Assessing the benefit of snow data assimilation for runoff modeling in Alpine catchments

Griessinger, Nena; Seibert, Jan; Magnusson, Jan; Jonas, Tobias

doi:https://doi.org/10.5194/hess-20-3895-2016

Articles | Volume 20, issue 9

https://doi.org/10.5194/hess-20-3895-2016

© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

https://doi.org/10.5194/hess-20-3895-2016

© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 20, issue 9

Research article

|

22 Sep 2016

Research article |

| 22 Sep 2016

Assessing the benefit of snow data assimilation for runoff modeling in Alpine catchments

Nena Griessinger, Jan Seibert, Jan Magnusson, and Tobias Jonas

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Final revised paper (published on 22 Sep 2016)
Preprint (discussion started on 25 Feb 2016)

Interactive discussion

Status: closed

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

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- Supplement

RC1: 'review', Juraj Parajka, 29 Mar 2016
- AC1: 'Reply to "RC1: 'review', Juraj Parajka, 29 Mar 2016"', Nena Griessinger, 29 Apr 2016
RC2: 'Review of : Griessinger et al. 2016', Anonymous Referee #2, 15 Apr 2016
- AC2: 'Reply to "RC2: 'Review of : Griessinger et al. 2016', Anonymous Referee #2, 15 Apr 2016"', Nena Griessinger, 29 Apr 2016
RC3: 'Very interesting manuscript that needs some clarifications', Ole Rössler, 28 Apr 2016
- AC3: 'Reply to "RC3: 'Very interesting manuscript that needs some clarifications', Ole Rössler, 28 Apr 2016"', Nena Griessinger, 02 May 2016

Peer-review completion

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

ED: Reconsider after major revisions (05 May 2016) by Ross Woods

AR by Nena Griessinger on behalf of the Authors (16 Jun 2016) Author's response Manuscript

ED: Referee Nomination & Report Request started (30 Jun 2016) by Ross Woods

RR by Ole Rössler (18 Jul 2016)

Suggestions for revision or reasons for rejection

The authors did a very good job in revising the manuscript. Nearly all previous concerns have been addressed and the manuscript and figure were changed accordingly. In many parts the manuscript has gained clarity and transparency.
I just found that one question of mine was misunderstood, most likely because I expressed myself incorrectly:
Comment by reviewer: a) The first concern addresses the interpretation of the results. What is exactly the added value of the assimilated data set. Is it a more sophisticated and correct snow melt model or is it rather the added indirect information of precipitation amounts fallen in high altitudes where the meteorological station network is not present. My interpretation would be the latter, as the differences between model M1 and M2 (e.g. assimilation) are considerable for the highest altitudes. I would appreciate a discussion on this question.

Answer by authors: As M1 and M2 differ in the use of the data assimilation algorithm only, and not in the snow melt model, the added value is based on the information coming from the point snow observations. Consequently, in both the discussion and the conclusions we highlight the value of data assimilation in M1. We have adapted the model description to clarify the exact difference between M1 and M2.

To clarify my comment:
As you emphasized, the effect of snow depth (e.g. SWE) assimilation is twofold: First, the snowfall amounts are corrected (lines 156-157). Second, snow melt rates and model state variables were corrected (lines 156-157). I would like a differentiated statement on why the M1 model is so much better than the M2 model version. Is it mainly due to the corrected input data, or due to corrected snow melt rates (cp. strong difference between M1 and M2 in accumulated snowmelt figure 2)
To elaborate on this comment:
The applied precipitation input data set RHiresD is suspected to (strongly) misrepresent the precipitation in higher elevations (>2000 m). This is because no or very sparse meteorological station data is available there. If I am correct, M2, in contrast to M1, only rely on RHiresD data, suggesting a strong deviation of snowfall amounts in higher elevations.
You highlighted that M1 and M2 model performance differ considerably, especially in your highest elevation class (>2000 m) – this finding indicates that data assimilation is crucial to overcome limitations in the input data set.
Furthermore, Figure 2 shows strong differences between snowmelt rates M1 and M2. I am not sure if this related also to this highest elevation (it is hard to read in the maps). This figure indicates a crucial need to substantially lower snow melt rates.
You replied: “, the added value is based on the information coming from the point snow observations.”
• I wanted to know which information of point snow observation is responsible for the added value.

I think this information is very helpful for other studies that want to make use of similar snow melt models or the RHiresD data sets.

I would very much appreciate such a statement and recommend publication after adding this clarification.

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ED: Publish subject to minor revisions (Editor review) (27 Jul 2016) by Ross Woods

AR by Nena Griessinger on behalf of the Authors (15 Aug 2016) Author's response Manuscript

ED: Publish as is (02 Sep 2016) by Ross Woods

AR by Nena Griessinger on behalf of the Authors (07 Sep 2016) Manuscript

Short summary

In Alpine catchments, snowmelt is a major contribution to runoff. In this study, we address the question of whether the performance of a hydrological model can be enhanced by integrating data from an external snow monitoring system. To this end, a hydrological model was driven with snowmelt input from snow models of different complexities. Best performance was obtained with a snow model, which utilized data assimilation, in particular for catchments at higher elevations and for snow-rich years.