ANALYSIS OF ANNUAL, SEASONAL AND MONTHLY TRENDS OF CLIMATIC DATA: A CASE STUDY of SAMSUN

Climate change impacts are perceived today as being very noticeable; determination of precipitation, wind, flow, evaporation and temperature trends has become essential for water resources and engineering projects in management and planning. Nowadays there is a lot of studies have been made in progress on global and regional climate changes in literature. In this study, monthly, annual and seasonal trends in average temperature, total precipitation, and average wind speed data calculated by Mann-Kendall, linear trend and Sen’s trend tests. The gauge station is located in Samsun, which is the largest city of the Turkey’s Black Sea region. The data sets are obtained for the period 1980 to 2015. According to the results, at the levels of significance of 5% and 10%, Mann-Kendall's statistical results were found to be generally similar to those of trend analysis methods.

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  • [1] Republic of Turkey Ministry of Environment and Urbanization, (2011). National Climate Change Action Plan 2011-2023.
  • [2] Xiong, L. and Guo, S., (2004). Trend Test and Change-Point Detection for the Annual Discharge Series of the Yangtze River at the Yichang Hydrological Station, Hydrological Sciences Journal, Vol:49(1), pp:99–112.
  • [3] Xu, Z.X., Takeuchi, K., and Ishidaira, H., (2003). Monotonic Trend and Step Changes in Japanese Precipitation, Journal of Hydrology, Vol:279(1-4), pp:144-150.
  • [4] Kumar, V., Jain, S.K., and Singh, Y., (2010). Analysis of Long-Term Rainfall Trends in India, Hydrological Sciences Journal, Vol:55(4), pp:484–496.
  • [5] Gocic, M. and Trajkovic, S., (2013). Analysis of Changes in Meteorological Variables Using Mann–Kendall and Sen’s Slope Estimator Statistical Tests in Serbia, Global and Planetary Change, Vol:100, pp:172–182.
  • [6] IPCC, (2014). Climate Change 2014 Impacts, Adaptation, and Vulnerability. Part B: Regional Aspects, Cambridge University Press, pp:688.
  • [7] Sen, Z., (2014). Trend Identification Simulation and Application, Journal of Hydrologic Engineering, Vol:19(3), pp: 635–642.
  • [8] Dogan, M., Ulke, A., and Cigizoglu, H.K., (2015). Trend Direction Changes of Turkish Temperature series in the First Half of 1990s, Theoretical and Applied Climatology, Vol:121(1), pp:23–39.
  • [9] Mann, H.B., (1945). Nonparametric Tests Against Trend, Econometrica, Vol:13(3), pp:245–259.
  • [10] Kendall, M.G., (1975). Rank Correlation Methods, Oxford University Press, New York.
  • [11] Sen, Z., (2012). Innovative Trend Analysis Methodology, Journal of Hydrologic Engineering, Vol: 17(9), pp: 1042–1046.
  • [12] Pettitt, A.N., (1979). A Non-Parametric Approach to the Change-Point Detection, Applied Statistics, Vol:28, pp:126-135.
  • [13] Von Neumann, J., (1941). Distribution of the Ratio of the Mean Square Successive Difference to the Variance, Annals of Mathematical Statistics, Vol:13, pp:367–395.
  • [14] Alexandersson, H., (1986). A Homogeneity Test Applied to Precipitation Data, Journal of Climatology, Vol:6, pp:661-675.
  • [15] Wijngaard, J.B., Tank, A.M.G.K., Können, G.P., (2003). Homogeneity of 20th Century European Daily Temperature and Precipitation Series, International Journal of Climatology, Vol:23, pp:679-692.
  • [16] Khaliq, M.N. and Quarda, T.B.M.J., (2007). Short Communication on the Critical Values of the Standard Normal Homogeneity Test, International Journal of Climatology, Vol:27, pp:681-687.
  • [17] Hamed, K.H., (2008). Trend Detection in Hydrologic Data: The Mann-Kendall Trend Test Under the Scaling Hypothesis, Journal of Hydrology, Vol:349, pp:350-363.
  • [18] Yue, S., Zou, S., and Whittemore, D., (1993). Non-parametric Trend Analysis of Water Quality Data of Rivers in Kansas, Journal of Hydrology, Vol:150(1), pp:61-80.
  • [19] Davidson, R. and MacKinnon J.G., (2003). Econometric Theory and Methods, Oxford University Press.
  • [20] Sen, Z., (2015). Innovative Trend Significance Test and Applications, Theoretical and Applied Climatology, Vol:127, pp:939–947.