پهنه بندی منطقه شرق دریاچه ارومیه براساس عملکرد دیم و بارش با روش های Ward،Means K- و PCA

نوع مقاله: مقاله پژوهشی

نویسندگان

1 دانش آموخته کارشناسی ارشد گروه مهندسی آب، دانشکده کشاورزی، دانشگاه تبریز

2 استاد، گروه مهندسی آب، دانشکده کشاورزی، دانشگاه تبریز.

3 دانشیار ، گروه مهندسی آب، دانشکده کشاورزی، دانشگاه تبریز.

4 استادیار، گروه مهندسی آب، دانشکده کشاورزی، دانشگاه تبریز.

چکیده

در بین عوامل اقلیمی مؤثر برای کشت دیم، بارندگی مهم­ترین عامل محسوب می­شود. تعیین اراضی مستعد دیم در کشور به­ویژه حوضه آبریز دریاچه ارومیه امری ضروری می­باشد. به­منظور پهنه­بندی منطقه شرق دریاچه ارومیه براساس عملکرد دیم و بارش برای بررسی قابلیت کشت دیم، آمار بارش روزانه ۲6 ایستگاه باران­سنجی برای دوره آماری 1370 تا 1392 به­کار گرفته شد. در این مطالعه گیاه گندم، به­عنوان یک محصول استراتژیک، انتخاب شد و و از آمار عملکرد گندم دیم طی دوره مذکور استفاده گردید. سپس با به­کارگیری روش­های تجزیه به مؤلفه­های اصلی،K- Means و وارد خوشه­بندی انجام گرفت. همگنی خوشه­های به­دست آمده با آزمون­های آماری H و S بررسی شد و خوشه­های همگن در محیط GIS رسم گردید. نتایج نشان داد که ضرایب عامل­های تجزیه به مؤلفه­های اصلی با خوشه­بندی K- Means از نظر درصد مساحت و درصد میانگین خوشه­های حاصل­شده بارش و عملکرد دیم همخوانی بیشتری باهم دارند و نتایج به هم نزدیک است. از طرفی دیگر، از نقطه نظر تغییرات مکانی، خطوط هم­عملکرد توام با خطوط هم­بارش رسم شد. نتایج افزایشی هم­سو برای عملکرد محصول و بارش در شمال­غرب و مرکز منطقه مورد مطالعه را نشان داد که با ماهیت فیزیکی روند عملکرد دیم همخوانی دارد؛ اما در بخش­های دیگر منطقه برخی مناطق ناهمگن مشاهده شد. مناطق همگن 47/24 درصد و مناطق ناهمگن 53/75 درصد مساحت منطقه مورد مطالعه را در برگرفت.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

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

نویسندگان [English]

  • Parva Mohammadi 1
  • Ahmad Fakherifard 2
  • Yaghob Dinpazhoh 3
  • Esmaeil Asadi 4
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.
چکیده [English]

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.

کلیدواژه‌ها [English]

  • Clustering
  • Precipitation
  • Regionalization
  • Rainfed yield

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.