Implementation of a new crop phenology and irrigation scheme in the ISBA land surface model using SURFEX_v8.1

Druel, Arsène; Munier, Simon; Mucia, Anthony; Albergel, Clément; Calvet, Jean-Christophe

doi:https://doi.org/10.5194/gmd-15-8453-2022

Articles | Volume 15, issue 22

https://doi.org/10.5194/gmd-15-8453-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Special issue:

The externalised surface model SURFEX

https://doi.org/10.5194/gmd-15-8453-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 15, issue 22

Model description paper

|

21 Nov 2022

Model description paper |

| 21 Nov 2022

Implementation of a new crop phenology and irrigation scheme in the ISBA land surface model using SURFEX_v8.1

Arsène Druel, Simon Munier, Anthony Mucia, Clément Albergel, and Jean-Christophe Calvet

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Final revised paper (published on 21 Nov 2022)
Supplement to the final revised paper
Preprint (discussion started on 11 Oct 2021)
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Interactive discussion

Status: closed

CEC1:
'Comment on gmd-2021-332', Juan Antonio Añel, 19 Nov 2021

Dear authors,
After checking your manuscript, it has come to our attention that it does not comply with our Code and Data Policy.

https://www.geoscientific-model-development.net/policies/code_and_data_policy.html

You have archived your code in GitHub and a repository that does not comply with our requirements (umr-cnrm.fr). However, neither of them are suitable repositories. GitHub itself instructs authors to use other alternatives for long-term archival and publishing, such as Zenodo. Therefore, please, publish your code in one of the appropriate repositories.

In this way, before the Discussions stage is closed, you must reply to this comment with the link to the repository for the code and the corresponding DOI. Also, you must include in a potential reviewed version of your manuscript the modified 'Code and Data Availability' section and the DOI of the code.

Two additional issues that you must fix are:

- You say in the manuscript that ISBA version 9.0 is scheduled for release by 2020. This seems to be a mistake.

- The title of your manuscript does not include the version of ISBA that you use. According to our guidelines, you must specify it.
Please, reply as soon as possible to this comment with the link for it so that it is available for the peer-review process, as it should be.
Best regards,
Juan A. Añel
Geosc. Mod. Dev. Executive Editor

Citation: https://doi.org/10.5194/gmd-2021-332-CEC1
- AC1: 'Reply on CEC1', Jean-Christophe Calvet, 22 Nov 2021
  
  Dear Juan Antonio Añel,
  Many thanks for your suggestions.
  This will be fixed in the revised version of the manuscript:
  - Title: "Implementation and validation of a new irrigation scheme in the ISBA land surface model using SURFEX_v8.1"
  - Code is now available on zenodo: https://doi.org/10.5281/zenodo.5718063
  - Code was integrated into SURFEX_v9.0, and SURFEX_v9.0 should be published early 2022.
  Best regards,
  Jean-Christophe Calvet, on behalf of all co-authors.
  
  Citation: https://doi.org/10.5194/gmd-2021-332-AC1
RC1:
'Comment on gmd-2021-332', Anonymous Referee #1, 04 Jan 2022

Please find attached comments

Citation: https://doi.org/10.5194/gmd-2021-332-RC1
- AC2: 'Reply on RC1', Jean-Christophe Calvet, 17 Feb 2022
  
  Response to Reviewer 1 is enclosed.
  
  Citation: https://doi.org/10.5194/gmd-2021-332-AC2
EC1:
'Comment on gmd-2021-332', Jinkyu Hong, 05 Jan 2022

This study describes a new irrigation parameterization for ISBA land surface model (LSM). Despite interesting subjective of this study, it is hard to read and to understand what the unique things in this study are. Please revise the manuscript carefully for better readability and provide clearly 1) how to parameterize irrigation processes in the model codes, 2) remove redundant sentences many places, 3) clarify the information on the data so that other people reproduce what this study did, and 4) rewrite fractured sentences. It is also important to organize sentences, paragraph, and figures to converge into clear goals and to support your conclusions.
Here I put some comments on the manuscript, but I believe that overall structure of this manuscript should be reorganized and rewritten carefully.
- There is not enough information to reproduce the modeling results in this manuscript. Please provide more details on the irrigation parameterization especially for different kinds of irrigation methods. How do you deal with different irrigation types in the model?

- Please provide specific information on water conservation and differences between different irrigation methods in the model.
- Writing is important and this manuscript is not well organized. This manuscript is not easy to read and understand because of inconsistent and poorly organized sentences and redundant statements. More efforts are necessary to revise the manuscript carefully.

- Fluxcom data by Jung et al. do not consider the irrigation process and I am not sure if these data are useful for the model evaluation.
- Irrigation process may change local climate around the irrigated lands and I am not sure if reanalysis data of coarse resolution is appropriate to simulate the irrigation effects or not. In-situ observation data instead of the reanalysis data and its related offline simulation is more useful in this respect.
- Line 115: Rewrite this sentence for better readability. I cannot understand the method used in this study. It seems to me that this is not downscaling. This is just to assign the same value to 1 km grid.

- Line 140: Is there any inconsistency between these data?

- Line 144: How to distribute the annual values and how to justify the process?

- Line147: What kinds of inconsistencies? Any impacts on the results and conclusion?
- Line 149: What LAI values are used for the initial conditions?
- Line 154: Clarify how to process the data
- Line 155: I am not sure if there is any GPP observation by the eddy-covariance method in this study region.
- Line 158: Clarify how to interpolate the data into the model grid.
- Line 169: what is for periods after “evaporation, sensible heat” . Absolutely, sensible heat is different from sensible heat fluxes.

- Line 176: One example of redundant sentences in this manuscript
- Line 178: What kinds of technical development?
- Line 182: I cannot figure out what were done to deal with heterogeneity.
- Line 188: One example of redundant sentences in this manuscript
- Line 197: I am not quite sure if this is realistic or not.
- Line 202: I am not quite sure if other types of irrigation also need evaluation.
- Line 210: This approach is valid only for sprinkler irrigation.
- Line 216: trough ?
- Line 218: references.
- Line 234: I am not quite sure if the sum of this irrigated water is consistent with the USGS annual irrigation data.
- Line 238: how to consider water conservation?
- Line 249: What are nature types and how to decide it?
- Line 254: One example of redundancy
- Line 304: One example of redundancy
- Line 334: I cannot understand the meaning of this sentence. What kinds of code changes?
- Results: I am not sure if the new parameterization give improvement of important variables. For example, I don’t believe that the new parameterization gives peak timing of LAI. Please check figures and numbers if they support the conclusion and results.

Citation: https://doi.org/10.5194/gmd-2021-332-EC1
- AC4: 'Reply on EC1', Jean-Christophe Calvet, 17 Feb 2022
  
  Enclosed the response to the Editor.
  
  Citation: https://doi.org/10.5194/gmd-2021-332-AC4
RC2:
'Comment on gmd-2021-332', Anonymous Referee #2, 10 Jan 2022

The comment was uploaded in the form of a supplement: https://gmd.copernicus.org/preprints/gmd-2021-332/gmd-2021-332-RC2-supplement.pdf

Citation: https://doi.org/10.5194/gmd-2021-332-RC2
- AC3: 'Reply on RC2', Jean-Christophe Calvet, 17 Feb 2022
  
  Enclosed the response to Reviewer 2.
  
  Citation: https://doi.org/10.5194/gmd-2021-332-AC3

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload

AR by Jean-Christophe Calvet on behalf of the Authors (14 Mar 2022) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (16 Mar 2022) by Jinkyu Hong

RR by Anonymous Referee #2 (29 Mar 2022)

RR by Anonymous Referee #3 (09 Apr 2022)

Suggestions for revision or reasons for rejection

General comments:

This study presents a new irrigation scheme within ISBA model used in combination with crop phenology and evaluates how the representation of crop phenology and irrigation improves states and fluxes in an irrigated Nebraska domain. Representing human activities in earth system models is necessary as this may help capture the human impact on hydrological and carbon cycles, however, I think the result/conclusion sections need to be improved and adjusted by discussing the relative role of crop phenology and irrigation in improving the simulation of the evaluated states and fluxes. It seems that major contribution is provided by crop phenology instead of the inclusion of irrigation. These need to be clearly discussed with a highlight of the unique contribution of this study.

Detailed comments:

1. Abstract L 24: “The ISBA simulations with and without irrigation scheme…” Is this conclusive as the comparisons are also made for simulations with/without crop phenology?
2. L82: typo – “LSI-Noah”
3. L105 – 110: may need to modify the description of the scope of this study as you also changed the title by including the role of crop phenology.
4. Figure 1: Please add the latitude and longitude coordinates for the maps.
5. L136: Why only mentioned the LAI comparison here?
6. L161: TYPO – “focusses”
7. What is the spatial resolution of the model simulations? Please clarify in section 2.
8. L176: Is there any sensitivity analysis done regarding the magnitude of this parameter (30mm)? Similarly, any sensitivity test conducted for the choice of SWI threshold? How sensitive your result would be to these irrigation parameters? And how does this sensitivity transfer to vegetation conditions?
9. L197: “Irrigation can optionally be triggered without considering…” It is a bit hard to understand this as you mentioned earlier that the irrigation time window is based on the emergence and harvest dates.
10. Why don’t the emergence and harvest dates differ among different vegetation types? Is this assumption reasonable? If mentioned as a limitation, could you populate the discussion on how this assumption may affect the states and fluxes?
11. Line 300: Is the model simulation conducted at a resolution of 300m * 300m? It is not quite clear why the irrigation map needs to be transferred to 300m*300m if the model resolution is not at this scale.
12. L343-345: “It is available every 10 days…does not cover the whole simulation time period (1979 to 2018)” These sentences contain contradictory information, please clarify.
13. I don’t think GLEAM ET is a good candidate reference datasets as the model does not explicitly include the irrigation signal. Please consider select other ET reference datasets that can detect the irrigation signal or include several widely used ET datasets for comparison besides GLEAM.
14. Figure 4: I would suggest converting the irrigation number to total irrigation amount in this figure. Your y-axis is irrigation number while some of the data shown are compared in terms of amount.
15. Section 3.1: Other than the averaged irrigation amount, could you comment on the model performance in simulating irrigation amount in wet/dry years? Even though it is not possible to evaluate the interannual variation as the data is only available every 5 years, it is possible to at least see how the model perform in wet/dry conditions.
16. Figure 6&7: I would suggest including correlation and RMSD difference between ISBA_pheno and ISBA_ref and then discuss the major contributor for this improvement. It seems that irrigation only plays an additive role in improving the vegetation seasonal cycle as compared to the role of including crop phenology.
17. Figure 9: Degradation in R for ET for most of the months considered may be evidence that GLEAM is not suitable to be referred for irrigation impacted ET comparison. Please investigate other reference datasets. Besides, ET products are subject to uncertainties in magnitude and RMSD needs to be discussed under this context.
18. For all the correlation related analysis, how the values are selected to mask out the grid cells with white color? It would be better to add a significance test and use the significance result to mask out the grid cells.
19. L521: Is it possible to derive emergence and harvest dates based on the LAI observation so that it can represent the interannual variation? Please comment on this.
20. Please make sure all the supplemental materials are mentioned in the main manuscript to support relevant statements.

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ED: Reconsider after major revisions (11 Apr 2022) by Jinkyu Hong

AR by Jean-Christophe Calvet on behalf of the Authors (07 Jun 2022) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (09 Jun 2022) by Jinkyu Hong

RR by Anonymous Referee #4 (23 Jun 2022)

RR by Anonymous Referee #3 (02 Jul 2022)

Suggestions for revision or reasons for rejection

The authors have done a commendable job in addressing most of the questions in the revised submission and the overall quality of the presentation has improved substantially. I have a few additional comments here and hope this helps.

1. Comment to response R2.5: a bit more detail is needed to argue the robustness of using a model resolution of 0.25 deg when other input datasets are available at a much finer resolution such as the irrigation map. I understand that the resolution of the ERA5 met-forcing might be a factor at play, but I think it would help by adding discussions on how the choice of this spatial resolution may affect resolving the smaller irrigation variabilities and whether this may affect the results under different conditions? It might be good to clarify this.
2. Comment to response R2.13: Regarding the discussion on the wet year overestimation of irrigation, could you explore and elaborate on what might be the main factor contributing to this overestimation? Could that be resulted by precipitation uncertainty or the relatively coarse spatial scale? Or could that be irrigation sensitivity to selected irrigation parameters? I would appreciate a short discussion of this aspect so that readers can have a better understanding of the ISBA irrigation performance.
3. Comment to response R2.15. Thanks for conducting additional analysis on ET. Figure 5&6 indicates that LAI seasonality differs largely between ISBA_ref and ISBA_pheno_irr, however, when looking at the seasonal cycle for ET, the peak timing seems to be not much affected. I wonder how the ISBA model estimate ET? Is LAI or vegetation related parameter at play? I think it would be good to elaborate the ET discussion in terms of its response to LAI from both the modeling and observational aspect.
4. Comment to response R2.16: It sounds that the R and RMSD values are arbitrarily selected to be presented in white and marked with white plus. I would suggest testing the significance of the R and RMSD differences and use white to mask out the insignificant ones. And it is not necessary to add white plus in the figure. You may use fisher’s z test for R difference and paired sample t test for RMSD difference.

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ED: Reconsider after major revisions (03 Jul 2022) by Jinkyu Hong

AR by Jean-Christophe Calvet on behalf of the Authors (11 Aug 2022) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (17 Aug 2022) by Jinkyu Hong

RR by Anonymous Referee #4 (17 Aug 2022)

RR by Fabian Stenzel (31 Aug 2022)

Suggestions for revision or reasons for rejection

The present study ("Implementation of a new crop phenology and irrigation scheme in the ISBA land surface model using SURFEX_v8.1") introduces a new phenology and irrigation scheme for the ISBA LSM and evaluates its performance against observational data from a densely irrigated region of Nebraska (USA).

It becomes clear that the main improvement for better performance regarding LAI and GPP stems from the improved phenology with prescribed emergence and harvest dates. The irrigation scheme does not add much with respect to the aforementioned variables, but provides reasonable water use values with respect to observations.

I have been only involved now where the manuscript is already in round 3 of revisions. Therefore I interpret my main responsibility is to judge whether the present version of the manuscript is fit for publication and the authors have taken care of all points raised by the two previous reviewers. This is the case. The only thing missing in terms of reproducibility of the study is a step-by-step explanation of how to use the data in the ZENODO archive(s) together with the SURFAX code to redo the simulations and any potential postprocessing scripts. I suggest to add a README file to the ZENODO archive containing this information. The manuscript itself is written very clearly and I enjoyed reading it.

However, since I read the article for the first time and with fresh eyes, I noticed some minor things that could still be improved and I ask the authors to include them in the final version of their manuscript. I apologize for this, because I know that for authors, introducing new reviewers late in the review process is annoying, but at the same time I hope that they seize the opportunity to further increase the quality of their (already good) manuscript by including my remarks. I am looking forward to read about future work on the global evaluation of the phenology and irrigation schemes in ISBA.

Best regards,
Fabian Stenzel

-----------------------------------

More general comment:

What purpose is the model generally used for? One main purpose of crop models is to provide harvest amounts. Therefore I was wondering, why (additional to the irrigation water amount, LAI and GPP) you did not look at how harvests compared between the 3 model versions? I would suspect that here you might see a stronger difference between ISBA_pheno and ISBA_pheno_irr, suggesting that including irrigation is worth while.

In-line comments:
- lines 58-72: It it great that you implement irrigation into ISBA. However your are making it sound like just very few other models have implemented irrigation. I don't think that is a fair point, as basically all crop models have implemented it. Many of them can (depending on your definition) be regarded as a LSM.
- lines 129-134: Your argument for having decreasing SWI thresholds for subsequent irrigation events is weak. The root fraction is already part of SWIroot_zone.
Additionally: If the irrigation water amount is still 30mm for subsequent irrigation events triggered at lower SWI, you would need more water to fill the soil again, right?
- lines 149-152: Please add that this first part describes sprinkler irrigation settings. Drip/flood description only starts in line 171.
- line 193: 20 non-irrigated + 20 irrigated * 3 types is 80
- line 240: The random picking of harvest/emergence date seems to complicate things and you did not mention it previously. However it is relevant, because irrigation would not be allowed, if emergence is happening later or harvest earlier than the default date.
- lines 313-314: I suggest to change “simulated number of yearly irrigation events” to “simulated irrigation water amount”. Events cannot be compared to amounts and you explain how the conversion is done in the next sentence.
- lines 360-364: Explain how you calculate the precipitation bias in Fig S4.6, either here in the text or in the figure caption. I assume it is ERA5 data minus weather station data for that pixel, right? You are arguing that the bias in ERA5 is the reason for too high simulated irrigation. But the absolute bias is also high in 2000 and 2005. If you want to take the relative change (2010 was a wetter year than 2005 and 2000) into account, I would think that this could be best seen in precipitation bias in [%] with respect to absolute precipitation. This should show higher deviations for 2010 than 2000 and 2005 and serve your point.
- lines 369-371: Where do I see this Boedhram data?
- line 371: You could mention here that you will do a comparison across all “nature types” in the next section.
- line 380: I assume you meant to say “without phenology (and without irrigation)” instead of “without irrigation” – the main difference here is the phenology, not the irrigation
- lines 409-410: I would argue that the wider distributions are due to the effect of not having imposed emergence and harvest dates for natural vegetation.
- line 489: I would say, that rather than “empirical” it is “random”.
- line 539: It is really hard to see the difference, please add a third panel with the difference to this figure.

- Table 1:
List all possible values as well, not only the default.
Irrigated “nature“ type, I understand that the surface type is called this way in ISBA, but to call a crop “nature” sounds wrong to me. How about (at least in the paper), you rename it to “vegetation”, or what it is: “land surface type”.
An irrigation water “turn” could be called “event”.
Explain the abbrv. “SWI” in the caption.
I believe a “time lapse” is sth. else, how about “time interval”/”lapse of time”/”time span”?

- Figure 3: I suggest to use 5-year steps for the x-axis starting 1985
- Figure 7: This figure is never mentioned in the text

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ED: Reconsider after major revisions (07 Sep 2022) by Jinkyu Hong

AR by Jean-Christophe Calvet on behalf of the Authors (19 Oct 2022) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (30 Oct 2022) by Jinkyu Hong

RR by Fabian Stenzel (01 Nov 2022)

ED: Publish as is (02 Nov 2022) by Jinkyu Hong

AR by Jean-Christophe Calvet on behalf of the Authors (03 Nov 2022)

Short summary

Crop phenology and irrigation is implemented into a land surface model able to work at a global scale. A case study is presented over Nebraska (USA). Simulations with and without the new scheme are compared to different satellite-based observations. The model is able to produce a realistic yearly irrigation water amount. The irrigation scheme improves the simulated leaf area index, gross primary productivity, evapotransipiration, and land surface temperature.