the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Influence of plant traits on water cycle processes in the Amazon Basin
Abstract. Plants play a key role in the soil-plant-atmosphere-climate hydrological continuum. Plants depend on the water cycle and, in return, several hydrological processes could be impacted via vegetation-induced mechanisms. Changes in plant composition are known to affect this relationship, however, detailed understanding on how plant characteristics, i.e., their traits, are seldom included in observational and modeling studies. Here we examine the effect of plant traits on water cycle processes in the Amazon Basin. We used remotely-sensed estimates of four plant traits, namely Specific Leaf Area (SLA), Leaf Dry Matter Content (LDMC), Leaf Phosphorus Content (LPC), Leaf Nitrogen Content (LNC), and two vegetation indices, the Normalised Difference Vegetation Index (NDVI), and Leaf Area Index (LAI), for 10 years between 2001 and 2010. We examined the relationship between plant traits and six parameters relevant for water cycle processes, namely Evapotranspiration (ET), Potential Evapotranspiration (PET), Vapour Pressure Deficit (VPD), Land Surface Temperature (LST) Day/Night and Soil Moisture (SM). We used multivariate and quantile regressions to analyse how plant traits explain the average and standard deviation in water cycle process parameters. We find that SLA, NDVI, and LAI exert the strongest effects across the whole of Amazon basin and the sub-basins, most important for the regulation of atmospheric water content and of land surface temperature, but little effect on the regulation of soil moisture content. These effects are exacerbated at extreme values of water process parameters, where plant traits exert an even stronger effect at low values of ET and PET and high values of VPD and LST. Leaf gas exchange traits are most important in comparison to the other traits, and these results also highlight that if water cycle process parameters achieve extreme values, plant traits are key to the persistence of hydrological processes fundamental to the resilience of the Amazon.
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Status: closed
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RC1: 'Comment on egusphere-2023-3069', Anonymous Referee #1, 20 Feb 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2023-3069/egusphere-2023-3069-RC1-supplement.pdf
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AC1: 'Reply on RC1', Kien Nguyen, 23 Mar 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2023-3069/egusphere-2023-3069-AC1-supplement.pdf
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AC1: 'Reply on RC1', Kien Nguyen, 23 Mar 2024
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RC2: 'Review of “Influence of plant traits on water cycle processes in the Amazon Basin”.', Anonymous Referee #2, 17 Mar 2024
In this work the authors analyze different remotely sensed plant traits on water fluxes, VPD and surface temperature. The plant traits analyzed are Specific Leaf Area (SLA), Leaf Dry Matter Content (LDMC), Leaf Phosphorous Content (LPC), Leaf Nitrogen Content (LNC) NDVI and LAI. The authors find that SLA, NDVI and LAI are strongly associated with water fluxes and surface temperature. The associations are exacerbated at extreme values.
I found the objectives of the article interesting, but think there are serious problems with the work and unfortunately cannot recommend publication.
Main comments
The physical relationships between NDVI/LAI, evapotranspiration, land surface temperature have been developed for decades and are a standard in land surface models. Earth system models also incorporate phosphorous and nitrogen. It was not clear to me that the authors were familiar with current ways to model these relationships, or what specific problem they were addressing. Are the authors are implying that the current parameterization of land surface traits is incorrect or insufficient? I would agree, there are likely severe problems with our current parameterizations, but without an overview of current methods and clear identification of the problems, I don’t understand how multivariate linear regression models will help us better understand these highly nonlinear relationships. I think that the work needs to clearly articulate how our current representation of the relationship between plant traits and water cycle variables is problematic, and how their remote sensing analysis will help.
I am assuming that the regressions are done using time-averaged quantities, and you do the regressions with different in space…correct? Could you please clearly explain how you are doing this?
There is insufficient background on the remote sensing estimates. Can you please specify the satellite used for each estimate? What satellite estimates are you using for soil moisture? VPD? LST? What are the uncertainties associated with each of these variables?
Critically, variables such as ET (which I believe are MODIS?) are derived quantities that use NDVI/LAI. As such, it is not surprising that you would find a relationship. This makes the entire analysis very problematic.
Citation: https://doi.org/10.5194/egusphere-2023-3069-RC2 -
AC2: 'Reply on RC2', Kien Nguyen, 23 Mar 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2023-3069/egusphere-2023-3069-AC2-supplement.pdf
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AC2: 'Reply on RC2', Kien Nguyen, 23 Mar 2024
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AC3: 'Comment on egusphere-2023-3069', Kien Nguyen, 07 Apr 2024
We have addressed the referees' comments and provided responses accordingly.
We would like to acknowledge the oversight regarding the inclusion of LAI in the analysis alongside ET/PET. Nevertheless, we remain committed to contributing novel insights to the scientific knowledge by assessing the association between plant traits and water cycle parameters at the regional scale in the Amazon Basin and at the extreme values of the water cycle parameters.
Should the preprint be accepted for revision, we intend to implement the following actions:
- Exclusion of analyses involving LAI and NDVI to focus solely on plant traits.
- Incorporation of only SLA and LPC in multivariate analyses to account for potential dependencies, due to the role of LDMC and LNC for gap-filling purposes.
- Integration of results obtained through generalised linear mixed models.
Once again, we express our gratitude to the anonymous referees and the associate editor for their thorough review of our paper.
Citation: https://doi.org/10.5194/egusphere-2023-3069-AC3
Status: closed
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RC1: 'Comment on egusphere-2023-3069', Anonymous Referee #1, 20 Feb 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2023-3069/egusphere-2023-3069-RC1-supplement.pdf
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AC1: 'Reply on RC1', Kien Nguyen, 23 Mar 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2023-3069/egusphere-2023-3069-AC1-supplement.pdf
-
AC1: 'Reply on RC1', Kien Nguyen, 23 Mar 2024
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RC2: 'Review of “Influence of plant traits on water cycle processes in the Amazon Basin”.', Anonymous Referee #2, 17 Mar 2024
In this work the authors analyze different remotely sensed plant traits on water fluxes, VPD and surface temperature. The plant traits analyzed are Specific Leaf Area (SLA), Leaf Dry Matter Content (LDMC), Leaf Phosphorous Content (LPC), Leaf Nitrogen Content (LNC) NDVI and LAI. The authors find that SLA, NDVI and LAI are strongly associated with water fluxes and surface temperature. The associations are exacerbated at extreme values.
I found the objectives of the article interesting, but think there are serious problems with the work and unfortunately cannot recommend publication.
Main comments
The physical relationships between NDVI/LAI, evapotranspiration, land surface temperature have been developed for decades and are a standard in land surface models. Earth system models also incorporate phosphorous and nitrogen. It was not clear to me that the authors were familiar with current ways to model these relationships, or what specific problem they were addressing. Are the authors are implying that the current parameterization of land surface traits is incorrect or insufficient? I would agree, there are likely severe problems with our current parameterizations, but without an overview of current methods and clear identification of the problems, I don’t understand how multivariate linear regression models will help us better understand these highly nonlinear relationships. I think that the work needs to clearly articulate how our current representation of the relationship between plant traits and water cycle variables is problematic, and how their remote sensing analysis will help.
I am assuming that the regressions are done using time-averaged quantities, and you do the regressions with different in space…correct? Could you please clearly explain how you are doing this?
There is insufficient background on the remote sensing estimates. Can you please specify the satellite used for each estimate? What satellite estimates are you using for soil moisture? VPD? LST? What are the uncertainties associated with each of these variables?
Critically, variables such as ET (which I believe are MODIS?) are derived quantities that use NDVI/LAI. As such, it is not surprising that you would find a relationship. This makes the entire analysis very problematic.
Citation: https://doi.org/10.5194/egusphere-2023-3069-RC2 -
AC2: 'Reply on RC2', Kien Nguyen, 23 Mar 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2023-3069/egusphere-2023-3069-AC2-supplement.pdf
-
AC2: 'Reply on RC2', Kien Nguyen, 23 Mar 2024
-
AC3: 'Comment on egusphere-2023-3069', Kien Nguyen, 07 Apr 2024
We have addressed the referees' comments and provided responses accordingly.
We would like to acknowledge the oversight regarding the inclusion of LAI in the analysis alongside ET/PET. Nevertheless, we remain committed to contributing novel insights to the scientific knowledge by assessing the association between plant traits and water cycle parameters at the regional scale in the Amazon Basin and at the extreme values of the water cycle parameters.
Should the preprint be accepted for revision, we intend to implement the following actions:
- Exclusion of analyses involving LAI and NDVI to focus solely on plant traits.
- Incorporation of only SLA and LPC in multivariate analyses to account for potential dependencies, due to the role of LDMC and LNC for gap-filling purposes.
- Integration of results obtained through generalised linear mixed models.
Once again, we express our gratitude to the anonymous referees and the associate editor for their thorough review of our paper.
Citation: https://doi.org/10.5194/egusphere-2023-3069-AC3
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