Comparing bias correction methods in downscaling meteorological variables for a hydrologic impact study in an arid area in China

Fang, G. H.; Yang, J.; Chen, Y. N.; Zammit, C.

doi:https://doi.org/10.5194/hess-19-2547-2015

Articles | Volume 19, issue 6

https://doi.org/10.5194/hess-19-2547-2015

© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

https://doi.org/10.5194/hess-19-2547-2015

© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 19, issue 6

Research article

|

02 Jun 2015

Research article |

| 02 Jun 2015

Comparing bias correction methods in downscaling meteorological variables for a hydrologic impact study in an arid area in China

G. H. Fang, J. Yang, Y. N. Chen, and C. Zammit

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Final revised paper (published on 02 Jun 2015)
Preprint (discussion started on 13 Nov 2014)

Interactive discussion

Status: closed

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

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- Supplement

RC C5552: 'comments on hess-2014-436', Anonymous Referee #1, 08 Dec 2014
- AC C5852: 'Response to Referee # 1', F. Gonghuan, 30 Dec 2014
RC C6111: 'Review of your manuscript', Markus Muerth, 17 Jan 2015
- AC C6745: 'Response to Referee #2', F. Gonghuan, 13 Apr 2015

Peer-review completion

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

ED: Reconsider after major revisions (30 Jan 2015) by Bettina Schaefli

AR by Svenja Lange on behalf of the Authors (14 Apr 2015) Author's response

ED: Referee Nomination & Report Request started (23 Apr 2015) by Bettina Schaefli

RR by Markus Muerth (05 May 2015)

Suggestions for revision or reasons for rejection

The revised manuscript of Fang et al is now much clearer than in the original version and hence can be accepted for publication in HESS. Yet, some clarifications of my original review are provided below, which also may be helpful for the final version of the manuscript.
I wish you all the best, Markus

Comments on the reply of Fang et al on the review:
Now the methodology of your approach is much more understandable, as there was some confusion on my side regarding the climate data used for modelling. In the first version, I assumed that the correction parameters found for one station would be applied on the whole RCM grid to run SWAT with gridded input data. This is of course now clarified, especially due to Fig. 2!
This misunderstanding in the original manuscript also led to my comment that the catchment runoff of about 310 mm per year, which equals the Banyaluk station precipitation corrected with LS of 267 mm. Of course, corrected catchment precipitation is 664 mm as you stated now. But it was a bit confusing before…
Still, as I am no expert in SWAT modelling: Would it change your SWAT results, if you generally cut off drizzle values of e.g. 0.1 mm or less, because these are obviously RCM artefacts? Would this increase your actual ET?
In the new conclusion text you state: “Note that LOCI and DM methods should be used with caution when analysing drought or extreme stream flows.” Please add here why!
Furthermore, you state “We applied PT instead of QM after drizzle correction because QM can correct the drizzle days and performs well in the literature (e.g., Teutschbein and Seibert, 2012). This makes the comparison between PT and QM meaningful.” What do you want to say with that, something like: They are similar, but QM has a drizzle correction? Maybe think of reformulating this, I don’t get it really.
Finally, of course I redraw my comment on the title. In your case you actually downscale RCM data to two stations, while I was thinking you are correcting the RCM grid over the catchment.

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ED: Publish subject to minor revisions (Editor review) (06 May 2015) by Bettina Schaefli

AR by Gonghuan Fang on behalf of the Authors (08 May 2015) Author's response Manuscript

ED: Publish as is (08 May 2015) by Bettina Schaefli

Short summary

This study compares the effects of five precipitation and three temperature correction methods on precipitation, temperature, and streamflow through loosely coupling RCM (RegCM) and a distributed hydrological model (SWAT) in terms of frequency-based indices and time-series-based indices. The methodology and results can be used for other regions and other RCM and hydrologic models, and for impact studies of climate change on water resources at a regional scale.