期刊
JOURNAL OF HYDROLOGY
卷 496, 期 -, 页码 71-78出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.jhydrol.2013.05.025
关键词
Synthetic streamflow generation; Continuous wavelet transform; Morlet wavelet; Random permutation; Pearl River basin
资金
- University of Hong Kong and Ji Chen for the research fellowship
- Australian Research Council [FT110100328]
- Australian Research Council [FT110100328] Funding Source: Australian Research Council
This study develops a scale-dependent synthetic data generation method for streamflow by using a continuous wavelet transform. The detailed information of streamflow variability across different timescales embedded in the data is obtained from the continuous wavelet transform. To take into account the time-dependent flow magnitudes, the wavelet coefficients are simply separated into two basic categories, namely high-flow part and low-flow part. The data reconstruction is based on the random permutation of the separated wavelet coefficients for the two categories. The synthetic generation is performed at both the individual timescales and the multiple timescales. The Morlet wavelet transform is considered as a representative continuous wavelet transform, and generation of daily streamflow data is attempted. The method is applied to a streamflow series observed in the Pearl River basin in South China. The results indicate that the proposed method: (1) is suitable for scale-controlled generation of streamflow time series and (2) provides reliable information as to the extent of spectral properties present in the original data that need to be preserved. (C) 2013 Elsevier B.V. All rights reserved.
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