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Detecting seasonal cycle shift on streamflow over Turkey by using multivariate statistical methods

Download PDF Theoretical and Applied Climatology pp 1–19 | Cite as Detecting seasonal cycle shift on streamflow over Turkey by using multivariate statistical methods
  • Dogan YildizEmail author
  • Mehmet Samil Gunes
  • Fulya Gokalp Yavuz
  • Dursun Yildiz
Original Paper First Online: 19 August 2017 Received: 08 March 2016 Accepted: 01 August 2017 Abstract Climate change analysis includes the study of several types of variables such as temperature, precipitation, carbon emission, and streamflow. In this study, we focus on basin hydrology and, in particular, on streamflow values. They are geographic and climatologic indicators utilized in the study of basins. We analyze these values to better understand monthly and seasonal change over a 40-year period for all basins in Turkey. Our study differs from others by applying multivariate analysis into the streamflow data implementations rather than on trend, frequency, and/or distribution-based analysis. The characteristics of basins and climate change effects are visualized and examined with monthly data by using cluster analysis, multidimensional scaling, and gCLUTO (graphical Clustering Toolkit). As a result, we classify months as low-flow and high-flow periods. Multidimensional scaling proves that there is a clockwise movement of months from one decade to the next, which is the indicator of seasonal shift. Finally, the gCLUTO tool is utilized in a novel way in the hydrology field by revealing the seasonal change and visualizing the current changing structure of streamflow.
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