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

Brunner, Manuela I.; Gilleland, Eric

doi:https://doi.org/10.5194/hess-24-3967-2020

Articles | Volume 24, issue 8

https://doi.org/10.5194/hess-24-3967-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/hess-24-3967-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 24, issue 8

Research article

|

12 Aug 2020

Research article |

| 12 Aug 2020

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

Manuela I. Brunner and Eric Gilleland

Download

Final revised paper (published on 12 Aug 2020)
Preprint (discussion started on 14 Jan 2020)

Interactive discussion

Status: closed

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

- Printer-friendly version

- Supplement

RC1: 'Phase randomisation paper by Brunner et al.', Anonymous Referee #1, 29 Jan 2020
- AC1: 'Response to reviewer 1', Manuela Irene Brunner, 17 Mar 2020
SC1: 'Some comments and suggestions for imporvement', Ioannis Tsoukalas, 19 Feb 2020
- AC3: 'Response to commentator', Manuela Irene Brunner, 17 Mar 2020
RC2: 'Brunner_Stochastic Simulation if streamflow Using Wavelet Phase Randomisation (Review report)', Sandhya Patidar, 26 Feb 2020
- AC2: 'Response to reviewer 2', Manuela Irene Brunner, 17 Mar 2020

Peer-review completion

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

ED: Reconsider after major revisions (further review by editor and referees) (23 Apr 2020) by Patricia Saco

AR by Manuela Irene Brunner on behalf of the Authors (23 Apr 2020) Author's response Manuscript

ED: Referee Nomination & Report Request started (27 May 2020) by Patricia Saco

RR by Anonymous Referee #1 (16 Jun 2020)

Suggestions for revision or reasons for rejection

GENERAL COMMENTS
Authors have addressed most comments in the previous review. I have some follow up comments and if these issues are addressed, I would suggest acceptance.

Comments:
COMMENT1: In Figure 4, the simulated discharges show overestimated ACF in most realizations. Any particular reason for this? Similar for the Figure 7, the simulated ccf captures the pattern but seems to overestimate among investigated stations.

COMMENT2: There is little discussion on the results of Figure 8. It is supposed to be discussed at Line 252 with comparison with Fig. A2. First, the stochastic simulations, are they 100 realizations or just one ensemble mean/median simulation? No matter which case, simulated variogram is underestimated while in Fig. A2 the phase randomized for each catchment, the simulated variograms are mostly overestimated. I would suggest authors include a discussion on this issue.

COMMENT3: If there is a generally good agreement between observed and simulated spatial dependence in Figure 11 using F-madogram, then the simulated variogram with phase randomized for each catchment in Fig.A2 can be seen having a good agreement with observations as well. Another doubt is that the observed and simulated spatial dependence seems like have different range in the x-axis. Why?

COOMENT4: In this work, authors have shown that the proposed technique to generate multisite hydrological extremes is able to capture both spatial dependencies and non-stationarities. The logic is very similar to the work of Jiang et al. (2020), where they modify the variance structure of the variables in the frequency domain to match the spectral attributes of one another. A discussion of the similarities in the two should be included. Although the focus here is to randomize the phases in the frequency domain, it is worth checking the difference of wavelet variance (Percival, 1995) before and after randomization, and this might be the reason that the overestimation and/or underestimation in your temporal and spatial dependence analysis. It is not necessary to include further results in the current manuscript but a short discuss of limitation and future development might be helpful.

JIANG, Z., SHARMA, A. & JOHNSON, F. 2020. Refining Predictor Spectral Representation Using Wavelet Theory for Improved Natural System Modeling. Water Resources Research, 56, e2019WR026962.
PERCIVAL, D. P. 1995. On estimation of the wavelet variance. Biometrika, 82, 619-631.

Hide

ED: Publish subject to minor revisions (review by editor) (16 Jun 2020) by Patricia Saco

AR by Manuela Irene Brunner on behalf of the Authors (18 Jun 2020) Author's response Manuscript

ED: Publish as is (07 Jul 2020) by Patricia Saco

AR by Manuela Irene Brunner on behalf of the Authors (07 Jul 2020)

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 domain 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.