عنوان مقاله [English]
The climatic classificationfrom the distant past has attracted the attention of climatologists. In traditional methods one or more climatic elements considered for classification but these methods cannot indicates the reality of climatic regions. Therefore in the recent years researchers have tried using the dominant parameters affecting climate and multivariate methods have provided a real images from climatic regions. The aim of this study is climatic regionalization of Markazi province by utilizing 29 climatic parameters and use the factor and cluster analysis. Combined use of these parameters in the climatic classification can improves accuracy and shows a real aspect of province. Recognition of microclimates can help us to identify the
strengths and weaknesses of regions climatic characteristics and useful for development planning proposes.
The 29 climate variables from 21 synoptic stations from province and adjacent areas were used. By using the statistics of adjacent stations, accuracy and resolutions of factors and climatic zones were increased. The statistical data were normalized and also, due to different scales of data, the standard scores were used in analysis. The factor analysis and clustering method were applied for classification. After estimation of stations factor loading scores, by using of IDW method, 5*5km nodes were created, using these nodes instead stations in classification improved the accuracy of climatic classification. Eventually by calculation of factor scores in stations, a cluster analysis was applied. For interpolation purpose the kriging methods in GIS were used.
2-1- Factor analysis
The factor analysis as multivariate statistical methods can reduce the number of variables. The advantage of this method is that not only reduces the number of variables, but also keeps the variance of main data.
If the internal correlation between variables is much closer, the number of emerged factors is to be less.
2-2- Cluster analysis
In this method, the grouping of observations based on their distances, this means that observations have short distances classified in one cluster. The aim of clustering method is construction some group that the within group variance less than between group variance. The distance method usually applied for two or multi criteria clustering.
In this method, Euclidean geometry was used for distances measuring of members. According to Euclidean distance between spatial and temporal points, the distance matrices to be created that based on these matrix distances, determined the spatial and temporal cluster.
The factor analysis over variables was showed that the 6 components explained about 90% of region climatic behaviors. The factors with regards to weight of them over the variables are named. These principle components are; Dust-coldness, precipitation, Cloudiness-humid, Thermal, precipitation- coldness and Cloudiness - Thunder. The dust-coldness factor has its maximum weights over Arak region. In south west of province, the precipitation factor were dominate and the cloudiness-humid factor active over the north of province. The thermal factor was affected over Arak and some of southeastern regions of province. Precipitation- coldness factor in Tafresh and north of province and finally Cloudiness- Thunder factor dominated over North West and Taftresh area. The cluster analysis over these 6 factors confirmed 7 climatic regions in Markazi province.
These regions are:
The temperate and semi-dust;
Dusty and semi humid;
Warm and semi arid;
Dusty Semi cold and semi humid;
Temperate and dusty semi arid;
Semi arid Temperate;
Cold and dusty semi arid;
Semi cold and humid thunder.
In the studied area despite the homogenous synoptic systems Because of vitiate geographic factors such as elevation, topographic orientation; latitude and etc, the role of synoptic systems are overshadowed. These caused numerous microclimates in the region. The results of factor analysis shown that climate of region affected by 6 components. These principle components are; Dust-coldness, precipitation, Cloudiness-humid, Thermal, precipitation- coldness and Coldness – Thunder. These components explained about 90% of region climatic behavior. Cluster analysis shown 7 different climatic regions. The factor-cluster analysis technique is found advantageous over many of traditional methods, as it produces richer regions and shows clear climate variations within this province.