Comparison of approaches to interpolating climate observations in steep terrain with low-density gauging networks

Ossa-Moreno, Juan; Keir, Greg; McIntyre, Neil; Cameletti, Michela; Rivera, Diego

doi:https://doi.org/10.5194/hess-23-4763-2019

Articles | Volume 23, issue 11

https://doi.org/10.5194/hess-23-4763-2019

© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/hess-23-4763-2019

© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 23, issue 11

Research article

|

22 Nov 2019

Research article |

| 22 Nov 2019

Comparison of approaches to interpolating climate observations in steep terrain with low-density gauging networks

Juan Ossa-Moreno, Greg Keir, Neil McIntyre, Michela Cameletti, and Diego Rivera

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Final revised paper (published on 22 Nov 2019)
Preprint (discussion started on 29 Oct 2018)

Interactive discussion

Status: closed

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

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RC1: 'Comments to the authors', Anonymous Referee #1, 03 Dec 2018
- AC1: 'Author response to reviewers', Juan Ossa-Moreno, 11 Feb 2019
RC2: 'Comments on “Comparison of approaches to interpolating climate observations in steep terrains with low-density gauging networks” by Juan Ossa-Moreno et al.', Anonymous Referee #2, 16 Jan 2019
- AC1: 'Author response to reviewers', Juan Ossa-Moreno, 11 Feb 2019

Peer-review completion

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

ED: Reconsider after major revisions (further review by editor and referees) (22 Feb 2019) by Daniel Viviroli

AR by Lorena Grabowski on behalf of the Authors (29 Apr 2019) Author's response

ED: Referee Nomination & Report Request started (06 May 2019) by Daniel Viviroli

RR by Anonymous Referee #3 (28 Jun 2019)

RR by Anonymous Referee #4 (30 Jun 2019)

Suggestions for revision or reasons for rejection

Dear Editor,
This paper presents an interesting assessment of different methodologies for interpolating temperature and precipitation measurements in an important river basin in the Central Andes of Chile. For South American standards, the amount of climate data available from this basin is relatively high, allowing the exercise of gradually excluding stations to test the overall skill of the different interpolation methodologies.

In my opinion the paper is well written and provides solid evidence showing that the methodologies that include a combination of free gridded climate datasets and residuals of station data are the most appropriate for this type of basins in the Andes. Considering that this kind of analyses could eventually be replicated by scientists and water managers in different sectors of the Andes, I like the fact that the recommended methodologies are relatively simple to understand and implement. The case of the GLMM method, which may have some merits, is too complex mathematically and computationally, making this option practically impossible to replicate for the vast majority of potential readers in the Andes.

I found a few typos and details that in my opinion need to be fixed before the paper can be accepted for publication. These specific details and comments are listed below.

Page - Line – Comment
2 – 29 – “KED” has not been defined above. Please explain what this acronym means.

3 – 1 – The part in parenthesis … “(except in dry areas… )” is repeated below in the same sentence. Remove?

3 – 6 to 11 – The Acronyms PGFv3, CHIRPS, TMPA, etc have not been defined. Please provide appropriate references.

3 – 19 - … precipitation MAGNITUDES and temperature…

4 – 5 – The “source” is located… Change to: The “western limit of the basin” is located in the Andean mountains “along” de border…

4 – 13 – The Anticyclone “retreats”… Explain better?

4 – 19 – There is considerable inter-annual variability… of what variables?

5 – Figure 1: Put Argentina and Chile in the second inset. Put also “Aconcagua river BASIN” and “Maipo river BASIN” in the map (the rivers are not shown, just their upper basins).

6 – 11 – “long-term gauges” which ones? List them in the table

7 – Figure 2. These diagrams show different things for different variables (annual cycle for temperatures, and monthly time series for precipitation). This is confusing and leaves the reader with the feeling that something is missing. We cannot see the temperature changes over the 2008-2013 study period, nor the annual cycle of precipitation in the basin. I believe it would be better to show the annual cycle for both T and PP, and below the monthly time series for both T and PP. Please show this to characterize the general climate variations and seasonality in the basin.

8 – Figure 3. This figure is not clear either. On the left we see the actual monthly CHIRPS data for May, and on the left the May climatology from Worldclim. At a first look these maps are quite different, not only due to the pixel resolution but also because their values for different sectors of the basin not consistent at all. This makes the reader believe the CHIRPS data is of little use, when in reality this is not the case. Why not showing the May climatology (long-term average) for CHIRPS too? Or at least make the difference between the maps more clear in the legend and in the maps themselves.

11 – 17 - …for this VARIABLE…

14 – 3 to 6 – Please discuss and assess the various diagrams in Figure 4 in order (first fig 4A, then Fig 4B, and so on). You should also mention that the diagrams with the low correlations between ENSO and T are not shown in Fig 4.

14 – 9 to 11 – Here you should also mention that the diagrams with the correlations between ENSO and PP are not shown in Fig 4.

14 – 16 – (23 TEMPERATURE gauges)

19 – Table 6. In the note below the table, Shouldn´t it should say … provided by WC to approximate daily PRECIPITATION at all sites…

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ED: Publish subject to minor revisions (review by editor) (05 Jul 2019) by Daniel Viviroli

AR by Anna Mirena Feist-Polner on behalf of the Authors (13 Aug 2019) Author's response

ED: Publish subject to technical corrections (26 Aug 2019) by Daniel Viviroli

AR by Juan Ossa-Moreno on behalf of the Authors (30 Aug 2019) Author's response Manuscript

Short summary

Water management in mountains is challenging when there are no climate data of good quality. This can be addressed by using statistical methods or by including alternative sources of information. This project tests a relatively complex statistical method and compares it with simpler alternatives while including satellite data. It was found that the simple alternative may behave as well as the complex one, and how good the alternative sources of information are could also be established.