A hydrography upscaling method for scale-invariant parametrization of distributed hydrological models

Eilander, Dirk; van Verseveld, Willem; Yamazaki, Dai; Weerts, Albrecht; Winsemius, Hessel C.; Ward, Philip J.

doi:https://doi.org/10.5194/hess-25-5287-2021

Articles | Volume 25, issue 9

https://doi.org/10.5194/hess-25-5287-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/hess-25-5287-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 25, issue 9

Research article

|

28 Sep 2021

Research article |

| 28 Sep 2021

A hydrography upscaling method for scale-invariant parametrization of distributed hydrological models

Dirk Eilander, Willem van Verseveld, Dai Yamazaki, Albrecht Weerts, Hessel C. Winsemius, and Philip J. Ward

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Final revised paper (published on 28 Sep 2021)
Preprint (discussion started on 24 Nov 2020)

Interactive discussion

Status: closed

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

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- Supplement

RC1: 'Referee comments for Eilander et al. 2020', Anonymous Referee #1, 22 Dec 2020
- AC1: 'Reply on RC1', Dirk Eilander, 14 Apr 2021
RC2: 'Review of Eilander et al.', Anonymous Referee #2, 16 Mar 2021
- AC2: 'Reply on RC2', Dirk Eilander, 14 Apr 2021

Peer-review completion

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

ED: Reconsider after major revisions (further review by editor and referees) (29 Apr 2021) by Roger Moussa

AR by Dirk Eilander on behalf of the Authors (16 May 2021) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (03 Jun 2021) by Roger Moussa

RR by Peter Burek (30 Jun 2021)

Suggestions for revision or reasons for rejection

2nd review:
A hydrography upscaling method for scale invariant parametrization of distributed hydrological models

I agree with the authors that the paper has significantly improved.
My questions from the former review have been answered in a very good way.
The paper is addressing a relevant problem in hydrological modeling and presents an improved solution to the existing methods.
Furthermore the solution is available as open source python code.

I tested the code. It is working and it is a fine piece of open source software which include code, license information, documentation, testing, coverage, changelog.
Even if this is not part of the paper, I put my ranking of the scientific quality to excellent also because of this round software package.

I still think it would have been a benefit to test the method against the DDM30 network at 30arcmin, because this is used at the moment as reference in the global hydrological model intercomparison project. But I accept that this paper uses 2 other methods to test against on 0.5, 5 and 15 arcmin.

Small remarks:

100: While it might be possible to use your routine on the fly, meaning you have a high resolution dataset and convert all data to a lower resolution, it might be more convenient to do a preprocessing and store the low resolution before you run the hydrological model. This will save memory and time.
And also your tool needs to keep at least part of high resolution of d8 in memory.
I cannot see this as a real disadvantage (a real disadvantage is the DRT is not open source, and one is limited to certain resolutions and a WGS84 lat/lon projections – maybe this changed meanwhile)

270: Not clear if or where there are data gaps in figure 2F

498/535ff It seem your approach show much better results in cases where rivers run parallel for some time (e.g. Rhine and Meuse). You point this out in 535ff but it seems your approach is giving better results for parallel rivers even with the D8 limitation. Maybe point this out that your approach is superior to the others.

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RR by Anonymous Referee #2 (11 Aug 2021)

ED: Publish subject to technical corrections (11 Aug 2021) by Roger Moussa

AR by Dirk Eilander on behalf of the Authors (30 Aug 2021) Author's response Manuscript

Short summary

Digital elevation models and derived flow directions are crucial to distributed hydrological modeling. As the spatial resolution of models is typically coarser than these data, we need methods to upscale flow direction data while preserving the river structure. We propose the Iterative Hydrography Upscaling (IHU) method and show it outperforms other often-applied methods. We publish the multi-resolution MERIT Hydro IHU hydrography dataset and the algorithm as part of the pyflwdir Python package.