Trend Identification Simulation and Application

Trend analysis occupy a significant role in the climate change studies for almost three decades. It is significant to try and identify monotonic trends in a given time series so as to make future predictions about the possible consequences on the urban environment, water resources, agriculture, and many other socioeconomic aspects of life. Although there are now classically accepted and frequently used trend tests in the open literature, such as Mann-Kendall trend analysis and Spearman’s rho test, they are based on some restrictive assumptions as normality, serial independence, and rather long sample sizes. Also, they search for a single monotonic trend without any specification such as low, medium, and high values, which may have different trend patterns. Many climatological records have serial dependence, and therefore, it is very helpful to provide a methodology that is not affected from such a restriction. It is the main purpose of this paper to provide simulation results and applications of an earlier innovative trend analysis methodology based on the $1:1$ (45°) line comparison of the scatter points on a Cartesian coordinate system. The plots are a result of available time series first-half values versus second half after sorting in ascending order. This method does not have any restriction, and it is applicable whether the time series is serially correlated, nonnormally distributed, or has short record length. It helps to identify trends in low, medium, and high records. The application of the methodology is provided for a set of temperature records from the Marmara region in Turkey.