Innovative Trend Analysis Methodology

Hydrometeorological time series include recent trends, especially over the past 30 years, as a result of climate change impact according to the Intergovernmental Panel on Climate Change (IPCC). Although there are commonly used trend identification techniques, such as Mann-Kendall (MK) and Spearman's rho (SR) tests, their validity is possible under a set of restrictive assumptions, such as independent structure of the time series, normality of the distribution, and length of data. It is also not possible to calculate trend magnitude (slope) except through regression approach, which brings additional assumptions for the theoretical validation in practical applications. This paper presents a new methodology on the basis of subsection time series plots derived from a given time series on a Cartesian coordinate system. In such a plot, trend free time series subsections appear along the 45° straightline. Increasing (decreasing) trends occupy upper (lower) triangular areas of the square area defined by the variation domain of the variable concerned. The validity of this new approach is documented through a set of Monte Carlo simulations by taking into consideration independent and dependent processes. In the new approach, all the aforementioned assumptions in the MK and SR tests are avoided, and additionally it is possible to calculate trend magnitude from square area plots. The application of this methodology is given for a set of precipitation and runoff time series from different parts of the world.