the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 21 Jul 2021
Modeling perennial bioenergy crops in the E3SM land model
Abstract. Perennial bioenergy crops are increasingly important for the production of ethanol and other renewable fuels, and as part of an agricultural system that alters the climate through its impact on biogeophysical and biogeochemical properties of the terrestrial ecosystem. The Energy Exascale Earth System Model (E3SM) Land Model (ELM) does not represent perennial bioenergy crops, however. In this study, we expand ELM’s crop model to include perennial bioenergy crops whose production increases in modeled socioeconomic pathways owing to their potential for mitigating climate change. We focus on high-productivity miscanthus and switchgrass, estimating various parameters associated with their different growth stages and performing a global sensitivity analysis to identify and optimize these parameters. The sensitivity analysis identifies eight parameters associated with phenology, carbon/nitrogen allocation, and photosynthetic capacity as the most sensitive parameters for carbon and energy fluxes. We calibrated the model against observations collected at the University of Illinois Energy Farm for carbon and energy fluxes, and found that the model closely captures the observed seasonality and the magnitude of carbon fluxes. The model accurately represents the seasonality of energy fluxes, but their magnitude is not well captured. This work provides a foundation for future analyses of the interactions between perennial bioenergy crops and carbon, water, and energy dynamics in the larger earth system and can also be used for studying the impact of future biofuel expansion on climate and terrestrial systems.
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Interactive discussion
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CEC1: 'Comment on gmd-2021-244', Astrid Kerkweg, 21 Jul 2021
Dear authors,
in my role as Executive editor of GMD, I would like to bring to your attention our Editorial version 1.2: https://www.geosci-model-dev.net/12/2215/2019/
This highlights some requirements of papers published in GMD, which is also available on the GMD website in the ‘Manuscript Types’ section: http://www.geoscientific-model-development.net/submission/manuscript_types.html
In particular, please note that for your paper, the following requirements have not been met in the Discussions paper:
- The main paper must give the model name and version number (or other unique identifier) in the title.
- "Code must be published on a persistent public archive with a unique identifier for the exact model version described in the paper or uploaded to the supplement, unless this is impossible for reasons beyond the control of authors. All papers must include a section, at the end of the paper, entitled "Code availability". Here, either instructions for obtaining the code, or the reasons why the code is not available should be clearly stated. It is preferred for the code to be uploaded as a supplement or to be made available at a data repository with an associated DOI (digital object identifier) for the exact model version described in the paper. Alternatively, for established models, there may be an existing means of accessing the code through a particular system. In this case, there must exist a means of permanently accessing the precise model version described in the paper. In some cases, authors may prefer to put models on their own website, or to act as a point of contact for obtaining the code. Given the impermanence of websites and email addresses, this is not encouraged, and authors should consider improving the availability with a more permanent arrangement. Making code available through personal websites or via email contact to the authors is not sufficient. After the paper is accepted the model archive should be updated to include a link to the GMD paper."
As the land model is the interesting part of your investigation, add the abbreviation and version number of the E3SM land model (ELMv1) to the title of the manuscript. E.g. "Modeling perennial bioenergy crops in the E3SM land model (ELMv1)"
As GitHub is not a persistent archive, please provide a persistent release for the exact source code version used for the publication in this paper. As explained in https://www.geoscientific-model-development.net/about/manuscript_types.html the preferred reference to this release is through the use of a DOI which then can be cited in the paper. For projects in GitHub a DOI for a released code version can easily be created using Zenodo, see https://guides.github.com/activities/citable-code/ for details.
Yours, Astrid Kerkweg
Citation: https://doi.org/10.5194/gmd-2021-244-CEC1 -
RC1: 'Comment on gmd-2021-244', Anonymous Referee #1, 29 Aug 2021
General comments:
This study implements two perennial bioenergy crops miscanthus and switchgrass in the E3SM land model ELM. The revised phenology, carbon/nitrogen allocation, and biomass harvest parametrizations are based on the generic grass and annual crop functional types in CLM4.5 with the main distinction that the perennials are planted once and have repeated leaf onset, senescence and harvest cycle each year with a longer growing season than annuals. The manuscript focuses on sensitivity analysis and parameter optimization using an approach combining surrogate construction with polynomial regression and Bayesian calibration with MCMC. While the sensitivity analysis and calibration methods are novel, there are some issues that need to be clarified (which I list below as three questions). The manuscript can also be substaintly improved by an independent validation with data not used in the sensitivity analysis and calibration.
- Are the constructed surrogate models each representing one quantity or variable of interest (QoI)?
- From Table 2, I see that each parameter has different optimized values for different QoIs (GPP, ER, LE, H). For example, the parameter ‘slatop’ for Miscanthus used values 0.02, 0.06, 0.04, and 0.02 for estimating GPP, ER, LE, and H, respectively, in Fig. 3ACEG, right? Does it imply that the MCMC calibration was done for each QoI independently and that the ELM model or surrogate models were run multiple times using different parameter values to simulate the four QoIs in Fig. 3 separately?
My initial understanding is ‘yes’ to the above questions (correct me if I misunderstood and you can skip to 3, but please then explain what is the final value used for each parameter in Table 1 & 2 and how is it determined?)
This is the problem – Model calibration should lead to one decided value for each parameter for each bioenergy crop regardless of the QoI. A land surface model should have a determined set of parameter values to be used together to simulate all the carbon, water, and energy flux and state varaibles, including the QoIs here simultanuously, because they are interconnected. You can not use different (optimized) values for each parameter to simulate different target variables separately. Otherwise, you will have to conduct numerous runs for the large number of variables in ELM, which is not feasible for real applications. The calibrated results presented in Fig. 3 and Table 3 essentially come from the surrogate based posterior simulations for the respective QoIs, not from the ELM. Ideally, each parameter should be calibrated against all the observed variables (e.g., GPP, ER, LE, H) at the same time based on the average performance, so that it is applicable to other variables of interest such as LAI, carbon stocks, transpiration… in the same model.
- The manuscript lacks an independent validation step to prove the model’s generalizability and applicability for cross-location, regional, or global simulations. Validation usually entails applying the set of determined model parameters for each crop from calibration to one or more new sites to evaluate the model performance on all concerned variables or, if without new sites, at least reserving some observation data for evaluating some variables that are not used in the calibration. Although the 2000 ELM simulations were split to 1600 and 400 for training and testing the surrogates, it is not the same concept as validation. Fig. 3 is merely a calibration plot (and it is partial calibration/over-fitted to the input data for each individual QoI, thus lacking applicability to the ELM model as a whole). Therefore, the new model is not sufficiently validated yet.
Before the above questions are clarified, the current model development and evaluation are immature. If I did not misunderstood, the current model description and calibration are problematic and need to be revised. Essential steps to obtain a consistent set of parameter values for the ELM model (not for each QoI) and further validation of the model using independent data are needed.
Specific comments:
Line 59: “ESM crop-models often use default global parameter values rather than crop- and region-specific values” – this is partly untrue. Take CLM4.5 for example, it uses crop-specific parameters for six major crop types, and it also considers regional differences for soy and maize by implementing tropical and subtropical cultivars for these two crops. It may be too challenging to use site-specific or region-specific parameters for a crop model in ESM if the objective of model development is for global applications.
Lines 83-86: are these distinctive characteristics of perennial crops being reflected in the current model development for miscanthus and switchgrass?
Section 2.1: The described phenology is very similar to annual crop phenology, except that annuals are planted every year, but perennials are planted once and have repeated leaf onset, growth, senescence and harvest cycle each year. The major difference of the two perennial crops from the generic grass is their longer growing season and “planting once”, while the phenological cycles are essentially the same as annual grass/crop in CLM4.5/ELM. It seems the Cheng et al. 2020 study also used this strategy to simulate miscanthus and switchgrass (please verify the statement in Line 291-294).
Section 3.3: Isn't there LAI and/or biomass harvest data for the two crops? It would be beneficial to have harvest data for validation of the model, given that this is the key output of interest for bioenergy crops.
Line 208: the seasonal dynamics of H is poorly simulated by the ensemble. It will be good to see more explanation why the observed trough of H in the summer season (May-Aug) is not captured by the model.
Lines 231-232: this could be mentioned earlier in the methods section when describing the construction of surrogates.
Section 4.4, Lines 236-239: From here I realize that the authors calibrated the parameters for the four QoIs separately and obtained different optimized parameter values for the different QoIs in Table 2. Thus, I asked the above main questions. Only if the authors have determined a final set of parameter values for each crop type (not for each QoI) and validate the whole model using this parameter set against various observations of (e.g., LAI, carbon stocks, harvested biomass, etc.) in addition to the variables used for calibration (GPP, ER, LE, H), can we trust that the new model is fully developed and validated for real world applications.
Lines 243-245: these less reliable parameters are the most sensitive ones -- What is the implication for the model accuracy? This needs to be discussed.
Lines 262-263: this same reason should also lead to decreased H during the summer season, which is not well captured by the model.
Line 264: the diurnal mismatch is likely related to plant hydraulics and soil hydraulics.
Line 265: not really captured the seasonality, esp. for Sep. and Oct. as shown in Fig. 3H
Section 5: The Discussion is focused on sensitivity analysis and calibrated parameters. While they are an important step of model development, the discussion could devote more to validation and comparison of model performance with reference to observation or the literature, and the implications for model application in potential research fields. For example, why the model failed to capture the seasonality of sensible heat? And can the model correctly simulate harvested biomass or soil carbon pools (which are important consideration for bioenergy crops)? Even if there are no direct observations at the study site, there may be published data or related information in the ecology or agricultural literature.
The manuscript can also be improved if the authors clearly list the limitations and uncertainties of the current model.
Line 300: Given the high sensitivity of the model to this parameter, even if the optimized range of slatop matches observed range, a single value of slatop should be decided for each crop functional type in order to be used in cross-site or regional simulations. Please see earlier comments about this issue.
Lines 325-326: Why water budgets but not carbon budgets and harvests? Carbon is the major concern for bioenergy crops. Again, calibrating a land surface model using spatially varying site data would lead to spatially varying parameter values, which may work for those specific sites used for calibration, but cannot guarantee their applicability to other places or to regional and global simulations. That’s why I commented that the current study lacks an independent validation using the calibrated and determined parameter set against different datasets and/or different variables of interest.
Figure 1: it would be helpful to add the ensemble mean of model simulations, not just the range.
Table 2: explain what the dash "-" means.
Citation: https://doi.org/10.5194/gmd-2021-244-RC1 -
RC2: 'Reply on RC1', Anonymous Referee #1, 29 Aug 2021
Correction for formatting issue: the paragrah "1. The manuscript lacks an independent validation..." should be labeled as item 3, which refers to my main comment/question 3.
Citation: https://doi.org/10.5194/gmd-2021-244-RC2
-
RC3: 'Comment on gmd-2021-244', Anonymous Referee #2, 29 Dec 2021
The authors attempted to improve the representation of perennial bioenergy crops in ELM and identified the sensitive parameters to evaluate the performance of the newly developed module - ELM-crop. I have some concerns. First, there are not many improvements that have been made in the ELM-crop. The authored adopted a lot of processes and parameters from ELMv1 or CLM4.5, described in section 2. I am not sure what is the major contribution that the authors have made to develop the ELM-crop module. Second, since the authors were modeling crops, fertilization should be considered. How does the ELM-crop deal with fertilization (nitrogen input, plant uptake, nitrogen emissions, and leaching/runoff)? I did not see any detailed description on this, nor about the input data sets. Third, the authors only calibrate the newly developed model at one site. How does the model perform at other sites? Fourth, although the authors spent a lot of energy on parameter optimization, I do not think they were necessary enough to improve the ELM-crop module that they developed. Moreover, the comparison of daily GPP shown in Fig. 1 looks far from good for Miscanthus.
Detailed comments:
Line 17: Here, does “agriculture” include pasture? Or do you mean “cropland only”?
Line 46: Provide the full name of “ISAM”. Also, in lines 49 and 52 for “ORCHIDEE” and “JULES”.
Lines 54-55: This sentence seems out of place. The authors should introduce the importance of parametrization optimization when they describe all improvements made in previous land models. Then, they can conclude this. I think it is necessary to add this description.
Lines 71-76: This paragraph fails to convey the objectives of this study. It is necessary to describe what the authors have done in order like (1) the improvements of perennial crops in ELM; (2) the calibration scheme; (3) validation if the authors have done; and (4) the key implication or the regional application of the newly developed module at the regional scale.
Lines 78-79: The authors should add one or two sentences to describe this. Also, at least one sentence for explaining the major difference between ELMv1 and CLM4.5.
Lines 85-86: ELMv1 has not (or partially?) considered nutrient input, allocation, and limitation, right? Did you improve all these processes in the newly developed version?
Section 2.2: I am confused. I think carbon and nitrogen allocation in perennial crops are different from the annual crops, but in this section, it seems that the authors just adopt these processes from the annual crops in ELM. So, what is the authors’ contribution?
Section 2.3: What do you mean “a single time step after occurrence of the leaf senescence.”? Any range for the number “70% of the available C and N contributes…”?
Line 135 and 146: Give the full of QoIs in the first place.
Section 3.1: It is necessary to describe your input data sets such as meteorological conditions, nitrogen fertilization, soil property, etc. To my knowledge, the authors should drive the ELM-crop with all inputs from the selected site at the University of Illinois Urbana-Champaign (UIUC), right? In line 173, the authors mentioned the eddy covariance flux towers. I think it is necessary to give the names of these flux towers. One more question about the calibration: The authors only described the calibration. Do they have any other site-level data for model validation?
Fig. 3a: The peaks of modeled GPP shifted compared to the observation for Miscanthus. Can you explain?
Citation: https://doi.org/10.5194/gmd-2021-244-RC3 -
AC1: 'Comment on gmd-2021-244', Eva Sinha, 05 Feb 2022
The comment was uploaded in the form of a supplement: https://gmd.copernicus.org/preprints/gmd-2021-244/gmd-2021-244-AC1-supplement.pdf
Interactive discussion
Status: closed
-
CEC1: 'Comment on gmd-2021-244', Astrid Kerkweg, 21 Jul 2021
Dear authors,
in my role as Executive editor of GMD, I would like to bring to your attention our Editorial version 1.2: https://www.geosci-model-dev.net/12/2215/2019/
This highlights some requirements of papers published in GMD, which is also available on the GMD website in the ‘Manuscript Types’ section: http://www.geoscientific-model-development.net/submission/manuscript_types.html
In particular, please note that for your paper, the following requirements have not been met in the Discussions paper:
- The main paper must give the model name and version number (or other unique identifier) in the title.
- "Code must be published on a persistent public archive with a unique identifier for the exact model version described in the paper or uploaded to the supplement, unless this is impossible for reasons beyond the control of authors. All papers must include a section, at the end of the paper, entitled "Code availability". Here, either instructions for obtaining the code, or the reasons why the code is not available should be clearly stated. It is preferred for the code to be uploaded as a supplement or to be made available at a data repository with an associated DOI (digital object identifier) for the exact model version described in the paper. Alternatively, for established models, there may be an existing means of accessing the code through a particular system. In this case, there must exist a means of permanently accessing the precise model version described in the paper. In some cases, authors may prefer to put models on their own website, or to act as a point of contact for obtaining the code. Given the impermanence of websites and email addresses, this is not encouraged, and authors should consider improving the availability with a more permanent arrangement. Making code available through personal websites or via email contact to the authors is not sufficient. After the paper is accepted the model archive should be updated to include a link to the GMD paper."
As the land model is the interesting part of your investigation, add the abbreviation and version number of the E3SM land model (ELMv1) to the title of the manuscript. E.g. "Modeling perennial bioenergy crops in the E3SM land model (ELMv1)"
As GitHub is not a persistent archive, please provide a persistent release for the exact source code version used for the publication in this paper. As explained in https://www.geoscientific-model-development.net/about/manuscript_types.html the preferred reference to this release is through the use of a DOI which then can be cited in the paper. For projects in GitHub a DOI for a released code version can easily be created using Zenodo, see https://guides.github.com/activities/citable-code/ for details.
Yours, Astrid Kerkweg
Citation: https://doi.org/10.5194/gmd-2021-244-CEC1 -
RC1: 'Comment on gmd-2021-244', Anonymous Referee #1, 29 Aug 2021
General comments:
This study implements two perennial bioenergy crops miscanthus and switchgrass in the E3SM land model ELM. The revised phenology, carbon/nitrogen allocation, and biomass harvest parametrizations are based on the generic grass and annual crop functional types in CLM4.5 with the main distinction that the perennials are planted once and have repeated leaf onset, senescence and harvest cycle each year with a longer growing season than annuals. The manuscript focuses on sensitivity analysis and parameter optimization using an approach combining surrogate construction with polynomial regression and Bayesian calibration with MCMC. While the sensitivity analysis and calibration methods are novel, there are some issues that need to be clarified (which I list below as three questions). The manuscript can also be substaintly improved by an independent validation with data not used in the sensitivity analysis and calibration.
- Are the constructed surrogate models each representing one quantity or variable of interest (QoI)?
- From Table 2, I see that each parameter has different optimized values for different QoIs (GPP, ER, LE, H). For example, the parameter ‘slatop’ for Miscanthus used values 0.02, 0.06, 0.04, and 0.02 for estimating GPP, ER, LE, and H, respectively, in Fig. 3ACEG, right? Does it imply that the MCMC calibration was done for each QoI independently and that the ELM model or surrogate models were run multiple times using different parameter values to simulate the four QoIs in Fig. 3 separately?
My initial understanding is ‘yes’ to the above questions (correct me if I misunderstood and you can skip to 3, but please then explain what is the final value used for each parameter in Table 1 & 2 and how is it determined?)
This is the problem – Model calibration should lead to one decided value for each parameter for each bioenergy crop regardless of the QoI. A land surface model should have a determined set of parameter values to be used together to simulate all the carbon, water, and energy flux and state varaibles, including the QoIs here simultanuously, because they are interconnected. You can not use different (optimized) values for each parameter to simulate different target variables separately. Otherwise, you will have to conduct numerous runs for the large number of variables in ELM, which is not feasible for real applications. The calibrated results presented in Fig. 3 and Table 3 essentially come from the surrogate based posterior simulations for the respective QoIs, not from the ELM. Ideally, each parameter should be calibrated against all the observed variables (e.g., GPP, ER, LE, H) at the same time based on the average performance, so that it is applicable to other variables of interest such as LAI, carbon stocks, transpiration… in the same model.
- The manuscript lacks an independent validation step to prove the model’s generalizability and applicability for cross-location, regional, or global simulations. Validation usually entails applying the set of determined model parameters for each crop from calibration to one or more new sites to evaluate the model performance on all concerned variables or, if without new sites, at least reserving some observation data for evaluating some variables that are not used in the calibration. Although the 2000 ELM simulations were split to 1600 and 400 for training and testing the surrogates, it is not the same concept as validation. Fig. 3 is merely a calibration plot (and it is partial calibration/over-fitted to the input data for each individual QoI, thus lacking applicability to the ELM model as a whole). Therefore, the new model is not sufficiently validated yet.
Before the above questions are clarified, the current model development and evaluation are immature. If I did not misunderstood, the current model description and calibration are problematic and need to be revised. Essential steps to obtain a consistent set of parameter values for the ELM model (not for each QoI) and further validation of the model using independent data are needed.
Specific comments:
Line 59: “ESM crop-models often use default global parameter values rather than crop- and region-specific values” – this is partly untrue. Take CLM4.5 for example, it uses crop-specific parameters for six major crop types, and it also considers regional differences for soy and maize by implementing tropical and subtropical cultivars for these two crops. It may be too challenging to use site-specific or region-specific parameters for a crop model in ESM if the objective of model development is for global applications.
Lines 83-86: are these distinctive characteristics of perennial crops being reflected in the current model development for miscanthus and switchgrass?
Section 2.1: The described phenology is very similar to annual crop phenology, except that annuals are planted every year, but perennials are planted once and have repeated leaf onset, growth, senescence and harvest cycle each year. The major difference of the two perennial crops from the generic grass is their longer growing season and “planting once”, while the phenological cycles are essentially the same as annual grass/crop in CLM4.5/ELM. It seems the Cheng et al. 2020 study also used this strategy to simulate miscanthus and switchgrass (please verify the statement in Line 291-294).
Section 3.3: Isn't there LAI and/or biomass harvest data for the two crops? It would be beneficial to have harvest data for validation of the model, given that this is the key output of interest for bioenergy crops.
Line 208: the seasonal dynamics of H is poorly simulated by the ensemble. It will be good to see more explanation why the observed trough of H in the summer season (May-Aug) is not captured by the model.
Lines 231-232: this could be mentioned earlier in the methods section when describing the construction of surrogates.
Section 4.4, Lines 236-239: From here I realize that the authors calibrated the parameters for the four QoIs separately and obtained different optimized parameter values for the different QoIs in Table 2. Thus, I asked the above main questions. Only if the authors have determined a final set of parameter values for each crop type (not for each QoI) and validate the whole model using this parameter set against various observations of (e.g., LAI, carbon stocks, harvested biomass, etc.) in addition to the variables used for calibration (GPP, ER, LE, H), can we trust that the new model is fully developed and validated for real world applications.
Lines 243-245: these less reliable parameters are the most sensitive ones -- What is the implication for the model accuracy? This needs to be discussed.
Lines 262-263: this same reason should also lead to decreased H during the summer season, which is not well captured by the model.
Line 264: the diurnal mismatch is likely related to plant hydraulics and soil hydraulics.
Line 265: not really captured the seasonality, esp. for Sep. and Oct. as shown in Fig. 3H
Section 5: The Discussion is focused on sensitivity analysis and calibrated parameters. While they are an important step of model development, the discussion could devote more to validation and comparison of model performance with reference to observation or the literature, and the implications for model application in potential research fields. For example, why the model failed to capture the seasonality of sensible heat? And can the model correctly simulate harvested biomass or soil carbon pools (which are important consideration for bioenergy crops)? Even if there are no direct observations at the study site, there may be published data or related information in the ecology or agricultural literature.
The manuscript can also be improved if the authors clearly list the limitations and uncertainties of the current model.
Line 300: Given the high sensitivity of the model to this parameter, even if the optimized range of slatop matches observed range, a single value of slatop should be decided for each crop functional type in order to be used in cross-site or regional simulations. Please see earlier comments about this issue.
Lines 325-326: Why water budgets but not carbon budgets and harvests? Carbon is the major concern for bioenergy crops. Again, calibrating a land surface model using spatially varying site data would lead to spatially varying parameter values, which may work for those specific sites used for calibration, but cannot guarantee their applicability to other places or to regional and global simulations. That’s why I commented that the current study lacks an independent validation using the calibrated and determined parameter set against different datasets and/or different variables of interest.
Figure 1: it would be helpful to add the ensemble mean of model simulations, not just the range.
Table 2: explain what the dash "-" means.
Citation: https://doi.org/10.5194/gmd-2021-244-RC1 -
RC2: 'Reply on RC1', Anonymous Referee #1, 29 Aug 2021
Correction for formatting issue: the paragrah "1. The manuscript lacks an independent validation..." should be labeled as item 3, which refers to my main comment/question 3.
Citation: https://doi.org/10.5194/gmd-2021-244-RC2
-
RC3: 'Comment on gmd-2021-244', Anonymous Referee #2, 29 Dec 2021
The authors attempted to improve the representation of perennial bioenergy crops in ELM and identified the sensitive parameters to evaluate the performance of the newly developed module - ELM-crop. I have some concerns. First, there are not many improvements that have been made in the ELM-crop. The authored adopted a lot of processes and parameters from ELMv1 or CLM4.5, described in section 2. I am not sure what is the major contribution that the authors have made to develop the ELM-crop module. Second, since the authors were modeling crops, fertilization should be considered. How does the ELM-crop deal with fertilization (nitrogen input, plant uptake, nitrogen emissions, and leaching/runoff)? I did not see any detailed description on this, nor about the input data sets. Third, the authors only calibrate the newly developed model at one site. How does the model perform at other sites? Fourth, although the authors spent a lot of energy on parameter optimization, I do not think they were necessary enough to improve the ELM-crop module that they developed. Moreover, the comparison of daily GPP shown in Fig. 1 looks far from good for Miscanthus.
Detailed comments:
Line 17: Here, does “agriculture” include pasture? Or do you mean “cropland only”?
Line 46: Provide the full name of “ISAM”. Also, in lines 49 and 52 for “ORCHIDEE” and “JULES”.
Lines 54-55: This sentence seems out of place. The authors should introduce the importance of parametrization optimization when they describe all improvements made in previous land models. Then, they can conclude this. I think it is necessary to add this description.
Lines 71-76: This paragraph fails to convey the objectives of this study. It is necessary to describe what the authors have done in order like (1) the improvements of perennial crops in ELM; (2) the calibration scheme; (3) validation if the authors have done; and (4) the key implication or the regional application of the newly developed module at the regional scale.
Lines 78-79: The authors should add one or two sentences to describe this. Also, at least one sentence for explaining the major difference between ELMv1 and CLM4.5.
Lines 85-86: ELMv1 has not (or partially?) considered nutrient input, allocation, and limitation, right? Did you improve all these processes in the newly developed version?
Section 2.2: I am confused. I think carbon and nitrogen allocation in perennial crops are different from the annual crops, but in this section, it seems that the authors just adopt these processes from the annual crops in ELM. So, what is the authors’ contribution?
Section 2.3: What do you mean “a single time step after occurrence of the leaf senescence.”? Any range for the number “70% of the available C and N contributes…”?
Line 135 and 146: Give the full of QoIs in the first place.
Section 3.1: It is necessary to describe your input data sets such as meteorological conditions, nitrogen fertilization, soil property, etc. To my knowledge, the authors should drive the ELM-crop with all inputs from the selected site at the University of Illinois Urbana-Champaign (UIUC), right? In line 173, the authors mentioned the eddy covariance flux towers. I think it is necessary to give the names of these flux towers. One more question about the calibration: The authors only described the calibration. Do they have any other site-level data for model validation?
Fig. 3a: The peaks of modeled GPP shifted compared to the observation for Miscanthus. Can you explain?
Citation: https://doi.org/10.5194/gmd-2021-244-RC3 -
AC1: 'Comment on gmd-2021-244', Eva Sinha, 05 Feb 2022
The comment was uploaded in the form of a supplement: https://gmd.copernicus.org/preprints/gmd-2021-244/gmd-2021-244-AC1-supplement.pdf
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