Socio-hydrological data assimilation: analyzing human–flood interactions by model–data integration

Sawada, Yohei; Hanazaki, Risa

doi:https://doi.org/10.5194/hess-24-4777-2020

Articles | Volume 24, issue 10

https://doi.org/10.5194/hess-24-4777-2020

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

https://doi.org/10.5194/hess-24-4777-2020

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

Articles | Volume 24, issue 10

Research article

|

05 Oct 2020

Research article |

| 05 Oct 2020

Socio-hydrological data assimilation: analyzing human–flood interactions by model–data integration

Yohei Sawada and Risa Hanazaki

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Final revised paper (published on 05 Oct 2020)
Supplement to the final revised paper
Preprint (discussion started on 19 Feb 2020)
Supplement to the preprint

Interactive discussion

Status: closed

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

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- Supplement

RC1: 'Review of "Socio-hydrologic data assimilation: Analyzing human-flood interactions by model-data integration"', Anonymous Referee #1, 21 Mar 2020
- AC2: 'Response to the comments of Anonymous Referee #1', Yohei Sawada, 25 Apr 2020
RC2: 'Manuscript review', Anonymous Referee #2, 24 Mar 2020
- AC3: 'Response to the comments of Anonymous Referee #2', Yohei Sawada, 25 Apr 2020
AC1: 'real-world experiment of socio hydrological data assimilation', Yohei Sawada, 30 Mar 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) (06 May 2020) by Giuliano Di Baldassarre

AR by Yohei Sawada on behalf of the Authors (19 May 2020) Author's response Manuscript

ED: Referee Nomination & Report Request started (11 Jun 2020) by Giuliano Di Baldassarre

RR by Anonymous Referee #2 (12 Jun 2020)

RR by Anonymous Referee #1 (17 Jul 2020)

Suggestions for revision or reasons for rejection

First of all, I would like to thank the authors for having carefully addressed all my comments. I also appreciate their effort in proposing a real-world application based on the data of Ciullo et al. (2017). However, I still do have a few comments.

- In my previous review, I asked to clarify how are the authors planning to estimate the accuracy of flood awareness observations. The authors replied that “several previous studies obtained the proxy of the social memory by interview data (Barendrecht et al. 2019) and the number of Google searches (Gonzales and Ajami 2017)”. However, it is still not clear to me how is the authors’ modeling framework going to assimilate such social observations and how are they going to assign an error to such observations. Maybe this is a limitation that should be included in the discussion of the results.

- I think it would be really interesting to read more about the use of such assimilation framework to better understand the human-flood dynamics. Right now the discussion of the results is more focused on the numerical tool, its performances, and observations availability (which is great). However, in my opinion, it would be also interesting to discuss more in detail how such a tool could help us in advancing our understanding of the complex feedback between the human and flood systems. What about using the proposed approach for predictions in socio-hydrological modeling?

- In the title use either “assimilation” or “integration”, they are synonymous but they do mean different things.

- How is it possible to get awareness higher than 1?

- I invite the authors to improve the overall quality of the figures and include all the information in the legend of figures 12-15 (ensemble, mean ensemble, observations).

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ED: Publish subject to minor revisions (review by editor) (03 Aug 2020) by Giuliano Di Baldassarre

AR by Yohei Sawada on behalf of the Authors (07 Aug 2020) Author's response Manuscript

ED: Publish subject to technical corrections (14 Aug 2020) by Giuliano Di Baldassarre

Short summary

In socio-hydrology, human–water interactions are investigated. Researchers have two major methodologies in socio-hydrology, namely mathematical modeling and empirical data analysis. Here we propose a new method for bringing the synergic effect of models and data to socio-hydrology. We apply sequential data assimilation, which has been widely used in geoscience, to a flood risk model to analyze the human–flood interactions by model–data integration.