한국수자원학회논문집 (Journal of Korea Water Resources Association)

  • 제50권2호
  • /
  • Pages.75-87
  • /
  • 2017
  • /
  • 2799-8746(pISSN)
  • /
  • 2799-8754(eISSN)
한국수자원학회 (Korea Water Resources Association)
권호 표지
DOI QR코드

DOI QR Code

유역단위에서의 연강수량의 변동점 분석을 위한 계층적 Bayesian 분석기법 개발

A development of hierarchical bayesian model for changing point analysis at watershed scale

  • 김진국 (전북대학교 토목공학과) ;
  • 김진영 (전북대학교 토목공학과) ;
  • 김윤희 (한국수자원공사 대구경북지역본부) ;
  • 권현한 (전북대학교 토목공학과)
  • Kim, Jin-Guk (Department of Civil Engineering, Chonbuk National University) ;
  • Kim, Jin-Young (Department of Civil Engineering, Chonbuk National University) ;
  • Kim, Yoon-Hee (Daegu-Gyeongbuk Regional Division, Korea Water Resources Corporation) ;
  • Kwon, Hyun-Han (Department of Civil Engineering, Chonbuk National University)
  • 투고 : 2016.09.27
  • 심사 : 2016.12.19
  • 발행 : 2017.02.28
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

최근 기후변화에 따른 기상변동성 증가로 기존 한반도의 기상패턴과 다른 이상강우 현상이 증가하고 있다. 이상강우현상에 따른 수문패턴의 변화는 수자원 계획을 수립하는데 있어 불확실성을 가중시키기고 있다. 이러한 점에서 수문 시계열의 변화양상을 효과적으로 인지할 수 있으며, 유역단위에서 일관된 변화를 평가할 수 있는 변동점 분석 개발이 필요하다. 이에 본 연구에서는 기존 변동점 분석방법에 계층적 베이지안(Hierarchical Bayesian) 기법을 연계하여 유역단위에서 계층적 변동점 분석이 가능한 모형을 개발하였다. 우리나라에 40년 이상 관측된 기상청 강수자료를 활용하여 연강수량 자료를 구축하였으며, 본 연구를 통해 개발된 모형의 적합성을 평가하였다. 분석결과, 1990년대의 강수자료의 변화 양상을 정량적으로 확인할 수 있었으며, 과거에 비해 강수의 증가 특성을 확인할 수 있었다. 최종적으로 추정된 수문자료의 변화시점 전후의 재해석자료를 이용하여 한반도 주변의 강수량과 해수면기압의 Anomaly를 분석해본 결과 변동점을 기준으로 강수량과 해수면기압의 명확한 차이를 확인하였다.

In recent decades, extreme events have been significantly increased over the Korean Peninsula due to climate variability and climate change. The potential changes in hydrologic cycle associated with the extreme events increase uncertainty in water resources planning and designing. For these reasons, a reliable changing point analysis is generally required to better understand regime changes in hydrologic time series at watershed scale. In this study, a hierarchical changing point analysis approach that can apply in a watershed scale is developed by combining the existing changing point analysis method and hierarchical Bayesian method. The proposed model was applied to the selected stations that have annual rainfall data longer than 40 years. The results showed that the proposed model can quantitatively detect the shift in precipitation in the middle of 1990s and identify the increase in annual precipitation compared to the several decades prior to the 1990s. Finally, we explored the changes in precipitation and sea level pressure in the context of large-scale climate anomalies using reanalysis data, for a given change point. It was concluded that the identified large-scale patterns were substantially different from each other.

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. Bai, J., and Perron, P. (2003). "Computation and Analysis of Multiple Structural Change Models." Journal of Applied Econometrics, Vol. 18, pp. 1-22. https://doi.org/10.1002/jae.659
  3. Barry, D., and Hartigan, J. A. (1993). "A Bayesian analysis for change point problems." Journal of the American Statistical Association, Vol. 88, No. 309-319, pp. 309-319.
  4. Brooks, S. P., and Gelman, A. (1998). "General methods for monitoring convergence of iterative simulations." Journal of computational and graphical statistics, Vol. 7, No. 4, pp. 434-455. https://doi.org/10.2307/1390675
  5. Brooks, S. P., and Roberts, G. O. (1998). "Assessing convergence of Markov chain Monte Carlo algorithms." Statistics and Computing, Vol. 8, No. 4, pp. 319-335. https://doi.org/10.1023/A:1008820505350
  6. Carlin, B. P., Gelfand, A. E., and Smith, A. F. M. (1992). "Hierarchical Bayesian analysis of change point problems." Applied Statistics, Vol. 41, No. 2, pp. 389-405. https://doi.org/10.2307/2347570
  7. Cox, D. R., Isham, V. S., and Northrop, P. J. (2002). "Floods: some probabilistic and statistical approaches." Philosophical Transactions of the Royal Society of London, Series A, Vol. 360, pp. 1389-1408. https://doi.org/10.1098/rsta.2002.1006
  8. Elsner, J. B., Niu, X., and Jagger, T. H. (2004). "Detecting shifts in hurricane rates using a Markov chain Monte Carlo approach." Journal of Climate, Vol. 17, pp. 2652-2666. https://doi.org/10.1175/1520-0442(2004)017<2652:DSIHRU>2.0.CO;2
  9. Findley, D. F. (1991). "Counter examples to Parsimony and BIC." Annals of the Institute of Statistical Mathematics, Vol. 43, No. 3, pp. 505-514. https://doi.org/10.1007/BF00053369
  10. Gelfand, A. E., and Smith, A. F. (1990). "Sampling-based approaches to calculating marginal densities." Journal of the American statistical association, Vol. 85, No. 410, pp. 398-409. https://doi.org/10.1080/01621459.1990.10476213
  11. Gelman, A., Carlin, J. B., Stern, H. S., and Rubin, D. B. (2003). "Bayesian data analysis." CRC press, United States of America.
  12. Gelman, A., Carlin, J. B., Stern, H. S., and Rubin, D. B. (2004). "Bayesian data analysis (2nd ed.)." Boca Raton: Chapman and Hall/CRC.
  13. Geman, S., and Geman, D. (1984). "Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images." IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 6, pp. 721-741.
  14. Hastings, W. K. (1970). "Monte Carlo Sampling Methods using Markov Chains and their applications." Biometrika, Vol. 57, No. 1, pp. 97-109. https://doi.org/10.1093/biomet/57.1.97
  15. Hollander, M., and Wolfe, D. A. (1973). "Nonparametric Statistical Methods." John Wiley & Sons, Newyork, USA.
  16. Hwang, S. H., Kim, J. H., Yoo, C. S., and Jung, S. W. (2010). "A probabilistic estimation of changing points of Seoul rainfall using BH Bayesian analysis." Journal of Korea Water Resources Association, Vol. 43, No. 7, pp. 645-655. https://doi.org/10.3741/JKWRA.2010.43.7.645
  17. Jung, H.-S., Choi, Y. E., Oh, J.-H., and Lim, G.-H. (2002). "Recent trends in temperature and precipitation over South Korea." International Journal of Climatology, Vol. 22, pp. 1327-1337. https://doi.org/10.1002/joc.797
  18. Kite, G. W. (1977). "Frequency and Risk Analysis in Hydrology." Water Resources Publication, Fort Collins, Colorado, USA.
  19. Lee, K. M., Jang, H. S., Kim, Y.-H., and Lee, S. H. (2011). "Changepoint Analysis of Mean Temperature and Extreme Temperature in the Republic of Korea." Journal of Korean Geographical Society, Vol. 46, No. 5, pp. 583-596.
  20. Kim, K. S., and Son, Y. S. (2004). "Bayesian Change Point Analysis for a Sequence of Normal Observations: Application to the Winter Average Temperature in Seoul." The Korean journal of applied statistics, Vol. 17, No. 2, pp. 281-301. https://doi.org/10.5351/KJAS.2004.17.2.281
  21. Kwon, H.-H, Casey, B., and Lall, U. (2008). "Climate Informed Flood Frequency Analysis and Prediction in Montana Using Hierarchical Bayesian Modeling." Geophysical Research Letters, Vol. 35, L05404.
  22. Kwon, H.-H., Kim, J.-Y., Kim, O.-K., and Lee, J.-J. (2013). "A Development of Regional Frequency Model Based on Hierarchical Bayesian Model." Journal of Korea Water Resources Association, Vol. 46, No. 1, pp. 13-24. https://doi.org/10.3741/JKWRA.2013.46.1.13
  23. Lee, J.-J., and Kwon, H.-H. (2011). "Analysis on Spatio-Temporal Pattern and Regionalization of Extreme Rainfall Data." Journal of Korean Society of Civil Engineers, Vol. 31, No. 1B, pp. 13-20.
  24. Lee, J.-J., Kwon, H.-H., and Kim, T.-W. (2010). "Concept of Trend Analysis of Hydrologic Extreme variables and Nonstationary Frequency Analysis.", Journal of Korean Society of Civil Engineers, Vol. 30, No. 4B, pp. 389-397.
  25. Lee, K. M., Kwon, W. T., and Lee, S. H. (2009). "A study on plant phenological trends in South Korea." Journal of the Korean Association of Regional Geographers, Vol. 15, No. 3, pp. 337-350.
  26. Lee, S. H., Kim, S. U., Lee, Y. S., and Sung, J. H. (2014). "Intercomparison of Change Point Analysis Methods for Identification of Inhomogeneity in Rainfall Series and Applications." Journal of Korea Water Resources Association, Vol. 47, No. 8, pp. 671-684. https://doi.org/10.3741/JKWRA.2014.47.8.671
  27. Lund, R., and Reeves, J. (2002). "Detection of undocumented change points: a revision of the two-phase regression model." Journal of Climate, Vol. 15, pp. 2547-2554. https://doi.org/10.1175/1520-0442(2002)015<2547:DOUCAR>2.0.CO;2
  28. Mann, H. B., and Whitney, D. R. (1947). "On a test of whether one of two ranmom variables is stochastically larger then the other." Ann Math Statist., Vol. 18, pp. 50-60. https://doi.org/10.1214/aoms/1177730491
  29. Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., and Teller, E. (1953). "Equations of state calculations by fast computing machines." Journal of Chemical Physics, Vol. 21, pp. 1087-1091. https://doi.org/10.1063/1.1699114
  30. NOAA Earth System Research Laboratory (http://www.esrl.noaa.gov/psd/cgi-bin/data/composites/printpage.pl)
  31. Olshen, A. B., Venkatraman, E. S., Lucito, R., and Wigler, M. (2004). "Circular binary segmentation for the analysis of array-based dna copy number data." Biostatistics, Vol. 5, No. 4, pp. 557-572. https://doi.org/10.1093/biostatistics/kxh008
  32. Sen, A., and Srivastava, M. S. (1975). "On tests for detecting change in mean." The Annals of Statistics, Vol. 3, No. 1, pp. 98-108. https://doi.org/10.1214/aos/1176343001
  33. Solow, A. R. (1987). "Testing for climate change: An application of two-phase regression model." Journal of Applied Meteorology, Vol. 26, pp. 1401-1405. https://doi.org/10.1175/1520-0450(1987)026<1401:TFCCAA>2.0.CO;2
  34. Yun, S.-H., and Lee, J.-T. (2001). "Climate change impacts on optimum ripening periods of rice plant and its countermeasure in rice cultivation." Korean Journal of Agricultural and Forest Meteorology, Vol. 3, No. 1, pp. 55-70.