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Non-parametric Stochastic Generation of Streamflow Series at Multiple Locations

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  • Đurica Marković
  • Jasna Plavšić
  • Nesa Ilich
  • Siniša Ilić

Abstract

A non-parametric method for generating stationary weekly hydrologic time series at multiple locations is presented. The procedure has three distinct steps: first, the Monte Carlo method is used to obtain 1000 years of simulated weekly flows having statistical properties as close as possible to the observed series; second, rearranging the order of simulated data in the series to achieve target spatial and temporal correlations within each simulated year; and third, the permutation of annual partial series to adjust the correlation of weekly streamflows at the beginning of a year with that at the end of a previous year while also adjusting the auto-correlation of annual flows. In this paper the method is applied for the first time on log-transformed data, and contributes to this methodology by introducing an additional criterion related to the possibility to obtain a desired frequency of occurrence of extremely dry years in the simulated series. The method is tested in two case studies, which use data from three hydrologic stations on the Studenica River in Serbia, and from seven stations in the Oldman River basin in Southern Alberta, Canada. The results show that the simulated data correspond to the observed data in all their stochastic properties and that they can be consequently used in the studies related to planning and design of reservoirs and other water management systems. Copyright Springer Science+Business Media Dordrecht 2015

Suggested Citation

  • 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.
    • Handle: RePEc:spr:waterr:v:29:y:2015:i:13:p:4787-4801
    DOI: 10.1007/s11269-015-1090-z
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    References listed on IDEAS

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    1. Juran Ahmed & Arup Sarma, 2007. "Artificial neural network model for synthetic streamflow generation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 21(6), pages 1015-1029, June.
    2. Jie Chen & François Brissette, 2015. "Combining Stochastic Weather Generation and Ensemble Weather Forecasts for Short-Term Streamflow Prediction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(9), pages 3329-3342, July.
    3. Ilich, Nesa, 2009. "A matching algorithm for generation of statistically dependent random variables with arbitrary marginals," European Journal of Operational Research, Elsevier, vol. 192(2), pages 468-478, January.
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    5. Wenquan Gu & Dongguo Shao & Yufang Jiang, 2012. "Risk Evaluation of Water Shortage in Source Area of Middle Route Project for South-to-North Water Transfer in China," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(12), pages 3479-3493, September.
    6. 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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    Cited by:

    1. Quansen Wang & Jianzhong Zhou & Kangdi Huang & Ling Dai & Benjun Jia & Lu Chen & Hui Qin, 2021. "A Procedure for Combining Improved Correlated Sampling Methods and a Resampling Strategy to Generate a Multi-Site Conditioned Streamflow Process," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(3), pages 1011-1027, February.
    2. Wenzhuo Wang & Zengchuan Dong & Wei Si & Yu Zhang & Wei Xu, 2018. "Two-Dimension Monthly River Flow Simulation Using Hierarchical Network-Copula Conditional Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(12), pages 3801-3820, September.

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