The CAMELS-CL dataset: catchment attributes and  meteorology for large sample studies – Chile dataset

Alvarez-Garreton, Camila; Mendoza, Pablo A.; Boisier, Juan Pablo; Addor, Nans; Galleguillos, Mauricio; Zambrano-Bigiarini, Mauricio; Lara, Antonio; Puelma, Cristóbal; Cortes, Gonzalo; Garreaud, Rene; McPhee, James; Ayala, Alvaro

doi:https://doi.org/10.5194/hess-22-5817-2018

Articles | Volume 22, issue 11

https://doi.org/10.5194/hess-22-5817-2018

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

https://doi.org/10.5194/hess-22-5817-2018

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

Articles | Volume 22, issue 11

Research article

|

13 Nov 2018

Research article |

| 13 Nov 2018

The CAMELS-CL dataset: catchment attributes and meteorology for large sample studies – Chile dataset

Camila Alvarez-Garreton, Pablo A. Mendoza, Juan Pablo Boisier, Nans Addor, Mauricio Galleguillos, Mauricio Zambrano-Bigiarini, Antonio Lara, Cristóbal Puelma, Gonzalo Cortes, Rene Garreaud, James McPhee, and Alvaro Ayala

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Final revised paper (published on 13 Nov 2018)
Preprint (discussion started on 16 Feb 2018)

Interactive discussion

Status: closed

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

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- Supplement

RC1: 'Review of HESS-2018-23', Anonymous Referee #1, 08 Apr 2018
- AC1: 'Response letter to Referees 1 and 2', Camila Alvarez-Garreton, 17 May 2018
RC2: 'Review: The CAMELS-CL dataset', Anonymous Referee #2, 23 Apr 2018
- AC1: 'Response letter to Referees 1 and 2', Camila Alvarez-Garreton, 17 May 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) (25 May 2018) by Jan Seibert

AR by Camila Alvarez-Garreton on behalf of the Authors (20 Jun 2018) Author's response Manuscript

ED: Referee Nomination & Report Request started (22 Jun 2018) by Jan Seibert

RR by Anonymous Referee #1 (24 Jul 2018)

RR by Margarita Saft (31 Jul 2018)

Suggestions for revision or reasons for rejection

The paper presents a newly available dataset for a region with limited prior data availablilty. The dataset combines a number of hydrological, climatological, and geographical characteristics allowing for interesting synthesis of these data. I have learned a number of things about Chilean hydrology including regional aspects, even though this did not seem to be the focus of this paper. Another strong side of the paper is that authors clearly describe the limitations of the data and the implications. In my opinion the paper is relevant for HESS audience, presents a valuable contribution to the field, so I recommend publication subject to addressing comments below.
Regarding the content and the context of the paper:
The way I see it there are three main aspects in this paper: 1) dataset presentation; 2) characterization of hydrology and related aspects of geography of Chile highlighting regional differences based on novel data; and 3) climate data product comparison in the terrain known to be difficult to get accurate estimates for. The presentation of the paper (abstract/introduction/conclusion) focuses on aspects 1 and 3 and largely omits aspect 2. However, the related parts of the manuscript on their own and in their current state appear somewhat detached. In particular, the core of the dataset is streamflow and catchment data, whereas analysis of uncertainty (in form of spread between different products) is focused solely on the climatic (P and PET) data. Yet aspect 2 (characterizing Chile’s hydrology including its social aspects using new data) appears to me to be a logical focus of the paper submitted to HESS, especially since the paper already includes this material. I suggest the authors address this in the abstract/introduction/conclusion.
Specific comments:
3.1.5. Streamflow: Do you include data for all available record or clip it to what is presented at Fig. 3? I have tried to download the dataset from the link provided to check that (as I would see including whole records as the strength of the dataset), but the download did not work (zip was 0b size, and individual downloads buttons created a ‘File not found or deleted from server’ message). Related question: what are mean and median record lengths (and may be min/max too)?
3.1.5. and Fig.10 b and f: It is surprising to see elasticities < 0 (rainfall increase results in runoff decrease?) and runoff ratios above 1. It would be good to refer to the relevant part of the discussion on >1 runoff ratios in part 4, and comment on the elasticities (is it <0 in Far North? Is it a consequence of opposite trends in P and Q?) here.
3.1.5. and Table 3 / Baseflow: the BFIs seems to be quite high (>0,5 everywhere) and I was wondering what parameter value(s) were used for the filter.
3.1.5. and Table 3 / Elasticity: What method was used to calculate elasticity?
3.1.9. and Fig. 4e: Snowmelt data is not shown in the northern and southern catchments, I was wondering whether lack of catchment markers indicates no snowmelt, or no data? Are catchments with no snowmelt were included on the map as orange points, or just not included on the map? I would expect to see some snowmelt in the Patagonia catchments. Can you please comment on the data availability in regard to the current paper study area?
p.13 line 4: I suppose Fig. 9d, not Fig. 4d?
3.2.5. Near-natural catchments: Plantations are another factor which can considerably change catchment hydrological signature, it might be beneficial to set some threshold for plantation % as well.
4.1. in particular p.15 lines 28-32: Assessment of the spread is closely related to mean values; may be comment on the absolute errors as well as relative.
p.17 lines 1-8: Nice clear description of the limitations
Histograms in the bottom of the maps (in a number of cases, e.g. Fig.6 a,d,e): Smaller bins near lower tail would be more informative.
Fig. 7 a, b: It would be good to see bar plot(s), possibly frequency-sorted, and may be even stacked.
Fig. 8: Nice to see no forest cover also presented on the plot – add to the legend?
Fig. 9i: Green column (I assume djf) is missing from the bar chart/legend
Table 3: barren_frac unit is missing

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RR by Anonymous Referee #4 (08 Aug 2018)

Suggestions for revision or reasons for rejection

Review of “The CAMELS-CL dataset: catchment attributes and meteorology for large sample studies – Chile dataset”, by Alvarez-Garreton et al.

The manuscript entitled “The CAMELS-CL dataset: catchment attributes and meteorology for large sample studies – Chile dataset” developed by Alvarez-Garreton C. et al. presents a dataset with daily resolution of several climatic variables. This is of high interest in order to develop climate research, especially in a country as Chile, particularly vulnerable to climate change effects and where water management is a priority for policymakers.
My first thought when I received and accepted to review was, coinciding with the editor, that this manuscript fit perfectly in Earth System Science Data journal, but this is a matter that has to be solved by the editorial team.
Nevertheless, the manuscript shows high hydrological interest (mainly for the inclusion of the human intervention on catchments) and has developed a great improvement since its original version following the other referees comments.
Despite this, I would like the authors to answer some final concerns.

MAJOR COMMENTS
Maybe the first concern about this manuscript is that I miss a more detailed explanation about how the authors deal with the fact that the precipitation datasets show projections that display large uncertainty in high and dry areas, as those located in the Far and Near North.
Another point is a matter of scale, but that affects directly the most strong point of this work to this reviewer’s eyes: how have been considered Andean peatlands in this work? They play a key role as water reservoirs and their presence modify significantly the water flow, but their dimensions make the “invisible” to many regional to global products (Tachikawa et al. 2011). They’re also very sensitive to annual climate variability, which makes me feel that the period used in Zhao et al. (2016) is not enough consider these ecosystems in the model. Finally, they have been traditionally managed by human activity, and their influence in the groundwater flow of high elevated is determinant. Do the authors have considered this?

MINOR COMMENTS
- for the whole manuscript, author’s guidelines in Physical dimensions and units, point h, says: “The symbol for the decimal marker is the dot. To facilitate reading, numbers may be divided in groups of three using a thin space (e.g. 12 345.6), starting with the ten-thousand digit. Neither dots nor commas are permitted as group separators”. Please, correct.

- p.2 l. 31: I don’t really see the point here to talk about the main results of the research and, next, start talking about the study area. Why not directly in the “Study area” section? I would delete these sentences.

- p. 3 l. 15: I miss a reference at the end of the enumeration, concretely for “climate change impacts on the hydrology of the CONUS”.

- p. 4 l. 13: strictly, Kottek et al. (2006) did not applied the Köppen’s but the Köppen-Geiger’s classification. See Sarricolea P., Herrera-Ossandon M.J., Meseguer-Ruiz O. (2017) Climatic Regionalisation of Continental Chile. Journal of Maps 13(2): 66-73. DOI: 10.1080/17445647.2016.1259592 for further details.

- p. 8 l. 12: is it possible to know some information about the multivariate regression models? Mainly mean square error and residuals…

- p. 10 l. 25: there is a repeated “6”, it should be “Figure 6b” nor “Figure 66b”.

- p. 13 l. 10 and l. 13: in English, months start with a capital letter, so it should be written “DJF” and “JJA” when referring to summer and winter.

- p. 14 l. 30: I think there is a missing letter: “It should be noted…”.

- p. 19 l. 5: it should be “quartile” instead of “quantile”.

- p. 20 l. 25: missing word: “that can be addressed”.

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ED: Publish subject to minor revisions (review by editor) (21 Aug 2018) by Jan Seibert

AR by Camila Alvarez-Garreton on behalf of the Authors (31 Aug 2018) Author's response Manuscript

ED: Publish subject to minor revisions (review by editor) (19 Sep 2018) by Jan Seibert

AR by Camila Alvarez-Garreton on behalf of the Authors (29 Sep 2018) Author's response Manuscript

ED: Publish subject to minor revisions (review by editor) (03 Oct 2018) by Jan Seibert

AR by Camila Alvarez-Garreton on behalf of the Authors (08 Oct 2018) Author's response Manuscript

ED: Publish as is (25 Oct 2018) by Jan Seibert

AR by Camila Alvarez-Garreton on behalf of the Authors (29 Oct 2018) Manuscript

Short summary

CAMELS-CL provides a catchment dataset in Chile, including 516 catchment boundaries, hydro-meteorological time series, and 70 catchment attributes quantifying catchments' climatic, hydrological, topographic, geological, land cover and anthropic intervention features. By using CAMELS-CL, we characterise hydro-climatic regional variations, assess precipitation and potential evapotranspiration uncertainties, and analyse human intervention impacts on catchment response.