Uncertainties in estimating regional methane emissions from rice paddies due to data scarcity in the modeling approach

Zhang, W.; Zhang, Q.; Huang, Y.; Li, T. T.; Bian, J. Y.; Han, P. F.

doi:https://doi.org/10.5194/gmd-7-1211-2014

Articles | Volume 7, issue 3

https://doi.org/10.5194/gmd-7-1211-2014

© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

https://doi.org/10.5194/gmd-7-1211-2014

© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 7, issue 3

Development and technical paper

|

27 Jun 2014

Development and technical paper |

| 27 Jun 2014

Uncertainties in estimating regional methane emissions from rice paddies due to data scarcity in the modeling approach

W. Zhang, Q. Zhang, Y. Huang, T. T. Li, J. Y. Bian, and P. F. Han

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Final revised paper (published on 27 Jun 2014)
Preprint (discussion started on 10 Jan 2014)

Interactive discussion

Status: closed

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

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- Supplement

RC C3: 'Referee Comment on gmd-2013-148', Anonymous Referee #1, 17 Jan 2014
- AC C142: 'Response to refree1', Wen Zhang, 18 Mar 2014
RC C43: 'Interactive comment on “Estimation of uncertainties due to data scarcity in model upscaling: a case study of methane emissions from rice paddies in China” by W. Zhang et al.', Anonymous Referee #2, 01 Mar 2014
- AC C152: 'Response to refree2', Wen Zhang, 18 Mar 2014

Peer-review completion

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

AR by Wen Zhang on behalf of the Authors (18 Mar 2014) Author's response Manuscript

ED: Referee Nomination & Report Request started (03 Apr 2014) by Michio Kawamiya

RR by Anonymous Referee #1 (12 Apr 2014)

Suggestions for revision or reasons for rejection

Concerns:
1. While both reviewers pointed out the same concern that reasons other than the data sharing (e.g., the similar geographical condition in temperature etc.) would lead to higher correlation across two grid cells and this could seriously affect the results and conclusions, as far as I read, it is rather hard to say that the authors did a good job to satisfy reviewers’ doubts. I am still in doubt about whether the aggregation method proposed here truly deals with the data scarcity as the authors are discussing.
2. Ironically, this study suffers from the data scarcity while trying to quantify the uncertainties in national-level methane emission associated with the data scarcity. This in part relates the concern mentioned above. A concern came to me after reading the revised manuscript is that the experimental design needs improvement to be more straightforward. For instance, while the authors conducted the Monte Carlo simulation at a county level, this is less informative when aggregating because the information on the spatial heterogeneities of some input variables within a county is lacking. And this lead to an unnecessary doubt regarding the dependency of the confidence interval of national-level estimate to the scale of simulation. As the central purpose of this study is to propose an aggregation method, it would be more straightforward to start analysis from a spatial resolution where the information on the spatial heterogeneities of all input variables within a calculation unit is available.
3. “Improving the data availability of the model input variables is expected to reduce the uncertainties significantly… (L29-30)”. The statement is one of main conclusion of this study (as far as I read), but conflict with the results. This comment is applicable to L264-266. I agree this in general terms, but what you present in Table 4 is that even a full set of spatially fine inputs are unavailable, the simulations at a finer level would help to reduce the uncertainties in national-level estimate. And there is no reasoning why the simulation at a finer scale showed the smaller uncertainty despite of the lacking of spatially-dense information on some input variables and relatively comparable model sensitivity across all input variables at a site level (see Table B1).
4. In my view, many statements in the manuscript are worded too strongly or without caution. The followings are the examples (but not all):
 “Data scarcity is a substantial cause of the uncertainties in estimating the methane emissions on regional scales (L14-15)”. It may be true, but you cannot say with absolute certainty as the relative importance of data scarcity against other sources of uncertainty is practically impossible to quantify due to the lack of a full set of spatially-dense inputs.
 “from site scale up to regional/national scale (L17)”. I don’t think you present a result at a site scale. The finest scale presented is a county scale. Table B1 shows to some degree the sensitivity of the model at a site scale, but this has little relevance to the data scarcity.
 The statement such as L25-28 is likely unfaithful if no caution related to the limitations of the method proposed here is accompanied by.

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RR by Anonymous Referee #3 (09 May 2014)

ED: Publish subject to minor revisions (Editor review) (16 May 2014) by Michio Kawamiya

AR by Wen Zhang on behalf of the Authors (19 May 2014) Author's response

ED: Publish as is (21 May 2014) by Michio Kawamiya

AR by Wen Zhang on behalf of the Authors (22 May 2014)