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

1-    Akinci, H., Ozalp, A.Y. and Turgut, B., 2013. Agricultural land use suitability analysis using GIS and AHP technique. Computers and Electronics in Agriculture, 97, pp. 71-82.
2-    Babaei, F., Vaezi, A.R. and Taheri, M., 2015. Modeling soil organic carbon content based on topographic indices and soil properties of wheat dryland. Journal of Soil and Water Conservation Research, 23 (3), pp. 111-129. (In Persian)
3-     Munoz-Diaz, D. and Rodrigo, F.S., 2004, April. Spatio-temporal patterns of seasonal rainfall in Spain (1912-2000) using cluster and principal component analysis: comparison. In Annales Geophysicae (Vol. 22, No. 5, pp. 1435-1448).
4-    Faizizadeh, B. Abdali, H., Rezaei Banafsheh, M. and Mohammadi, Gh. H., 2012. Zoning of rainfed wheat cultivation capability in East Azarbaijan province using GIS spatial analysis. Journal of Agriculture, 96, pp. 75-91. (In Persian).
5-    Hasheminasab Khabisi, F. Mousavi Baigi, M., Bakhtari, B. and Bnaianaval, M. 2014. Effect of rainfall on dryland wheat yield and satisfaction index of water need at different time scales. Journal of Irrigation and Water Engineering, 17, pp. 1-13. (In Persian).
6-    Hassan, B.G.H. and Ping, F., 2012. Formation of homogenous regions for Luanhe basin by using L-moments and cluster techniques. International Journal of Enviromental Science and Development, 3(2), pp. 205- 210.
7-    Hosking, J.R.M. and Wallis, J.R., 1993. Some statistics useful in regional frequency analysis. Water Resource Research, 29 (2), pp. 281- 671.
8-    Houshyar, E., Sheikh-Davoodi, MJ., Almassi, M., Bahrami, H., Azadi, H., Omidi, M., Sayyad, G. and Witlox, F., 2014. Silage corn production in conventional and conservation tillage systems. Part 1: Sustainability analysis using combination of GIS/AHP and multi-fuzzy modeling. Ecological Indicators, 30, pp. 102-114.
9-    Kamali, Gh.A., Sadeghianipour, A., Sedaghatkerdar, A., Asgari, Gh., 2008. Climatic potential of rainfed wheat cultivation in East Azarbaijan province. Journal of Soil and Water Science and Technology, 22 (2), pp. 467-483. (In Persian).
10- Kitsara, G., Pappaioannou, G., Mitropoulo, A. and Markopoulos, P., 2005. Reference Crop evapotranspiration and agricultural rainfall index. In the 9th International Conference on Environmental Science and Technology, Rhodes island, Greece.
11- Macqueen, J., 1967. Some methods for classtification and analysis of multivariate observation. In Proceeding of the 5th Berkeley Symposiumon Mathematical Statistics and Probability, Berkeley, CA: University of California.
12- Masoodian, S.A., Darand, M., and Karsaz, S.A., 2011. Precipitation zoning west and northwest of Iran by cluster analysis method. Journal of Natural Geography, 11, pp. 35- 44. (In Persian).
13- Mohammadi, P., Fakherifard, A., Dinpazhoh, Y., and Asadi, E., 2017. Regionalization of the East part of Lake Urmia Basin based on impact of seasonal precipitation on rainfed yield using the ward and K-means methods. Iranian Journal of Ecohydrology, 4(2), pp. 489-498. (In Persian).
14- Nazmfar, H. and Goldoost, A., 2013. Identification of climatic sub-regions of Yazd province using multivariate statistical methods. Geographical Space Journal. 48, pp. 161-147. (In Persion)
15- Nosrati, K., Mohseni Sarovi, M., Islamian, S., Sharifi, F. and Mahdavi, M., 2004. Determination of homogeneous zones for low flow frequency analysis. Iranian Journal of Natural Resources, 57 (1), pp. 45-58. (In Persian).
16- Parracho, A.C., Melo-Goncalves, P. and Rocha, A., 2016. Regionalisation of precipitation for the Iberian Peninsula and climate change. Physics and Chemistry of the Earth, Parts A/B/C, 94, pp. 146-154.
17- Pelczer, I.J. and Cisneros-Iturbe, H.L., 2008. Identification of rainfall patterns over the valley of Mexico. In 11th International Conference on Urban Drainge, Edinburgh, Scatland, UK.
18- Pineda-Martinez,  L.F. and Carbajal, N. and Median –Roldan, E., 2007. Regionalization and classification of bioclimatic zones in the central- northeastern region of Mexico using principal component analysis. Journal of Atomofera, 20(2), pp. 133- 145.
19- Raziei., T., 2017. A precipitation regionalization and regime for Iran based on multivariate analysis. Theoretical and Applied Climatology,131(3-4), pp. 1429-1448.
20- Rencher, A.C., 2002. Methods of multivariate analysis. John Wiley and Sons, INC publication.
21- Romero, R., Sumner, G., Ramis, C., Genovés, A., 1999. A classification of the atmospheric circulation patterns producing significant daily rainfall in the Spanish Mediterranean area. International Journal of Climatology: A Journal of the Royal Meteorological Society, 19(7), pp. 765-785.
22- Sattari, N., Fakhri Fard, A. and Hasaniha,. A.H., 2014. Northwest zoning of the country based on the ratio of precipitation to evapotranspiration by principal component analysis and Ward. Iranian Water Research Journal, 9 (4), pp. 1-8. (In Persian).
23- Shirvani, A. and Nazem al-Sadat, S.M. J., 2012. Precipitation Zoning in Iran Using Principal Component Analysis and Cluster Analysis. Iranian Water Resources Research, 8 (1), pp. 81-85. (In Persian).
24- Stathis, D. and Myronidis, D., 2009. Principal component analysis of precipitation in Thessaly region (Central Greece). Journal of Global Nest, 11(4), pp. 467-476.
25- Ward, J.R., 1963. Hierarchical grouping to optimize an objective function. Journal of the American Statistical Asssociation, 58 (301), pp. 236-244.
26- Wiltshire, S.E., 1986. Identification of homogeneous regions for flood frequency analysis. Journal of Hydrology, 84(3), pp.287-302.
Volume 42, Issue 4
December 2019
Pages 45-59
  • Receive Date: 03 October 2016
  • Revise Date: 26 December 2017
  • Accept Date: 30 December 2017
  • First Publish Date: 22 December 2019