Articles | Volume 23, issue 8
Hydrol. Earth Syst. Sci., 23, 3175–3187, 2019
https://doi.org/10.5194/hess-23-3175-2019
Hydrol. Earth Syst. Sci., 23, 3175–3187, 2019
https://doi.org/10.5194/hess-23-3175-2019

Technical note 02 Aug 2019

Technical note | 02 Aug 2019

Technical note: Stochastic simulation of streamflow time series using phase randomization

Manuela I. Brunner et al.

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Cited articles

Blum, A. G., Archfield, S. A., and Vogel, R. M.: On the probability distribution of daily streamflow in the United States, Hydrol. Earth Syst. Sci., 21, 3093–3103, https://doi.org/10.5194/hess-21-3093-2017, 2017. a, b, c
Borgomeo, E., Farmer, C. L., and Hall, J. W.: Numerical rivers: A synthetic streamflow generator for water resources vulnerability assessments, Water Resour. Res., 51, 5382–5405, https://doi.org/10.1002/2014WR016259, 2015. a, b, c
Bracken, C., Rajagopalan, B., and Zagona, E.: A hidden Markov model combined with climate indices for multidecadal streamflow simulation, Water Resour. Res., 50, 7836–7846, https://doi.org/10.1002/2014WR015567, 2014. a
Brunner, M. I. and Furrer, R.: PRSim: Stochastic Simulation of Streamflow Time Series using Phase Randomization, CRAN, available at: https://cran.r-project.org/web/packages/PRSim/index.html, last access: July 2019. a
Burr, I. W.: Cumulative frequency functions, Ann. Math. Stat., 13, 215–232, 1942. a
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Short summary
This study proposes a procedure for the generation of daily discharge data which considers temporal dependence both within short timescales and across different years. The simulation procedure can be applied to individual and multiple sites. It can be used for various applications such as the design of hydropower reservoirs, the assessment of flood risk or the assessment of drought persistence, and the estimation of the risk of multi-year droughts.