The relation between periods' identification and noises in hydrologic series data

Identification of dominant periods is a typical and important issue in hydrologic series data analysis, since it is the basis of building effective stochastic models, understanding complex hydrologic processes, etc. However it is still a difficult task due to the influence of many interrelated factors, such as noises in hydrologic series data. In this paper, firstly the great influence of noises on periods' identification has been analyzed. Then, based on two conventional methods of hydrologic series analysis: wavelet analysis (WA) and maximum entropy spectral analysis (MESA), a new method of periods' identification of hydrologic series data, main series spectral analysis (MSSA), has been put forward, whose main idea is to identify periods of the main series on the basis of reducing hydrologic noises. Various methods (include fast Fourier transform (FFT), MESA and MSSA) have been applied to both synthetic series and observed hydrologic series. Results show that conventional methods (FFT and MESA) are not as good as expected due to the great influence of noises. However, this influence is not so strong while using the new method MSSA. In addition, by using the new de-noising method proposed in this paper, which is suitable for both normal noises and skew noises, the results are more reasonable, since noises separated from hydrologic series data generally follow skew probability distributions. In conclusion, based on comprehensive analyses, it can be stated that the proposed method MSSA could improve periods' identification by effectively reducing the influence of hydrologic noises. (c) 2009 Elsevier B.V. All rights reserved.