Study on Mother Wavelet Optimization Framework Based on Changepoint Detection of Hydrological Time Series

Abstract

Abstract. Hydrological time series (HTS) is the key basis of water conservancy project planning and construction. However, under the influence of climate change, human activities and other factors, the consistency of HTS has been destroyed and cannot meet the requirements in mathematical statistics. It is urgent to find a better way to divide HTS. Wavelet transform is an effective way to catch the evolution of HTS, but its accuracy is highly dependent on the mother wavelet (MWT). To address these issues, we constructed a potential changepoint set based on two traditional detection methods and wavelet changepoint detection (WTCPD). Then, the degree of change before and after the potential changepoint was calculated with the Kolmogorov-Smirnov test, and a changepoint detection framework (CPDF) was proposed. Finally, according to the difference of detection accuracy between MWT in WTCPD, a mother wavelet optimal framework (MWTOF) was proposed, and continuous wavelet transform was carried out to analyse HTS evolution. We used Pingshan Station and Yichang Station in the Yangtze River as study cases. The result shows: (1) CPDF can quickly locate potential changepoints, determine the change trajectory and complete the division of HTS. (2) MWTOF can select the MTW that conforms to HTS characteristics and ensure the accuracy and uniqueness of the transformation. This study analyses the HTS evolution and provides a better basis for hydrological and hydraulic calculation, which will improve the design flood estimation and the operation scheme preparation.