Learning from satellite observations: increased understanding  of catchment processes through stepwise model improvement

Hulsman, Petra; Savenije, Hubert H. G.; Hrachowitz, Markus

doi:https://doi.org/10.5194/hess-25-957-2021

Articles | Volume 25, issue 2

https://doi.org/10.5194/hess-25-957-2021

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

https://doi.org/10.5194/hess-25-957-2021

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

Articles | Volume 25, issue 2

Research article

|

24 Feb 2021

Research article |

| 24 Feb 2021

Learning from satellite observations: increased understanding of catchment processes through stepwise model improvement

Petra Hulsman, Hubert H. G. Savenije, and Markus Hrachowitz

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Final revised paper (published on 24 Feb 2021)
Supplement to the final revised paper
Preprint (discussion started on 14 May 2020)
Supplement to the preprint

Interactive discussion

Status: closed

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

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- Supplement

RC1: 'Review', Anonymous Referee #1, 14 Jun 2020
- AC1: 'Reply to Anonymous Referee #1', Petra Hulsman, 07 Jul 2020
RC2: 'Review', Anonymous Referee #2, 25 Aug 2020
- AC2: 'Reply to Anonymous Referee #2', Petra Hulsman, 14 Sep 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) (06 Nov 2020) by Markus Weiler

AR by Petra Hulsman on behalf of the Authors (26 Nov 2020) Author's response Manuscript

ED: Referee Nomination & Report Request started (07 Dec 2020) by Markus Weiler

RR by Anonymous Referee #2 (08 Jan 2021)

Suggestions for revision or reasons for rejection

The authors have responded in detail to the questions raised and fundamentally maintained the structure of the paper and argued that some of the suggestions for major revisions are outside the scope of the paper.
I appreciate the added discussion on limitations of the study and in particular the data scarse and uncertain study area. I especially appreciate the added analysis of alternative precipitation data (figure S15) and an attempt to address parameter distribution (Model G, Figure S14). I would still argue that the paper would have contributed more towards actually learning from satellite data, if the parametrization had been addressed, both through sensitivity analysis towards different performance metrics and different parametrisation schemes. This would, as argued by the authors require an undesired expansion of the manuscript, and would consequently require a restructuring of the entire study and reduction of the already presented analysis.
I find the manuscript ready for publication, but I do have one request, which I find reasonable. That is to address the limitations of the study in the conclusions. It would be appropriate to highlight, that due to the limitations of the selected study area, model type and data available, the study will mainly be a novel exemplification of a framework for exploiting satellite data for improved process representation. Meaning that the main interest of this paper is on the idea and framework, and much less on the actual improved process understanding. I simply do not believe the model, data and approach can clearly claim to have increased the process understanding significantly. However, you have exemplified a very interesting approach to potentially use satellite information for increased process understanding. E.g.Your Model F performs best, but weather groundwater upwelling is the correct process, is not exhaustively examined, especially when considering all the uncertainties.

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ED: Publish subject to minor revisions (review by editor) (16 Jan 2021) by Markus Weiler

AR by Petra Hulsman on behalf of the Authors (20 Jan 2021) Author's response Author's tracked changes Manuscript

ED: Publish as is (25 Jan 2021) by Markus Weiler

AR by Petra Hulsman on behalf of the Authors (26 Jan 2021) Manuscript

Short summary

Satellite observations have increasingly been used for model calibration, while model structural developments largely rely on discharge data. For large river basins, this often results in poor representations of system internal processes. This study explores the combined use of satellite-based evaporation and total water storage data for model structural improvement and spatial–temporal model calibration for a large, semi-arid and data-scarce river system.