Wavelet Transform Method for Synthetic Generation of Daily Streamflow

Synthetic generation of daily streamflow sequences is one of the most critical issues in stochastic hydrology. In this study, a new wavelet transform method is developed for synthetic generation of daily streamflow sequences. Firstly, daily streamflow sequences with different frequency components are decomposed into the series of wavelet coefficients W <Subscript>1</Subscript>(t), W <Subscript>2</Subscript>(t),...,W <Subscript> P </Subscript>(t) and scale coefficients (the residual) C <Subscript> P </Subscript>(t) at a resolution level P using wavelet decomposition algorithm. Secondly, the series of W <Subscript>1</Subscript>(t), W <Subscript>2</Subscript>(t),...,W <Subscript> P </Subscript>(t) and C <Subscript> P </Subscript>(t) are divided into a number of sub-series based on a yearly period. Thirdly, random sampling is performed from sub-series of W <Subscript>1</Subscript>(t), W <Subscript>2</Subscript>(t),...,W <Subscript> P </Subscript>(t) and C <Subscript> P </Subscript>(t), respectively. Based on these sampled sub-series, a large number of synthetic daily streamflow sequences are obtained using wavelet reconstruction algorithm. The advantages of this newly developed method include: (1) it is a nonparametric approach; (2) it is able to avoid assumptions of probability distribution types (Normal or Pearson Type III) and of dependence structure (linear or nonlinear); (3) it is not sensitive to the original data length and suitable for any hydrological sequences; and (4) the generated sequences from this method could capture the dependence structure and statistical properties presented in the data. Finally, a case study in Jinsha River, China, indicates that the new method is valid and efficient in generating daily streamflow sequences based on historical data. Copyright Springer Science+Business Media B.V. 2011