Determination of Best Fit Probability Distribution and Frequency Analysis of Threshold Rainfall under different Climate Change Scenarios

It is necessary to study and analyze the frequency of extreme rainfall events to determine the best-fit distribution that can predict the occurrence of the certain natural phenomena such as rainfall, flood, etc. In this study assessed to determine the best-fit distribution, the frequency analysis of threshold rainfalls considering Coupled Model Intercomparison Project phase 5 General Circulation Models (CMIP5 GCMs) under two Representative Concentration Pathways (RCP) scenarios (2.6 and 8.5). For this purpose, four empirical formulas (Hazen, Weibull, Tukey, and Cunnane) were used to estimate the return periods of threshold precipitation. Also, various probabilistic distributions including normal distributions, log normal (LN), log normal 3 (LN3), Gumble, Pearson type 3 (P3), and log Pearson type 3 (LP3) were applied to predict the distribution of threshold rainfalls. Kolmogorov-Smirnov test was used to determine the best-fit probability distribution function (PDF). Results revealed that the Hazen formula obtained the most estimate in the period of observation and future periods, and the near future (2015-2040) and the far future periods (2041-2065). According to the results, the LN3, LP3 and GEV probabilistic distributions presented the best PDF for threshold rainfalls in most periods. Among the best-fit distributions, LN3 was received 45 percent and LP3 and GEV received 20 and 30 percent of the best result, respectively. These results indicate there are severe abnormalities in the threshold precipitations, especially in high amounts. The results of this study can be used to develop more accurate models against the dangers, and damages caused by Extreme weather and flood.

کلیدواژه های Determination of Best Fit Probability Distribution and Frequency Analysis of Threshold Rainfall under different Climate Change Scenarios:

Tehran Province ، Threshold Precipitation ، Climate Change Scenarios ، Frequency Analysis ، Probabilistic Distribution