Comparison of sub-series with different lengths using sen-innovative trend analysis

Climate change causes trends in hydro-meteorological series. Traditional trend analysis methods such as Mann-Kendall and Spearman Rho are sensitive to dependent series and cannot detect non-monotonic trends. Sen-innovative trend analysis method is launched into literature in order to overcome these restrictions. It does not require any restrictive assumptions as serial independence and normal distribution and examines a given time series as equally divided into two sub-series. The Sen multiple innovative trend analysis methodology is improved to detect partial trends on different sub-series, again with equal lengths. Climate change strongly affects hydro-meteorological parameters today compared to the last twenty or thirty years and gives asymmetrical trend change points in hydro-meteorological time series. Due to asymmetric trend change points, it may be necessary to analyze sub-series with different lengths to use all measured data. In this study, the Sen innovative trend analysis method is revised to satisfy these requirements (ITA_DL). The new approach compared with the traditional Mann-Kendall (MK) and Sen innovative trend analysis (Sen_ITA) gives successful and consistent results. The ITA_DL gives four monotonic trends on May, July, September, and October rainfall series of Oxford although the MK gives three monotonic trends in the May, July, and December and cannot detect trends on the September and October. In the ITA_DL visual inspection, the December rainfall series does not show an overall or partial trend. The ITA_DL trend results are consistent with the Sen_ITA except for the September rainfall series, although it has different trend slope amounts.