Snowpack dynamics in the Lebanese mountains from quasi-dynamically downscaled ERA5 reanalysis updated by assimilating remotely sensed fractional snow-covered area

Alonso-González, Esteban; Gutmann, Ethan; Aalstad, Kristoffer; Fayad, Abbas; Bouchet, Marine; Gascoin, Simon

doi:https://doi.org/10.5194/hess-25-4455-2021

Articles | Volume 25, issue 8

https://doi.org/10.5194/hess-25-4455-2021

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

Special issue:

Changes in the Mediterranean hydrology: observation and...

https://doi.org/10.5194/hess-25-4455-2021

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

Articles | Volume 25, issue 8

Research article

|

19 Aug 2021

Research article |

| 19 Aug 2021

Snowpack dynamics in the Lebanese mountains from quasi-dynamically downscaled ERA5 reanalysis updated by assimilating remotely sensed fractional snow-covered area

Esteban Alonso-González, Ethan Gutmann, Kristoffer Aalstad, Abbas Fayad, Marine Bouchet, and Simon Gascoin

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Final revised paper (published on 19 Aug 2021)
Supplement to the final revised paper
Preprint (discussion started on 08 Jul 2020)

Interactive discussion

Status: closed

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

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- Supplement

SC1: 'Model top elevation chosen for the ICAR simulations', Johannes Horak, 15 Jul 2020
- AC1: 'Response to 'Model top elevation chosen for the ICAR simulations'', Esteban Alonso-González, 10 Oct 2020
SC2: 'Comment on a citation', Bertrand Cluzet, 28 Jul 2020
- AC2: 'Response to 'Comment on a citation'', Esteban Alonso-González, 10 Oct 2020
RC1: 'Referee Comment', Anonymous Referee #1, 08 Aug 2020
- AC3: 'Response to reviewer 1', Esteban Alonso-González, 10 Oct 2020
RC2: 'Referee Comments', Anonymous Referee #2, 09 Aug 2020
- AC4: 'Response to reviewer 2', Esteban Alonso-González, 10 Oct 2020
RC3: 'Reviewer comments', Anonymous Referee #3, 29 Aug 2020
- AC5: 'Response to reviewer 3', Esteban Alonso-González, 10 Oct 2020

Peer-review completion

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

ED: Publish subject to revisions (further review by editor and referees) (21 Oct 2020) by María José Polo

AR by Esteban Alonso-González on behalf of the Authors (16 Nov 2020) Author's response

ED: Referee Nomination & Report Request started (14 Dec 2020) by María José Polo

RR by Anonymous Referee #1 (15 Jan 2021)

Suggestions for revision or reasons for rejection

The authors examined a methodology to obtain snowpack reanalysis using ICAR and data assimilation methodology over data scarce regions. The work is valued since the authors take the advantage of using atmospheric reanalysis data together with remote sensing and integrating techniques of downscaling and data assimilation. There are number of analysis in comparison with ground truth (AWS data sets) and remote sensed observations (fSCA of MODIS) in the study. In this regard, the study addresses an interesting topic which fits well with the scope of HESS and it is also an important contribution to the snow science especially for the mountainous regions where there is a data scarcity.
The authors replied a number of comments in the previous review step which has already added value to the study. There are still couple of important aspects that need to be refined so that the possible impact of the study would further increase.
Each process has its own contribution with different advantages, WRF improves the resolution compared to ERA5 and ICAR improves WRF results even more with computational efficiency. It would be remarkable to see the contribution of ERA5, WRF and ICAR in temperature and precipitation reanalysis separately with simply showing the same error analysis provided in Figure 3 and Figure 4. Even if ICAR will not improve WRF in terms of statistical performance it is still required for any analyzes/processes need a better resolution.
According to the text, ICAR snowpack reanalysis data is acquired as a result of ICAR output. This step should be indicated in the flow chart as an output (in Figure 2). Then, the authors improve ICAR snowpack reanalysis since these are not very consistent with the observations (Observed and ICAR in Figure 5), so in the next step they prefer to implement data assimilation using satellite snow cover data. The authors should clarify this part and refine the role of Flexible Snow Model (FSM) in the study (which seems also to be the concern of reviewers previously); it looks as if there are two snowpack data sets: one as a direct output of ICAR reanalysis (ICAR in Figure 5) and the other is an output of FSM where the forcing variables are ICAR precipitation and temperature reanalysis data without data assimilation (which is not provided in the text or figures). The assimilation of MODIS fSCA is applied to the later as indicated, ICAR_assim, in Figure 2 and Figure 5. If this is so, to make a fair comparison, my suggestion is to use FSM results without data assimilation instead of ICAR snowpack reanalysis output (ICAR) in Figure 5 and also in the statistical performance analysis. This will also change the conceptualization in the study and objectives since ICAR_assim is not a direct output of ICAR snowpack reanalysis as authors emphasize that it is an ensemble output of FSM as a result of perturbation of ICAR reanalysis forcings.
There is an important impact of PBS assimilation on the results but this is rather unrealistic for some cases where there is almost no snow water equivalent before assimilation (Figure 5). Providing FSM results with forcing of ICAR before and after data assimilation as suggested above might better explain this remarkable contribution of data assimilation by PBS. If the description above (on direct ICAR snowpack output and FSM snowpack output without data assimilation) is not accurate then the authors should better explain the reasoning behind such a spectacular change in SWE with data assimilation through the use of fSCA of MODIS.
Since there is a remark on NDSI reformulation using Theia in fSCA analysis to improve the results compared to the ordinary equation, it should also be important to deal with cloud removal process and give more explanation in section 3.2.1.

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RR by Anonymous Referee #2 (20 Jan 2021)

ED: Publish subject to revisions (further review by editor and referees) (09 Feb 2021) by María José Polo

AR by Esteban Alonso-González on behalf of the Authors (05 Mar 2021) Author's response Manuscript

ED: Referee Nomination & Report Request started (22 Apr 2021) by María José Polo

RR by Anonymous Referee #1 (23 May 2021)

RR by Anonymous Referee #2 (24 May 2021)

ED: Publish subject to technical corrections (17 Jun 2021) by María José Polo

AR by Esteban Alonso-González on behalf of the Authors (21 Jun 2021) Author's response Manuscript

Short summary

Snow water resources represent a key hydrological resource for the Mediterranean regions, where most of the precipitation falls during the winter months. This is the case for Lebanon, where snowpack represents 31 % of the spring flow. We have used models to generate snow information corrected by means of remote sensing snow cover retrievals. Our results highlight the high temporal variability in the snowpack in Lebanon and its sensitivity to further warming caused by its hypsography.