Assessment of simulated soil moisture from WRF Noah, Noah-MP, and CLM land surface schemes for landslide hazard application

Zhuo, Lu; Dai, Qiang; Han, Dawei; Chen, Ningsheng; Zhao, Binru

doi:https://doi.org/10.5194/hess-23-4199-2019

Articles | Volume 23, issue 10

https://doi.org/10.5194/hess-23-4199-2019

© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/hess-23-4199-2019

© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 23, issue 10

Research article

|

18 Oct 2019

Research article |

| 18 Oct 2019

Assessment of simulated soil moisture from WRF Noah, Noah-MP, and CLM land surface schemes for landslide hazard application

Lu Zhuo, Qiang Dai, Dawei Han, Ningsheng Chen, and Binru Zhao

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Final revised paper (published on 18 Oct 2019)
Preprint (discussion started on 22 Mar 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, 14 Apr 2019
- AC1: 'Reply to Reviewer 1', Lu Zhuo, 10 Jun 2019
RC2: 'Revision', Anonymous Referee #2, 23 Apr 2019
- AC2: 'Reply to Reviewer 2', Lu Zhuo, 10 Jun 2019

Peer-review completion

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

ED: Reconsider after major revisions (further review by editor and referees) (18 Jun 2019) by Roberto Greco

AR by Lu Zhuo on behalf of the Authors (05 Jul 2019) Author's response Manuscript

ED: Referee Nomination & Report Request started (12 Jul 2019) by Roberto Greco

RR by Anonymous Referee #1 (01 Aug 2019)

RR by Anonymous Referee #2 (14 Aug 2019)

Suggestions for revision or reasons for rejection

Dear Authors
The manuscript has been certainly improved. However, some flaws are still present. A few of them are quite relevant and need to be thoroughly addressed before publication.
I therefore suggest another round of major revisions. If the major weaknesses are not satisfactorily addressed, I will be forced to suggest the rejection of the paper during the next stage of review.
About line numbering, I refer to the track-change document (named “hess-2019-95-author_response-version2”).

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MAJOR FLAWS

1- The use of a single measuring station located in a completely different setting is still an open issue in my opinion. The authors provided some justification (only available station) and heavily modified the evaluation section. This is not enough. In my opinion a publication in an important journal like HESS demands good data and good results. I appreciated the methodology developed in your paper but you just do not have good amount and quality of data, therefore you are borderline. You cannot use an excuse that these are the best data you could get in this test site: limited data prevent to get reliable and robust outcomes, thus endangering publication. In my first report (general issue #4), I suggested an approach to overcome this situation, but you didn’t implement it in the revised manuscript. I briefly outline it again (this is just a quick sum-up, please develop it further and add these concepts in different places in the manuscript, where appropriate). One measuring station in the whole area is not sufficient to adequately calibrate the model (this must be stated clearly) and can be used only to build a methodology and to obtain only preliminary results. However, from this preliminary steps, relevant outcomes could be obtained: other studies in the same test site established empirical correlations between landslides and hydrogeological variables on smaller territorial units (see details in my previous general issue #4). Thus, it could be inferred that the proposed methodology is preliminary but it could be further refined (and better result could be obtained) if data from a denser measuring network would be available.

2- Validation. What I understand from the latest version of your paper is the following: You model soil moisture and rainfall. You provide a spatial validation only for the modeled rainfall. You use the soil moisture modeled across the whole region. I think this is not correct. This issue is related to the previous one: a measuring station in the alluvial plain cannot be used to calibrate a soil moisture model in mountains hundreds of kilometers away. However, you did it, you got some results in trying to predict landslides, you need to add that more measuring instruments would allow for a better calibration potentially improving the overall results.

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PREVIOUS COMMENTS NOT ADDRESSED

- Former general issue #4: see also my general comment. You “dodged” the problem by validating rainfall instead of soil moisture. But in the manuscript you use soil moisture, therefore the problem remains still open.

- Former specific issue L40-42. I can0t find any trace of this. Maybe because you delated relecant parts of the introduction?

- Former specific issue Line 628. I suggested to check line 628, not to check the references. Sorry for the misunderstanding (I wrote last comment and the reference list too close each other).

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GENERAL COMMENTS
The validation of the rainfall does not seem to provide good results: a R value of 0.40 seems rather low to me. You make reference to another work in the USA that gets similar values, but I think that the standards in the international literature are higher than this.
Results, discussion and conclusion are not well separated. The results section includes some interpretation of the results (usually more convenient in the discussion). If the discussion/conclusion section becomes too long, maybe it would be better to split discussion and conclusions.

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MINOR COMMENTS
Please, check the bi-directional correspondence between the references in the text and in the reference list, as you ou modified the introduction and it seems to me that some discrepancies exists (e.g. Zhuo et al 2016 is cited at line 80 but it is not in the reference list).
L78. The new added sentence needs some reference.
In the first part of section 4.1 (e.g. L365 or 366), please clarify that the comparison is carried out at a single point: the one where the measuring station is located.
L441. Which works? Please be more specific.
L465-466. I don’t agree with this reason. You are dealing with landslides, not with pure statistics, therefore the statistical reliability of this approach is questionable. I think it is better to state in the premises that your objective is to have equal coverage areas, consequently you identified those class-break values.
L519. Many shallow instead of very shallow?
L549. Which study? Please provide a reference.
L560-568- I suggest deleting this whole part as it includes issues like civil protection procedures, risk perception, risk management and it goes beyond the scopes of your work.
L570 Which studies? Please provide references.
L580 Here some reference are needed. I suggest using Nichol and Wong 2005 for remote sensing (feel free to add more examples). In addition, I suggest to add also the possibility to use internet news to detect all relevant landslides (all landslides with a relevant impact on society will be reported on internet news), also using automatic methods (Battisitni et al., 2013).
L588. I would add: “However, the methodology could be replicated to derive site-specific calibrations of the approach proposed.”

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REFERENCES CITED:
Battistini, A., Segoni, S., Manzo, G., Catani, F., & Casagli, N. (2013). Web data mining for automatic inventory of geohazards at national scale. Applied Geography, 43, 147-158.
Nichol, J., & Wong, M. S. (2005). Satellite remote sensing for detailed landslide inventories using change detection and image fusion. International journal of remote sensing, 26(9), 1913-1926.

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ED: Reconsider after major revisions (further review by editor and referees) (16 Aug 2019) by Roberto Greco

AR by Lu Zhuo on behalf of the Authors (02 Sep 2019) Author's response Manuscript

ED: Referee Nomination & Report Request started (06 Sep 2019) by Roberto Greco

RR by Anonymous Referee #2 (06 Sep 2019)

ED: Publish as is (16 Sep 2019) by Roberto Greco

Short summary

This study assesses the usability of WRF model-simulated soil moisture for landslide monitoring in northern Italy. In particular, three advanced land surface model schemes (Noah, Noah-MP, and CLM4) are used to provide multi-layer soil moisture data. The results have shown Noah-MP can provide the best landslide monitoring performance. It is also demonstrated that a single soil moisture sensor located in plain area has a high correlation with a significant proportion of the study area.