Ensemble modeling of stochastic unsteady open-channel flow in terms of its time–space evolutionary probability  distribution – Part 1: theoretical development

Dib, Alain; Kavvas, M. Levent

doi:https://doi.org/10.5194/hess-22-1993-2018

Articles | Volume 22, issue 3

https://doi.org/10.5194/hess-22-1993-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/hess-22-1993-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 22, issue 3

Research article

|

28 Mar 2018

Research article |

| 28 Mar 2018

Ensemble modeling of stochastic unsteady open-channel flow in terms of its time–space evolutionary probability distribution – Part 1: theoretical development

Alain Dib and M. Levent Kavvas

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Final revised paper (published on 28 Mar 2018)
Preprint (discussion started on 09 Aug 2017)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

RC1: 'Comment', Anonymous Referee #1, 31 Aug 2017
- AC1: 'Response to Anonymous Referee #1 (Incl. Revised Manuscript)', Alain Dib, 01 Nov 2017
RC2: 'derivation of a stochastic version of the classic De Saint Venant equation', Anonymous Referee #2, 12 Sep 2017
- AC2: 'Response to Anonymous Referee #2 (Incl. Revised Manuscript)', Alain Dib, 01 Nov 2017

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

ED: Publish as is (05 Mar 2018) by Sabine Attinger

AR by Alain Dib on behalf of the Authors (07 Mar 2018) Manuscript

Short summary

A new method is proposed to solve the stochastic unsteady open-channel flow system in only one single simulation, as opposed to the many simulations usually done in the popular Monte Carlo approach. The derivation of this new method gave a deterministic and linear Fokker–Planck equation whose solution provided a powerful and effective approach for quantifying the ensemble behavior and variability of such a stochastic system, regardless of the number of parameters causing its uncertainty.