Evaluation of multiple climate data sources for managing environmental resources in East Africa

Gebrechorkos, Solomon Hailu; Hülsmann, Stephan; Bernhofer, Christian

doi:https://doi.org/10.5194/hess-22-4547-2018

Articles | Volume 22, issue 8

https://doi.org/10.5194/hess-22-4547-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/hess-22-4547-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 22, issue 8

Research article

|

28 Aug 2018

Research article |

| 28 Aug 2018

Evaluation of multiple climate data sources for managing environmental resources in East Africa

Solomon Hailu Gebrechorkos, Stephan Hülsmann, and Christian Bernhofer

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Final revised paper (published on 28 Aug 2018)
Supplement to the final revised paper
Preprint (discussion started on 29 Sep 2017)
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: 'MS No.: hess-2017-558', Anonymous Referee #1, 11 Dec 2017
- AC1: 'Reply to Anonymous Referee #1', Solomon Hailu Gebrechorkos, 20 Jan 2018
RC2: 'Evaluation of Multiple Climate Data Sources for Managing Environmental Resources in East Africa', Anonymous Referee #2, 20 Dec 2017
- AC2: 'Reply to Anonymous Referee #2', Solomon Hailu Gebrechorkos, 20 Jan 2018

Peer-review completion

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

ED: Reconsider after major revisions (further review by editor and referees) (08 Feb 2018) by Shraddhanand Shukla

AR by Solomon Hailu Gebrechorkos on behalf of the Authors (07 Mar 2018) Author's response Manuscript

ED: Referee Nomination & Report Request started (26 Mar 2018) by Shraddhanand Shukla

RR by Anonymous Referee #2 (01 May 2018)

RR by Anonymous Referee #1 (10 May 2018)

Suggestions for revision or reasons for rejection

Overall, the paper has improved, but I don’t think that the paper is ready for publication. I have provided some additional comments here that i think would make the paper more publishable in a journal like HESS. Overall, given the stated goal of being a rainfall evaluation i did not find it to be very comprehensive - Why didn’t they include additional rainfall datasets like TAMSAT? ENACTS? (No citation of Tufa Dinku are surprising in an africa rainfall evaluation paper). And why not include additional temperature datasets like MERRA-2, CFS-R? I recognize that one can’t compare all products, but then it needs to be justified how these product are chosen e.g. relevant to a larger project. The paper mentions the need for daily data for hydrologic modeling, and CORDEX for climate applications but did not include either of these applications. So I would suggest either making the rainfall evaluation more comprehensive (more comments below) or potentially adding an application piece.

I do think that the daily analysis is novel, interesting and should be published, as well as their careful consideration of the point to pixel problem. But in the current presentation is not sufficient for HESS publication.

Major Comments:
Page 7 line 9: Example of a not very clearly written sentence. I would suggest having an outside reader (or co-author) review the paper for clarity. I have trouble following this section.

For example: CHIRPS is a satellite-gauge blended product. The CHIRPS-prelim is updated every 5 days, at 2 day latency (not sure if that is 100% accurate) and blends in GTS stations. The CHIRPS-final product, updated around the 20th of each month blends in GTS, GSOD and other local or regional stations (including some NMA) that the product developers are able to acquire. For the final product, these stations are used to adjust monthly totals.

Page 7, line 15: Last update Feb 18 (should say: to present), and 1981-2018 (should also say “to present”) since Feb 2018 won't be accurate by the time this paper is published.

I appreciate that the authors tried to add the explanation page 8 lines 10-25, but it was not very clearly written and using the original data from NMA doesn't necessarily make it independent --since some of those stations should be in the CHIRPS. That said, the ENACT data said does contain many of the NMA stations. The authors should acknowledge that ENACTS dataset exists and include it in the analysis or justify why it was not included https://link.springer.com/content/pdf/10.1186%2F2194-6434-1-15.pdf

The authors include CORDEX but mostly say that it doesn't compare well with stations. But there are important applications for this dataset. So, the message in the paper that CORDEX rainfall isn't good isn't very useful since people look to CORDEX for its climate applications. What do the authors suggest for improvements?

Minor:
Page 7 line 9: Stations data should be “station data”
Line 10 page 15: 17, three, and one [use numbers in a list]

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ED: Reconsider after major revisions (further review by editor and referees) (14 May 2018) by Shraddhanand Shukla

AR by Solomon Hailu Gebrechorkos on behalf of the Authors (24 May 2018) Author's response Manuscript

ED: Reconsider after major revisions (further review by editor and referees) (29 Jun 2018) by Shraddhanand Shukla

Dear Authors,

Thank you for revising the manuscript and providing response to the reviewer #1. I think the manuscript is improved however I think a few more changes (moderate in nature) are needed. Please see my comments below. They mainly intend to improve upon the presentation of the results.

(1) Please consider converting table 3, 5 and 6 into graphs. There are just too many numbers to keep track of there. It would be much easier to keep track of the relative performance of each of the datasets if these results were presented in a graphical format.

(2) Please use a different color bar for Figure 2. Please limit the range from 0 to 12 (why is there below 0 in a precipitation color scale?). Perhaps choose a color scale that is sequential going from a light color to a darker color. Also in this figure and in fig 3, it may be best to show absolute value of rainfall or temperature using one of the reference dataset and show only difference between other datasets and that reference dataset.

(3) Table 4: I like this analysis but it would be useful to see how each of these the spatial variability of daily rainfall characteristics as indicated by different datasets. The datasets probably don't compare as well over the high altitude and rainfall regions than low altitude and low rainfall regions. It would be good see the spatial variability in those characteristics and how each of the dataset performs relative to observations.

(4) Please also consider reducing the number of sub-figures in Figure 4 by simply showing the correlation value for each of the sub-regions for a given dataset in one sub-figure hence you'll have a 5 columns single row figure. Not sure if it's necessary to show all the scatter diagrams. You could also just show correlation values for one reference dataset (such as CHIRPS) and show the difference in the correlation of other datasets and the correlation of the reference dataset.

Thanks and I look forward to seeing the next version of the manuscript.

Best,
Shrad

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AR by Solomon Hailu Gebrechorkos on behalf of the Authors (05 Jul 2018) Manuscript

ED: Publish subject to minor revisions (further review by editor) (20 Jul 2018) by Shraddhanand Shukla

AR by Solomon Hailu Gebrechorkos on behalf of the Authors (23 Jul 2018) Author's response Manuscript

ED: Publish as is (02 Aug 2018) by Shraddhanand Shukla

AR by Solomon Hailu Gebrechorkos on behalf of the Authors (06 Aug 2018)

Short summary

In Africa field-based meteorological data are scarce; therefore global data sources based on remote sensing and climate models are often used as alternatives. To assess their suitability for a large and topographically complex area in East Africa, we evaluated multiple climate data products with available ground station data at multiple timescales over 21 regions. The comprehensive evaluation resulted in identification of preferential data sources to be used for climate and hydrological studies.