Determining suitable probability distribution models for annual precipitation data (a case study of Mazandaran and Golestan provinces)

Mohammad Mahdavi, Khaled Osati, Sayed Ali Naghi Sadeghi, Bakhtiar Karimi, Jalil Mobaraki


Statistical distributions can be used for data development in shortage data situations, as in many part of Iran station. The aims of this study are select the best frequency distribution to estimate average annual precipitation and assess the effects of data length on the selection of suitable distribution. Therefore 65 stations data of Mazandaran and Golestan provinces were analyzed. Relative residual mean square (RMS) was used to determine the best fitted distribution to any annual series and precipitation was estimated for different return periods. Relative frequency of first classes of fitted distributions showed that normal and Pearson distributions decreased and Gumbel distribution had more fitness with data series by increasing statistical period length.  The best-fitted distribution is Pearson with 15-year data; log Pearson for 20, 25 and 30-year periods. Based on Moment method and total given scores, two-parameter normal distribution has the best fitness in all statistical periods.

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