Probability Distribution Modeling of Extremes Rainfall Series in Makassar City using the L-Moments Method

Authors

  • Wahidah Sanusi
  • Syafruddin Side Department of Mathematics, Faculty of Mathematics and Natural Science, Universitas Negeri Makassar, 90224
  • Muhammad Kasim Aidid

Keywords:

extreme rainfall, L-moments, and probability distribution

Abstract

Information on probability distribution of extreme rainfall is very important for planning of water resources and studying related to climatic change. The objective of this study is to identify the best fit probability distribution of extreme rainfall series using L-moments method for three rainfall stations in Makassar city for the period 1985-2014. The results of study show that Generalized Logistic distribution (GLO) is the best fit probability model for the annual maximum rainfall at Maritime Meteorological station of Paotere. Meanwhile, Generalized Pareto distribution (GPA) and Generalized Extreme distribution (GEV) were found as the best fit for Biring Romang station of Panakukkang and BBMKG region IV station of Panaikang, respectively.

References

Buishand, T.A. 1991. Extreme rainfall estimation by combining data from several sites. Hydrologkal Sciences, 36(4): 345-362.

Central Agency on Statistics of Makassar city, 2010. Makassar in Figure 2010. Makassar: UD Areso.

Deka, S., Borah, M. and Kakaty, S.C. 2009. Distributions of annual maximum rainfall series in North-West India. European Water, 27/28: 3-14.

Dodangeha, E., Shaob, Y. and Daghestanic, M. 2011. L-Moments and fuzzy cluster analysis of dust storm frequencies in Iran. Aeolian Research, doi: 10.1016/j.aeolia.2011.10.004.

Du, H., Xia, J., Zeng, S, She, D. and Liu, J. 2014. Variations and statistical probability characteristic analysis of extreme precipitation events under climate change in Haihe River Basin, China. Hydrological Processes, 28(3): 913–925.

Eslamian, S.S. and Feizi, H. 2007. Maximum monthly rainfall analysis using L-moments for an arid region in Isfahan province, Iran. Journal of Applied Meteorology and Climatology, 46: 496-503.

Feng, S., Nadarajah, S., and Hu, Q. 2007. Modeling annual extreme precipitation in China using the Generalized Extreme Value distribution. Journal of the Meteorological Society of Japan, 85(5): 599-613.

Hosking, J. R. M. & Wallis, J. R. 1997. Regional Frequency Analysis. An approach based on L- moment. UK: Cambridge University Press.

Li, Z., Li, Z., Zhao, W., and Wang, Y. 2015. Probability modeling of precipitation extremes over two river basins in Northwest of China. Advances in Meteorology, 1-12.

Marx, L, and Kinter, J.L. 2007. Estimating the representation of extreme precipitation events in atmospheric general circulation models using L-Moments. Vol. 250. Center for Ocean-Land-Atmosphere Studies.

Mayooran, T. and Laheetharan, A. 2014. The statistical distribution of annual maximum rainfall in Colombo district. Sri Lankan Journal of Applied Statistics,15(2): 107-130.

Modarres, R. 2010. Regional dry spells frequency analysis by L-moment and multivariate analysis. Water Resour Manage, 24: 2365-2380.

Park, J.S., Jung, H.S, Kim, R.S, and Oh, J.H. 2001. Modeling summer extreme rainfall over the Korean peninsula using Wakeby distribution. Int. J. Climatol., 21: 1371-1384.

Park, J.S., and Jung, H.S, 2002. Modeling Korean extreme rainfall using a Kappa distribution and maximum likelihood estimate. Theor. Appl. Climatol., 72: 55-64.

Sanusi, W., Side, S and Aidid, M.K. 2015. Analysis of rainfall distributions of Makassar city. Proceeding of the national seminar of the UNM research institute, 395-405. In Indonesian.

Shabri, A. and Ariff, N.M. 2009. Frequency analysis of maximum daily rainfalls via Lmoment approach. Sains Malaysiana 38 (2):149–158.

Von Storch, H. , and Zwiers, F. W. 1999. Statistical Analysis in Climate Research. Cambridge: University Press.

Downloads

Published

2015-10-27

How to Cite

Sanusi, W., Side, S., & Aidid, M. K. (2015). Probability Distribution Modeling of Extremes Rainfall Series in Makassar City using the L-Moments Method. Asian Journal of Applied Sciences, 3(5). Retrieved from https://www.ajouronline.com/index.php/AJAS/article/view/3258