Trend detection in hydrological time series by segment regression with application to Shiyang River Basin

Hydrological time series are generally subject to shift trends and abrupt changes. However, most of the methods used in the literature cannot detect both shift trends and abrupt changes simultaneously and have weak ability to detect multiple change points together. In this study, the segmented regression with constraints method, which can model both trend analysis and abrupt change detection, is introduced. The modified Akaike’s information criterion is used for model selection. As an application, the method is employed to analyse the mean annual temperature, precipitation, runoff and runoff coefficient time series in the Shiyang River Basin for the period from 1958 to 2003. The segmented regression model shows that the trends of the mean annual precipitation, temperature and runoff change over time, with different join (turning) points for different stations. The runoff pattern can potentially explained by the climate variables (precipitation and temperature). Runoff coefficients show slightly decreasing trends for Xiying, Huangyang, Gulang and Zamu catchments, slight increasing trends for Dongda and Dajing catchments and nearly no change for Xida catchment. No change points are found in runoff coefficient in all catchments.