the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 06 Sep 2024
Effects of plant traits on the regulation of water cycle processes in the Amazon Basin
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.
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RC1: 'Comment on egusphere-2024-2544', Anonymous Referee #1, 25 Oct 2024
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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'.Â
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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.Â
General comments:Â* The problem statement premise of this manuscript appears to be:Â
"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. Â
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* 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.Â
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* 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'.Â
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* 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)
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* 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.Â
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* I did not understand the justification for relating these regressions to the sub-basins.Â
* 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.Â
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* 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.Â
Figure comments:ÂFigure 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.Â
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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?
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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?Â
Citation: https://doi.org/10.5194/egusphere-2024-2544-RC1 -
AC1: 'Reply on RC1', Kien Nguyen, 20 Dec 2024
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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.
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AC1: 'Reply on RC1', Kien Nguyen, 20 Dec 2024
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