Dear Editor,

in their submitted paper 'Simulation of crop yield using the global hydrological model H08 (crp.v1)', the authors enhance the H08 crop sub-model with parameter calibration and algorithm improvement.

Thereby, the CO2 fertilization effect and the effect of vapor pressure deficit change has been included to the model. Additionally, a model calibration has been applied.

In order to evaluate the model results, simulated yields are compared with statistical yields and other global crop models for the major 4 crops (maize, wheat, rice, soybean) at country and grid-level.

The paper is well written and understandable. Nevertheless, the paper has a main weakness:

If you calibrate your model towards yields that you also use to validate/evaluate your model, it's not a surprise that R2 is >0.99.

But does that mean that your model improved? I wouldn't say so. It just says that the calibration was successful.

First, I'd suggest to say 'calibrated simulations' and 'default simulations' instead of 'improved' and 'default' simulations throughout the manuscript.

Second, given the fact that you added the effects of CO2 and vapor pressure deficit to the H08 model, it would be interesting in this study to quantify the difference between considering these effects and not.

As in Deryng et al. (2016) I would encourage you to quantify the difference of CO2 effect on crop water productivity for C3 and C4 crops. 

A calibration can be done in a next step but after the validation. The quantification of water flows would require to validate crop evapotranspiration, which is not done in this study.

Given the contextual and structural deficits in this study, I'd suggest major revisions.

Specific Comments:

Abstract ln 1: 'Food and water are essential for life'. To me, this is trivial and a bit pathethic.

Abstract ln 19: What means 'reasonably'? Can you quantify that with a statistical value?

Line 35: You could add the PROMET model to this list of models, because it is also a hydrological model with an enhanced crop growth module included that has been applied at global (Zabel et al. 2019) and regional scale (Degife et al. 2021).

Line 76: Is it still a 'process-based model', after calibrating parameters to match statistical yields that are subsequently used for model evaluation?

Line 125: Nitrogen and phosphorous stress are implicitly considered in your calibration procedure! It possibly one of the main factors that influences your calibration.

Line 224: Two full stops.

Line 222-231: See above comment on calibration and validation.

Line 233-239: Interannual yield variabilities are much higher in both, calibrated and default simulations than in observations. Can you explain why?

Line 243: According to the GGCMI phase 3 protocol, none of the models in Jägermeyr et al. (2021) are calibrated to yields.

Line 295: Same problem than with constant irrigation occurs for the crop calendar, I guess?

Line 305: What means 'remain good references'? Don't you contradict yourself with what is said before? Of course, we need better data and this is a strong limitation.

Line 307: 'factors' or better say 'processes' here.

Line 317: Therefore, it would be required to validate simulated crop evapotranspiration. If you can reproduce yields, it does not mean that evapotranspiration is simulated correctly.

Line 331: You just mentioned that irrigated areas are kept constant over time, which impacts the results. Another question is the amount of irrigation that is applied. Do you consider only full irrigation, or do you also consider deficit irrigation?

Line 336: As I understood, H08 had the capacity to simulate yields before. You added two processes and applied a calibration.

Line 343: Please avoid qualitative statements like 'a good tool'. Nobody can say what is a good tool.

Figures:

Figure 1: Font size too small. Country names, etc. are very hard to read, even after zooming in.

Figure 3: Interesting to see R and RMSE values as a bar plot, which is not intuitive. I'd suggest to show a Taylor diagram or a scatterplot.

Literature:

Deryng, D., Elliott, J., Folberth, C. et al. Regional disparities in the beneficial effects of rising CO2 concentrations on crop water productivity. Nature Clim Change 6, 786–790 (2016). https://doi.org/10.1038/nclimate2995

Degife, A. W., Zabel, F., Mauser, W. (2021): Climate change impacts on potential maize yields in Gambella region, Ethiopia. Regional Environmental Change, 21:60. doi: 10.1007/s10113-021-01773-3.

Zabel, F., Delzeit, R., Schneider, J. M., Seppelt, R., Mauser, M., Václacík, T. (2019): Global impacts of future cropland expansion and intensification on agricultural markets and biodiversity. Nature Communications, 10:2844, 11. 

doi: 10.1038/s41467-019-10775-z.

General comments

This paper attempts to improve the capacity of a global hydrological model H08 to simulate the yields of four major crops. The authors substantially improve the crop growth algorithm and add additional calibration procedures to aim at a better fit with national crop yields reported by FAO. The latter is arguably the main point of concern here. Calibration and validation were performed using two very similar reference datasets, both based on the long-term mean of FAO’s yields. Due to this similarity, the validation shows great agreement with reference data. However, once the calibrated model is compared to long-term (not mean) annual yields, the agreement substantially drops and hence the model provides a weak representation of the actual historical yields. Another issue lies in the insufficient description of Materials and Methods. As a reader, it is difficult to understand the details of crop growth processes, simulation setup, and calibration procedure. Therefore, this section would greatly benefit from adding figures (e.g. crop sub-model scheme) and tables (e.g. main input data). Lastly, it would be interesting to see if the improved model can simulate the water cycle more accurately since H08 is primarily a hydrological model. Given mentioned-above, I would suggest major revisions.

Specific comments

Title: What does (crp.v1) mean? do you really need to include it in a title?

Line 9-14: The authors put too much emphasis on the “good consistency” (how do you define good?) with the long-term mean of FAO’s yields. However, they fail to acknowledge the poor correlation when yields were analysed not as a mean but as a time series over the same period. The latter, one could argue, is much more important in the context of solving water-food nexus problems.

Line 20: Why do you refer to “food-water-land-energy nexus”? so far you only talk about water and food. Similar thing for line 345.

Line 35: If you are trying to list all global crop models, then add the ones you miss from the recent round of ISIMIP simulations, e.g. ACEA, PROMET, SIMPLACE etc. (Jägermeyr et al., 2021).

Lines 108-162: The list of formulas is helpful but it is difficult to follow. For example, the parameter “Ihun” is first used in Eq.1 but is only explained in Eq.9. I would strongly recommend starting Chapter 2.2 with a general description of the crop sub-model (preferably with supporting figure) and after that diving into details.

Line 165-174: you roughly explain the calibration of “blai” but you do not describe the calibration of “Harvest”. As I understand, you first check if changing “blai” is sufficient to reach FAO’s yield and only then you check if further changing of “Harvest” is needed, correct? In general, it is not clear how the calibration procedure works.

Line 243: Please indicate which models and time period you take from Jägermeyr et al. (2021).

Line 248-251: Jägermeyr et al. (2021) were not aiming at representing actual historical crop yields so it is not surprising that their data mostly demonstrates climatic signals.

Line 254: Double check that you actually take total precipitation and mean temperature for the duration of the growing season and not for the whole calendar year. Also, indicate how you weigh the grid cells here, is it according to harvested areas?

Line 276: Instead of referring to a “wide range of regions” you may refer to the percentage of grid cells with significant correlation. Same for areas without correlation.

Line 294: There are ways to represent the historical trend in rainfed/irrigated harvested areas. To start, you can scale the total area to FAOSTAT and only then scale the yields. On top of this, you can include historical dynamics in overall rainfed and irrigated croplands as did for maize Mialyk et al. (2022).

Line 137, 343: Please avoid using the word “good” and use academic alternatives instead.

Line 342: Where does the word “bioenergy” come from? You do not use it throughout the text.

Figure 2: It seems that for some countries the improved model shows the same numbers as the default one. Please explain why this is happening in Chapter 3.1.

Figure 8: Consider removing it. The message in lines 331-332 is very clear already.

References

Jägermeyr, J., Müller, C., Ruane, A.C., Elliott, J., Balkovic, J., Castillo, O., Faye, B., Foster, I., Folberth, C., Franke, J.A., Fuchs, K., Guarin, J.R., Heinke, J., Hoogenboom, G., Iizumi, T., Jain, A.K., Kelly, D., Khabarov, N., Lange, S., Lin, T.-S., Liu, W., Mialyk, O., Minoli, S., Moyer, E.J., Okada, M., Phillips, M., Porter, C., Rabin, S.S., Scheer, C., Schneider, J.M., Schyns, J.F., Skalsky, R., Smerald, A., Stella, T., Stephens, H., Webber, H., Zabel, F., Rosenzweig, C., 2021. Climate impacts on global agriculture emerge earlier in new generation of climate and crop models. Nat. Food 2, 873–885. https://doi.org/10.1038/s43016-021-00400-y

Mialyk, O., Schyns, J.F., Booij, M.J., Hogeboom, R.J., 2022. Historical simulation of maize water footprints with a new global gridded crop model ACEA. Hydrol. Earth Syst. Sci. 26, 923–940. https://doi.org/10.5194/hess-26-923-2022