Period analysis of hydrologic series through moving-window correlation analysis method

Abstract

Summary Period analysis is of great significance for understanding various hydrologic processes and predicting the future hydrological regime of a watershed or region. Hence, many period analysis methods including fast Fourier transform (FFT), maximum entropy spectral analysis (MESA) and wavelet analysis (WA) have been developed to study this issue. However, due to the complex components of hydrologic series and the limitations of these conventional methods, the problem is still difficult to be solved. In this paper, the moving-window correlation analysis method (MWCA) has been proposed for analyzing the periodic component of hydrologic series, which includes construction of periodic processes, significant test of periods and time frequency analysis. Three commonly used methods (FFT, MESA and WA) and MWCA are employed to investigate the periods of synthetic series and observed hydrologic series, respectively. The results show that FFT, MESA and WA are not always as good as expected when detecting periods of a time series. By contrast, MWCA has better application effects, which could identify the true periods of time series, extract the reliable periodic components, find the active time ranges of periodic components and resist the disturbance of noises. In conclusion, MWCA is suitable to identify the true periods of hydrologic time series.