On the similarity of hillslope hydrologic function:  a clustering approach based on groundwater changes

Maina, Fadji Z.; Wainwright, Haruko M.; Dennedy-Frank, Peter James; Siirila-Woodburn, Erica R.

doi:https://doi.org/10.5194/hess-26-3805-2022

Articles | Volume 26, issue 14

https://doi.org/10.5194/hess-26-3805-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/hess-26-3805-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 26, issue 14

Research article

|

19 Jul 2022

Research article |

| 19 Jul 2022

On the similarity of hillslope hydrologic function: a clustering approach based on groundwater changes

Fadji Z. Maina, Haruko M. Wainwright, Peter James Dennedy-Frank, and Erica R. Siirila-Woodburn

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Final revised paper (published on 19 Jul 2022)
Preprint (discussion started on 26 Oct 2021)

Interactive discussion

Status: closed

RC1:
'Comment on hess-2021-520', A. D. Parsekian, 05 Feb 2022

please see the attached PDF.

Citation: https://doi.org/10.5194/hess-2021-520-RC1
- AC1: 'Reply on RC1', Fadji Zaouna Maina, 09 Mar 2022
  
  Responses to the referre can be found attached.
  
  Citation: https://doi.org/10.5194/hess-2021-520-AC1
RC2:
'Comment on hess-2021-520', Anonymous Referee #2, 10 Feb 2022

I think this is interested study that is worthy of publication. However, a lot of improvements have to be made for its current form. I agree with that ground water dynamics may be a good proxy of surface hydrological processes in some places. However, this may not be the truth in some other areas. So this proposed approach may have its limits. This have to be clarified in the introduction and discussion. There are a lot of indices and methods used in this study were not presented in the Methodology section. I also feel that the methods section did not clearly present how the authors process the data and generate the results. In addition, and especially, discussion of the findings of this study has to be strengthen. currently, the discussion is weak, maybe due to the reason that the results and discussion were combined. References are needed for the interpretations. Explanation of the results and comparions with other published studies have to be improved.

1. the abstract lacks quantified description

2. line 41-44, references are needed to support this statement.

3. line 106, maybe give some examples of such models

4. line 138, maybe starting with a sentence to tell the reads the pupose or the reason of using ParFlow-CLM in this study

5. line 164, provide the examples of the application of ParFlow-CLM

6. line 239-240, what were those thresholds tested, specify

7. line 238-243, what were the thredholds of drainage area you finally used?

8. line 316, the clustering approaches have to be introduced in the methods section

9. line 369-379, most of the part would be better to move to the methods section

10. section 3.2.3, why were surface runoff not considered? I thinks it might be one of the most important hydrological processes.

Citation: https://doi.org/10.5194/hess-2021-520-RC2
- AC2: 'Reply on RC2', Fadji Zaouna Maina, 09 Mar 2022
  
  Responses to the referre can be found attached.
  
  Citation: https://doi.org/10.5194/hess-2021-520-AC2

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload

ED: Reconsider after major revisions (further review by editor and referees) (09 Mar 2022) by Hilary McMillan

AR by Fadji Zaouna Maina on behalf of the Authors (13 Apr 2022) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (15 Apr 2022) by Hilary McMillan

RR by Anonymous Referee #2 (08 May 2022)

RR by A. D. Parsekian (08 Jun 2022)

ED: Publish as is (12 Jun 2022) by Hilary McMillan

AR by Fadji Zaouna Maina on behalf of the Authors (13 Jun 2022)

Short summary

We propose a hillslope clustering approach based on the seasonal changes in groundwater levels and test its performance by comparing it to several common clustering approaches (aridity index, topographic wetness index, elevation, land cover, and machine-learning clustering). The proposed approach is robust as it reasonably categorizes hillslopes with similar elevation, land cover, hydroclimate, land surface processes, and subsurface hydrodynamics, hence a similar hydrologic function.