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비정상성 확률분포 및 재현기간을 고려한 홍수빈도분석

Flood Frequency Analysis Considering Probability Distribution and Return Period under Non-stationary Condition

  • 김상욱 (강원대학교 공과대학 토목공학과) ;
  • 이영섭 (강원대학교 공과대학 토목공학과)
  • Kim, Sang Ug (Department of Civil Engineering, Kangwon National University) ;
  • Lee, Yeong Seob (Department of Civil Engineering, Kangwon National University)
  • 투고 : 2015.03.04
  • 심사 : 2015.06.01
  • 발행 : 2015.07.31

초록

본 연구에서는 모수(parameter)가 시간에 따라 변화하는 비정상성 확률분포를 훙수빈도분석에 적용하였다. 또한, 비정상성을 가정한 재현기간 및 위험도를 추정하였다. GEV (Generalized Extreme Value) 분포를 사용하여 정상성 및 비정상성 모형 4개를 구축하였으며 비정상성 모형은 위치모수(location parameter)만 선형경향성을 가지는 경우, 규모모수(scale parameter)만 선형경향성을 가지는 경우, 위치 및 규모모수가 모두 선형경향성을 가지는 경우의 3가지로 구분되었다. 구축된 4개의 모형 중 적합모형을 선정하기 위해 상대적 우도비 검정과 Akaike 정보기준을 사용하였으며, 우리나라의 8개 다목적댐(충주댐, 소양강댐, 안동댐, 임하댐, 합천댐, 대청댐, 섬진강댐, 주암댐)으로부터 취득된 과거 관측 댐 유입량을 사용하여 제안된 절차를 적용하고 결과를 비교분석하였다. 적합모형 선정 결과 합천댐과 섬진강댐이 비정상성 GEV 모형에 적합한 것으로 분석되었고, 나머지 6개 지점의 다목적댐들은 정상성 모형에 적합한 것으로 분석되었다. 특히 합천댐과 섬진강댐의 경우 비정상성 가정에서 산정된 재현기간이 정상성 가정에서 산정된 재현기간보다 작게 산정되었음을 알 수 있었다.

This study performed the non-stationary flood frequency analysis considering time-varying parameters of a probability density function. Also, return period and risk under non-stationary condition were estimated. A stationary model and three non-stationary models using Generalized Extreme Value(GEV) were developed. The only location parameter was assumed as time-varying parameter in the first model. In second model, the only scale parameter was assumed as time-varying parameter. Finally, the both parameters were assumed as time varying parameter in the last model. Relative likelihood ratio test and Akaike information criterion were used to select appropriate model. The suggested procedure in this study was applied to eight multipurpose dams in South Korea. Using relative likelihood ratio test and Akaike information criterion it is shown that the inflow into the Hapcheon dam and the Seomjingang dam were suitable for non-stationary GEV model but the other six dams were suitable for stationary GEV model. Also, it is shown that the estimated return period under non-stationary condition was shorter than those estimated under stationary condition.

키워드

참고문헌

  1. Akaike, H. (1974). "A new look at the statistical model identification." IEEE Transactions on Automatic Control, Vol. 19, No. 6, pp. 716-723. https://doi.org/10.1109/TAC.1974.1100705
  2. Bedient, P.B., and Hurber, W.C. (2001). Hydrology and floodplain analysis, 3rd Ed. Prentice-Hall, Upper Saddle River, NJ 07458, USA.
  3. Coles, S. (2001). An introduction to statistical modeling of extreme values. Springer-Verlag, London, UK.
  4. Delgado, J.M., Apel, H., and Merz, B. (2010). "Flood trends and variability in the Mekong river." Hydrology and Earth System Sciences, Vol. 14, pp. 407-418. https://doi.org/10.5194/hess-14-407-2010
  5. Fisher, R.A., and Tippett, L.H.C. (1928). "Limiting forms of the frequency distribution of the largest or smallest member of a sample." Mathematical Proceedings of the Cambridge Philosophical Society, Vol. 24, No. 2, pp. 180-190. https://doi.org/10.1017/S0305004100015681
  6. Gumbel, E.J. (1941). "The return period of flood flows." The Annals of Mathematical Statistics, Vol. 12, No. 2, pp. 163-190. https://doi.org/10.1214/aoms/1177731747
  7. Gumbel, E.J. (1958). Statistics of extremes. Columbia University Press, New York, USA.
  8. He, Y., Bardossy, A., and Brommundt, J. (2006). "Nonstationary flood frequency analysis in southern Germany." The 7th International Conference on Hydro Science and Engineering, Philadelphia, USA.
  9. Hirsch, R.M., and Ryberg, K.R. (2012). "Has the magnitude of floods across the USA changed with global $CO_2$ levels?" Hydrological Sciences Journal, Vol. 57, No. 1, pp. 1-9. https://doi.org/10.1080/02626667.2011.621895
  10. Jenkinson, A.F. (1955). "The frequency distribution of the annual maximum(or minimum) values of meteorological elements." Quarterly Journal of the Royal Meteorological Society, Vol. 81, pp. 158-171. https://doi.org/10.1002/qj.49708134804
  11. Katz, R.W., Parlange, M.B., and Naveau, P. (2002). "Statistics of extremes in hydrology." Advances in Water Resources, Vol. 25, No. 8-12, pp. 1287-1304. https://doi.org/10.1016/S0309-1708(02)00056-8
  12. Koutsoyiannis, D. (2004). "Statistics of extremes and estimation of extreme rainfall: I. Theoretical investigation." Hydrological Sciences Journal, Vol. 49, No. 4, pp. 575-590. https://doi.org/10.1623/hysj.49.4.575.54430
  13. Milly, P.C.D., Betancourt, J., Falkenmark, M., Hirsch, R.M., Kundzewicz, Z.W., Lettenmaier, D.P., and Stouffer, R.J. (2008). "Stationarity is dead: Whiter water management?" Science, Vol. 319, pp. 573-574. https://doi.org/10.1126/science.1151915
  14. Novotny, E.V., and Stefan, H.G. (2007). "Streamflow in Minnesota: indicator of climate change." Journal of Hydrology, Vol. 334, No. 3-4, pp. 319-333. https://doi.org/10.1016/j.jhydrol.2006.10.011
  15. Olsen, J.R., Lambert, J.H., and Haimes, Y.Y. (1998). "Risk of extreme events under nonstationary condition." Risk Analysis, Vol. 18, No. 4, pp. 497-510. https://doi.org/10.1111/j.1539-6924.1998.tb00364.x
  16. Olsen, J.R., Stedinger, J.R., Matalas, N.C., and Stakhiv, E.Z. (1999). "Climate variability and flood frequency estimation for the upper Mississippi and lower Missouri rivers." Journal of American Water Resources Association, Vol. 35, No. 6, pp. 1509-1523. https://doi.org/10.1111/j.1752-1688.1999.tb04234.x
  17. Parey, S., and Hoang, T.T.H. (2010). "Different ways to compute temperature return levels in the climate change context." Environmetrics, Vol. 21, pp. 698-718. https://doi.org/10.1002/env.1060
  18. Parey, S., Malek, F., Laurent, C., Dacunha-Castelle, D. (2007). "Trends and climate evolution: statistical approach for very high temperatures in France." Climate Change, Vol. 81, No. 3, pp. 331-352. https://doi.org/10.1007/s10584-006-9116-4
  19. Rao, A.R., and Hamed, K.H. (2000). Flood frequency analysis. CRC Press, Boca Raton, Florida 33431, USA.
  20. Salas, J.D., and Obeysekera, J. (2014). "Revisiting the concepts of return period and risk for nonstationary hydrologic extreme events." Journal of Hydrologic Engineering, Vol. 19, No. 3, pp. 554-568. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000820
  21. Shin, H.J., Ahn, H.J., and Heo, J.H. (2014). "A study on the changes of return period considering nonstationarity of rainfall data." Journal of Korea Water Resources Association, Vol. 47, No. 5, pp. 447-457. (In Korean) https://doi.org/10.3741/JKWRA.2014.47.5.447
  22. Shin, J.Y., Park, Y.J., and Kim, T.W. (2013). "Estimation of future design rainfalls in administrative districts using nonstationary GEV model." Journal of Korean Society of Hazard Mitigation, Vol. 13, No. 3, pp. 147-156. (In Korean) https://doi.org/10.9798/KOSHAM.2013.13.3.147
  23. Sivapalan, M., and Samuel, J.M. (2009). "Transcending limitations of stationarity and the return period: process-based approach to flood estimation and risk assessment." Hydrological Processes, Vol. 23, pp. 1671-1675. https://doi.org/10.1002/hyp.7292
  24. Stephenson, A.G. (2011). ismev: An introduction to statistical modeling of extreme values, R package version 1.35 ed.
  25. Strupczewski, W.G., Singh, V.P., and Feluch, W. (2001a). non-stationary approach to at-stie flood frequency modelling. I. Maximum likelihood estimation." Journal of Hydrology, Vol. 248, No. 1, pp. 123-142. https://doi.org/10.1016/S0022-1694(01)00397-3
  26. Strupczewski, W.G., Singh, V.P., and Mitosek, H.T. (2001b). "Non-stationary approach to at-site flood frequency modeling. III. Flood frequency analysis of Polish rivers." Journal of Hydrology, Vol. 248, No. 1, pp. 152-167. https://doi.org/10.1016/S0022-1694(01)00399-7
  27. Wilks, S.S. (1938). "The large-sample distribution of the likelihood ratio for testing composite hypotheses." The Annals of Mathematical Statistics, Vol. 9, No. 1, pp. 60-62. https://doi.org/10.1214/aoms/1177732360

피인용 문헌

  1. A Study on the Changes of Design Flood Quantiles based on Rainfall Quantile Estimation Methods in Han River Basin vol.16, pp.1, 2016, https://doi.org/10.9798/KOSHAM.2016.16.1.73
  2. Introduction and application of non-stationary standardized precipitation index considering probability distribution function and return period pp.1434-4483, 2018, https://doi.org/10.1007/s00704-018-2500-y