Spatiotemporal variations of the precipitation in the Yellow River Basin using a novel hierarchical discrete-continuous wavelet decomposition model

Periods detected by an advanced, systematic technology can provide reliable basis for water resources prediction and management. One of the main challenges of period mining is getting rid of the effects of climate change and noise. This study presents the hierarchical discrete-continuous wavelet decomposition (HDCWD) model. The method provides a three-layer identification framework of detrending, denoising and mining by combining discrete wavelet transform and continuous wavelet transform. The dominating periods and their spatiotemporal features of precipitation in the Yellow River Basin are identified by applying HDCWD to different catchments. Results show the following: (1) Noise exists in the precipitation series in the Yellow River Basin and leads to overlooked period. (2) Precipitation in the Yellow River Basin has the dominating periods of 2–4 years and 7–9 years from 1956 to 1984, and period of 2 years from 1998 to 2002. (3) The periodicity of precipitation in the Yellow River Basin varies among different catchments that the ones in higher latitude exhibit a longer period and those in the lower east exhibit a shorter period.