Identification of precipitation regimes of Iran using multivariate methods


Assistant Professor, Soil Conservation and Watershed Management Research Institute (SCWMRI), Agricultural Research, Education and Extension Organization (AREO), Tehran, Iran


Delineation of precipitation regimes is very important for large countries such as Iran which is characterized with complex topography and different climates. The very rare previous studies on precipitation regimes of Iran have used very limited and unevenly scattered stations across the country; thus making it necessary to identify the most realistic precipitation regimes for Iran using as much as available stations. Henco, the data of 155 synoptic stations with relatively regular distribution over Iran; mostly having full data records for the common period of 1990 to 2014, were used for identifying the updated precipitation regimes for the country. For each station, the percentage of monthly precipitation in relation to total annual precipitation was computed for all the time period and the mean of the time period was considered for the analysis. A principal Component Analysis (PCA) was applied to the inter-stations correlations matrix (155×12) that is composed of 155 stations and 12 mean monthly percentage of precipitation for each station. The computed Kaiser-Meyer-Olkin measure of sampling adequacy for the considered matrix with the value of 0.79 indicates that the considered matrix is approximately meritorious for a PCA application. The first 5 leading significant PCs were considered for further analysis based on the Scree plot and the sampling errors of the PCs (North et al., 1982). The retained PCs were then rotated using varimax orthogonal and promax oblique criterion. The PC scores of both rotated solutions and un-rotated solution were separately used as input for Cluster Analysis (CA) to partition the considered stations into distinctive clusters. Moreover, all agglomerative CA methods as well as K-means CA were examined to find out the most appropriate method for partitioning the data. The cophenetic correlation coefficient was used to measure how well the hierarchical dendrogram of a given CA candidate represents the relationships within the input data. The results indicate that all the clustering approaches well represented the inherent structure of the input data, but the Ward method was selected as the most appropriate method since it resulted in much realistic clusters that well matched the topographic and geographical features of the country. The correct number of clusters was also selected based on the Silhouette index (Rousseeuw, 1987) that measures how well objects lie within their cluster, and which ones are merely somewhere in between clusters. The average silhouette width provides an evaluation of clustering validity, and might be used to select an ‘appropriate’ number of clusters. Computing the index for a set of predefined cluster numbers (2 to 15 clusters) suggests that 9 clusters is the most appropriate cluster number that better represents the inherent structure of the data. As such, all 155 stations were classified into five clusters applying Ward CA method on the 5 leading un-rotated PC scores. However, the 8th cluster that grouped stations from two distant areas into a single cluster was subjectively partitioned into 2 distinctive clusters to better represent the precipitation regimes of these two areas. Moreover, the 5 leading varimax rotated PC scores were also mapped to present spatial variability of seasonal precipitation across the country.
The maps of varimax rotated PC scores well represent areas characterized with seasonal precipitation maximum.  For example, summer precipitation in the coastal areas of the Caspian Sea and south eastern Iran are presented by the rotated PC score 1 while the rotated PC score 2 points to the spring precipitation maxima in north western Iran. By taking into account the seasonal displacement of maximum precipitation across the country in the Ward clustering, the identified clusters labeled with its core geographic position or the season of the maximum precipitation well portrait the Iranian precipitation regimes. The Caspian Sea precipitation regime is the most humid precipitation regime in the country with relatively well distributed precipitation during the year that maximizes in autumn. The northern and southern Azerbayjan in northwestern Iran are represented by two distinct precipitation regimes, both being characterized with relatively uniform precipitation distribution during the year but getting their maximum precipitation in a different month of the spring. The south-eastern monsoon precipitation regime featured south-eastern Iran where summer monsoon precipitation has a considerable contribution in annual total precipitation. Similarly, the southeastern coastal precipitation regime characterizing coastal areas of Oman Sea that benefits from summer monsoon but with a lesser magnitude and duration. The western mountainous regime is characterized with a precipitation regime spanning from October to May that maximizes in March. The south-western precipitation regime that encompasses south-western and southern Iran along the Persian Gulf is characterized with a winter rainy season that maximizes in January. Central-eastern and central-northeastern Iran also exhibit two distinct precipitation regimes, both getting their maximum proportion of precipitation in winter but the rainy season is much shorter in central-eastern Iran. And finally, central Alborz is characterized with a precipitation regime in which summer precipitation is relatively high.


Main Subjects

جهانبخش‌اصل، س.، ذوالفقاری، ح. 1381. بررسی الگوهای همدیدیدی بارش‌های روزانه در غرب ایران، فصلنامه تحقیقات جغرافیایی، شماره پیاپی 63 و 64، 234-258.
خلیلی،ع.، حجام،س.، ایران‌نژاد، پ. 1370. تقسیمات آب و هوائی ایران، انتشارات وزارت نیرو، طرح جامع آب کشور (جاماب)، 259 صفحه و یک نقشه با مقیاس یک میلیونیم.
ذوالفقاری ، ح.، ساری‌صراف، ب. 1378. بررسی بارش‌های شمال غرب ایران با تاکید بر تحلیل خوشه ای، آب و توسعه، سال هفتم، شماره دوم و سوم، 134-142.
عدل، ا.ح.، آب و هوای ایران، 1339، انتشارات دانشگاه تهران.
گنجی، م.ح.، 1353. 32 مقاله جغرافیایی. موسسه جغرافیایی و کارتوگرافی سحاب. تهران، 101-139.
مسعودیان، ا.، 1384، شناسایی رژیم های بارش ایران به روش تحلیل خوشه ای، پژوهشهای جغرافیایی، دوره 37، شماره 52، 47-59.
مسعودیان، ا.، عطایی، ه. 1384. شناسایی فصول بارشی ایران به روش تحلیل خوشه ای، مجله پژوهشی دانشگاه اصفهان (علوم انسانی)، جلد هجدهم، شماره 1، 1-12.
 Ahmed S. M., Hussain, M. and Abderrahman, W., 2005, Using multivariate factor analysis to assess surface/logged water quality and source of contamination at a large irrigation project at Al-fadhli, eastern province, Saudi Arabia, Bull Eng Geol Environ, 64: 319-327.
Bunkers, M. J., Miller, J. R. and DeGaetano, A. T., 1996, Definition of climate regions in the Northern plains using and objective cluster modification technique, J. Climate, Vol. 9, 130-146.
Comrie, A. C. and Glenn, E. C., 1998, Principal components-based regionalization of precipitation regimes across the southwest United States and northern Mexico, with an application to monsoon precipitation variability, Clim. Res., Vol. 10: 201-215.
De Martonne, E., 1926, Aréisme et indice artidite. Comptes Rendus de L’Acad Sci, Paris, 182, 1395–1398.
DeGaetano, A. T., 1996, Delineation of mesoscale climate zones in the northeastern United States using novel approach to cluster analysis, J. climate, Vol. 9: 1765-1782.
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, 109–123
Domroes, M., Kaviani, M. and Schaefer, D., 1998, An Analysis of Regional and Intra-annual Precipitation Variability over Iran using Multivariate Statistical Methods. Theor. Appl. Climatol. 61, 151-159.
Ehrendorfer, M., 1987, A regionalisation of Austria’s precipitation climate using principal component analysis, J. Climatol. 7, 71–89.
Fernández Mills, G., 1995, Principal Component Analysis of precipitation and rainfall regionalization in Spain, Theoretical and Applied Climatology, 50 (3), 169-183.
Fovel, R. G. and Fovel, M. C., 1993, Climate zones of coterminous United States defined using cluster analysis, Journal of Climate, 6, 2103-2135.
Green, M. C., Flocchini, G. and Myrup, L. O., 1993, Use of temporal principal components analysis to determine seasonal periods. Journal of Applied Meteorology 32(5), 986-995.
Janowalk J. E., 1988, An Investigation of interannual rainfall variability in Africa, J. Climate, 1, 240-255.
Kansakar, S. R., Hannah, D. M., Gerrard, A. J. and Rees, G., 2004, Spatial pattern in the precipitation regime of Nepal. Int. J. Climatol. 24, 1645–1659.
Köppen, W., 1936, Das geographische System der Klimate. In: Köppen W, Geiger R (eds) Handbuch der Klimatologie. Gebrüder Borntraeger, Berlin, p 1−44.
Lolis C. J., Bartzokas, A. and Metaxas, D. A., 1999, Spatial covariability of the climatic parameters in the Greek area, Int. J. Climatol. 19, 185-196.
Modarres, R., 2006, Regional precipitation climates of Iran, Journal of Hydrology (NZ), 45 (1): 15-29.
Modarres, R. and Sarhadi, A., 2011, Statistically-based regionalization of rainfall climates of Iran, Global and Planetary Change, 75, 67–75.
North, G. R., Bell, T. L. and Cahalan, R. F., 1982, Sampling errors in the estimation of empirical orthogonal functions. Mon. Wea. Rev., 110, 699–706.
Rao, A. R. and Srinivas V. V., 2006, Regionalization of watersheds by hybrid-cluster analysis. Journal of Hydrology, 318, 37–56.
Raziei, T., Bordi I. and Pereira, L. S., 2008, A precipitation-based regionalization for Western Iran and regional drought variability. Hydrol Earth Syst Sci, 12, 1309–1321.
Reghunath, R., Sreedhara Murthy, T. R. and Raghavan, B. R., 2002, The utility of multivariate statistical techniques in hydrogeochemical studies: an example from Karnataka, India, Water Research, 36, 2437-2442.
Richman M. B., 1981, Obliquely rotated principal components: An improved meteorological map typing technique, Journal of Appl. Meteo, 20, 1145-1159.
Richman, M. B., 1986, Rotation of principal components, J. Climatol., 6, 293-335.
Rousseeuw, P.J., 1987, Silhouettes: A graphical aid to the interpretation and validation of cluster analysis, J. Comput. Appl. Math., 20, 53-65.
Saraçli, S., Doğan, N. and Doğan, I., 2013, Comparison of hierarchical cluster analysis methods by cophenetic correlation. Journal of Inequalities and Applications, 2013:203.
Saris, F., Hannah, D. M. and Eastwood, W. J., 2010, Spatial variability of precipitation regimes over Turkey, Hydrological Sciences Journal, 55(2), 234-249.
Sheskin, D., 2007, Handbook of parametric and nonparametric statistical procedures, Chapman & Hall/CRC, 1736 pp.
Singh, K. K. and Singh, S. K., 1996, Space-time variation and regionalization of seasonal and monthly summer monsoon rainfall of the sub-Himalayan region and Gangetic plains of India, Clim. Res, 6, 251-262.
Thornthwaite, C. W., 1948, An approach toward a rational classification of climate. Geographical Review, 38(1), 55–94.
Todhunter, P. E., Mearns, L. O., Terjung, W. H., Hayes, J. T. and Ji, H. Y., 1989, Effects of Mosoonal fluctuations on Grains in China. Part I: Climatic conditions for 1961-1975, J. Climate, 2: 5-17.
Unal, Y., Kindap, T. and Karaca, M., 2003, Redefining the climate zones of Turkey using cluster analysis. Int. J. Climatol, 23(9), 1045–1055.
Yarnal, B., 1993, Synoptic climatology in environmental analysis: A primer, Belhaven Press, London, UK.