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설계강우량 산정을 위한 매개변수 추정방법 평가

Evaluation of Parameter Estimation Method for Design Rainfall Estimation

  • Kim, Kwihoon (Department of Rural Systems Engineering, College of Agriculture and Life Sciences, Seoul National University) ;
  • Jun, Sang-Min (Research Institute of Agriculture and Life Sciences, College of Agriculture and Life Sciences, Seoul National University) ;
  • Jang, Jeongyeol (Rural Research Institute, Korea Rural Community Corporation) ;
  • Song, Inhong (Department of Rural Systems Engineering, College of Agriculture and Life sciences, Research Institute of Green Bio Science and Technology, Global Smart Farm Convergence Major, Seoul National University) ;
  • Kang, Moon-Seong (Department of Rural Systems Engineering, College of Agriculture and Life Sciences, Research Institute of Green Bio Science and Technology, Seoul National University) ;
  • Choi, Jin-Yong (Department of Rural Systems Engineering, College of Agriculture and Life sciences, Research Institute of Green Bio Science and Technology, Global Smart Farm Convergence Major, Seoul National University)
  • 투고 : 2021.03.25
  • 심사 : 2021.07.02
  • 발행 : 2021.07.31

초록

Determining design rainfall is the first step to plan an agricultural drainage facility. The objective of this study is to evaluate whether the current method for parameter estimation is reasonable for computing the design rainfall. The current Gumbel-Kendall (G-K) method was compared with two other methods which are Gumbel-Chow (G-C) method and Probability weighted moment (PWM). Hourly rainfall data were acquired from the 60 ASOS (Automated Synoptic Observing System) stations across the nation. For the goodness-of-fit test, this study used chi-squared (𝛘2) and Kolmogorov-Smirnov (K-S) test. When using G-K method, 𝛘2 statistics of 18 stations exceeded the critical value (𝑥2a=0.05,df=4=9.4877) and 10, 3 stations for G-C method, PWM method respectively. For K-S test, none of the stations exceeded the critical value (Da=0.05n=0.19838). However, G-K method showed the worst performances in both tests compared to other methods. Subsequently, this study computed design rainfall of 48-hour duration in 60 ASOS stations. G-K method showed 5.6 and 6.4% higher average design rainfall and 15.2 and 24.6% higher variance compared to G-C and PWM methods. In short, G-K showed the worst performance in goodness-of-fit tests and showed higher design rainfall with the least robustness. Likewise, considering the basic assumptions of the design rainfall estimation, G-K is not an appropriate method for the practical use. This study can be referenced and helpful when revising the agricultural drainage standards.

키워드

과제정보

이 논문은 2020년도 정부 (과학기술정보통신부)의 재원으로 한국연구재단의 지원을 받아 수행된 연구임 (No. 2017R1E1A1A01077413).

참고문헌

  1. Ball, J., M. Babister, R. Nathan, W. Weeks, E. Weinmann, M. Retallick, and I. Testoni, 2019. Australian Rainfall and Runoff: A Guide to Flood Estimation. Symonston, Canberra, Australia: Commonwealth of Australia (Geoscience Australia).
  2. Greenwood, J. A., J. M. Landwehr, N. C. Matalas, and J. R. Wallis, 1979. Probability weighted moments: definition and relation to parameters of several distributions expressible in inverse form. Water Resources Research 15(5): 1049-1054. doi:10.1029/WR015i005p01049.
  3. Haan, C. T., 2002. Statistical methods in hydrology, 2nd edition. Ames, Iowa: Iowa State Press.
  4. Hosking, J. R. M., J. R. Wallis, and E. F. Wood, 1985. Estimation of the generalized extreme-value distribution by the method of probability-weighted moments. Technometrics 27(3): 251-261. doi:10.2307/1269706.
  5. Hosking, J. R. M., and J. R. Wallis, 1986. Paleoflood hydrology and flood frequency analysis. Water Resources Research 22(4): 543-550. doi:10.1029/WR022i004p00543.
  6. Kim, K. C., Y. Kim, J. Song, and S. Chung, 2014. Revision of agricultural drainage design standards. KCID Journal 21(1): 32-44 (in Korean).
  7. Kite, G. W., 1978. Frequency and risk analyses in hydrology. Fort Collins, CO: Water Resources Publications.
  8. Kjeldsen, T. R., D. A. Jones, and A. C. Bayliss, 2008. Improving the FEH statistical procedures for flood frequency estimation. Bristol, UK: Environment Agency.
  9. MCT (Ministry of Construction and Transportation), 2000. Report for research and survey of water resources management, probability rainfall in Korea (Republic of).
  10. ME (Ministry of Environment), 2019. Standard guideline for flood estimation.
  11. ME (Ministry of Environment), 2020. Report for developing program of rainfall frequency analysis.
  12. MLTMA (Ministry of Land, Transport and Maritime Affairs), 2011. Study on improvement and supplement of probability rainfall.
  13. Moon, Y. I., D. H. Park, and K. S. Yoon, 2003. The study of design flood for road drainage system. Korean Society of Civil Engineers Proceedings: 2586-2589 (in Korean).
  14. Park, J., T. Kang, and S. Lee, 2019 . A temporal distribution method of probable rainfall for planning a storm sewer network in an urban area. Journal of Korean Society of Hazard Mitigation 19(1): 85-94 (in Korean). doi:10.9798/KOSHAM.2019.19.1.85.
  15. Stuart, A., and J. Keith Ord, 1994. Kendall's advanced theory of statistics, 6th edition. New York, NY.: John Wiley & Sons Inc..
  16. Sturges, H. A., 1926. The choice of a class interval. Journal of the American Statistical Association 21: 65-66. doi:10.1080/01621459.1926.10502161.
  17. Yoon, Y. N., 2013. Hydrology. Paju: Cheong-moon-gak.
  18. Perica, S., S. Dietz, S. Heim, L. Hiner, K. Maitaria, D. Martin, S. Pavlovic, I. Roy, C. Trypaluk, D. Unruh, F. Yan, M. Yekta, T. Zhao, G. Bonnin, D. Brewer, L. Chen, T. Parzybok, and J. Yarchoan, 2011. Precipitation-frequency Atlas of the United States. Baltimore, MD: National Weather Service.