DOI QR코드

DOI QR Code

Evaluation of Probability Rainfalls Estimated from Non-Stationary Rainfall Frequency Analysis

비정상성 강우빈도해석법에 의한 확률강우량의 평가

  • Lee, Chang-Hwan (Dept. of Civil and Environmental Engineering, Hanyang University) ;
  • Ahn, Jae-Hyun (Dept. of Civil Engineering, Seokyeong University) ;
  • Kim, Tae-Woong (Dept. of Civil and Environmental System Engineering, Hanyang University)
  • 이창환 (한양대학교 대학원 건설환경공학) ;
  • 안재현 (서경대학교 토목공학과) ;
  • 김태웅 (한양대학교 건설환경시스템공학)
  • Published : 2010.02.28

Abstract

This study evaluated applicability and confidence of probability rainfalls estimated by the non-stationary rainfall frequency analysis which was recently developed. Using rainfall data at 4 sites which have an obvious increasing trend in observations, we estimated 3 type probability rainfalls; probability rainfalls from stationary rainfall frequency analysis using data from 1973-1997, probability rainfalls from stationary rainfall frequency analysis using data from 1973-2006, probability rainfalls from non-stationary rainfall frequency analysis assuming that the current year is 1997 and the target year is 2006. Based on the comparison of residuals from 3 probability rainfalls, the non-stationary rainfall frequency analysis provided more effective and well-directed estimates of probability rainfalls in the target year. Using Bootstrap resampling, this study also evaluated the parameter estimation methods for the non-stationary rainfall frequency analysis based on confidence intervals. The confidence interval length estimated by the maximum likelihood estimation (MLE) is narrower than the probability weighted moments (PWM). The results indicated that MLE provides more proper confidence than PWM for non-stationary probability rainfalls.

본 연구는 최근에 개발된 비정상성 강우빈도해석법을 적용하여 추정한 확률강우량에 대한 적용성 및 신뢰성을 평가하였다. 이를 위하여 기상청 관할 강우관측소 중 자료의 증가 경향성이 유의한 4개 지점에 대하여 3가지 형태의 확률강우량을 산정하였다. 첫 번째 확률강우량은 1973-1997년의 관측자료를 가지고 일반적인 강우빈도해석을 적용하여 추정한 확률강우량(SPR1997)이고, 두 번째 확률강우량은 1973-2006년의 관측자료를 가지고 일반적인 강우빈도 해석을 적용하여 추정한 확률강우량(SPR2006), 그리고 세 번째 확률강우량은 1973-1997년의 강우량 자료를 가지고 1997년을 현재시점이라 가정하여 2006년의 확률강우량을 비정상성 강우빈도해석법을 적용하여 추정한 확률강우량(NSPR2006)이다. 2006년을 목표연도라 가정하고, 확률강우량을 비교분석한 결과, 비정상성 강우빈도해석법에 의한 확률강우량(NSPR2006)이 정상성 확률강우량(SPR1997)에 비해 목표연도의 확률강우량에 대하여 적절한 값을 추정하는 것으로 나타났다. 본 연구는 또한 Bootstrap 기법을 이용한 신뢰구간을 비교하여 비정상성 확률강우량 추정에 적용되는 매개변수 추정법에 대한 평가를 수행하였다. 최우도법에 의한 신뢰구간 길이가 확률가중모멘트법에 의한 신뢰구간 길이보다 좁게 나타났으며, 이는 최우도법이 비정상성 강우빈도해석법에 적용되어 신뢰성 높은 확률강우량을 추정하는 것으로 판단된다.

Keywords

References

  1. 건설교통부 (2000). 1999년도 수자원관리기법개발 연구조사 보고서, 제 1권 한국 확률강우량도 작성. 건설교통부.
  2. 김병식, 서병하, 김남원 (2003). “전이함수모형과 일기발생모형을 이용한 유역규모 기후변화시나리오의 작성.” 한국수자원학회논문집, 한국수자원학회, 제36권, 제3호, pp. 345-363.
  3. 김경덕, 허준행 (2004). “수문자료 크기에 따른 지역빈도해석 적용성 기준 검토.” 2004년 한국수자원학회학술발표대회논문집, 한국수자원학회, pp. 190-194.
  4. 권현한, 문영일 (2004). “수문시계열의 Bootstrap 신뢰구간 추정기법 응용.” 대한토목학회논문집, 대한토목학회, 제24권, 제6B호, pp. 567-576.
  5. 권영문, 박진원, 김태웅 (2009). “강우의 증가 경향성을 고려한 목표연도 확률강우량 산정.” 대한토목학회논문집, 대한토목학회, 제29권, 제2B호, pp. 131-139.
  6. 안재현, 김태웅, 유철상, 윤용남 (2000). “자료기간 증가에 따른 확률강우량의 거동 특성 분석.” 한국수자원학회논문집, 한국수자원학회, 제33권, 제5호, pp. 569-580.
  7. 오재호, 홍성길(1995). “대기중 CO2 증가에 따른 한반도 강수량 변화.” 한국수자원학회지, 한국수자원학회, 제28권, 제3호, pp. 143-157.
  8. 유철상, 박정훈, 김중훈 (2006). “기후변화에 따른 선행토양함수조건(AMC)의 변화.” 대한토목학회논문집, 대한토목학회, 제26권, 제3B호, pp. 233-240.
  9. 윤용남, 유철상, 이재수, 안재현 (1999). “지구온난화에 따른 홍수 및 가뭄 발생빈도의 변화와 관련하여: 1. 연/월 강수량의 변화에 따른 일강수량 분포의 변화분석.” 한국수자원학회논문집, 한국수자원학회, 제32권, 제6호, pp. 617-625.
  10. 정대일, 제리 스테딘져, 성장현, 김영오 (2008). “기후 변화를 고려한 홍수 위험도 평가.” 대한토목학회논문집, 대한토목학회, 제28권, 제1B호, pp. 55-64.
  11. 정종호, 윤용남 (2007). 수자원설계실무, 구미서관.
  12. 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
  13. Efron, B. (1997). “Bootstrap Method: Another Look at the Jack-knife.” The Annuals of Statistics, Institute of Mathematical Statistics, Vol. 7, No 1, pp. 1-26. https://doi.org/10.1214/aos/1176344552
  14. Flower, H.J., and Kilsby, C.G. (2003a). “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
  15. Flower, H.J., and Kilsby, C.G. (2003b). “Implications of change in seasonal and annual extreme rainfall.” Geophysical Research Letters, Vol. 30, No. 13, pp. 1720 doi:10.1029/2003GL017327.
  16. 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
  17. 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. https://doi.org/10.1061/40927(243)69
  18. 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.
  19. 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
  20. 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. 43, No. 6, pp. 845-858. https://doi.org/10.1080/02626669809492182
  21. 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
  22. Robinson, P.J., and Finkelstein, P.L. (1991). “The development of impact-oriented climate scenario.” Bull. Ameri. Meteolor. Soc., Vol. 72, pp. 481-490. https://doi.org/10.1175/1520-0477(1991)072<0481:TDOIOC>2.0.CO;2
  23. Stedinger, J.R., Vogel, R.M., and Foufoula-Georgious, E. (1993). “Frequency analysis of extreme events, Chapter 18.” Handbook of Hydrology, D. Maidment (ed.), McGraw-Hill, Inc., New York, USA.
  24. 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. https://doi.org/10.1061/40577(306)7
  25. 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
  26. Sharma, A., Tarboton, D.G., and Lall, U. (1997). “Streamflow simulation: a non-parametric approach.” Water Resources Research, Vol. 33, No. 2, pp. 291-308 https://doi.org/10.1029/96WR02839
  27. 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
  28. Zhao, B., Tung, Y.K., Yeh, K.C., and Yang, J.C. (1997). “Storm resampling for uncertainty analysis of a multiple-storm unite hydrograph.” Journal of Hydrology, Vol. 194, pp. 366-384. https://doi.org/10.1016/S0022-1694(96)03112-5

Cited by

  1. Trend Analysis of Extreme Precipitation Using Quantile Regression vol.45, pp.8, 2012, https://doi.org/10.3741/JKWRA.2012.45.8.815
  2. Estimation of Future Design Rainfalls in Administrative Districts Using Nonstationary GEV Model vol.13, pp.3, 2013, https://doi.org/10.9798/KOSHAM.2013.13.3.147
  3. Non-stationary Frequency Analysis for Extreme Precipitation based on Representative Concentration Pathways (RCP) Climate Change Scenarios vol.12, pp.2, 2012, https://doi.org/10.9798/KOSHAM.2012.12.2.231
  4. The Recent Increasing Trends of Exceedance Rainfall Thresholds over the Korean Major Cities vol.34, pp.1, 2014, https://doi.org/10.12652/Ksce.2014.34.1.0117
  5. Comparison Study on the Various Forms of Scale Parameter for the Nonstationary Gumbel Model vol.48, pp.5, 2015, https://doi.org/10.3741/JKWRA.2015.48.5.331
  6. A Study on the Changes of Return Period Considering Nonstationarity of Rainfall Data vol.47, pp.5, 2014, https://doi.org/10.3741/JKWRA.2014.47.5.447
  7. Estimation and Assessment of Future Design Rainfall from Non-stationary Rainfall Frequency Analysis using Separation Method vol.48, pp.6, 2015, https://doi.org/10.3741/JKWRA.2015.48.6.451
  8. Ensemble Prediction of Future Design Rainfalls Considering Climate Change vol.12, pp.2, 2012, https://doi.org/10.9798/KOSHAM.2012.12.2.159
  9. Comparative Assessment of a Method for Extraction of TC-induced Rainfall Affecting the Korean Peninsula vol.47, pp.12, 2014, https://doi.org/10.3741/JKWRA.2014.47.12.1187
  10. Bayesian Nonstationary Probability Rainfall Estimation using the Grid Method vol.48, pp.1, 2015, https://doi.org/10.3741/JKWRA.2015.48.1.37
  11. Geographical Impact on the Annual Maximum Rainfall in Korean Peninsula and Determination of the Optimal Probability Density Function vol.17, pp.3, 2015, https://doi.org/10.17663/JWR.2015.17.3.251
  12. Assessment of uncertainty associated with parameter of gumbel probability density function in rainfall frequency analysis vol.49, pp.5, 2016, https://doi.org/10.3741/JKWRA.2016.49.5.411
  13. 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
  14. Non-Stationary Frequency Analysis of Future Extreme Rainfall using CMIP5 GCMs over the Korean Peninsula vol.18, pp.3, 2018, https://doi.org/10.9798/KOSHAM.2018.18.3.73