Modifying Annual Precipitation Mean in Iran Concerning the Effects of Heteroscedasticity; Part 2: Catchment Spatial Scale

Iran is a very rugged country with a fairly high variation of climate. These different climates can affect the heteroscedasticity of precipitation data. Because, precipitation is a missing variable in heteroscedastic. To investigate the spatial and seasonal variation of heteroscedasticity in this study, building on the catchment scale and investigating the precipitation data from 146 synoptic stations -the effect of heteroscedasticity on mean precipitation was investigated by the Levene test. The results show that precipitation data on the catchment scale are also heteroscedastic. Also, the current network of synoptic stations cannot help decrease heteroscedasticity in precipitation data on the catchment scale. The most heteroscedasticity and homoscedasticity are located in the Mazandaran Sea and the Central Plateau catchments. Hence, heteroscedasticity of precipitation data challenges the efficiency of mean precipitation on the catchment scale, like the climatic zoning scale. Therefore, to achieve an efficient mean of annual precipitation in the country, it is necessary to do practice to enhance the calculated mean precipitation, by avoiding calculating the mean precipitation in the 2nd grade catchment and climatic regions.   Also, heteroscedasticity is reduced by determining the cause of heteroscedasticity of precipitation data errors and the selection of the best model that can consider the heteroscedasticity on the small catchment scale (3, 4, 5, 6 and 7 grades).