Innovative trend significance test and applications

Hydro-climatological time series might embed characteristics of past changes concerning climate variability in terms of shifts, cyclic fluctuations, and more significantly in the form of trends. Identification of such features from the available records is one of the prime tasks of hydrologists, climatologists, applied statisticians, or experts in related topics. Although there are different trend identification and significance tests in the literature, they require restrictive assumptions, which may not be existent in the structure of hydro-climatological time series. In this paper, a method is suggested with statistical significance test for trend identification in an innovative manner. This method has non-parametric basis without any restrictive assumption, and its application is rather simple with the concept of sub-series comparisons that are extracted from the main time series. The method provides privilege for selection of sub-temporal half periods for the comparison and, finally, generates trend on objective and quantitative manners. The necessary statistical equations are derived for innovative trend identification and statistical significance test application. The application of the proposed methodology is suggested for three time series from different parts of the world including Southern New Jersey annual temperature, Danube River annual discharge, and Tigris River Diyarbakir meteorology station annual total rainfall records. Each record has significant trend with increasing type in the New Jersey case, whereas in other two cases, decreasing trends exist.