Journal of the Korean Society of Hazard Mitigation (한국방재학회 논문집)

Korean Society of Hazard Mitigation (한국방재학회)
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Non-stationary Frequency Analysis for Extreme Precipitation based on Representative Concentration Pathways (RCP) Climate Change Scenarios

RCP 기후변화 시나리오 기반의 미래 극한강수의 비정상성 빈도해석

  • 성장현 (국립기상연구소 기후연구과) ;
  • 김병식 (국립강원대학교 방재전문대학원 도시환경방재학과) ;
  • 강현석 (국립기상연구소 기후연구과) ;
  • 조천호 (국립기상연구소 기후연구과)
  • Received : 2012.04.03
  • Accepted : 2012.04.16
  • Published : 2012.04.30
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Abstract

Due to the Climate Change and Climate Variability in the world, the temperature, precipitation, evaporation etc, hydrologic cycle components are changing rapidly compared with the past. Previous researches have presented many results of extreme hydrological events and frequency increases. In Korea, during latest 10years (1999~2008), the frequency of localized heavy rain above 100 mm in a day occurred totally 385 times, compared 1970~80s when the frequency was 222 times, it increased 1.7 times. For 2011, from early July to mid-August, due to the rainy season and localized heavy precipitation, the accumulated precipitation reached 1285.3 mm. In Seoul and metropolitan areas, the localized heavy precipitation over design precipitation of 100year frequency happened, causing widely flooded area and causing property damage and human casualties in the downtown area. According to the latest extreme flood moved to upper tail of existing probability density function, it is expected that future flood damage frequency and intensity will increase comparing present situation. In this paper, in order to project the future influence of extreme flood of the Korean peninsula, comparing the IPCC 5 Report (AR5), according to the Representative Concentration Pathways (RCP), newly promoted and simulated regional climate model was applied to summary precipitation and considering to the exterior factors, the non-stationary frequency analysis was performed. This result range across the South Korea areas, and the whole increase of extreme precipitation was checked. Especially, following to RCP 4.5 and 8.5 parts, the future 20year frequency average would be reduced to 12.5years, 11.9years separately in the place, so it is expected that the flood safety will be reduced.

전 세계적으로 기후변화와 변동으로 인해 기온, 강수 등의 수문순환 요소들이 과거와는 다르게 빠른 속도로 변화하고 있으며 많은 선행 연구들은 극한 수문사상의 규모 및 빈도가 증가한다는 결과를 보여 주었다. 우리나라의 경우 최근 10년간(1999~2008년) 1일 100 mm 이상 집중호우의 발생빈도는 총 385회로, 70~80년대 222회에 비해 무려 1.7배나 증가했으며 2011년의 경우 7월 초부터 8월 중순까지 지속적인 장마와 집중호우로 인해 1285.3 mm의 누적강수량이 발생하였으며, 서울 및 수도권 지역에서 100년 빈도 설계강수량을 초과하는 집중호우 발생으로 서울의 도심지역 곳곳이 침수되어 많은 재산피해와 인명피해를 입혔다. 최근 발생하는 극한강수는 기존 확률밀도함수의 상위 꼬리부분 쪽으로 이동함으로써 미래에 발생하게 될 물 관련 재해의 빈도 및 강도가 현재에 비하여 증가할 것으로 전망되고 있다. 본 연구에서는 기후변화가 한반도의 미래 극한강수의 발생에 미치는 영향을 전망하기 위해 IPCC 5차 보고서(AR5)에 대비하여 새롭게 권장되는 대표농도경로(Representative Concentration Pathways, RCP)에 따라 모의된 지역기후모형의 여름철 강수량을 외부인자로 고려한 비정상성 빈도해석을 실시하였다. 그 결과, 남한지역에 걸쳐서 극한강수의 전반적인 증가를 확인하였으며 특히, RCP4.5와 8.5를 따르는 미래에는 20년 빈도가 지점평균 각각 12.5년, 11.9년으로 줄어들어 치수안전도가 저하되는 것으로 전망되었다.

Keywords

Acknowledgement

Supported by : 한국건설교통기술평가원, 한국연구재단

References

  1. 국립기상연구소 (2011) IPCC 5차 평가보고서 대응을 위한 기후 변화 시나리오 보고서 2011, pp. 2.
  2. 권영문, 박진원, 김태웅 (2009) 강우의 증가 경향성을 고려한 목표연도 확률강우량 산정, 대한토목학회 논문집, 대한토목학회, 29(2B), pp. 131-139.
  3. 권현한, 김병식 (2009) 비정상성 Markov Chain Model을 이용한 통계학적 Downscaling 기법 개발, 한국수자원학회 논문집, 한국수자원학회, 제42권, 제3호, pp. 213-225. https://doi.org/10.3741/JKWRA.2009.42.3.213
  4. 김병식, 서병하, 김남원 (2003) 전이함수모형과 일기발생모형을 이용한 유역규모 기후변화시나리오의 작성, 한국수자원학회 논문집 한국수자원학회, 제36권 제3호, pp. 345-363.
  5. 신진호, 이효신, 권원태 (2010) 역학적 상세화 기법을 활용한 우리나라 극한 강수사상 전망: 일최대강수량 변화 분석, 한국수자원학회 학술발표회 초록집, 한국수자원학회, pp. 350.
  6. 오재호, 홍성길 (1995) 대기중 CO2 증가에 따른 한반도 강수량 변화, 한국수자원학회지, 한국수자원학회, 제28권, 제3호, pp. 143-157.
  7. 오태석, 문영일, 오근택 (2008) 군집분석과 변동핵밀도함수를 이용한 지역빈도해석의 확률강우량 산정, 대학토목학회 논문집, 대한토목학회, 28(2B), pp. 169-278.
  8. 윤용남, 유철상, 이재수, 안재현 (1999) 지구온난화에 따른 홍수 및 가뭄 발생빈도의 변화와 관련하여: 1. 연/월 강수량의 변 화에 따른 일강수량 분포의 변화분석, 한국수자원학회 논문집, 한국수자원학회, 제32권, 제6호, pp. 617-625.
  9. 이정주, 권현한, 황규남 (2010) 극치수문자료의 계절성 분석 개념 및 비정상성 빈도해석을 이용한 확률강수량 해석, 한국수자원학회 논문집, 한국수자원학회, 제43권, 제8호, pp. 733-745. https://doi.org/10.3741/JKWRA.2010.43.8.733
  10. 이창환, 안재현, 김태웅 (2010) 비정상성 강우빈도해석법에 의한 확률강우량의 평가, 한국수자원학회 논문집, 한국수자원학회, 제43권, 제2호, pp. 187-199. https://doi.org/10.3741/JKWRA.2010.43.2.187
  11. 정대일, 제리 스테딘져, 성장현, 김영오 (2008) 기후변화를 고려한 홍수 위험도 평가, 대한토목학회 논문집 대한토목학회, 28(1B), pp. 55-64.
  12. 허준행, 이영석, 신홍준, 김경덕 (2007) 우리나라 강우자료의 지역빈도해석 적용성, 대한토목학회 논문집, 대한토목학회, 27(2B), pp. 101-111.
  13. Beniston, M. and Coauthors (2007) Future extreme events in European climate: An exploration of regional climate model projections. Climatic Change, 81, pp. 71-95.
  14. Bister, M. and Emanuel, K.A. (1998) Dissipative Heating and Hurricane Intensity. Meteorol. Atmos. Phys. 65, pp. 233-240. https://doi.org/10.1007/BF01030791
  15. Boo, K.-O., Kwon, W.-T., Oh, J.-H. and Baek, H.-J. (2004) Response of global warming on regional climate change over Korea: an experiment with the MM5 model. Geophysical Research Letters, 31, L21206, DOI: 10.1029/2004 GL021171.
  16. Kwon, W.-T. and Baek, H.-J. (2006) Change of extreme events of temperature and precipitation over Korea using regional projection of future climate change. Geophysical Research Letters, 33, L01701, doi: 10.1029/2005GL023378.
  17. 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, 35, pp. 415-434 https://doi.org/10.1023/A:1005372407881
  18. Chang, H. and Kwon, W.-T. (2007) Spatial variation of summer precipitation trends in South Korea, 1973-2005. Environ. Res. Lett., doi: 10.1088/1748-9326/2/4/045012.
  19. Collins, W.J., Bellouin, N., Doutriaux-Boucher, M., Gedney, N., Hinton, T., Jones, C.D., Liddicoat, S., Martin, G., O'Connor, F., Rae, J., Senior, C., Totterdell, I., Woodward, S., Reichler, T., and Kim, J. (2008) Evaluation of HadGEM2 model, Hadley Centre Technical Note, 74, pp. 44.
  20. Douglas, E.M., Vogel, R.M. and Kroll, C.N. (2001) Trends in floods and low flows in the United States: impact of spatial correlation. Journal of Hydrology, 240(1-2), pp. 90-105.
  21. El Adlouni, S., Ouarda, T.B.M. J., Zhang, X., Roy, R. and Bobee, B. (2005) Estimation of non-stationary GEV model parameters.In Proceedings, 4th Conference on Extreme Value Analysis, Gothenburg, August 15-19, 2005.
  22. Emanuel, K.A. and D.S. Nolan (2004) Tropical cyclone activity and global climate. 26th Conf. on Hurricanes and Tropical Meteorology, Miami, FL, Amer. Meteor. Soc., pp. 240-241.
  23. 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.
  24. Ganguly, A.R. (2007) Climate extremes hydro-meteorological extremes and impacts, fall creek Falls 2007 Workshop (www.ccs.ornl.gov)
  25. Gellens, D. and Roulin, E. (1998) Streamflow response of Belgian catchment to IPCC climate change scenario. Journal of Hydrology, 210, pp. 242-258. https://doi.org/10.1016/S0022-1694(98)00192-9
  26. Greenwood, J.A., Landwehr, J.M., Matalas, N.C. and Wallis, J.R. (1979) Probability weighted moments: definition and relation to parameters of several distributions expressible inverse form. Water Resources Research, 15, pp. 1049-1054. https://doi.org/10.1029/WR015i005p01049
  27. Groisman, P.Y., Karl, T.R., Easterling, D.R., Knight, R.W., Jamason, P.B., Hennessy, J.K., Suppiah, R., Page, C.M., Wibig, J., Fortuniak, K., Razuvaev, V.N., Douglas, A., Forland, E., Zhai, P.M. (1999) Changes in the probability of heavy precipitation: important indicators of climatic changes. Climatic Change, Vol. 42, No. 1, pp. 243-283. https://doi.org/10.1023/A:1005432803188
  28. He, Y., Brdossy, A. and Brommundt, J. (2006) Non-stationary flood frequency analysis in southern Germany, The 7th International Conference on HydroScience and Engineering, Philadelphia.
  29. Hosking, J.R., Wallis, J.R. and Wood, E.F. (1985) Estimation of the GEV distribution by the method of probability-weighted moment. Technometrics, Vol. 27, No. 3, pp. 251-261. https://doi.org/10.1080/00401706.1985.10488049
  30. Im, E.-S. and Kwon, W.-T. (2007) Characteristics of extreme climate sequences over Korea using a regional climate change scenario. SOLA, 3, pp. 17-20. https://doi.org/10.2151/sola.2007-005
  31. Jain, S. and Lall, U. (2000) Magnitude and timing of annual maximum floods: Trends and large-scale climatic associations for the Blacksmith Fork River, Water Resources Research, Vol. 36, No. 12, pp. 3641-3651. https://doi.org/10.1029/2000WR900183
  32. Jain, S. and Lall, U. (2001) Floods in a changing climate: Does the past represent the future?. Water Resources Research, Vol. 37, No. 12, pp. 3193-3205. https://doi.org/10.1029/2001WR000495
  33. Kalra, T., Piechota, T.C., Davies, R. and Tootle, G.A. (2006) Is climate change evident in U.S. streamflow?. Proceedings World Environmental and Water Resources Congress, ASCE, Omaha, Nebraska, U.S.A.
  34. Kite, G.W. (1993) Application of a land class hydrological model to climate change. Water Resources Research, 29, pp. 2377-2384. https://doi.org/10.1029/93WR00582
  35. Koo, G.-S., Boo, K.-O. and Kwon, W.-T. (2009) Projection of temperature over Korea using an MM5 regional climate simulation. Climate Research, 40, 241-248. https://doi.org/10.3354/cr00825
  36. Leclerc, M. and Ouarda, T.B.M.J. (2007) Non-stationary regional flood frequency analysis at ungauged site.Journal of Hydrology, 343(3-4), pp. 254-265. https://doi.org/10.1016/j.jhydrol.2007.06.021
  37. Lins, H.F. and Slack, J.R. (1999) Streamflow trends in the United States. Geophysical Research Letters, 26, pp. 227-230. https://doi.org/10.1029/1998GL900291
  38. Lu, L. and Stedinger, J.R. (1992) Variance of 2- and 3-paremeter GEV/PWM quantile estimators: formulas, confidence intervals and a comparison, Journal of Hydrology, 138, pp. 247-268. https://doi.org/10.1016/0022-1694(92)90167-T
  39. MaCabe, G.J. and Wolock, D.M. (2002) A step increase in streamflow in the conterminous United States.Geophysical Research Letters, 29(24), 2185, doi: 10.1029/2002GL015999.
  40. Martin, E.S. and Stedinger, J.R. (2000) Generalized maximum likelihood GEV quantile estimator for hydrologic data. Water Resources Research, 28(11), 3001-3010.
  41. May, W. (2008) Potential future changes in the characteristics of daily precipitation in Europe simulated by the HIRHAM regional climate model. Climate Dyn., 30, pp. 581-603. https://doi.org/10.1007/s00382-007-0309-y
  42. Milly, P.C.D., Wetherald, R.T., Dunne, K.A. and Delworth, T.L., (2002) Increasing risk of great floods in a changing climate. Nature, 415(6871), pp. 514-517. https://doi.org/10.1038/415514a
  43. 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, No. 6, pp. 845-858.
  44. Moss, R., M. Babiker, S. Brinkman, E. Calvo, T. Carter, J. Edmonds, I. Elgizouli, S. Emori, L. Erda, K. Hibbard, R. Jones, M. Kainuma, J. Kelleher, J. F. Lamarque, M. Manning, B. Matthews, J. Meehl, L. Meyer, J. Mitchell, N. Nakicenovic, B. O'Neill, R. Pichs, KeywanRiahi, Steven Rose, Paul Runci, Ron Stouffer, Detlef van Vuuren, John Weyant, Tom Wilbanks, J. P. van Ypersele, and M. Zurek, 2008: Towards New Scenarios for Analysis of Emissions, Climate Change, Impacts, and Response Strategies. Intergovernmental Panel on Climate Change, Geneva, pp. 132.
  45. Nogaj, M.,Dacunha-Castelle, D., and Yiou, P. (2005) Analysis of climate extreme events under non-stationary conditions.In Proceedings, 4th Conference on Extreme Value Analysis, Gothenburg, August 15-19, 2005.
  46. 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 the American Water Resource Association, 35, pp. 1509-1524. https://doi.org/10.1111/j.1752-1688.1999.tb04234.x
  47. Panagoulia, D. and Dimou, G. (1997) Sensitivity of flood events to global climate change, Journal of Hydrology, 191, pp. 208-222. https://doi.org/10.1016/S0022-1694(96)03056-9
  48. Pagano, T. and Garen, D. (2005) A recent increase in western U.S. streamflow variability and persistence. Journal of Hydrometeorology, 6, pp. 173-179. https://doi.org/10.1175/JHM410.1
  49. Park, J.-S., Kang, H.-S., Lee, Y. S. and Kim, M.-K. (2010) Changes in the extreme daily rainfall in South Korea. International Journal of Climatology, DOI: 10.1002/joc.2236.
  50. Pizaro, G. and Lall, U. (2002) El Nino and Floods in the US West: What can we expect?.Eos Trans. AGU, Vol. 83, No. 32, pp. 349-352.
  51. Porparto, A. and Ridolfi, L. (1998) Influence of weak trends on exceedance probability.Stochastic Hydrol.Hydraul., Vol. 12, No. 1, pp. 1-15. https://doi.org/10.1007/s004770050006
  52. Renard, B., Garreta, V., Lang, M. and Bois, P. (2005) Bayesian analysis of extremes in hydrology: A powerful tool for knowledge integration and uncertainties assessment. In Proceedings, 4th Conference on Extrame Value Analysis, Gothenburg, August 15-19, 2005.
  53. Sankarasubramanian, A. and Lall, U. (2003) Flood quantiles in a changing climate: Seasonal forecasts and causal relations. Water Resources Research, Vol. 39, No. 5, pp. 1134. https://doi.org/10.1029/2002WR001593
  54. Smith, R.L. (1989) Extreme value analysis of environmental time series: an application to trend detection in ground-level ozone. Statistical Science, 4, pp. 367-393. https://doi.org/10.1214/ss/1177012400
  55. Stedinger, J.R., Vogel, R.M. and Foufoula-Georgiou, E. (1993) Chapter 18, Frequency analysis of extreme events, Handbook of Hydrology, edited by Maidment, D. R., McGraw-Hill.
  56. 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, Santa Barbara, CA, Oct. 15-20, 2000, pp. 77-86, American Society of Civil Engineers, Reston, 2001.
  57. Strupczewski, W.G., Singh, V.P. and Feluch, W. (2001) Non-stationary approach to at-site flood frequency modeling I. Maximum likelihood estimation. Journal of Hydrology, 248, pp. 123-142. https://doi.org/10.1016/S0022-1694(01)00397-3
  58. Um, M.-J., Cho., W. and Heo, J.-H. (2010) A comparative study of the adaptive choice of thresholds in extreme hydrologic events. Stoch. Environ. Res. Risk Assess., 24, pp. 611-623. https://doi.org/10.1007/s00477-009-0348-5
  59. Walters, D.N., Best, M.J., Bushell, A.C., Copsey, D., Edwards, J.M., Falloon, P.D., Harris, C.M., Lock, A.P., Manners, J.C., Morcrette, C.J., Roberts, M.J., Stratton, R.A., Webster, S., Wilkinson, J.M., Willett, M.R., Boutle, I.A., Earnshaw, P.D., Hill, P.G., MacLachlan, C., Martin, G.M., Moufouma-Okia, W., Palmer, M.D., Petch, J.C., Rooney, G.G., Scaife, A.A. and Williams, K. D. (2011) The Met office unified model global atmosphere 3.0/ 3.1 and JULES global land 3.0/3.1 configurations, Geosci. Model Dev. Discuss., 4, pp. 1213-1271. https://doi.org/10.5194/gmdd-4-1213-2011
  60. Wang, J. and Yang, P. (2005) A compound reconstructed prediction model for nonstationary climate processes, Journal of Climatology, 25, pp. 1265-1277. https://doi.org/10.1002/joc.1158

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