Articles | Volume 20, issue 23
https://doi.org/10.5194/bg-20-4795-2023
© Author(s) 2023. This work is distributed under the Creative Commons Attribution 4.0 License.
Temporal variability of observed and simulated gross primary productivity, modulated by vegetation state and hydrometeorological drivers
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- Final revised paper (published on 06 Dec 2023)
- Supplement to the final revised paper
- Preprint (discussion started on 12 Jun 2023)
Interactive discussion
Status: closed
Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor
| : Report abuse
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RC1: 'Comment on egusphere-2023-994', Anonymous Referee #1, 18 Jul 2023
- AC1: 'Reply on RC1', Jan De Pue, 03 Oct 2023
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RC2: 'Comment on egusphere-2023-994', Anonymous Referee #2, 02 Sep 2023
- AC2: 'Reply on RC2', Jan De Pue, 03 Oct 2023
Peer review completion
AR – Author's response | RR – Referee report | ED – Editor decision | EF – Editorial file upload
ED: Publish subject to minor revisions (review by editor) (07 Oct 2023) by Paul Stoy
AR by Jan De Pue on behalf of the Authors (09 Oct 2023)
Author's response
Author's tracked changes
Manuscript
ED: Publish as is (16 Oct 2023) by Paul Stoy
AR by Jan De Pue on behalf of the Authors (20 Oct 2023)
The manuscript aims to verify the variance exhibited by common GPP models and products in repsonse to the driving factors of GPP. The manuscript is well written in general. In my personal opinion, the abstract and discussion sections are excellent as they nicely summarize the key results in a crisp manner. I have few queries/suggestions for the consideration of the authors.
1) What is the reason for not including the accuracy assessment of the models? Is it because of the lack of independent data for validation? Idea about absolute accuracy would further help the readers to know about the GPP models.
2) Why the downscaled SIF product was considered in this study? It will have the effect of LUE model used in the downscaling and any artefacts of downscaling will be present. Spatial resolution is an issue especially when the SIF based GPP has to be compared with flux tower observations. But the downscaling procedure might have affected the spatiotemporal characeteristics of the SIF product affecting the variance metrics studied in this work. If the effect of SIF to be studied, it is better to use the original datasets without much modification.
3) What is the temporal frequency of the reflectance data (SPV) that were used to derive vegetation indices? I understand it to be 16-days. If yes, then it will strongly impact the variances at daily and monthly scales. Why can't daily/weekly data be used?
4) As you rightly pointed out, empirical models (Vegetation Indices models) must be developed for each landcover/climate zone combination. There can be significant improvement in the results had it been the case rather than having one generalized regression model. Landcover specific models can be developed to see if there are any changes in the performance of the VI-based models.
5) In Section 2.2, it will be beneficial to add a brief summary of the tests conducted to test the validity of ERA-5 data in place of in-situ observations. If not in the main article, include it as appendix.
6) In page 6, Equation (4), was PAR considered as a constant fraction of incoming solar radiation? A brief mention about PAR estimation will be beneficial.
7) Equation 6 can be explained with an example.
8) When referring to the appendix in the main text, always refer the figure or table to be looked upon. It became little difficult to identify which figure/table to refer. Further, it was difficult to read the figures given in the appendix. Please improve the readability of all the figures.