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Hydrological homogeneous region delineation for bivariate frequency analysis of extreme rainfalls in Korea

다변량 L-moment를 이용한 이변량 강우빈도해석에서 수문학적 동질지역 선정

  • Shin, Ju-Young (Department of Civil and Environmental Engineering, Yonsei University) ;
  • Jeong, Changsam (Department of Civil and Environmental Engineering, Induk University) ;
  • Joo, Kyungwon (Department of Civil and Environmental Engineering, Yonsei University) ;
  • Heo, Jun-Haeng (Department of Civil and Environmental Engineering, Yonsei University)
  • 신주영 (연세대학교 토목환경공학과) ;
  • 정창삼 (인덕대학교 토목환경공학과) ;
  • 주경원 (연세대학교 토목환경공학과) ;
  • 허준행 (연세대학교 토목환경공학과)
  • Received : 2017.10.16
  • Accepted : 2017.11.15
  • Published : 2018.01.31

Abstract

The multivariate regional frequency analysis has many advantages such as an adaption of regional parameters and consideration of a correlated structure of the data. The multivariate regional frequency analysis can provide the broader and more detailed information for the hydrological variables. The multivariate regional frequency analysis has not been attempted to model hydrological variables in South Korea yet. Therefore, it is required to investigate the applicability of the multivariate regional frequency analysis in the modeling of the hydrological variables. The current study investigated the applicability of the homogeneous region delineation and their characteristics in bivariate regional frequency analysis of annual maximum rainfall depth-duration data. The K-medoid method was employed as a clustering method. The discordancy and heterogeneous measures were used to assess the appropriateness of the delineation results. According to the results of the clustering analysis, the employed stations could be grouped into five regions. All stations at three of the five regions led to acceptable values of discordancy measures than the threshold. The stations where have short record length led to the large discordancy measures. All grouped regions were identified as a homogeneous region based on heterogeneous measure estimates. It was observed that there are strong cross-correlations among the stations in the same region.

다변량 지역빈도해석은 기존에 사용되어온 다변량 빈도해석과 지역빈도해석의 장점을 가지고 있는 방법으로 다양한 변수를 고려함으로써 수문현상에 대하여 많은 정보를 얻을 수 있다. 현재까지는 우리나라의 수문자료를 이용하여 다변량 지역빈도해석이 시도된 적이 없어 국내의 수문자료를 대상으로 다변량 지역빈도해석의 적용성을 검토할 필요가 있다. 본 연구에서는 다변량 지역빈도해석의 수문학적 동질지역을 설정하는 단계에 집중하여 이변량 수문자료인 연최대 강우량-지속기간 자료에 대하여 수문학적 동질지역을 설정하였다. 이변량 지역빈도해석에서 사용되는 지역구분방법의 한국의 연최대 강우량-지속기간 자료에 대한 적용성을 평가하였고 그 특성을 분석하였다. 기상청 71개 지점에 대하여 분석을 실시하였다. 군집해석방법으로는 K-medoid 방법을 적용하였고, 불일치 척도와 이질성 척도를 이용하여 지역구분이 적절히 되었는지를 판정하였다. 군집해석 결과 한국은 총 5개의 지역으로 나누어지며, 두 지역을 제외하고는 지역 내 모든 지점의 불일치 척도가 기준치 이하인 것으로 나타났다. 자료연수가 짧은 지점에서 불일치 척도가 높게 나오는 것을 확인하였다. 구분된 모든 지역은 지역 내 지점들의 자료들이 동질한 것으로 나타났고 각 지점간의 상관성이 매우 높은 것으로 나타났다.

Keywords

References

  1. Abdi, A., Hassanzadeh, Y., Talatahari, S., Fakheri-Fard, A., Mirabbasi, R., and Ouarda, T .B. M. J. (2017). "Multivariate regional frequency analysis: two new methods to increase the accuracy of measures." Advances in Water Resources, Vol. 107, pp. 290-300. https://doi.org/10.1016/j.advwatres.2017.07.006
  2. Ben Aissia, M. A., Chebana, F., Ouarda, T .B. M. J., Bruneau, P., and Barbet, M. (2015). "Bivariate index-flood model: case study in Quebec, Canada." Hydrological Sciences Journal, IAHS, Vol. 60, No. 2, pp. 247-268. https://doi.org/10.1080/02626667.2013.875177
  3. Chebana, F., and Ouarda, T. B. M. J. (2007). "Multivariate L-moment homogeneity test." Water Resources Researches, AGU, Vol. 43, No. 8.
  4. Chebana, F., and Ouarda, T. B. M. J. (2009), "Index flood-based multivariate regional frequency analysis." Water Resource Researches, AGU, Vol. 45, No. 10.
  5. Heo, J.-H., Lee, Y. S., Nam, W. S., and Kim, K.-D. (2007b). "Application of regional rainfall frequency analysis in South Korea (II): monte Carlo simulation and determination of appropriate method." Journal of Korean Society of Civil Engineering, KSCE, Vol. 27, No. 2B, pp. 113-123.
  6. Heo, J.-H., Lee, Y. S., Shin, H., and Kim, K.-D. (2007a). "Application of regional rainfall frequency analysis in South Korea (I): rainfall quantile estimation." Journal of Korean Society of Civil Engineering, KSCE, Vol. 27, No. 2B, pp.101-111.
  7. Hosking, J. R. M., and Wallis, J. R. (2005). Regional frequency analysis: an approach based on L-moments. Cambridge University Press.
  8. Joo, K., Shin, J.-Y., and Heo, J.-H. (2012). "Bivariate frequency analysis of rainfall using copula model." Journal of the Korea Water Resources Association, KWRA, Vol. 45, No. 8, pp. 827-837. https://doi.org/10.3741/JKWRA.2012.45.8.827
  9. Kaufman, L., and Rousseeuw, P. J. (1990). Partitioning around medoids (program PAM), in finding groups in data: an introduction to cluster analysis. John Wiley & Sons, Inc., Hoboken, N.J., USA.
  10. Kim, J.-Y., So, B.-J., Kim, T.-W., and Kwon, H.-H., (2016). "A development of trivariate drought frequency analysis approach using copula function." Journal of the Korea Water Resources Association, KWRA, Vol. 49, No. 10, pp. 823-833. https://doi.org/10.3741/JKWRA.2016.49.10.823
  11. Kim, U.-G., Ahn, W.-S., Lee, C.-Y., and Um, M.-J. (2012). "The optimal analysis of data preprocessing method for clustering the region of precipitation." Journal of the Korean Society of Hazard Mitigation, Korean Society of Hazard Mitigation, Vol. 12, No. 5, pp. 233-240. https://doi.org/10.9798/KOSHAM.2012.12.5.233
  12. Lee, D.-J., and Heo, J.-H. (2001). "Frequency analysis of daily rainfall in Han river basin based on regional L-moments algorithm." Journal of Korea Water Resources Association, KWRA, Vol. 34, No. 2, pp. 119-130.
  13. Lee, J.-Y., Park, D.-H., Shin, J.-Y., and Kim, T.-W. (2016). "Estimating design floods for ungauged basins in the geum-river basin through regional flood frequency analysis using L-moments method." Journal of Korea Water Resources Association, KWRA, Vol. 49, No. 8, pp. 646-656.
  14. Nam, W. S., Kim, T., Shin, J.-Y., and Heo, J.-H. (2008). "Regional rainfall frequency analysis by multivariate techniques." Journal of Korea Water Resources Association, KWRA, Vol. 41, No. 5, pp. 517-525. https://doi.org/10.3741/JKWRA.2008.41.5.517
  15. Park, H.-S., and Jun, C.-H. (2009), "A simple and fast algorithm for K-medoids clustering." Expert Systems with Applications, Vol. 36, No. 2, pp. 3336-3341, DOI:10.1016/j.eswa.2008.01.039.
  16. Requena, A. I., Chebana, F., and Mediero, L. (2016). "A complete procedure for multivariate index-flood model application." Journal of Hydrology, Vol. 535, pp. 559-580. https://doi.org/10.1016/j.jhydrol.2016.02.004
  17. Serfling, R., and Xiao, P. (2007). "A contribution to multivariate L-moments: L-comoment matrices." Journal of Multivariate Analysis, Vol. 98, No. 9, pp. 1765-1781. https://doi.org/10.1016/j.jmva.2007.01.008
  18. Song, H.-K., Joo, K., Jeong, J., and Heo, J.-H. (2016). "A comparative study on the inter-event time with the time-resolution of rainfall data." Proceedings of the Korea Water Resources Association Conference 2016, KWRA.