Regionalization of the Eastern Part of Urmia Lake Basin Based on the Rainfed Yield and Precipitation Using the Ward, K-Means and PCA Methods

Document Type : Research Paper


1 MSc, Water Resources Engineering, Faculty of Agriculture, Tabriz University

2 Professor, Water Resources Engineering, Faculty of Agriculture, Tabriz University.

3 Associate Professor, Water Resources Engineering, Faculty of Agriculture, Tabriz University.

4 Assistant Professor, Water Resources Engineering, Faculty of Agriculture, Tabriz University.


Rainfall is among the most important climatic factors affecting the rainfed cultivation. Thus, in order to maintain water consumption in current agriculture, with the view of water resources management, the country needs to convert some irrigated land areas to rainfed cultivation in the near future. Indeed, it is necessary to conduct an analytical study on rainfed agriculture and identify appropriate areas for rainfed agriculture in the country, especially in Urmia Lake basin. Principal component analysis (PCA), K-Means and Ward have been already used to assess climate regionalization in different regions such as Spain (Diaz and Rodrigo, 2004), Greece (Kitsara et al, 2005), central-northeastern region of Mexico (Pineda-Martinez et al, 2007), Luanhe basin (Hassan and Ping, 2012) and Iberian Peninsula (Parracho et al, 2015). This study was, thus, intended to study the regionalization of the eastern part of Urmia Lake basin on the basis of the precipitation and yield of rainfed wheat using PCA, K-Means and Ward methods. To that end, the maps were drawn in the GIS environment and three methods of clustering were compared. Finally, using the clustering of precipitation and rainfed yield, wheat cultivability was investigated in the eastern part of Urmia Lake. To that end, the daily rainfall dataset of 26 rain gauge stations were used and the yield of rainfed wheat was considered during the period. Then, PCA, K-Means and Ward clustering were performed and the results were compiled. The homogenousity of the resulting clusters were analyzed by H and S statistical tests and homogeneous clusters were drawn in the GIS environment. The analytical factor coefficients to the main components, through K-Means clustering method, showed that the clusters point of view, precipitation and rainfed yield were more consistent and the results were close to each other.


Main Subjects

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Volume 42, Issue 4
December 2019
Pages 45-59
  • Receive Date: 03 October 2016
  • Revise Date: 26 December 2017
  • Accept Date: 30 December 2017
  • Publish Date: 22 December 2019