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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Preprints
https://doi.org/10.5194/hess-2019-658
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-2019-658
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  14 Jan 2020

14 Jan 2020

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A revised version of this preprint was accepted for the journal HESS and is expected to appear here in due course.

Stochastic simulation of streamflow and spatial extremes: a continuous, wavelet-based approach

Manuela I. Brunner and Eric Gilleland Manuela I. Brunner and Eric Gilleland
  • Research Applications Laboratory, National Center for Atmospheric Research, Boulder CO, USA

Abstract. Stochastically generated streamflow time series are used for various water management and hazard estimation applications. They provide realizations of plausible but yet unobserved streamflow time series with the same temporal and distributional characteristics as the observed data. However, the representation of non-stationarities and spatial dependence among sites remains a challenge in stochastic modeling. We investigate whether the use of frequency-domain instead of time-domain models allows for the joint simulation of realistic, continuous streamflow time series at daily resolution and spatial extremes at multiple sites. To do so, we propose the stochastic simulation approach called Phase Randomization Simulation using wavelets PRSim.wave which combines an empirical spatio-temporal model based on the wavelet transform and phase randomization with the flexible four-parameter kappa distribution. The approach consists of five steps: (1) derivation of random phases, (2) fitting of kappa distribution, (3) wavelet transform, (4) inverse wavelet transform, and (5) transformation to kappa distribution. We apply and evaluate PRSim.wave on a large set of 671 catchments in the contiguous United States. We show that this approach allows for the generation of realistic time series at multiple sites exhibiting short- and long-range dependence, non-stationarities, and unobserved extreme events. Our evaluation results strongly suggest that the flexible, continuous simulation approach is potentially valuable for a diverse range of water management applications where the reproduction of spatial dependencies is of interest. Examples include the development of regional water management plans, the estimation of regional flood or drought risk, or the estimation of regional hydropower potential among others.

Manuela I. Brunner and Eric Gilleland

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Interactive discussion

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Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Manuela I. Brunner and Eric Gilleland

Manuela I. Brunner and Eric Gilleland

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Latest update: 11 Aug 2020
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Short summary
Stochastically generated streamflow time series are used for various water management and hazard estimation applications. They provide realizations of plausible but yet unobserved streamflow time series with the same characteristics as the observed data. We propose a stochastic simulation approach in the frequency instead of the time domain. Our evaluation results suggest that the flexible, continuous simulation approach is valuable for a diverse range of water management applications.
Stochastically generated streamflow time series are used for various water management and hazard...
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