The Determination of the Most Appropriate Probability Distribution Models for the Meteorological Variables

Abstract

Every component of the hydrological cycle is essential for controlling water supplies and assessing potentially catastrophic events like floods and droughts. The variables of the hydrological system are unexpected and unique to each place. In this paper, the most crucial variables, including precipitation, temperature, relative humidity, and evaporation, are examined for Ankara province. For meteorological parameters, the Lognormal, Log-logistic, Gamma, Weibull, Normal, and Gumbel models are used to find the best suitable distributions. Kolmogorov-Smirnov, Cramers-von Mises, Akaike's Information Criterion, Bayesian Information Criterion, AndersonDarling, and Maximum Loglikelihood methods are utilized to test these models. The results show that there is a distinct distribution model for each parameter. In particular, it has been determined that the Gumbel distribution is a better model for annual total precipitation, whereas the Normal distribution is a better model for annual minimum temperature. At stations 17130 and 17664, the gamma distribution is observed to be the best fit distribution at annual total precipitation, but station 17128 is found to be the most appropriate for both Log-logistic and normal distribution. Stations 17128, 17130, and 17664 for annual maximum temperature series are fitted with the Normal, Log-logistic, and Lognormal, respectively. Gamma is found to be the best fit when analyzing annual mean temperature for stations 17128 and 17130, whereas Lognormal is selected for station 17664. It is expected that these results will contribute to the planning of water resources projects in the region.