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Storage Capacity for River Reservoirs by Wavelet-Based Generation of Sequent-Peak Algorithm

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  • Hafzullah Aksoy

Abstract

Selection of a storage capacity for the design ofa river reservoir is made traditionally by the Rippl masscurve method or the sequent-peak algorithm. Both methods offera single value of storage capacity to a water resourcesengineer. Synthetic hydrology approach by which long syntheticflow data are generated is also commonly used. In the presentstudy a hybrid technique combining these two approaches isdeveloped. The technique is based on wavelets, which arefunctions of zero-mean and finite variance, and it generatessynthetic sequent-peak algorithms. Haar wavelet is used in thegeneration scheme. The generation scheme is applicable to non-skewed sequences. The application of the technique to a 64-year long annual flow data suggests that the technique can bea useful tool in the selection of a storage capacity for thedesign of a river reservoir. Copyright Kluwer Academic Publishers 2001

Suggested Citation

  • 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.
    • Handle: RePEc:spr:waterr:v:15:y:2001:i:6:p:423-437
    DOI: 10.1023/A:1015525317135
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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. A. Adeloye & M. Montaseri, 1999. "Predicting Critical Period to Characterise Over-Year and Within-Year Reservoir Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 13(6), pages 383-407, December.
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    1. Aksoy, Hafzullah & Fuat Toprak, Z & Aytek, Ali & Erdem Ünal, N, 2004. "Stochastic generation of hourly mean wind speed data," Renewable Energy, Elsevier, vol. 29(14), pages 2111-2131.
    2. Vahid Moosavi & Mehdi Vafakhah & Bagher Shirmohammadi & Negin Behnia, 2013. "A Wavelet-ANFIS Hybrid Model for Groundwater Level Forecasting for Different Prediction Periods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(5), pages 1301-1321, March.
    3. Sajjad Abdollahi & Jalil Raeisi & Mohammadreza Khalilianpour & Farshad Ahmadi & Ozgur Kisi, 2017. "Daily Mean Streamflow Prediction in Perennial and Non-Perennial Rivers Using Four Data Driven Techniques," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(15), pages 4855-4874, December.
    4. 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.

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