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Wavelet Transform Method for Synthetic Generation of Daily Streamflow

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  • Wensheng Wang
  • Shixiong Hu
  • Yueqing Li

Abstract

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 1 (t), W 2 (t),...,W P (t) and scale coefficients (the residual) C P (t) at a resolution level P using wavelet decomposition algorithm. Secondly, the series of W 1 (t), W 2 (t),...,W P (t) and C P (t) are divided into a number of sub-series based on a yearly period. Thirdly, random sampling is performed from sub-series of W 1 (t), W 2 (t),...,W P (t) and C P (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

Suggested Citation

  • Wensheng Wang & Shixiong Hu & Yueqing Li, 2011. "Wavelet Transform Method for Synthetic Generation of Daily Streamflow," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(1), pages 41-57, January.
    • Handle: RePEc:spr:waterr:v:25:y:2011:i:1:p:41-57
    DOI: 10.1007/s11269-010-9686-9
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    References listed on IDEAS

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    1. Mehmetcik Bayazit & Hafzullah Aksoy, 2001. "Using wavelets for data generation," Journal of Applied Statistics, Taylor & Francis Journals, vol. 28(2), pages 157-166.
    2. Hafzullah Aksoy, 2001. "Storage Capacity for River Reservoirs by Wavelet-Based Generation of Sequent-Peak Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 15(6), pages 423-437, December.
    3. Wensheng Wang & Juliang Jin & Yueqing Li, 2009. "Prediction of Inflow at Three Gorges Dam in Yangtze River with Wavelet Network Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 23(13), pages 2791-2803, October.
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    Cited by:

    1. Yan-Fang Sang & Zhonggen Wang & Changming Liu, 2015. "Wavelet Neural Modeling for Hydrologic Time Series Forecasting with Uncertainty Evaluation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(6), pages 1789-1801, April.
    2. Jiang Wu & Jianzhong Zhou & Lu Chen & Lei Ye, 2015. "Coupling Forecast Methods of Multiple Rainfall–Runoff Models for Improving the Precision of Hydrological Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(14), pages 5091-5108, November.
    3. Mohamed Shenify & Amir Danesh & Milan Gocić & Ros Taher & Ainuddin Abdul Wahab & Abdullah Gani & Shahaboddin Shamshirband & Dalibor Petković, 2016. "Precipitation Estimation Using Support Vector Machine with Discrete Wavelet Transform," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(2), pages 641-652, January.
    4. R. Venkata Ramana & B. Krishna & S. Kumar & N. Pandey, 2013. "Monthly Rainfall Prediction Using Wavelet Neural Network Analysis," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(10), pages 3697-3711, August.
    5. Yan-Fang Sang, 2012. "A Practical Guide to Discrete Wavelet Decomposition of Hydrologic Time Series," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(11), pages 3345-3365, September.
    6. Đurica Marković & Jasna Plavšić & Nesa Ilich & Siniša Ilić, 2015. "Non-parametric Stochastic Generation of Streamflow Series at Multiple Locations," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(13), pages 4787-4801, October.
    7. Jenq-Tzong Shiau & Chian-You Huang, 2014. "Detecting Multi-Purpose Reservoir Operation Induced Time-Frequency Alteration Using Wavelet Transform," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(11), pages 3577-3590, September.
    8. Marco Gallegati, 2022. "Multiscale evaluation of CMIP5 models using wavelet-based descriptive and diagnostic techniques," Climatic Change, Springer, vol. 170(3), pages 1-16, February.

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