Assimilation of Soil Moisture and Ocean Salinity (SMOS) brightness temperature into a large-scale distributed conceptual hydrological model to improve soil moisture predictions: the Murray–Darling basin in Australia as a test case

Hostache, Renaud; Rains, Dominik; Mallick, Kaniska; Chini, Marco; Pelich, Ramona; Lievens, Hans; Fenicia, Fabrizio; Corato, Giovanni; Verhoest, Niko E. C.; Matgen, Patrick

doi:https://doi.org/10.5194/hess-24-4793-2020

Articles | Volume 24, issue 10

https://doi.org/10.5194/hess-24-4793-2020

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

https://doi.org/10.5194/hess-24-4793-2020

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

Articles | Volume 24, issue 10

Research article

|

12 Oct 2020

Research article |

| 12 Oct 2020

Assimilation of Soil Moisture and Ocean Salinity (SMOS) brightness temperature into a large-scale distributed conceptual hydrological model to improve soil moisture predictions: the Murray–Darling basin in Australia as a test case

Renaud Hostache, Dominik Rains, Kaniska Mallick, Marco Chini, Ramona Pelich, Hans Lievens, Fabrizio Fenicia, Giovanni Corato, Niko E. C. Verhoest, and Patrick Matgen

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Final revised paper (published on 12 Oct 2020)
Preprint (discussion started on 23 Aug 2019)

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, 01 Sep 2019
- AC1: 'Response to Anonymous Referee #1', Renaud Hostache, 24 Jan 2020
RC2: 'Review_of_Hostache_et_al_2019', Anonymous Referee #2, 08 Oct 2019
- AC2: 'Response to Anonymous Referee #1', Renaud Hostache, 24 Jan 2020
RC3: 'Comments on Renaud Hostache et al. “Assimilation of SMOS TB into a large-scale distributed conceptual hydrological model”', Anonymous Referee #3, 24 Nov 2019
- AC3: 'Response to Anonymous Referee #3', Renaud Hostache, 24 Jan 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) (26 Jan 2020) by Harrie-Jan Hendricks Franssen

AR by Anna Wenzel on behalf of the Authors (07 May 2020) Author's response

ED: Referee Nomination & Report Request started (07 May 2020) by Harrie-Jan Hendricks Franssen

RR by Anonymous Referee #1 (16 May 2020)

RR by Anonymous Referee #3 (18 May 2020)

Suggestions for revision or reasons for rejection

Further comments:
1. The author only provided some brief points to explain CLM is more complex than SFX model. On the other hand, it is not specifically clear what are difference in complexity, in terms of physical processes. This matters, particularly, for soil moisture predictions. This reviewer does understand it is not needed to present all the difference between two models, while still believe a tabulated comparison is needed, especially for those parts matter the most to the discussions of results (ET, SM, etc.).

This is to verify the claim in the abstract “Our empirical results show that the SUPERFLEX-CMEM modelling chain is capable of predicting soil moisture at a performance level similar to that obtained for the same study area and with a quasi-identical experimental set up using the CLM land surface model”.

This reviewer cannot understand how ‘quasi-identical’ it is.

2. ‘In this study, since we focus on the two upper root zone layers that are of interest for simulating soil moisture, the deeper reservoirs and the routing function are switched off and not further referred to in the remainder.’
The above treatment means a twist of the physical processes of SFX. This reviewer is interested how this treatment will affect the final assimilation results.

3. “The model is therefore distributed over grid cells of 0.25 aligned on the grid used in the ERA-Interim dataset and simulations are carried out at an hourly time step.”
This reviewer is very curious on how this is possible. ERA-Interim only provides four analyses per day, at 00, 06, 12 and 18 UTC, while the simulation is carried out at an hourly time step. Is there certain interpolation involved and how?

4. For me the improvement of correlation coefficient with 0.03 (for SM) and 0.02 (for ET) do not seem significant, even though William’s significance test was carried out. Does it mean that the assimilation of TB does not matter that much? Or please help to discuss with similar studies, or compare with those studies using spatial information (soil moisture spatial info) to do calibration while not using DA.

5. ‘highomputational demand’  ‘high computational demand’

6. In Figure 8 “maps of the number of assimilated SMOS Tb observation at each soil moisture measurement station, in relation with inter-annual average rainfall (bottom left panel) and inter-annual average Ep (bottom right panel).” This reviewer cannot see the number of assimilated SMOS Tb in this figure, and a bit confused by the caption here.

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RR by Anonymous Referee #2 (01 Jun 2020)

ED: Publish subject to revisions (further review by editor and referees) (05 Jun 2020) by Harrie-Jan Hendricks Franssen

AR by Anna Wenzel on behalf of the Authors (24 Jul 2020) Author's response

ED: Referee Nomination & Report Request started (31 Jul 2020) by Harrie-Jan Hendricks Franssen

RR by Anonymous Referee #3 (13 Aug 2020)

ED: Publish subject to technical corrections (17 Aug 2020) by Harrie-Jan Hendricks Franssen

AR by Renaud Hostache on behalf of the Authors (21 Aug 2020) Author's response Manuscript

Short summary

Our objective is to investigate how satellite microwave sensors, particularly Soil Moisture and Ocean Salinity (SMOS), may help to reduce errors and uncertainties in soil moisture simulations with a large-scale conceptual hydro-meteorological model. We assimilated a long time series of SMOS observations into a hydro-meteorological model and showed that this helps to improve model predictions. This work therefore contributes to the development of faster and more accurate drought prediction tools.