Journal of Korean Society of Coastal and Ocean Engineers (한국해안·해양공학회논문집)

Korean Society of Coastal and Ocean Engineers (한국해안·해양공학회)
권호 표지
DOI QR코드

DOI QR Code

Hierarchical Bayesian Model Based Nonstationary Frequency Analysis for Extreme Sea Level

계층적 베이지안 모델을 적용한 극치 해수위 비정상성 빈도 분석

  • Kim, Yong-Tak (Department of Civil Engineering, Chonbuk National University) ;
  • Uranchimeg, Sumiya (Department of Civil Engineering, Chonbuk National University) ;
  • Kwon, Hyun-Han (Department of Civil Engineering, Chonbuk National University) ;
  • Hwang, Kyu Nam (Department of Civil Engineering, Chonbuk National University)
  • Received : 2016.01.07
  • Accepted : 2016.02.03
  • Published : 2016.02.29
Download PDF
⟨ Previous Next ⟩

Abstract

Urban development and population increases are continuously progressed in the coastal areas in Korea, thus it is expected that vulnerability towards coastal disasters by sea level rise (SLR) would be accelerated. This study investigated trend of the sea level data using Mann-Kendall (MK) test, and the results showed that the increasing trends of annual average sea level at 17 locations were statistically significant. For annual maximum extremes, seven locations exhibited statistically significant trends. In this study, non-stationary frequency analysis for the annual extreme data together with average sea level data as a covariate was performed. Non-stationary frequency analysis results showed that sea level at the coastal areas of Korean Peninsula would be increased from a minimum of 60.33 mm to a maximum of 214.90 mm by 2100.

국내의 연안은 지속적 발전으로 해수면 상승(sea level rise, SLR)으로 인한 연안재해 취약성이 가중될 것으로 전망되고 있다. 본 연구에서는 평균해수면 상승에 따른 극치조위 자료에 대한 비정상성 빈도분석을 수행하였다. Mann-Kendall(MK) 검정 결과 연평균조위(annual average tide)의 경우 17개 지점에서 경향성이 통계적으로 유의한 것으로 나타났으며, 연극치 조위의 경우에는 7개 지점에서 유의한 것으로 나타났다. 비정상성 빈도 해석 결과 2100년에 한반도 연안의 극치 해수면 변화는 최소 60.33 mm에서 최대 214.90 mm까지 증가하는 것으로 분석되었다.

Keywords

References

  1. Cho, K.W., Lee, H.M., Roh, B.H., Kang, J.E. and Nobuoka, H. (2011). National Assessment on Sea Level Rise Impact of Korean Coast in the Socioeconomic Context I. 11-41-01, Korea Environment Institute (in Korean).
  2. Cho, K.W. and Maeng, J.H. (2007). Some Thoughts on Direction to Cope with the Sea level Rise in Korea. Journal of the Korean Society for Marine Environmental Engineering, 10(4), 227-234, (in Korean).
  3. Flick, R.E., Knuuti, K. and Gill, S.K. (2013). Matching mean sea level rise projections to local elevation datums. J. Waterway Port Coastal Ocean Eng, 139(2), 142-146. https://doi.org/10.1061/(ASCE)WW.1943-5460.0000145
  4. Gilks, W. R., Best, N.G. and Tan, K.K.C. (1995). Adaptive rejection metropolis sampling within Gibbs sampling. Journal of the Royal Statistical Society. Series C, 44(4), 455-472.
  5. IPCC, (2001): Climate Change 2001: The Scientific Basis. Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change [Houghton, J.T.,Y. Ding, D.J. Griggs, M. Noguer, P.J. van der Linden, X. Dai, K. Maskell, and C.A. Johnson (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 881
  6. IPCC, (2014): Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, R.K. Pachauri and L.A. Meyer (eds.)]. IPCC, Geneva, Switzerland, 151.
  7. Kendall, M. and Gibbons, J.D. (1990). Rank correlation methods. E.Arnold. London, 260.
  8. Kim, G.S. (2006). Review of Statistical Methods for Hydrologic-Meteorological Data Analysis. 2006 Subcommittee Research Task Report, 373-398, (in Korean).
  9. Kwon, H.H., Brown, C., Xu, K. and Lall, U. (2009). Seasonal and annual maximum streamflow forecasting using climate information: application to the Three Gorges Dam in the Yangtze River basin, China. Hydrological Sciences Journal des Sciences Hydrologiques, 54(3), 582-595. https://doi.org/10.1623/hysj.54.3.582
  10. Lee, C.E., Kim, S.U. and Lee, Y.S. (2014). Estimation of the regional future sea level rise using long-term tidal data in the Korean peninsula. J. Korea water resources association, 47(9) 753-766, (in Korean).
  11. Lee, J.J., Kwon, H.H. and Kim, T.W. (2010). Concept of Trend Analysis of Hydrologic Extreme Variables and Nonstationary Frequency Analysis. Korean society of civil engineers, 30(4b), 389-397, (in Korean).
  12. Milne, G.A., Gehrels, W.R., Hughes, C.W. and Tamisiea, M.E. (2009). Identifying the causes of sea-level change. Nature Geoscience, 2(7), 471-478. https://doi.org/10.1038/ngeo544
  13. Oh, J.S., Jang, D.W., Kim, H.S. and Seo, B.H. (2005). A study on Trend Analysis of Time Series Data. Korean Society of Civil Engineers, 1408-1411, (in Korean).
  14. Rahmstorf, S. (2007). A semi-empirical approach to projecting future sea-level rise. Science, 315(5810), 368-370. https://doi.org/10.1126/science.1135456
  15. Stalnacke, P., Aakeroy, P.A., Blicher-Mathiesen, G., Iital, A., Jansons, V., Koskiaho, J., Kyllmar, K., Lagzdins, A., Pengerud, A. and Povilaitis, A. (2014). Temporal trends in nitrogen concentrations and losses from agricultural catchments in the Nordic and Baltic countries. Agriculture, Ecosystems and Environment, 198, 94-103. https://doi.org/10.1016/j.agee.2014.03.028
  16. U.S. Army Corps of Engineers. (2011). Sea-level change considerations for civil works programs. Circular No.1165-2-212, Washington DC.
  17. Uranchimeg, S., Kim, Y.T., Kwon, H.H. and Hwang, K.N. (2015). A Study on Trend Analysis in Sea Level Data Through MK Test and Quantile Regression Analysis. Journal of Korean Society of Coastal and Ocean Engineers. 27(2), 94-104, (in Korean). https://doi.org/10.9765/KSCOE.2015.27.2.94