- Email: [email protected]
Peer review report 2 On “Influence of Groundwater on Plant Water Use and Productivity: Development of an Integrated Ecosystem - Variably Saturated Soil Water Flow Model”
Agricultural and Forest Meteorology 201S (2015) 356
Contents lists available at ScienceDirect
Agricultural and Forest Meteorology journal homepage: www.elsevier.com/locate/agrformet
Peer review report
Peer review report 2 On “Influence of Groundwater on Plant Water Use and Productivity: Development of an Integrated Ecosystem - Variably Saturated Soil Water Flow Model”
Original Submission Recommendation: Minor Revision Comments to Author: This manuscript is an important contribution to the improvement of LSM with respect to their water cycle and the effect on plants and crops. The model has potential but it would certainly be interesting to challenge the model with other types of data such as tile-drained vs no-tile-drained or systems in which the drainage is managed. These last conditions are especially useful for the current state of the US Midwest agriculture. Given the modeling effort and the ambitions of the project is feels that the amount of data used to test the model is somewhat scarce. There is a discussion about predictions about corn physiology and phenology which are not contrasted with data, which leads to a bit too much speculation. Also the discussion about VPD is not compared with measurements. I’m left with the feeling that the authors have done a great work and proposed very interesting ideas and simulations but the comparison between model predictions and data is lacking making the paper somewhat weak. I still think it is a very valuable contribution that can lead to more interesting testing. Specific comments LN 63 ‘elevating’ might not be general enough. I suppose that ground water can either elevate or decrease soil temperature depending on the situation, so ‘modify’ would be better. LN 83-86 Needs to be reworded LN 92-94 Plants do not always maximize photosynthesis rates. Source-sink studies and FACE experiments show that there are a variety of negative feedbacks on photosynthesis. LN 92-102 This paragraph is not well connected to the rest of the introduction. At the moment it reads as phrases taken from other sources. For example, ‘stomatal conductance’ is not best described as a physiological characteristic, it is a specific measurement. ‘Stomatal opening and closing’ could be said to be a physiological
DOI of original article: http://dx.doi.org/10.1016/j.agrformet.2014.01.019. 0168-1923/$ – see front matter http://dx.doi.org/10.1016/j.agrformet.2015.08.183
characteristic. The important connection to be made here is that in some proposed models of the Soil-Plant-Atmosphere continuum (Campbell and others) stomatal conductance is thought to respond to the leaf water potential which depends on the soil water potential. So this is where the ground water comes into play. However, note that for some plants stomata can close in direct response to VPD irrespective of soil moisture. This is, to some extent, captured in the Ball-Berry model. LN 348-349. I’m hoping that the authors do not believe that because a model has been shown to work well (tested or validated) under a range of situations it is guaranteed to always work well. Even the best most thoroughly tested models fail to represent ‘reality’ at times because natural processes are always more complex than what our models are able to do. The authors use the term validated which I personally dislike because it suggests that once a model is ‘valid’ that is it; no more testing is required. LN 352-354. Here it says the model was compared against 2012 data but Figure 2 does not have 2012 data for LAI or NPP. Given 2012 was a drought year (and particularly interesting for this application) could it be included in the paper? Also, are NPP data available for years prior to 2008? LN 360. ‘Daily average’? Can the model be tested against subdaily fluctuations? ˆ although widely used, is a poor index for model LN 369 The R2, agreement since it does not account for systematic bias. My suggestion would be to not use it, but I understand if the authors want to include it anyway. Figure 2d If the bars are the standard deviation as opposed to the CI then 2009 would encompass zero? The model performed well but the data are highly variable. Figure 4 is great! LN 553 “affects” instead of “effects” LN 620 “affecting” vs. “effecting” Anonymous Available online 6 August 2015
Contents lists available at ScienceDirect
Agricultural and Forest Meteorology journal homepage: www.elsevier.com/locate/agrformet
Peer review report
Peer review report 2 On “Influence of Groundwater on Plant Water Use and Productivity: Development of an Integrated Ecosystem - Variably Saturated Soil Water Flow Model”
Original Submission Recommendation: Minor Revision Comments to Author: This manuscript is an important contribution to the improvement of LSM with respect to their water cycle and the effect on plants and crops. The model has potential but it would certainly be interesting to challenge the model with other types of data such as tile-drained vs no-tile-drained or systems in which the drainage is managed. These last conditions are especially useful for the current state of the US Midwest agriculture. Given the modeling effort and the ambitions of the project is feels that the amount of data used to test the model is somewhat scarce. There is a discussion about predictions about corn physiology and phenology which are not contrasted with data, which leads to a bit too much speculation. Also the discussion about VPD is not compared with measurements. I’m left with the feeling that the authors have done a great work and proposed very interesting ideas and simulations but the comparison between model predictions and data is lacking making the paper somewhat weak. I still think it is a very valuable contribution that can lead to more interesting testing. Specific comments LN 63 ‘elevating’ might not be general enough. I suppose that ground water can either elevate or decrease soil temperature depending on the situation, so ‘modify’ would be better. LN 83-86 Needs to be reworded LN 92-94 Plants do not always maximize photosynthesis rates. Source-sink studies and FACE experiments show that there are a variety of negative feedbacks on photosynthesis. LN 92-102 This paragraph is not well connected to the rest of the introduction. At the moment it reads as phrases taken from other sources. For example, ‘stomatal conductance’ is not best described as a physiological characteristic, it is a specific measurement. ‘Stomatal opening and closing’ could be said to be a physiological
DOI of original article: http://dx.doi.org/10.1016/j.agrformet.2014.01.019. 0168-1923/$ – see front matter http://dx.doi.org/10.1016/j.agrformet.2015.08.183
characteristic. The important connection to be made here is that in some proposed models of the Soil-Plant-Atmosphere continuum (Campbell and others) stomatal conductance is thought to respond to the leaf water potential which depends on the soil water potential. So this is where the ground water comes into play. However, note that for some plants stomata can close in direct response to VPD irrespective of soil moisture. This is, to some extent, captured in the Ball-Berry model. LN 348-349. I’m hoping that the authors do not believe that because a model has been shown to work well (tested or validated) under a range of situations it is guaranteed to always work well. Even the best most thoroughly tested models fail to represent ‘reality’ at times because natural processes are always more complex than what our models are able to do. The authors use the term validated which I personally dislike because it suggests that once a model is ‘valid’ that is it; no more testing is required. LN 352-354. Here it says the model was compared against 2012 data but Figure 2 does not have 2012 data for LAI or NPP. Given 2012 was a drought year (and particularly interesting for this application) could it be included in the paper? Also, are NPP data available for years prior to 2008? LN 360. ‘Daily average’? Can the model be tested against subdaily fluctuations? ˆ although widely used, is a poor index for model LN 369 The R2, agreement since it does not account for systematic bias. My suggestion would be to not use it, but I understand if the authors want to include it anyway. Figure 2d If the bars are the standard deviation as opposed to the CI then 2009 would encompass zero? The model performed well but the data are highly variable. Figure 4 is great! LN 553 “affects” instead of “effects” LN 620 “affecting” vs. “effecting” Anonymous Available online 6 August 2015