Determining the Climatic Type of Different Regions Using Principal Components Analyses Method

Climate change on a large scale increases changes in boundary indicators. Since these indicators play an important role in the occurrence of droughts, floods and other climate disasters, it is necessary to study their behavior in the context of climate change. In the present study, 24-hour precipitation data of 11 Synoptic Stations during statistical periods (1987-2017) have been used. Climate change indicators (ETCCDI) have been used to extract trends using R-ClimDex software Also to check the linear trend of the test TFPW-MN and in order to zoning the precipitation conditions, PCA method has been used in the form of Minitab statistical software. The results of m-Kendall test show that precipitation indicators in all studied stations have a decreasing and negative trend and homogeneous slope. After forming the variable correlation correlation matrix, the principal components were reduced to 11 components using the analysis method and rotated using a varimax rotation. By examining the results of PCA algorithm, four climatic types were identified. The fourth climatic type (Shiraz station) has the most effective role in creating climatic conditions in precipitation with a relatively 85% specific variance of the total changes. As a result, it can be said that the overall structure of precipitation in the study area is affected by latitude, the existence of rough configuration and air masses. And by changing any of these factors, the rainfall will change.