Spatio- Temporal delineation of Iran’s Precipitation Climate and Selection of Indicator Stations Using the Multivariate Statistical Methods

Authors

1 Ph.D. Student, Dept. of Water Engineering, Faculty of Agric., Univ. of Tabriz, Iran

2 Assoc. Prof., Dept. of Water Engineering, Faculty of Agric., Univ. of Tabriz, Iran

Abstract

In the present study, the information of 31 synoptic weather stations in the period of 1987-2010 were used to delineate Iran’s precipitation regions. For this purpose, each month’s data were standardized and written in the (n*m) matrix, where n is the number of stations (31) and m is the number of months (12). Principal component analysis was conducted on the mentioned matrix. The main components were determined according to the criterion of having an eigen value greater than one. Then principal components scores were calculated for the selected components. These valeus were used as an input for Ward method of cluster analysis. Then according to the cluster diagram, precipitation climates of the country were identified. The Procrustes analysis (PA) was used to answer the question of which stations could be considered as the indicator of rainfall climates? Furthermore, from this method those months, which their precipitation can be recognized as an indicator of annual rainfall, were selected. Results showed that the first three components incorporated more than 97% of total variance. Based on the selected components the six distinct precipitation regions were distinguished across the country. Furthermore, PA results indicated that the precipitation of May, August and December approximately had whole of the annual precipitation information. Moreover, it was found that seven stations located in different points of the country namely Zahedan, Tehran, Urmia, Ilam, Yasooj, Gorgan and Rasht could be considered as the indicator stations. These stations incorporated over 87 percent of total variance of data of all selected stations in the country.

Keywords


Carvalho MJ, Melo-Gonçalves P, Teixeira JC and Rocha A, 2016. Regionalization of Europe based on a K-Means Cluster Analysis of the climate change of temperatures and precipitation. Physics and Chemistry of the Earth, Parts A/B/C, 94: 22-28.‏
Dinpashoh Y, Fakheri-Fard A, Moghaddam M, Jahanbakhsh S and Mirnia M, 2004. Selection of variables for the purpose of regionalization of Iran's precipitation climate using multivariate methods. Journal of Hydrology 297(1): 109-123.‏
Gocic, M and Trajkovic S, 2014. Spatio-temporal patterns of precipitation in Serbia. Theoretical and Applied Climatology 117(3-4): 419-431.‏
Johnson GL and Hanson CL, 1995. Topographic and atmospheric influences on precipitation variability over a mountainous watershed. Journal of Applied Meteorology 34(1): 68-87.‏
Jolliffe IT, 1986. Principal Component Analysis. Springer-Verlag, 271p.
Kalkstein LS, Tan G and Skindlov JA, 1987. An evaluation of three clustering procedures for use in synoptic climatological classification. Journal of Climate and Applied Meteorology 26(6): 717-730.‏
Krzanowski WJ, 1987. Selection of variables to preserve multivariate data structure, using principal components. Applied Statistics 36(1): 22-33.‏
Nam W, Shin H, Jung Y, Joo K and Heo JH, 2015. Delineation of the climatic rainfall regions of South Korea based on a multivariate analysis and regional rainfall frequency analyses. International Journal of Climatology 35(5): 777-793.‏
Othman M, Ash’aari ZH and Mohamad ND, 2015. Long-term daily rainfall pattern recognition: Application of principal component analysis. Procedia Environmental Sciences 30: 127-132.‏
Pansera WA, Gomes BM, Boas AV and Mello EL, 2013. Clustering rainfall stations aiming regional frequency analysis. Journal of Food, Agriculture & Environment 11(2): 877-885.