Determination of the Best Fit Probability Distribution for Annual Rainfall in Karkheh River at Iran
Authors: Karim Hamidi Machekposhti, Hossein Sedghi
Abstract:
This study was designed to find the best-fit probability distribution of annual rainfall based on 50 years sample (1966-2015) in the Karkheh river basin at Iran using six probability distributions: Normal, 2-Parameter Log Normal, 3-Parameter Log Normal, Pearson Type 3, Log Pearson Type 3 and Gumbel distribution. The best fit probability distribution was selected using Stormwater Management and Design Aid (SMADA) software and based on the Residual Sum of Squares (R.S.S) between observed and estimated values Based on the R.S.S values of fit tests, the Log Pearson Type 3 and then Pearson Type 3 distributions were found to be the best-fit probability distribution at the Jelogir Majin and Pole Zal rainfall gauging station. The annual values of expected rainfall were calculated using the best fit probability distributions and can be used by hydrologists and design engineers in future research at studied region and other region in the world.
Keywords: Log Pearson Type 3, SMADA, rainfall, Karkheh River.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.2571967
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[1] Alahmadi, F., Abd Rahman, N., Abdulrazzak, M. “Evaluation of the best fit distribution for partial duration series of daily rainfall in Madinah, western Saudi Arabia”, Evolving water resources system: understanding, predicting and managing watersociety interactions proceedings of ICWRS2014, Bologna, Italy, 2014.
[2] Alghazali, N. O., Alawadi, D. A. “Fitting statistical distributions of monthly rainfall for some Iraq stations”, Civil and Environmental Research, 2014, 6(6): 40-46.
[3] Abdullah, M. A., AL-Mazroui, M. A. “Climatological study of the southwestern region of Saudi Arabia, I. Rainfall analysis”, Climate Research, 1998, 9: 213-223.
[4] Amin, M. T., Rizwan, M., Alazba, A. A. “A best-fit probability distribution for the estimation of rainfall in northern regions of Pakistan”, Open Life Sciences, 2016, 11(1): 432–440.
[5] Bhakar S. R., Iqbal M., Devanda M., Chhajed N., Bansal A. K. “Probability analysis of rainfall at Kota, Indian”, J. Agri. Res, 2008, 42: 201-206
[6] Hann, C. T. “Statistical methods in hydrology”, The Iowa State University Press, 1977.
[7] Mohamed, T. M., Ibrahim, A. A. A. “Fitting Probability Distributions of Annual Rainfall in Sudan”, SUST Journal of Engineering and Computer Sciences (JECS), 2016, 17(2): 34-39.
[8] Osati, K., Mohammed, M., Karimi, B, Naghi, S, Mobaraki, J. “determining suitable probability distribution models for annual precipitation data (A case study of Mazandaran and Golestan provinces)”, Journal of Sustainable Development, 2010, 3(1): 159-168.
[9] Sheng Y., Michio H. “probability distribution of annual, seasonal and monthly precipitation in Japan”, Hydrological Sciences Journal, 2007, 52(5): 863-877.
[10] Tao D. Q., Nguyen V. T., Bourque A. “On selection of probability distributions for representing extreme precipitations in Southern Quebec”, Annual Conference of the Canadian Society for Civil Engineering, 5th-8th June 2002, 1-8.
[11] Waylen, P. R., Qusesada, M. E. and Caviedes, C. N. “Temporal and spatial variability of annual precipitation in Costa Rica and southern oscillation”, Int J Climatol, 1996, 14: 173-193.