한국습지학회지 (Journal of Wetlands Research)

  • 제13권3호
  • /
  • Pages.499-514
  • /
  • 2011
  • /
  • 1229-6031(pISSN)
  • /
  • 2384-0056(eISSN)
한국습지학회 (Korean Wetlands Society)
권호 표지
DOI QR코드

DOI QR Code

기후변동을 고려한 조건부 GEV 분포를 이용한 비정상성 빈도분석

Non-stationary Frequency Analysis with Climate Variability using Conditional Generalized Extreme Value Distribution

  • 김병식 (강원대학교 도시환경방재전공) ;
  • 이정기 (인하대학교 사회기반시스템공학부) ;
  • 김형수 (인하대학교 사회기반시스템공학부) ;
  • 이진원 (한국건설기술연구원 수자원환경연구본부 하천해안항만연구실)
  • 투고 : 2011.08.23
  • 심사 : 2011.11.15
  • 발행 : 2011.12.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

전통적 수문빈도분석의 기본가정은 기후와 수문사상이 정상성이라는 것으로 즉, 분포형의 매개변수들이 시간에 따라 불변이라는 것이다. 댐, 제방, 운하, 교량 등 수공 관련 기간시설물을 계획하고 설계할 때는 과거 상황을 이해하고 미래에도 그 상황이 유지될 것이라는 것을 근거로 한다. 그러나 현실은 기본가정과는 달리 수문자료들은 비정상성을 지니고 있으며 수자원관리자들에 의해 항상 기간시설물을 계획하고 설계 할 때 비정상성을 다루고자 끊임없이 노력해 왔다. 본 논문에서는 비정상성 수문빈도분석기법을 소개하고, 조건부 Generalized Extreme Value(GEV) 분포를 이용하여 비정상성 빈도분석을 실시하였다. 본 논문에서는 6개 기상관측소지점의 24시간 연최고치 강우량을 대상으로 비정상성 빈도분석을 실시하였으며 최우도법(Maximum Likelihood)을 사용하여 GEV 분포형의 매개변수를 추정하였다. 그 결과 비정상성 GEV 분포가 확률 강우량을 산정하는데 있어 적합함을 확인 할 수 있었다. 또한 ENSO(El Nino Southern Oscillation)를 나타내는 지수인 SOI(Southern Oscillation Index)를 이용하여 기후변동 고려한 비정상성 빈도분석을 실시하였다.

An underlying assumption of traditional hydrologic frequency analysis is that climate, and hence the frequency of hydrologic events, is stationary, or unchanging over time. Under stationary conditions, the distribution of the variable of interest is invariant to temporal translation. Water resources infrastructure planning and design, such as dams, levees, canals, bridges, and culverts, relies on an understanding of past conditions and projection of future conditions. But, Water managers have always known our world is inherently non-stationary, and they routinely deal with this in management and planning. The aim of this paper is to give a brief introduction to non-stationary extreme value analysis methods. In this paper, a non-stationary hydrologic frequency analysis approach is introduced in order to determine probability rainfall consider changing climate. The non-stationary statistical approach is based on the conditional Generalized Extreme Value(GEV) distribution and Maximum Likelihood parameter estimation. This method are applied to the annual maximum 24 hours-rainfall. The results show that the non-stationary GEV approach is suitable for determining probability rainfall for changing climate, sucha sa trend, Moreover, Non-stationary frequency analyzed using SOI(Southern Oscillation Index) of ENSO(El Nino Southern Oscillation).

키워드

참고문헌

  1. 권영문, 박진원, 김태웅 (2009). "강우의 증가 경향성을 고려한 목표연도 확률강우량 산정." 대한토목학회논문집, 대한토목학회, 제29권, 제2B호, pp. 131-139.
  2. 권현한, 김병식 (2009). "비정상성 Markov Chain Model을 이용한 통계학적 Downscaling 기법개발." 한국수자원학회논문집, 제42권, 제3호, pp. 213-225.
  3. 이정주, 권현한, 황규남 (2010), "극치수문자료의 계절성 분석 개념 및 비정상성 빈도해석을 이용한 확률강수량 해석", 한국수자원학회논문집, Vol. 43(8), pp. 733-745
  4. 이창환, 안재현, 김태웅 (2010), "비정상 강우빈도 해석법에 의한 확률강우량의 평가", 한국수자원학회논문집, Vol. 43(2), pp. 187-199
  5. Boorman, D.B. and Sefton, C.E,M. (1997). "Recognizing the uncertainty in the quantification of the effect of climate on hydrological response." Climate Change, Vol. 35, pp. 415-434. https://doi.org/10.1023/A:1005372407881
  6. Coles, S. (2001). An introduction to statistical modelling of extreme values, Springer, London.
  7. Cunderlik, J.M. and Burn, D.H. (2003). "Non-stationary pooled flood frequency analysis." Journal of Hydrology, Vol. 276, pp. 210-223 https://doi.org/10.1016/S0022-1694(03)00062-3
  8. Flower, H.J., and Kilsby, C.G. (2003). "A regional frequency analysis of United Kingdom extreme rainfall from 1961 to 2000." International Journal of Climatology, Vol. 23, pp. 1313-1334. https://doi.org/10.1002/joc.943
  9. Franks, S.W., and Kuczera, G. (2002), "Flood frequency analysis: Evidence and implications of secular climate variability", New South Wales. Water Resources Research, Vol. 38, No. 2, pp. 432-439.
  10. Ganguly, A.R.(2007) Climate Extremes Hydro-Meteorological Extremes and Impacts, Fall Creek Falls 2007 Workshop(www.ccs.ornl.gov)
  11. Gellens, D., and Roulin, E. (1998). "Streamflow response of Belgian catchment to IPCC climate change scenario." Journal of Hydrology, Vol. 210, pp. 242-258 https://doi.org/10.1016/S0022-1694(98)00192-9
  12. Griffis, V.W., and Stedinger, J.R. (2007). "Incorporating climate change and variability into Bulletin 17B LP3 Model." World Environmental and Water Resource Congress 2007, ASCE, Tampa, FL, USA.
  13. Griffith, G.M., Chambers, L.E.,Haylock, M.R.,Mamtom, M.J., Nicholls, N., Baek, H.J., Choi, Y., Della-Marta, P.M., Gosal, A., Iga, N.,Lata, R., Laurent, V., Maitrepierre, L., Nakamigawa, H., Ouprasitwong, N., Solofa, D, Tahani, L., Thuy, T., Tibig, L., Trewin, B., Vediapan, K., and Zhai, P.(2005) "Change in mean temperature as a predictor of extreme temperature change in the Asia-Pacific Region", International Journal of Climatology, Vol. 25, pp. 1301-1330 https://doi.org/10.1002/joc.1194
  14. Groisman PY, Karl TR, Easterling DR, Knight RW, Jamason PB, Hennessy JK, Suppiah R, Page ChM, Wibig J, Fortuniak K, Razuvaev VN, Douglas A, Forland E, Zhai PM. 1999. Changes in the probability of heavy precipitation: important indicators of climatic changes. Climatic Change Vol. 42(1): 243-283. https://doi.org/10.1023/A:1005432803188
  15. Halpert, M. S., and C. F. Ropelewski (1992), "Surface-temperature patterns associated with the Southern Oscillation", J. Clim., Vol. 5, pp. 577-593. https://doi.org/10.1175/1520-0442(1992)005<0577:STPAWT>2.0.CO;2
  16. He, Y., Bardossy, A. and Brommundtm, J. (2006). "Non-stationary flood frequency analysis in southern Germany." The 7th International Conference on HydroScience and Engineering, Philadelphia, USA
  17. Hershfield DM. (1973). "On the probability of extreme rainfall events.", Bulletin of the American Meteorological Society 54: 1013-018. Reiss R-D, Thomas M. 1997. Statistical Analysis of Extreme Values. Birkhauser: Basel, Boston, MA, Berlin.
  18. Jain, S., and U. Lall (2000), "Magnitude and timing of annual maximum floods: Trends and large-scale climatic associations for the Blacksmith Fork River", Utah, Water Resour. Res., 36(12), 3641-3651, https://doi.org/10.1029/2000WR900183
  19. Jain, S., and U. Lall (2001), "Floods in a changing climate: Does the past represent the future?", Water Resour. Res., Vol. 37(12), pp. 3193-3205 https://doi.org/10.1029/2001WR000495
  20. Karl T, Knight RW. (1998). "Secular trends of precipitation amount, frequency, and intensity in the United States.", Bulletin of the American Meteorological Society, Vol. 79(2), pp. 231-241. https://doi.org/10.1175/1520-0477(1998)079<0231:STOPAF>2.0.CO;2
  21. Katz RW (2002). "Stochastic modeling of hurricane damage.", J Appl Meteorol., Vol. 41, pp. 754-62 https://doi.org/10.1175/1520-0450(2002)041<0754:SMOHD>2.0.CO;2
  22. Katz RW, Parlange MB, Naveau P. (2002). "Statistics of extremes in hydrology.", Advances in Water Resources, Vol. 25(8-12), pp. 1287-1304. https://doi.org/10.1016/S0309-1708(02)00056-8
  23. Kendall, C., Sklash, M.G. and Bullen, T.D. (1995), "Isotope Tracers ofWater and Solute Sources in Catchments.", In Trudgill, S.T. (ed). Solute Modelling in Catchment Systems, pp. 261-303. Wiley, London.
  24. Kite, G.W. (1993). "Application of a land class hydrological model to climate change." Water Resour. Res., Vol. 29, pp. 2377-2384. https://doi.org/10.1029/93WR00582
  25. Klein Tank, A.M.G. and Konneb, G. P.(2003), "Trends in Indices of Daily Temperature and Precipitation Extremes in Europe, 1946-1999", Journal of Climate, Vol. 16, pp. 3665-3680 https://doi.org/10.1175/1520-0442(2003)016<3665:TIIODT>2.0.CO;2
  26. Milly, P. C. D., R. T. Wetherald, K. A. Dunne, and T. L. Delworth (2002), "Increasing risk of great floods in a changing climate", Nature, Vol. 415(6871), pp. 514-517 https://doi.org/10.1038/415514a
  27. Mirza. M.Q., Warrick, R.A., Ericksen, N.J., and Kenny, K.J. (1998). "Trend and persistence in precipitation in the Ganges, Brahmaputa and Meghna basin in the south Asia." Hydrol. Sci. J., Vol. 439(6), pp. 845-858.
  28. Mudersbach, C. and Jensen, J. (2010) "Nonstationary extreme value analysis of annual maximum water levels for designing coastal structures on the German North Sea Coastline.", Journal of Flood Risk Management, Vol. 3, pp. 52-62 https://doi.org/10.1111/j.1753-318X.2009.01054.x
  29. Panagoulia, D., and Dimou, G. (1997). "Sensitivity of flood events to global climate change." Journal of Hydrology, Vol. 191, pp. 208-222. https://doi.org/10.1016/S0022-1694(96)03056-9
  30. Piechota, T. C., and J. A. Dracup (1996), "Drought and regional hydrologic variation in the United States: Associations with the El Nino Southern Oscillation", Water Resour. Res., Vol. 32(5), pp. 1359-1373 https://doi.org/10.1029/96WR00353
  31. Pizaro, G., and U. Lall (2002), "El Nino and Floods in the US West: What can we expect?", Eos Trans. AGU, Vol. 83(32), pp. 349-352
  32. Porparto, A., and L. Ridolfi (1998), "Influence of weak trends on exceedance probability", Stochastic Hydrol. Hydraul., Vol. 12(1), pp. 1-15 https://doi.org/10.1007/s004770050006
  33. Rao,C.R. (1973), "Linear Statistical Inference and Its Applications(2nd ed.).", Wiley, New York
  34. Ropeleweski, C. F., and M. S. Halpert (1987), "Global and regional scale precipitation patterns associated with the El Nino/Southern Oscillation", Mon. Weather Rev., Vol. 115, pp. 1606-1626 https://doi.org/10.1175/1520-0493(1987)115<1606:GARSPP>2.0.CO;2
  35. Sankarasubramanian, A. and Upmanu Lall (2003), "Flood quantiles in a changing climate: Seasonal forecasts and causal relations", Water Resources Research, Vol. 39(5), pp. 1134,
  36. Smith RL. (1989), "Extreme value analysis of environmental time series: an application to trend detection in ground-level ozone.", Statistical Science Vol. 4, pp. 367-393. https://doi.org/10.1214/ss/1177012400
  37. Stedinger, J.R., and Crainiceanu, C.M. (2001). "Climate variability and flood-risk management, risk-based decision making in water resources." IX Proceedings of the Ninth Conference, United Engineering Foundation, ASCE, Santa Barbara, CA USA, pp. 77-86.
  38. Strupczewski, W.G., Singh, V.P., and Flench, W. (2001). "Non-stationary approach to at-site flood frequency modeling I. Maximum likelihood estimation." Journal of Hydrology, Vol. 248, pp. 123-142. https://doi.org/10.1016/S0022-1694(01)00397-3
  39. Wang, J., and Yang, P. (2005). "A compound reconstructed prediction model for nonstationary climate processes." Journal of Climatology, Vol 25, pp. 1265-1277. https://doi.org/10.1002/joc.1158