Development of the Community Water Model (CWatM v1.04) – a high-resolution hydrological model for global and regional assessment of integrated water resources management

Burek, Peter; Satoh, Yusuke; Kahil, Taher; Tang, Ting; Greve, Peter; Smilovic, Mikhail; Guillaumot, Luca; Zhao, Fang; Wada, Yoshihide

doi:https://doi.org/10.5194/gmd-13-3267-2020

Articles | Volume 13, issue 7

https://doi.org/10.5194/gmd-13-3267-2020

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

https://doi.org/10.5194/gmd-13-3267-2020

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

Articles | Volume 13, issue 7

Model description paper

|

21 Jul 2020

Model description paper |

| 21 Jul 2020

Development of the Community Water Model (CWatM v1.04) – a high-resolution hydrological model for global and regional assessment of integrated water resources management

Peter Burek, Yusuke Satoh, Taher Kahil, Ting Tang, Peter Greve, Mikhail Smilovic, Luca Guillaumot, Fang Zhao, and Yoshihide Wada

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Final revised paper (published on 21 Jul 2020)
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Preprint (discussion started on 14 Aug 2019)
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Interactive discussion

Status: closed

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

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RC1: 'Review', Anonymous Referee #1, 01 Oct 2019
- AC2: 'Reply to reviewer 1', Peter Burek, 19 Nov 2019
RC2: 'Review', Anonymous Referee #2, 19 Oct 2019
- AC1: 'Reply to reviewer 2', Peter Burek, 04 Nov 2019

Peer-review completion

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

AR by Peter Burek on behalf of the Authors (28 Nov 2019) Author's response Manuscript

ED: Referee Nomination & Report Request started (08 Dec 2019) by Wolfgang Kurtz

RR by Anonymous Referee #3 (18 Jan 2020)

Suggestions for revision or reasons for rejection

General comments

The paper describes a new macroscale hydrological model called CWatM. Although the model does not include much new functions compared with the most advanced similar models, but it does have several advantages due to its well organized code and adoption of modern technology. The unique feature of CWatM is that although global model, it can be easily applicable to specific basins. It also provides some tools for parameter tuning. These functions are seldom seen in earlier global models (exception is H08, Mateo et al. 2014; Masood et al. 2015). The text is well written and organized. The Section 5.4 and 5.5 are interesting, but too short to understand the methods and uncertainties in results.

Specific comments

Line 323-324 “With a simplification, the 1D Richards equation, is entirely gravity-driven and the matrix potential gradient is zero. This implies a flow that is always in a downward direction at a rate equal to the conductivity of the soil“: It sounds like that the authors assume the water transport in unsaturated zone is same as saturated one. Is this really the case?

Line 487 “Irrigation water demand”: Does CWatM consider multiple cropping? If so, how was it implemented?

Table 1: The original spatial resolution for DDM30 must be 30’.

Table 5 “Withdrawal of agricultural sector 2000 km3/yr:” I think the estimate is quite lower than that is shown in other reports. I am wondering this is partly due to the exclusion of multiple cropping.

Line 615 “Global calibration results”: During the calibration, were the models for human activities (e.g. water abstraction, reservoir operation, and others) enabled?

Line 640 “Section 5.4”: This section is very interesting, but too short to understand the methods and uncertainties in results. For instance, I hardly understood how the lake, the dam (the Owen Falls Dam), and water use in the study domain were treated. Are there any reports which include more detailed information?

Line 640 Section 5.5: Same as above. Are there any reports which include more detailed information?

Figure 7 the hydrograph for Matundo-Cais: It seems that the station is below the massive reservoir of Cahora Bassa. The observation in Fig 7 apparently shows the influence of reservoir operation (i.e. the seasonal fluctuation during low flow period is “unnaturally” stable). The simulated hydrograph also shows “unnatural” behavior which is quite different from the observation. Here comes my two questions. First, did the author consider the reservoir operation of Cahora Bassa in this study? If this is the case, how was the reproducibility of the dam operation (the dynamics of storage and release)?

Figure 8 I couldn’t understand how to understand the “violin diagram”. Elaborate what it shows.

Figure 8 Caption “at different values of a GHM”: Unclear. What does it mean?

Line 855 “Using CMIP6 datasets, (Eyring et al., 2016) is expected to reduce uncertainty”: The sentence seems incomplete.

Reference

Masood, M., Yeh, P. J. F., Hanasaki, N., and Takeuchi, K.: Model study of the impacts of future climate change on the hydrology of Ganges–Brahmaputra–Meghna basin, Hydrol. Earth Syst. Sci., 19, 747-770, 10.5194/hess-19-747-2015, 2015.

Mateo, C. M., Hanasaki, N., Komori, D., Tanaka, K., Kiguchi, M., Champathong, A., Sukhapunnaphan, T., Yamazaki, D., and Oki, T.: Assessing the impacts of reservoir operation to floodplain inundation by combining hydrological, reservoir management, and hydrodynamic models, Water Resources Research, 50, 7245-7266, 10.1002/2013wr014845, 2014.

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ED: Reconsider after major revisions (04 Feb 2020) by Wolfgang Kurtz

Dear authors,

thank you very much for the revision of your manuscript. I checked the changes with respect to the reviewer comments and also asked one additional referee to provide comments on the manuscript.

From my perspective, some of the critical points from reviewer 1 still remain. The new statements on modularity/ novelty are still rather broad and general. More technical details on the software engineering approach should be provided (see specific comments below). As it is a model description paper, this is the right place to provide (and focus on) such technical details of the model implementation. With respect to novelty, it would also be helpful to relate the concept and implementation of CWatM to already existing global hydrological models. In addition, the validation deserves more attention and should be extended and placed more prominent in the paper.

Therefore, I would like to ask you to revise your manuscript according to the items listed below and the comments of reviewer 3.

Kind regards,
Wolfgang Kurtz

Specific comments:
- It should be clarified how the modularity exactly manifests in the software architecture. For example, if someone wants to use an alternative representation of a certain process (e.g., water transport in the soil column, river routing), how does the architecture of CWatM support the implementation of such new code in terms of modularity? Is there an API or specific data structures that support modularity? The mentioned separation of input specifications and computation (line 129-133) as well as the separation of (hydrological) processes in different subroutines/ modules is also common for many other models in my opinion. How does CWatM differ in that respect?

- It is mentioned that CWatM can be easily coupled to other models (particularly due to its modularity). More details would be advantageous here. How does the architecture of CWatM support such model coupling? Is there a dedicated API for model coupling and how do the established model couplings work in detail? Is this done by some standardized (API-related) library calls or via transfer of information by specific input/output files (customized to each model separately)?

- It is emphasized that usage of netCDF as a standard format is an advantage of CWatM, e.g. because of the possibility of metadata annotation. How is that realized in the program? Does CWatM e.g. use CF conventions for model output (as other hydrological models like WRF-Hydro)? Which metadata are (or can be) annotated to the model output?

- It is also mentioned that CWatM can use different sources of meteorological input data sets (due to usage of netCDF). How does CWatM support the usage of these different data sources? Does it automatically care e.g. for different names of input variables in the different data sets? Does it support the user by regridding the meteorological forcings to the model grid (if yes, which interpolation/regridding schemes are available)?

- Section 5.2, lines 609-614: Model description papers in GMD should also focus on a sufficient validation of the model. Given the fact that comparisons with discharge and/or GRACE data are already available, some of these results should also be included and discussed in this model description paper.

- Section 5.3: lines 619-620: Validation results should be discussed in more detail in the main part of the paper itself.

- Figures 4+7: Discharge is shown for the respective calibration period. It would be worthwhile to additionally show results for an independent validation period.

- The incorporated calibration feature is interesting and it would also be helpful to the reader to get some more information on expected simulation times for the calibration exercise mentioned in the manuscript.

- Tables 3+4: It would be helpful to also provide non-cumulative runtime percentages.

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AR by Svenja Lange on behalf of the Authors (20 Mar 2020) Author's response Manuscript

ED: Referee Nomination & Report Request started (05 Apr 2020) by Wolfgang Kurtz

RR by Anonymous Referee #3 (19 Apr 2020)

ED: Publish subject to minor revisions (review by editor) (06 May 2020) by Wolfgang Kurtz

AR by Peter Burek on behalf of the Authors (07 May 2020) Author's response Manuscript

ED: Publish as is (29 May 2020) by Wolfgang Kurtz

Short summary

We present the new global hydrological model "Community Water Model" (CWatM), which can be used globally and regionally. The model is open source and written with the Python programming language. It uses global, freely available data in a smart and state-of-the-art format. It includes the major hydrological processes but also takes into account human activities, such as water use and reservoir regulation, by calculating water demand from the agriculture, domestic, and industrial sectors.