Using the Principal Component Analysis Approach for Weighting Statistical, Climatic and Geographical Attributes of the Maximum 24-hour Rainfall and Spatial Clustering Analysis (A Case Study: Urmia Lake Basin)

Regionalization is one of the useful tools for carrying out effective analyses in regions lacking data or with having only incomplete data. One of the regionalization methods widely used in the hydrological studies is the clustering approach. Moreover, another effective factor on clustering is the degree of importance and participation level for each of these attributes. In this study, it was tried to use a broad range of attributes to compare their performance in regionalization. Then, according to the importance and role of each attribute in regionalization, the appropriate weight for each of the attributes in each category was determined using the principal component analysis (PCA) method, and the effect of this weighting in forming the homogenous regions was investigated by the Ward's clustering method. In this regard, the maximum 24-hour rainfall data of 63 meteorological stations located in Urmia Lake Basin (ULB) was used in this study during a time period of 30 years (1979-2008). Furthermore, seven categories of attributes were defined in order to regionalize the rainfall. The results showed that by considering different attributes and combining them with each other, a different clustering is obtained in each category in terms of the number of clusters and stations. Among seven categories of attributes, it was found that the geographical and climatic-geographical categories of attributes showed a more appropriate clustering over the ULB. Additionally, the weighting of attributes could have more effect on improving homogeneity and forming the independent clusters in most cases in terms of the scattering of station and how to locate over the basin.