Application of Wavelet Transform for Extraction of long- and Short-term Components in a Hydrological Time Series
Young-Hoon Jin, Sung-Chun Park, Yeon-Kil Lee · Journal of the Korean Society of Civil Engineers · 2005
In the present study, we applied discrete wavelet transform to extract the short- and long-term periodic components from the monthly precipitation data and used a Daubechies wavelet of order 9 (‘db9’) for the transform. The results from the wavelet transform detected not only the usual seasonality and annual-periodicity but also the maximum periodicity up to eight-years from the precipitation in the study station of Mokpo. Quantitative index in which the raw data can be explained by the respective details and approximation was estimated and revealed the detail at the third level and the approximation at the maximum decomposition level can explain the raw data up to about 77%. Consequently, the wavelet transform as a time-frequency analysis method has proved its applicability for the detection of the characteristics of a time series and its results with the long-term periodic components might be expected that it can provide basic information for the guarantee of safe and sustainable water resources in the future.