, Watanabe M, Nozawa T, Sakamoto TT, Shiogama H, Awaji T, Sugiura N, Toyoda T, Yasunaka S, Tatebe H (2010) Pacific decadal oscillation hindcasts relevant to near-term climate prediction. Proc Natl Acad Sci 107:1833–1837. doi:10.1073/pnas.0906531107 CrossRefGoogle Scholar
  47. Morris JT, Sundareshwar PV, Nietch CT, Kjerfve B, Cahoon DR (2002) Responses of coastal wetlands to rising sea level. Ecology 83:2869–2877CrossRefGoogle Scholar
  48. Nicholls RJ (2002) Analysis of global impacts of sea-level rise: a case study of flooding. Phys Chem Earth A B C 27:1455–1466. doi: 10.1016/S1474-7065(02)00090-6 CrossRefGoogle Scholar
  49. Nielsen P (2009) How storm size matters for surge height. Coast Eng 56:1002–1004. doi: 10.1016/j.coastaleng.2009.02.006 CrossRefGoogle Scholar
  50. Park J, Jung H, Kim R, Oh J (2001) Modelling summer extreme rainfall over the Korean peninsula using Wakeby distribution. Int J Climatol 21:1371–1384. doi: 10.1002/joc.701 CrossRefGoogle Scholar
  51. Pickands III J (1975) Statistical inference using extreme order statistics. Ann Stat 119–131Google Scholar
  52. Rybczyk J, Cahoon D (2002) Estimating the potential for submergence for two wetlands in the Mississippi River Delta. Estuar Coasts 25:985–998CrossRefGoogle Scholar
  53. Sobey RJ (2005) Extreme low and high water levels. Coast Eng 52:63–77. doi: 10.1016/j.coastaleng.2004.09.003 CrossRefGoogle Scholar
  54. Tawn JA (1988) An extreme-value theory model for dependent observations. J Hydrol 101:227–250. doi: 10.1016/0022-1694(88)90037-6 CrossRefGoogle Scholar
  55. Tian B, Zhang L, Wang X, Zhou Y, Zhang W (2010) Forecasting the effects of sea-level rise at Chongming Dongtan Nature Reserve in the Yangtze Delta Shanghai, China. Ecol Eng 36:1383–1388. doi: 10.1016/j.ecoleng.2010.06.016 CrossRefGoogle Scholar
  56. Tibshirani RJ, Hastie TJ (1987) Local likelihood estimation. J Am Stat Assoc 559–567Google Scholar
  57. Tsimplis MN, Blackman D (1997) Extreme sea-level distribution and return periods in the Aegean and Ionian Seas. Estuar Coast Shelf Sci 44:79–89. doi: 10.1006/ecss.1996.0126 CrossRefGoogle Scholar
  58. Van G.P, Neykov NM (1998) Regional frequency analysis of extreme water levels along the dutch coast using L-moments: a preliminary study, Stochastic models of hydrological processes and their applications to problems of environmental preservation 14–20Google Scholar
  59. Viessman WJ, Lewis GL (1996) Introduction to Hydrology. Harper Collins College Publishers, New YorkGoogle Scholar
  60. Wakelin SL, Woodworth PL, Flather RA, Williams JA (2003) Sea-level dependence on the NAO over the NW European Continental Shelf. Geophys Res Lett 30:1403. doi: 10.1029/2003GL017041 CrossRefGoogle Scholar
  61. Walton TL (2000) Distributions for storm surge extremes. Ocean Eng 27:1279–1293. doi: 10.1016/S0029-8018(99)00052-9 CrossRefGoogle Scholar
  62. Webster PJ, Holland GJ, Curry JA, Chang HR (2005) Changes in tropical cyclone number, duration, and intensity in a warming environment. Science 309:1844–1846. doi: 10.1126/science.1116448 CrossRefGoogle Scholar
  63. Wu L, Wang B (2008) What has changed the proportion of intense hurricanes in the last 30 years? J Clim 21:1432–1439. doi: 10.1175/2007JCLI1715.1 CrossRefGoogle Scholar
  64. Yan Y, Qi Y, Zhou W (2012) Variability of tropical cyclone occurrence date in the South China Sea and its relationship with SST warming. Dyn Atmos Oceans 6:45–59. doi: 10.1016/j.dynatmoce.2012.05.001 CrossRefGoogle Scholar
  65. Zhang H, Sheng J (2015) Examination of extreme sea levels due to storm surges and tides over the northwest Pacific Ocean. Cont Shelf Res 93:81–97. doi: 10.1016/j.csr.2014.12.001 CrossRefGoogle Scholar
  66. Zhang WZ, Hu JY, Shang SP, Chen MN, She WM (2004) On the characteristics of storm surges along Fujian coast. Mar Sci Bull 23(3):12–19, in Chinese, English abstractGoogle Scholar
  67. Zhang Q, Xu C, Chen YD, Liu C (2009) Extreme value analysis of annual maximum water levels in the Pearl River Delta China. Front Earth Sci China 3:154–163CrossRefGoogle Scholar
  68. Zhang WZ, Shi FY, Hong HS, Shang SP, Kirby JT (2010) Tide-surge interaction intensified by the Taiwan Strait, J Geophys Res Oceans, C06012. doi:  10.1029/2009JC005762
  69. Zhou J, Yan X (2008) Frequency analysis on annual maximum water level in Huangpu River. In Conference of Association of Professional Committee of China Maritime 482–488Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Physical Oceanography LaboratoryOcean University of ChinaQingdaoChina
  2. 2.Laboratory of Marine Environment and EcologyOcean University of ChinaQingdaoChina

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