the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Forest fluxes and mortality response to drought: model description (ORCHIDEE-CAN-NHA, r7236) and evaluation at the Caxiuanã drought experiment
- 1Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif-sur-Yvette, 91191, France
- 2Centre National de Recherche Meteorologique, Unite mixte de recherche 3589 Meteo-France/CNRS, 42 Avenue Gaspard Coriolis, Toulouse, 31100, France
- 3Laboratoire Evolution et Diversité Biologique UMR 5174 CNRS, IRD, Université Paul Sabatier, Toulouse, 31062, France
- 4Department of Ecology and Evolutionary Biology, University of California Los Angeles, Los Angeles, California, 90095, USA
- 5Department of Viticulture & Enology, University of California, Davis, California, 95616, USA
- 6Research School of Biology, Australian National University, Canberra, ACT 2601 Australia
- 7CICERO Centre for International Climate and Environmental Research, Oslo, Norway
- 8Faculty of Science, Vrije Universiteit Amsterdam, Netherlands
- These authors contributed equally to this work.
- 1Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif-sur-Yvette, 91191, France
- 2Centre National de Recherche Meteorologique, Unite mixte de recherche 3589 Meteo-France/CNRS, 42 Avenue Gaspard Coriolis, Toulouse, 31100, France
- 3Laboratoire Evolution et Diversité Biologique UMR 5174 CNRS, IRD, Université Paul Sabatier, Toulouse, 31062, France
- 4Department of Ecology and Evolutionary Biology, University of California Los Angeles, Los Angeles, California, 90095, USA
- 5Department of Viticulture & Enology, University of California, Davis, California, 95616, USA
- 6Research School of Biology, Australian National University, Canberra, ACT 2601 Australia
- 7CICERO Centre for International Climate and Environmental Research, Oslo, Norway
- 8Faculty of Science, Vrije Universiteit Amsterdam, Netherlands
- These authors contributed equally to this work.
Abstract. Extreme drought events in Amazon forests are expected to become more frequent and more intense with climate change, threatening ecosystem function and carbon balance. Yet large uncertainties exist on the resilience of this ecosystem to drought. A better quantification of tree hydraulics and mortality processes is needed to anticipate future drought effects on Amazon forests. Most state-of-the-art dynamic global vegetation models are relatively poor in their mechanistic description of these complex processes. Here, we implement a mechanistic plant hydraulic module within the ORCHIDEE-CAN-NHA r7236 land surface model to simulate the percentage loss of conductance (PLC) and changes in water storage among organs via a representation of the water potentials and vertical water flows along the continuum from soil to roots, stems and leaves. The model was evaluated against observed seasonal variability in stand-scale sap flow, soil moisture and productivity under both control and drought setups at the Caxiuanã throughfall exclusion field experiment in eastern Amazonia between 2001 and 2008. A relationship between PLC and tree mortality is built in the model from two empirical parameters, the cumulated drought exposure duration that triggers mortality, and the mortality fraction in each day exceeding the exposure. Our model captures the large biomass drop in the year 2005 observed four years after throughfall reduction, and produces comparable annual tree mortality rates with observation over the study period. Our hydraulic architecture module provides promising avenues for future research in assimilating experimental data to parameterize mortality due to drought-induced xylem dysfunction. We also highlight that species-based (isohydric or anisohydric) hydraulic traits should be further tested to generalize the model performance in predicting the drought risks.
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Yitong Yao et al.
Status: final response (author comments only)
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RC1: 'Comment on gmd-2021-362', Anonymous Referee #1, 11 Mar 2022
In this paper, Yao et al. developed and implemented a new plant hydraulic architecture module “NHA” into ORCHIDEE-CAN based on soil-root-stem-leaf water transport continuum and the relationship between PLC and tree mortality. They compared the model performance of NHA model with two previous versions of the model to prove the efficacy of the new model in capturing the change of sap flow, soil moisture content, and GPP under drought events. They also evaluated model results against field measurements of leaf water potentials, biomass and mortality rates from a tropical lowland rainforest in eastern Amazonia. Their results show great potential of the NHA model to capture the drought-related tree biomass loss and mortality for tropical forests. The new model represents the state-of-the-art development of plant hydraulic model and will be of interest to the research community and readers of GMD. This paper is well written, and the results are nicely presented. I have some general comments as below.
For improvement, first, they should fit their new model into a broader field of mechanistic plant hydraulic models. They mentioned some previous work such as SPA model and Xu et al. (2016) but it’s still not very clear how they were motivated, how the new model was built on, and what are the strengths and weaknesses of their new model compared with other similar plant hydraulic models. They had some discussion starting from Line 547, but adding more details would be great.
Second, one of the key limitations of the usage of such plant hydraulic models is numerous parameters, as shown in Table 1 in this paper. The authors focused on one site simulation with well-recorded plants’ traits. However some topics such as how sensitive and uncertain these parameters are, and how to parameterize the model at the regional and global scales might be interesting to add to the discussion. The authors may find this paper relevant to their discussion:
Liu, Y., Kumar, M., Katul, G. G., Feng, X., & Konings, A. G. (2020). Plant hydraulics accentuates the effect of atmospheric moisture stress on transpiration. Nature Climate Change, 10(7), 691-695.
Third, some references when the authors described the equations in Methods are missing. More information about throughfall exclusion experiment and model simulation set up is needed as well.
Below, I provide more specific comments:
-Line 225: Any references for the sigmoidal relationship? How about other relationships such as linear, logistic, or exponential?
-Line 275: Please provide reference and a simple description for the gs model. L is not defined either.
-Line 280: What’s the gs model in the SPA model, is that the same one used in this study?
-Line 332: How is LAI modeled in this study?
-Line 346: More information such as the plot size and duration of the experiment about the TFE site could be added here so readers don’t need to read the cited papers.
-Line 353: What are the similarities and differences between SPA model and your model?
-Line 360: What meteorological forcing was used to drive the model, at what temporal resolution? Were the simulations coupled with a climate model or offline?
How was the TFE simulation carried out? Was the precipitation be reduced to 50% of CTL level at each model time step?
Is the model also initialized with real forest inventory data? How do 20 circumference classes correspond to the real-world situation?
-Line 365: Past tense for “run”, and “compare” in line 367.
-Line 384: Could the authors discuss why their new model underestimated sap flow in the dry season but overestimated it under TFE conditions?
-Line 421: What mechanism leads to the larger seasonal amplitude of modeled GPP compared with SPA model?
-Line 550: What’s the leaf-level demand of Xu et al. (2016)?
-Figure 2: Color for Ψ50 = -1.6 is too weak to be seen.
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RC2: 'Comment on gmd-2021-362', Anonymous Referee #2, 01 Apr 2022
This paper describes the implementation of a hydraulics scheme into the land surface model ORCHIDEE and its evaluation against the Caixuana drought experiment. In general, this is a nice piece of work, but the presentation could be quite significantly improved.
One important point is the need to show how the model simulations have changed since the new routines were added. The figures only show output from the new version of the model. To assess the value of the added subroutines, the paper needs to show output from previous versions of the model for comparison. There is some in the supplementary, but there is insufficient quantitative assessment of how each version of the model performs. The R values for sap flow are lower in the new model version than the previous one, suggesting a degradation of model performance. The comparison of GPP with previous models is qualitative only. It would be valuable to add some statistics to compare performance of different model versions.
There were quite a few questions about the model description.
It would be of great value to go through the symbols used and try to make them consistent, instead of using a mixture of abbreviations and symbols. It is confusing to have WD the wood density, rho-root the root density, WC the amount of water per unit volume of sapwood, and rootwc the amount of water per gram root biomass. Try to come up with a more systematic set of symbols. In particular, avoid abbreviations instead of symbols (e.g. use D rather than dbh) and avoid using variable names from code such as circ_class_mor or counterPLC50. Give these symbols. Also use capitals consistently, e.g. Cleaf, Cstem and Croot should all have capital “C”.
Ensure to give all units clearly in text and ensure they are consistent. For example, is capacitance in units of mmol (line 203) or mmol m-2 MPa-1 (line 207) or in kg m-3 MPa-1 (Table A1)? I suggest checking over all of the equations thoroughly to ensure units are correct throughout the text.
Eqn 4: msap,max = vstem *WC
Vstem is the volume of a cylinder of diameter DBH and height h (eqn 6) so overestimates volume of a stem. How is the stem form factor corrected for? How is this then converted to sapwood?
WC is defined as the mass of water per unit sapwood volume in mol m-3. It should be defined as the maximum mass of water, or the mass of water when water potential = 0. Clarify, is this per unit sapwood volume or per unit stem volume?
Figure 2 does not seem important or relevant enough to include as a main figure. It just shows the form of the sigmoidal relationship for different parameter values. The different values are not used in the paper, however, so it’s not clear why this wide range of parameter values are shown.
What happens when the canopy is wet? I note that in Figure 4, the canopy evaporation is a tiny fraction of ET, which seems very unlikely for this wet, high-LAI forest. These numbers need a reality check.
I found the representation of Tdemand (eqn 23) to be remarkably simple – one would normally expect a land surface model such as Orchidee to have a more complex representation of T, including a boundary layer conductance and some scaling of gs to the canopy. Is the canopy transpiration the same as Tdemand?
Does this value of gs affect assimilation? How has the assimilation (and GPP) calculation changed?
Be more specific about how water potentials are found.
Line 270: we decrease leaf water potential until the difference between leaf water supply and demand is “close to zero” – How does this algorithm work? How close is tolerable?
Line 294 and line 304: we “try to solve” Why only “try” ? How is the water potential found, and what happens if one can’t be found?
Give some indication of how parameter values are chosen. The table does list references, but it is not clear how values are chosen from the references.
It’s unfortunately not acceptable to refer to other papers that are still in review. The Joetzer et al. (in review) paper was not accepted in Biogeosciences in 2018.
https://bg.copernicus.org/preprints/bg-2018-308/
The fact it has not yet appeared raises some questions. This paper does rely quite heavily on that one, so it seems essential that that paper be accepted before this one can be. There may of course be some extenuating circumstances.
Section 2.1.6 is quite disconnected from the rest of the model implementation and it is not clear what has changed here from previous versions of the model.
It would be valuable to add more interpretation of the outputs of the model in terms of underlying assumptions. For example, it’s noted that leaf water potentials are lower in the taller trees. The effect of height should be about -0.1MPa / 10m. Once this is accounted for there are similar LWPs across cohorts, which is somewhat surprising given that cohorts have different rooting depth and see different soil moisture. There also doesn’t appear to be a lot of difference in the PLC by cohort (Figure 9). The discussion later talks about the larger mortality rates in large trees, but it’s not clear how this arises from the model structure. It would be useful to talk through how this works in the text.
It does also seem odd that the lower soil layers dry out much more than the upper soil layers. It seems that the plants are preferentially using water from lower in the soil profile. Again, it would be useful to talk through what the model is doing in terms of water uptake.
Smaller points:
Line 200 mentions “the first time-step” but is that just the very first half hour of a ten year simulation or is it every day? If water potentials are assumed the same in the first time step, what value do they take?
Please justify eqns 14 and 15.
Line 246: Please give correct units for J. (mmol m-2 s-1 ?)
Please include values and units for the parameters in eqn 24.
Line 349, “morality” should be “mortality”
Figure 3, how is sapflow extracted from the model? Is it the same as “T” in Figure 4 and “Tsupply” in Figure S4, or are these different outputs?
Please give full figure captions for the supplementary material. What is shown in Figure S1, exactly? What do the grey bands represent? Are the values given on a half-hourly or daily basis, and if daily, how are they averaged? Do the gs values differ by cohort?
What are the values shown in Figure S2? Were the data from Lin et al. filtered to show just high-PAR values? Are the observational values in fact comparable with the modelled values?
What are the measured and modelled values shown in Figure S8? How are the modelled values averaged over the cohorts? Where are the obs measured, and what is the uncertainty?
I did go looking at the code repository but it’s very large and not clear where the new code resides. It would be useful to indicate which subroutines were modified / added in this version of the code.
Yitong Yao et al.
Yitong Yao et al.
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