Browse > Article
http://dx.doi.org/10.5532/KJAFM.2010.12.2.083

The Determination of Probability Distributions of Annual, Seasonal and Monthly Precipitation in Korea  

Kim, Dong-Yeob (Dept. of Forest Sciences, Seoul National University)
Lee, Sang-Ho (Dept. of Forest Sciences, Seoul National University)
Hong, Young-Joo (Dept. of Forest Sciences, Seoul National University)
Lee, Eun-Jai (Dept. of Forest Sciences, Seoul National University)
Im, Sang-Jun (Dept. of Forest Sciences, Seoul National University)
Publication Information
Korean Journal of Agricultural and Forest Meteorology / v.12, no.2, 2010 , pp. 83-94 More about this Journal
Abstract
The objective of this study was to determine the best probability distributions of annual, seasonal and monthly precipitation in Korea. Data observed at 32 stations in Korea were analyzed using the L-moment ratio diagram and the average weighted distance (AWD) to identify the best probability distributions of each precipitation. The probability distribution was best represented by 3-parameter Weibull distribution (W3) for the annual precipitation, 3-parameter lognormal distribution (LN3) for spring and autumn seasons, and generalized extreme value distribution (GEV) for summer and winter seasons. The best probability distribution models for monthly precipitation were LN3 for January, W3 for February and July, 2-parameter Weibull distribution (W2) for March, generalized Pareto distribution (GPA) for April, September, October and November, GEV for May and June, and log-Pearson type III (LP3) for August and December. However, from the goodness-of-fit test for the best probability distributions of the best fit, GPA for April, September, October and November, and LN3 for January showed considerably high reject rates due to computational errors in estimation of the probability distribution parameters and relatively higher AWD values. Meanwhile, analyses using data from 55 stations including additional 23 stations indicated insignificant differences to those using original data. Further studies using more long-term data are needed to identify more optimal probability distributions for each precipitation.
Keywords
Probability distribution; Precipitation; L-moment; Average weighted distance (AWD);
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
연도 인용수 순위
1 Yue, S., and M. Hashino, 2007: Probability distribution of annual, seasonal and monthly precipitation in Japan. Hydrological Sciences Journal 52(5), 863-877.   DOI
2 Zhang, J., and M. A. Stephens, 2009: A new and efficient estimation method for the generalized Pareto distribution. Technometrics 51(3), 306-615.   DOI
3 건설교통부, 2000: 한국확률강우량도 작성. 1999년도 수자원관리기법개발연구조사 보고서. 건설교통부.
4 윤용남, 2007: 수문학 -기초와 응용-. 청운각, 1152pp.
5 Kroll, C. N., and R. M. Vogel, 2002: Probability distribution of low streamflow series in the United States. Journal of Hydrologic Engineering 7(2), 137-146.   DOI
6 Lee, J., J. Lee, B. Kim, and J. Park, 2000: Derivation of probable rainfall intensity formula of individual zone based on the representative probability distribution. Proceedings of the Korea Water Resources Association Conference, The Korea Water Resources Association, 124-129. (in Korean)   과학기술학회마을
7 Lee, D., and J. Heo, 2001: Frequency analysis of daily rainfall in Han River basin based on regional L-moment algorithm. Journal of Korean Water Resources Association 34(2), 119-130. (in Korean with English abstract)   과학기술학회마을
8 Peel, M. C., Q. J. Wang, R. Vogel, and T. A. McMahon, 2001: The utility of L-moment ratio diagrams for selecting a regional probability distribution. Hydrological Sciences Journal 46(1), 147-155.   DOI
9 Markovic, R. D., 1965: Probability of best fit to distributions of annual precipitation and runoff. Hydrology Paper no. 8, Colorado State Univ., Fort Collins, Colorado, USA, 35pp.
10 Oh, T. S., J. S. Kim, Y. Moon, and S. Y. Yoo, 2006: The study on application of regional frequency analysis using kernel density function. Journal of Korean Water Resources Association 39(10), 891-904. (in Korean with English abstract)   과학기술학회마을   DOI
11 Royston, P., 1992: Which measures of skewness and kurtosis are best? Statistics in Medicine 11(3), 333-343.   DOI
12 Vogel, R. M., and N. M. Fennessey, 1993: L moment diagrams should replace product moment diagrams. Water Resources Research 29(6), 1745-1752.   DOI
13 Vogel, R. M., and I. Wilson, 1996: Probability distribution of annual maximum, mean, and minimum streamflows in the United States. Journal of Hydraulic Engineering 1, 69-76.
14 Hosking, J. R. M., 1990: L-moments: analysis and estimation of distributions using linear combinations of order statistics. Journal of the Royal Statistical Society: Series B 52, 105-124.
15 Chow, K. C. A., and W. E. Watt, 1994: Practical use of the L-moments. Stochastic and Statistical Methods in Hydrology and Environmental Engineering vol. 1, K. W. Hipel (Eds.), Kluwer Academic Publishers, 55-69.
16 Guttman, N. B., J. R. M. Hosking, and J. R. Wallis, 1993: Regional precipitation quantile values for the continental United States computed from L-moments. Journal of Climate 6, 2326-2340.   DOI
17 Heo, J., and K. Kim, 1995: A study of the selection of probability distribution for rainfall data in Korea. Journal of the Engineering Research Institute, Yonsei University 27(2), 193-200. (in Korean with English abstract)
18 Hosking, J. R. M., 1996: Fortran routines for use with the method of L-moments. IBM Research Report RC 20525 (90933), 33pp.
19 Hosking, J. R. M., and J. R. Wallis, 1997: Regional Frequency Analysis. Cambridge University Press, 224pp.