An improved version of innovative trend analyses

Systematic and random changes occur in any hydro-meteorological record and have significant effects on human activities on time and space scales. Although it is almost impossible to forecast the future behavior of any natural event accurately, researchers can identify trend on systematic variations and seasonality rather accurately with random residual parts. On the other hand, the role of trend is very significant in climate change studies and the Mann-Kendall test is the most employed method for trend identification. However, this method requires restrictive assumptions such as the data length, normality, and serial independence. Recently, innovative trend analysis (ITA) method is suggested for detailed trend determination and classification interpretations in a given time series without restrictive assumptions. In this study, an ITA-change boxes (CB) approach is proposed, taking into account quantitative changes with increasing or decreasing trends of the two half-time series obtained from the data. Given the periodicity of the hydro-meteorological data, the data group is used to obtain two half-time series instead of the data. This allows the researcher to numerically observe changes in trends beyond visualization. This approach assists to make more detailed interpretations about trend possibilities within a given time series. The applications of the proposed approach are presented for daily temperature and monthly rainfall and discharge records from Turkey, UK, and the USA.