Trend Analysis of Rainfall in North Cyprus

  • Rahme Seyhun
  • Bertuğ AkıntuğEmail author


Cyprus, as the third largest island in the Mediterranean Sea, is located at the South of Turkey and the West of Syria and Lebanon. With a semiarid climate, rainfall is the only source of water in the island. Therefore, changes in rainfall regime directly affect the water resource management and ecosystem in the island. In order to improve water management strategies, it is vital to investigate the changes in the rainfall pattern. In this study, a nonparametric Mann–Kendall rank correlation method is employed to identify the existence of a linear trend in annual and monthly rainfall series. After application of homogeneity test and filling in missing data, this method is applied to the observed rainfall data from 20 rain-gauge stations that are located in the northern part of the island for the period of 1978–2009. The results show that there is no significant trend in the annual rainfall; however, upward trends in September rainfall and downward trends in March rainfall have been observed in most of the stations. This indicates that there are no significant changes in annual total rainfall; however, there is a shift in monthly rainfall regime.


Rainfall trend analysis Mann–Kendall test North Cyprus 



Most probable time point of change or the last time point of the sub-series with mean \( \overline{{{z_1}}} \)


Distance from the location of gauged station to the ungauged station


Null hypotheses


Alternative hypotheses


Total number of reference stations


Length of the data set


Number of surrounding stations


Estimate of rainfall for the ungauged station


Rainfall values of rain gauges used for estimation


Difference and ratio between the candidate and reference series at time step i


Mean values of Q i series


Mann–Kendall test statistic


Standard normal homogeneity test statistic


Number of ties of extent


Square of the correlation coefficient between the candidate and a reference station


Reference series (the jth of a total of k)

\( \overline{{{X_j}}} \)

Mean values of the X series


Data values at times i


Data values at times j


Candidate series at year i (or other time unit)

\( \overline{Y} \)

Mean values of Y series


Standardized series with zero mean and unit standard deviation

\( \overline{{{z_1}}} \)

Averages of the \( {Z_i} \) sequences before the shift

\( \overline{{{z_2}}} \)

Averages of the \( {Z_i} \) sequences after the shift


Standardized test statistic

Greek Symbols


Summation function

\( {\mu_1} \)

Theoretical mean level of standardized differences (or ratios) before a possible shift or trend

\( {\mu_2} \)

Theoretical mean level of standardized differences (or ratios) after a possible shift or trend


Standard deviation of the Qi series



Standard normal homogeneity test


Variance of s


  1. 1.
    Easterling DR, Meehl GA, Parmesan C, Changnon SA, Karl TR, Mearns LO (2000) Climate extremes: observations, modeling, and impacts. Science 289:2068–2074CrossRefGoogle Scholar
  2. 2.
    Intergovernmental Panel on Climate Change (2007) The physical science basis, summary for policymakers. Cambridge University Press, New York, CambridgeGoogle Scholar
  3. 3.
    Feidas H, Makrogiannis T, Bora-Senta E (2004) Trend analysis of air temperature time series in Greece and their relationship with circulation using surface and satellite data: 1955–2001. Theor Appl Climatol 79:185–208CrossRefGoogle Scholar
  4. 4.
    Kostopoulou E, Jones PD (2005) Assessment of climate extremes in the Eastern Mediterranean. Meteorol Atmos Phys 89:69–85CrossRefGoogle Scholar
  5. 5.
    Luterbacher J, Dietrich D, Xoplaki E, Grosjen M, Wanner H (2004) European seasonal and annual temperature variability, trends, and extremes since 1500. Science 303:1499–1503CrossRefGoogle Scholar
  6. 6.
    Partal T, Kahya E (2006) Trend analysis in Turkish precipitation data. Hydrol Process 20:2011–2026CrossRefGoogle Scholar
  7. 7.
    Kysely J (2009) Trends in heavy precipitation in the Czech Republic over 1951–2005. Int J Climatol 29:1745–1758CrossRefGoogle Scholar
  8. 8.
    Clark JS, Yiridoe EK, Burns ND, Astatkie T (2000) Regional climate change: trend analysis of temperature and precipitation series at selected Canadian sites. Can J Agric Econ 48:27–38CrossRefGoogle Scholar
  9. 9.
    Giannakopoulos C, Hadjinicolaou P, Kostopoulou E, Varotsos KV, Zerefos C (2010) Precipitation and temperature regime over Cyprus as a result of global climate change. Adv Geosci 23:17–24CrossRefGoogle Scholar
  10. 10.
    Alpert P, Ben-gai T, Baharad A (2002) The paradoxical increase of Mediterranean extreme daily rainfall in spite of decrease in total values. Geophys Res Lett 29:31-1–31-4CrossRefGoogle Scholar
  11. 11.
    Yosef Y, Saaroni H, Alpert P (2009) Trends in daily rainfall intensity over Israel 1950/1–2003/4. Open Atmos Sci J 3:196–203CrossRefGoogle Scholar
  12. 12.
    Kadıoglu M (1997) Trends in surface air temperature data over Turkey. Int J Climatol 17:511–520CrossRefGoogle Scholar
  13. 13.
    Kahya E, Kalaycı S (2004) Trend analysis of stream flow in Turley. J Hydrol 289:128–144CrossRefGoogle Scholar
  14. 14.
    Burn DH, Elnur HMA (2002) Detection of hydrologic trends and variability. J Hydrol 225:107–122CrossRefGoogle Scholar
  15. 15.
    Vincent LA, Peterson TC, Barros VR, Marino MB, Rusticucci M, Carrasco G, Ramirez E, Alves LM, Ambrizzi T, Berlato MA, Grimm AM, Marengo JA, Molion L, Moncunill DF, Rebello E, Anunciaçao YMT, Quintana J, Santos JL, Baez J, Coronel G, Garcia J, Trebejo I, Bidegain M, Haylock MR, Karoly D (2005) Observed trends in indices of daily temperature extremes in South America 1960–2000. Am Meteorol Soc 18:5011–5023Google Scholar
  16. 16.
    Tabari H, Somee BS, Zadeh MR (2011) Testing for long-term trends in climatic variables in Iran. Atmos Res 100:132–140CrossRefGoogle Scholar
  17. 17.
    Elshorbagy AA (2000) Group-based estimation of missing hydrological data: Approach and general methodology. Hydrol Sci J 45(6):849CrossRefGoogle Scholar
  18. 18.
    Khaliq MN, Quarda TBMJ (2007) On the critical values of the standard normal homogeneity test (SNHT). Int J Climatol 27:681–687CrossRefGoogle Scholar
  19. 19.
    Wijngaard JB, Klein Tank AMG, Können GP (2003) Homogeneity of 20th century European daily temperature and precipitation series. Int J Climatol 23:679–692CrossRefGoogle Scholar
  20. 20.
    Douglas EM, Vogel RM, Kroll CN (2000) Trends in floods and low flows in the United States: impacts of spatial correlation. J Hydrol 240:90–105CrossRefGoogle Scholar
  21. 21.
    Cannarozzo M, Noto LV, Viola F (2006) Spatial distribution of rainfall trends in Sicily (1921–2000). Phys Chem Earth 31:1201–1211CrossRefGoogle Scholar
  22. 22.
    Silva RPD, Dayawansa NDK, Ratnasiri MD (2007) A comparison of methods used in estimating missing rainfall data. J Agric Sci 3(2):101–108Google Scholar
  23. 23.
    Alexandersson H (1986) A homogeneity test applied to precipitation data. J Climatol 6:661–675CrossRefGoogle Scholar
  24. 24.
    Alexandersson H, Moberg A (1997) Homogenization of Swedish temperature data. Part I. Homogenization test for linear trends. Int J Climatol 17:25–34CrossRefGoogle Scholar
  25. 25.
    Tuomenvirta H (2002) Homogeneity testing and adjustment of climatic time series in Finland. Geophysical 38(1–2):15–41Google Scholar
  26. 26.
    Hirsch RM, Slack JR (1984) A nonparametric trend test for seasonal data with serial dependence. Water Resour Res 20(6):727–732CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Middle East Technical UniversityMersinTurkey

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