Effects of plant traits on the regulation of water cycle processes in the Amazon Basin

Nguyen, Kien; Santos, Maria J.

doi:https://doi.org/10.5194/egusphere-2024-2544

Preprints

https://doi.org/10.5194/egusphere-2024-2544

Preprints

06 Sep 2024

| 06 Sep 2024

Effects of plant traits on the regulation of water cycle processes in the Amazon Basin

Kien Nguyen and Maria J. Santos

Abstract. Plants play a key role in the soil-plant-atmosphere-climate hydrological continuum as they depend on water for their persistence and in turn affect water exchange processes. Changes in plant composition may affect these relationships through induced changes in cover, composition and functionality; however, detailed understanding on how feedbacks that involve plant traits develop are still seldom included in observational, experimental and modeling studies. To address this gap, here we make use of datasets derived from Earth Observation and models to examine the effect of plant traits on water cycle processes in the Amazon Basin. We used quantile regression to examine how plant traits (Specific Leaf Area (SLA), Leaf Dry Matter Content (LDMC), Leaf Phosphorus Content (LPC) and Leaf Nitrogen Content (LNC)), respond to parameters related to regulation of atmospheric water content (Evapotranspiration (ET), Potential Evapotranspiration (PET), Vapour Pressure Deficit (VPD)), land surface temperature (Land Surface Temperature (LST) day and night)), and soil moisture content (Soil Moisture (SM)) along their range of values. We found that SLA had the strongest relationships with parameters involved in the regulation of atmospheric water content and land surface temperature, but weak relationships with regulation of soil moisture content, for the Amazon basin and its sub-basins. Plant traits show even stronger relationships at the 5th and the 95th quantiles; this is particularly strong at low values of ET and PET and high values of VPD and LST. The associations remain strong and localised in some particular sub-basins. Our results highlight the role of plant traits in mediating hydrological processes, which are not yet included in current models. Further, the results suggest that if climate change induces shifts in water cycle parameters to more extreme values, the functional response of plants may exacerbate these effects and affect the resilience of the Amazon forest.

Received: 12 Aug 2024 – Discussion started: 06 Sep 2024

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.

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Kien Nguyen and Maria J. Santos

Status: closed

RC1:
'Comment on egusphere-2024-2544', Anonymous Referee #1, 25 Oct 2024

The authors present a large number of quantile regression analyses modelling the linear relationships between modelled plant functional traits, and long-term averages of climate data across the Amazon basin, which they call 'parameters'.

I apologise, as my comments will sound harsh and I can fully believe the lead author has done a lot of work to produce this. I would also like to state that I did read and attempt to comprehend the manuscript. However in my opinion, the manuscript is poorly organised, written, and ultimately very difficult to read and review. The abstract is too confusing for me to understand what the analysis was. The introduction is unfocused and loaded with long paragraphs containing generic statements about plant functional traits and vegetation hydrology. The methods are exceptionally brief and under detailed. Figure 1 is useful, but the other figures have serious presentation issues. Overall, I found it very difficult to comprehend what this manuscript was about, the justification for its analyses, and its results.

General comments:
* The problem statement premise of this manuscript appears to be:
"however, detailed understanding on how feedbacks that involve plant traits develop are still seldom included in observational, experimental and modelling studies."
but this is a strawman argument. The authors do not cite studies to support this argument. Many studies are cited, but there is no coherent narrative. It is a jumbled and superficial description of plant functional traits and vegetation hydrology.

* A number of off the shelf data products are used uncritically. I note some lip service is paid to this in the final paragraph of the discussion, but that did not seem to influence the rationale of the analyses. There does not appear to be any thought given to the evaluation of the accuracy of these products, particularly with respect to their extreme values. In any process or empirical modelling context, the extreme values tend to be the hardest to accurately predict. This study seems to be based on linear relationships of the extreme values of modelled plant functional traits to the extreme values of modelled climate data. I think this results in regressions of speculative model noise and outliers against other model outliers.

* I also must note that weather and climate data (which are intrinsically dynamic) are not typically called parameters (which are usually static in a modelling context), nor is it very compelling to frame a story around 'parameters'.

* I question the logic of the assumed causality in the conclusions presented here. Why is it concluded that the plant functional traits will affect VPD and LST, and not vice-versa? In reality, this is at least partially circular. LST, or rather the departure of LST from Tair will reflect latent heat flux. Plant transpiration is primarily a function of stomatal conductance and VPD, amongst other things. But the manuscript does not delve into what modulates stomatal conductance, nor relate it (stomatal slope) to the modelled plant functional traits. Ultimately, I think this analysis is naively empirical and ignores decades of theory from ecophysiology and land-surface modelling. My impression is that this represents a data mining exercise (48 data combinations with three quantiles)

* The data analysis paragraph is exceptionally short and does not provide sufficient detail for me to begin to understand what was done, nor how. Were these single or multiple regressions? This is far from reproducible.

* I did not understand the justification for relating these regressions to the sub-basins.
* The highest R2 value is 0.082, which is still quite low, nor does there appear to be any attempt at correcting for model overfitting. If one fits a large number of models to random data, I would expect some occasional high R2 values.

* There are 24 appendix tables. This seems extreme. Who will read these and for what purpose? Their presence is not strongly justified in the main text. The only appendix table referenced in the main text is A1.

Figure comments:
Figure 2: All of these panels are lacking units, and these scalebars are not colorblind friendly. The numbers on LSTDAY range from 13750 to 15500. What does this correspond to? Average based on what time period? This is really lacking a lot of standard details.

Figure 3: I am at a loss for how to interpret this. Does every single row for each panel represent the R2 of a different quantile regression model?

Figures 4, 5: Again, I am at a loss for how to interpret this. What do the numbers on the x-axis correspond to? Specifically what does standardised mean on the y-axis? Z-score transformed?

Citation: https://doi.org/10.5194/egusphere-2024-2544-RC1
- AC1: 'Reply on RC1', Kien Nguyen, 20 Dec 2024
  
  With the attached letter, we provide our reply to the comments provided by RC1 to our submitted manuscript. We appreciate the time that RC1 took to review our work and have seriously considered and appreciated all the comments by the reviewer. We detail in the letter our plan to address the comments in a revised version.
  
  Citation: https://doi.org/10.5194/egusphere-2024-2544-AC1
RC2:
'Comment on egusphere-2024-2544', Anonymous Referee #2, 28 Feb 2025

This study aims to investigate the effects of plant traits on regulating water cycle processes in the Amazon basin. However, the author refers to ‘water process parameters’: yet, ET, PET, VPD, LST, and SM are not strictly ‘parameters’. Additionally, the paper lacks clear organization; the Discussion section contains significant overlap with the Introduction.

Abstract: could the author specify how much of the variation in ET, PET, and other variables can be explained by plant traits?

Clarity of research focus - *... to examine how plant traits respond to parameters related to regulation of atmospheric water content …* This is unclear. It suggests an analysis of how external processes influence plant traits, while the rest of the paper seems to focus on the opposite direction - how plants regulate water cycle processes. This should be clarified.

*We found that SLA had the strongest relationships with parameters … * The author should specify the strength of these relationships - e.g. correlation coefficients.

Do Figure 4 and Figure 5 provide new insights, or do they largely repeat the information already presented in Figure 3?

While the paper highlights the importance of plant trait diversity and spatial heterogeneity, it assumes a unidirectional relationship between plant traits and water cycle processes. In reality, these interactions are bidirectional—plant traits regulate transpiration, leaf temperature, and soil moisture dynamics, but environmental conditions also influence plant traits through acclimation or adaptation. The paper focuses on analyzing forward relationships, but this approach alone may not be sufficient. I strongly suggest employing causality inference methods to examine these relationships in a more robust manner.

Minor comments

L260-263: The author should provide stronger evidence to support their claims rather than attributing unexpected results solely to data limitations. Simply stating that the data are insufficient is not a compelling explanation.

Establishing a direct link between LPC and VPD seems problematic, as VPD is primarily a meteorological factor. While vegetation dynamics can influence background climate conditions, this relationship should be better justified.

Section 4.3: Why do plant traits significantly relate to transpiration but not to soil moisture? A more detailed discussion or additional analysis would be helpful.

Figure 1: I suggest using illustrations (e.g., arrows, plus/minus symbols) to better depict the proposed mechanisms while keeping explanatory text within the main text. Additionally, the left-side arrow should be explicitly labeled (e.g., transpiration and soil rehydration) for clarity.

Figure 2: the unit for each panel is missing. What is the unit of LST? And the tick labels on both axes are wrong. You may either leave them blank or use the correct latitude and longitude like panel (a).

The caption for Table 1 is too brief.

Citation: https://doi.org/10.5194/egusphere-2024-2544-RC2
- AC2: 'Reply on RC2', Kien Nguyen, 01 Mar 2025
  
  With the attached letter, we provide our reply to the comments provided by RC2 to our submitted manuscript. We appreciate the time and effort that RC2 took to review our work.
  
  Citation: https://doi.org/10.5194/egusphere-2024-2544-AC2

Status: closed

RC1:
'Comment on egusphere-2024-2544', Anonymous Referee #1, 25 Oct 2024

The authors present a large number of quantile regression analyses modelling the linear relationships between modelled plant functional traits, and long-term averages of climate data across the Amazon basin, which they call 'parameters'.

I apologise, as my comments will sound harsh and I can fully believe the lead author has done a lot of work to produce this. I would also like to state that I did read and attempt to comprehend the manuscript. However in my opinion, the manuscript is poorly organised, written, and ultimately very difficult to read and review. The abstract is too confusing for me to understand what the analysis was. The introduction is unfocused and loaded with long paragraphs containing generic statements about plant functional traits and vegetation hydrology. The methods are exceptionally brief and under detailed. Figure 1 is useful, but the other figures have serious presentation issues. Overall, I found it very difficult to comprehend what this manuscript was about, the justification for its analyses, and its results.

General comments:
* The problem statement premise of this manuscript appears to be:
"however, detailed understanding on how feedbacks that involve plant traits develop are still seldom included in observational, experimental and modelling studies."
but this is a strawman argument. The authors do not cite studies to support this argument. Many studies are cited, but there is no coherent narrative. It is a jumbled and superficial description of plant functional traits and vegetation hydrology.

* A number of off the shelf data products are used uncritically. I note some lip service is paid to this in the final paragraph of the discussion, but that did not seem to influence the rationale of the analyses. There does not appear to be any thought given to the evaluation of the accuracy of these products, particularly with respect to their extreme values. In any process or empirical modelling context, the extreme values tend to be the hardest to accurately predict. This study seems to be based on linear relationships of the extreme values of modelled plant functional traits to the extreme values of modelled climate data. I think this results in regressions of speculative model noise and outliers against other model outliers.

* I also must note that weather and climate data (which are intrinsically dynamic) are not typically called parameters (which are usually static in a modelling context), nor is it very compelling to frame a story around 'parameters'.

* I question the logic of the assumed causality in the conclusions presented here. Why is it concluded that the plant functional traits will affect VPD and LST, and not vice-versa? In reality, this is at least partially circular. LST, or rather the departure of LST from Tair will reflect latent heat flux. Plant transpiration is primarily a function of stomatal conductance and VPD, amongst other things. But the manuscript does not delve into what modulates stomatal conductance, nor relate it (stomatal slope) to the modelled plant functional traits. Ultimately, I think this analysis is naively empirical and ignores decades of theory from ecophysiology and land-surface modelling. My impression is that this represents a data mining exercise (48 data combinations with three quantiles)

* The data analysis paragraph is exceptionally short and does not provide sufficient detail for me to begin to understand what was done, nor how. Were these single or multiple regressions? This is far from reproducible.

* I did not understand the justification for relating these regressions to the sub-basins.
* The highest R2 value is 0.082, which is still quite low, nor does there appear to be any attempt at correcting for model overfitting. If one fits a large number of models to random data, I would expect some occasional high R2 values.

* There are 24 appendix tables. This seems extreme. Who will read these and for what purpose? Their presence is not strongly justified in the main text. The only appendix table referenced in the main text is A1.

Figure comments:
Figure 2: All of these panels are lacking units, and these scalebars are not colorblind friendly. The numbers on LSTDAY range from 13750 to 15500. What does this correspond to? Average based on what time period? This is really lacking a lot of standard details.

Figure 3: I am at a loss for how to interpret this. Does every single row for each panel represent the R2 of a different quantile regression model?

Figures 4, 5: Again, I am at a loss for how to interpret this. What do the numbers on the x-axis correspond to? Specifically what does standardised mean on the y-axis? Z-score transformed?

Citation: https://doi.org/10.5194/egusphere-2024-2544-RC1
- AC1: 'Reply on RC1', Kien Nguyen, 20 Dec 2024
  
  With the attached letter, we provide our reply to the comments provided by RC1 to our submitted manuscript. We appreciate the time that RC1 took to review our work and have seriously considered and appreciated all the comments by the reviewer. We detail in the letter our plan to address the comments in a revised version.
  
  Citation: https://doi.org/10.5194/egusphere-2024-2544-AC1
RC2:
'Comment on egusphere-2024-2544', Anonymous Referee #2, 28 Feb 2025

This study aims to investigate the effects of plant traits on regulating water cycle processes in the Amazon basin. However, the author refers to ‘water process parameters’: yet, ET, PET, VPD, LST, and SM are not strictly ‘parameters’. Additionally, the paper lacks clear organization; the Discussion section contains significant overlap with the Introduction.

Abstract: could the author specify how much of the variation in ET, PET, and other variables can be explained by plant traits?

Clarity of research focus - *... to examine how plant traits respond to parameters related to regulation of atmospheric water content …* This is unclear. It suggests an analysis of how external processes influence plant traits, while the rest of the paper seems to focus on the opposite direction - how plants regulate water cycle processes. This should be clarified.

*We found that SLA had the strongest relationships with parameters … * The author should specify the strength of these relationships - e.g. correlation coefficients.

Do Figure 4 and Figure 5 provide new insights, or do they largely repeat the information already presented in Figure 3?

While the paper highlights the importance of plant trait diversity and spatial heterogeneity, it assumes a unidirectional relationship between plant traits and water cycle processes. In reality, these interactions are bidirectional—plant traits regulate transpiration, leaf temperature, and soil moisture dynamics, but environmental conditions also influence plant traits through acclimation or adaptation. The paper focuses on analyzing forward relationships, but this approach alone may not be sufficient. I strongly suggest employing causality inference methods to examine these relationships in a more robust manner.

Minor comments

L260-263: The author should provide stronger evidence to support their claims rather than attributing unexpected results solely to data limitations. Simply stating that the data are insufficient is not a compelling explanation.

Establishing a direct link between LPC and VPD seems problematic, as VPD is primarily a meteorological factor. While vegetation dynamics can influence background climate conditions, this relationship should be better justified.

Section 4.3: Why do plant traits significantly relate to transpiration but not to soil moisture? A more detailed discussion or additional analysis would be helpful.

Figure 1: I suggest using illustrations (e.g., arrows, plus/minus symbols) to better depict the proposed mechanisms while keeping explanatory text within the main text. Additionally, the left-side arrow should be explicitly labeled (e.g., transpiration and soil rehydration) for clarity.

Figure 2: the unit for each panel is missing. What is the unit of LST? And the tick labels on both axes are wrong. You may either leave them blank or use the correct latitude and longitude like panel (a).

The caption for Table 1 is too brief.

Citation: https://doi.org/10.5194/egusphere-2024-2544-RC2
- AC2: 'Reply on RC2', Kien Nguyen, 01 Mar 2025
  
  With the attached letter, we provide our reply to the comments provided by RC2 to our submitted manuscript. We appreciate the time and effort that RC2 took to review our work.
  
  Citation: https://doi.org/10.5194/egusphere-2024-2544-AC2

Kien Nguyen and Maria J. Santos

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Short summary

Plants affect the Amazon's water cycle through processes such as transpiration and evaporation. Characteristics related to leaf size and biomass have the most significant impact on atmospheric water content and land surface temperature, but they affect soil moisture content only minimally. These relationships also vary at extreme water cycle values and across Amazon subbasins. The study underscores the critical role of plants in maintaining the resilience of the Amazon’s hydrological processes.


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