Determining the Climatic Type of Different Regions Using Principal Components Analyses Method

Document Type : Research Paper


1 MSc. Department of Meteorology, Faculty of Planning and Environmental Sciences, University of Tabriz, Tabriz, Iran.

2 Assistant Professor, Department of Water Engineering and Sciences, Faculty of Agricultural Sciences and Food Industry, Islamic Azad University, Science and Research Branch, Tehran, Iran.

3 MSc., Department of Water Engineering and Sciences, Science and Research Branch, Islamic Azad University, Tehran, Iran.

4 Associate Professor, Department of New Energy and Environment, Faculty of Modern Science and Technology, University of Tehran, Tehran, Iran.

5 Professor, Department of Meteorology, Faculty of Planning and Environmental Sciences, University of Tabriz, Tabriz, Iran.


Climate change on a large scale increases changes in boundary indicators. Since these indicators play an important role in the occurrence of droughts, floods and other climate disasters, it is necessary to study their behavior in the context of climate change. In the present study, 24-hour precipitation data of 11 Synoptic Stations during statistical periods (1987-2017) have been used. Climate change indicators (ETCCDI) have been used to extract trends using R-ClimDex software Also to check the linear trend of the test TFPW-MN and in order to zoning the precipitation conditions, PCA method has been used in the form of Minitab statistical software. The results of m-Kendall test show that precipitation indicators in all studied stations have a decreasing and negative trend and homogeneous slope. After forming the variable correlation correlation matrix, the principal components were reduced to 11 components using the analysis method and rotated using a varimax rotation. By examining the results of PCA algorithm, four climatic types were identified. The fourth climatic type (Shiraz station) has the most effective role in creating climatic conditions in precipitation with a relatively 85% specific variance of the total changes. As a result, it can be said that the overall structure of precipitation in the study area is affected by latitude, the existence of rough configuration and air masses. And by changing any of these factors, the rainfall will change.


1. Ahmadi, M., Lashkarry, H., Freedom, M., & keykhsrovi, Q. (2015). Detection of climate change using parameters and precipitation in khorasan. Knowledge of Earth Knowledge Research, 6(23), 34-52. (In Persian).
2. Alaetaleghani, M. (2009). Geomorphology of Iran. Tehran,ghomos publication.
3. Alijani, B. (2011). Spatial analysis of critical damages and critical pressures in iran. Journal of Applied Geosciences Research, 11(20), 9-30.
4. Cheeloong, W., Juneng, L., Zulkifli, Y., Tarmizi, I., Raymond, V., & Stefan, U. (2016). Rainfall characteristics and regionalization in peninsular malaysia based on a high resolution gridded data set. Journal of Water Research, 8(11), 500.
5. Darend, M. (2014). Analysis of variations in rainfall and temperature extremes in urmia as indicators of climate change. Journal of Water and Soil Conservation Research, 21(2), 1-29. (In Persian).
6. Delima, M., Santo, F., Ramos, A., & Delima, J. (2013). Recent changes in daily precipitation and surface air temperature extremes in mainland portugal. Journal of Atmospheric Research, 127, 195-209.
7. Hejazizadeh, Z., Fatahi, T., Salighe, M., & Arsalani, F. (2013). Investigating the effect of climate signals on iran central region rainfall using artificial neural network. Journal of Applied Geosciences Research, 13 (29), 75-89. (In Persian).
8. Hirsch, R., & James, R. (1984). Anonparametric trend test for seasonal data with serial dependence. Journal of Water Resource, 20(6), 727-732.
9. Jahanshahi, A., Shahedi, K., Solaimani, K., & Moghaddamnia, A. (2019). Determination of hydrological homogenous regions in the west of hamounjazmourian river basin. Iran Water Resource Research Jounral, 15(1), 223-235. (In Persian).
10. Kendall, MG. (1975). Rank correlation methods and ed. Newyork hafner. Mann, H.B., 1945, Nonparametric tests against trend. Jounral of Econometrica Research, 3, 245-259.
11. Gan, T. Y. (1998). Hydroclimatic trends and possible climatic warming in the Canadian Prairies. Water resources research, 34(11), 3009-3015.
12. Khorshiddost, M., & Zanganeh, S. (2013). Analysis and evaluation of the trend of extreme temperature and precipitation indicators based on daily synoptic station series of kermanshah in the 48-year statistical period (1961-2009). The thirty-second gathering and the first international congress of geosciences, Tehran, Iran, 1-7.
13. Kosegran, S., & Mousavibaghi, M. (2015). Investigating the trend of extreme weather events in the northeast. Journal of Water and Soil Science and Technology of Agriculture, 29(3), 750-764. (In Persian).
14. Kouhestani, SH., Eslamian, S., Abedi-Koupai, J., & Besalatpour, A. (2016). Projection of climate change impacts on precipitation using soft-computing techniques a case study in zayandehrud basin iran. Jounral of Global and Planetary Change, Volume 144, 158-170.
15. Li, Q., Li, Z., Zhu, Y., Deng, Y., Zhang, K., & Yao, Ch. (2018). Hydrological regionalisation based on available hydrological information for runoff prediction at catchment scale. Jounral of Proceeding of the International Association of Hydrological Sciences, 379, 13-19.
16. Massah Bavani, A., Goodarzi, E., & Zohrabi, N. (2013). Detection of temperature and precipitation trends and their attribution it to the greenhouse gases (case study: west azerbaijan province). Journal of Earth and Space Physics, 39)3), 111-128. (In Persian).
17. Mohammadi, H., Azizi, Gh., Khoshaykalah, F., & Rancid, F. (2017). The trend analysis of daily rainfall indexes in iran. Natural Geographic Research, 49(1), 21-37. (In Persian).
18. Mohammadyariyan, M., Tavosi T., Khosravi, M., & Hamidiyanpour, M. (2019). Zoning of iranian heavy precipitation regime. Geographical Researches Quarterly Journal, 34(2), 183-192. (In Persian).
19. Paul, A., Riddhidipa, V., Chowdary, Dibyendu, Dutta, U., Sreedhar, H., & Ravi, S. (2017). Trend analysis of time series rainfall data using robust statistics. Journal of Water and Climate Change, 8(4), 691-700.
20. Peraltahernandez, A., Balling, R., & Barbamartinez, L. (2009). Comparative analysis of indices of extreme rainfall events. Variations and Trends from Southern Mexico Atmosfera, 22(2), 219-228.
21. Rajabi, A., & Shabanlou, S. (2012). The analysis of uncertainty of climate change by means of SDSM model case study- kermanshah. World Applied Sciences Journal, 23(10), 1392-1398. (In Persian).
22. Rustum, R., Adebayo, J., & Mwale, F. (2017). Spatial and temporal trend analysis of long term rainfall records in data-poor catchments with missing data a case study of lower shire floodplain in malawi for the period. Hydrology and Earth System Sciences Discussions, 1-30.
23.Sarr, M.A.,  Gachon, P.,  Seidou, O.,  Bryant, Ch.,  Ndione, J., & Comby, J. (2014). Inconsistent linear trends in senegalese rainfall indices from 1950-2007. Hydrological Sciences Journal, 60, 1538-1549.
24. Tipping, M., & Bishop, C. (1999). Probabilistic principal component analysis. Journal of Royal Statistical soc, 61(3), 611-622.
25. Yilmaz, A. (2015). The effects of climate change on historical and future extreme rainfall in antalya turkey. Hydrological SciencesJjournal, 60, 2148-2162.
26. Yue, S., Pilon, P., & Phinney, B. (2003). Canadian streamflow trend detection impacts of serial and crosscorrelation. Hydrogical Sciences Journal, 48(1), 51-64.
27. Zhang, X. (2007). ETCCDI/CRD climate change indices software. Climate Research Division of Environment Canada,