the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 17 Oct 2024
TROLL 4.0: representing water and carbon fluxes, leaf phenology and intraspecific trait variation in a mixed-species individual-based forest dynamics model – Part 1: Model description
Abstract. TROLL 4.0 is an individual-based forest dynamics model that is capable of jointly simulating forest structure, diversity and ecosystem functioning, including the ecosystem water balance and productivity, leaf area dynamics and the tree community functional and taxonomic composition. It represents ecosystem flux processes in a manner similar to dynamic global vegetation models, while adopting a representation of plant community structure and diversity at a resolution consistent with that used by field ecologists. Specifically, trees are modeled as three-dimensional individuals with a metric-scale spatial representation, providing a detailed description of ecological processes such as competition for resources and tree demography. Carbon assimilation and plant water loss are explicitly represented at tree level using coupled photosynthesis and stomatal conductance models, depending on the micro-environmental conditions experienced by trees. Soil water uptake by trees is also modelled. Physiological and demographic processes are parameterized using plant functional traits measured in the field. Here we provide a detailed description and discussion of the implementation of TROLL 4.0. An evaluation of the model at two tropical forest sites is provided in a companion paper (Schmitt et al., submitted companion paper). TROLL 4.0’s representation of processes reflects the state of the art, and we discuss possible developments to improve its predictive capability and its capacity to address challenges in forest monitoring, forest dynamics and carbon cycle research.
- Preprint
(3197 KB) - Metadata XML
- BibTeX
- EndNote
Status: open (until 12 Dec 2024)
-
CEC1: 'Comment on egusphere-2024-3104: No compliance with the policy of the journal', Juan Antonio Añel, 30 Oct 2024
reply
Dear authors,
Unfortunately, after checking your manuscript, it has come to our attention that it does not comply with our "Code and Data Policy".
https://www.geoscientific-model-development.net/policies/code_and_data_policy.htmlYou have archived your code on GitHub. However, GitHub is not a suitable repository for scientific publication. GitHub itself instructs authors to use other long-term archival and publishing alternatives, such as Zenodo. Therefore, the current situation with your manuscript is irregular, as no manuscript can be accepted in Discussions without fully comply with the code policy of the journal. Statements like the one you include in your manuscript, saying that the code will be stored after acceptance of the manuscript for publication, are not acceptable. Therefore, you must publish your code in one of the appropriate repositories and reply to this comment with the relevant information (link and a permanent identifier for it (e.g. DOI)) as soon as possible, as we can not accept manuscripts in Discussions that do not comply with our policy.
Please, note that if you do not fix this problem, we will have to reject your manuscript for publication in our journal.
Also, you must include the modified 'Code and Data Availability' section in a potentially reviewed manuscript, the DOI of the code.
Moreover, I have found that only one of your posted git has a license (GPLv3); for the other no license is listed. If you do not include a license the code remains your property and nobody can use it. Therefore, when uploading the model's code to new repositories, you could want to choose a free software/open-source (FLOSS) license. We recommend the GPLv3, which you already uses for the part of the code you have posted in GitHub.
Juan A. Añel
Geosci. Model Dev. Executive EditorCitation: https://doi.org/10.5194/egusphere-2024-3104-CEC1 -
AC1: 'Reply on CEC1', Isabelle Maréchaux, 31 Oct 2024
reply
Dear Editor,
We are sorry that the way we archived the code associated to our two submitted companion manuscripts did not comply with the code policy of Geoscientific Model Development.
We have solved this issue by creating three new Zenodo repositories:
1. A first one for the C++ code of TROLL 4.0:
Maréchaux, I., Fischer, F. J., Schmitt, S., & Chave, J. (2024). TROLL-code/TROLL: GMD preprint (4.0.0-GMD). Zenodo. https://doi.org/10.5281/zenodo.14013147
2. A second one for the R wrapper of this C++ code, rcontroll:
Schmitt, S., Salzet, G., Fischer, F.J., Maréchaux, I., & Chave, J. (2024). sylvainschmitt/rcontroll: GMD preprint (v0.2.0). Zenodo. https://doi.org/10.5281/zenodo.14012116
3. A third one for the R scripts used to perform all the simulations and analyses of our evaluation manuscript:
Schmitt, S. (2024). sylvainschmitt/troll_eval: GMD preprint (0.1.0). Zenodo. https://doi.org/10.5281/zenodo.14012085
The three citations and associated DOI will be added to any future revised version of our two manuscripts, in the “Code and Data Availability” section, as well as in the reference lists.
The three repositories have a GPLv3 license as recommended.
We hope that these modifications will make our manuscripts appropriate for publication in Geoscientific Model Development.
Yours sincerely,
Isabelle Maréchaux & Sylvain Schmitt, on behalf of all co-authors
Citation: https://doi.org/10.5194/egusphere-2024-3104-AC1 -
CEC2: 'Reply on AC1', Juan Antonio Añel, 31 Oct 2024
reply
Dear authors,
Thanks for addressing this issue so quickly. I have checked the repositories and we can consider now the current version of your manuscript in compliance with the code policy of the journal.
Juan A. Añel
Geosci. Model Dev. Executive Editor
Citation: https://doi.org/10.5194/egusphere-2024-3104-CEC2
-
CEC2: 'Reply on AC1', Juan Antonio Añel, 31 Oct 2024
reply
-
AC1: 'Reply on CEC1', Isabelle Maréchaux, 31 Oct 2024
reply
-
RC1: 'Comment on egusphere-2024-3104', Xiangtao Xu, 12 Nov 2024
reply
The manuscript describes a new version of TROLL, which represents 3D variation in canopy heterogeneity and individual-level dynamics that can best match field observations. The main development is to include (1) water fluxes, plant water stress, and some degree of micro-environment variability, (2) random intra-species variation, and (3) more realistic crown shapes.
As a vegetation modeler, I read the manuscript and the companion paper with great interests because representing species-level dynamics and 3D heterogeneity are important yet challenging in predicting tropical forest dynamics. I appreciate the efforts by the authors to push in this direction. At the same time, I feel the organization and presentation can be improved to better highlight the novel development and some of the model assumptions warrant justification and further discussions.
Overall, I would recommend reporting more key intermediate variables in the new modules (beyond ecosystem-level outputs in Fig. 4), such as predawn water potential, stomatal conductance, evaporation/transpiration partitioning, LAI_opt, etc. These will help the readers understand the behavior of the model and better interpret the other companion paper. Given the unique 3D configuration, it would be cool to show the simulated vertical/horizontal variation of the new processes/variables.
In addition, the leaf phenology section is tough to read through. Some schematic figures to explain the phenological dynamics would be helpful.
Finally, it can be useful to conduct/report some sensitivity tests of key model parameters (e.g. crown area allometry, rooting allometry, vertical temperature/VPD gradient etc.) so that we know how results might change with these parameters qualitatively.
Below I list my specific comments along the order of the manuscript
Line 52-65, there are some efforts to extend gap models regional to global scales. See the two reviews by H. Shugart
Shugart, H. H., Wang, B., Fischer, R., Ma, J., Fang, J., Yan, X., Huth, A., & Armstrong, A. H. (2018). Gap models and their individual-based relatives in the assessment of the consequences of global change. Environmental Research Letters, 13(3), 033001. https://doi.org/10.1088/1748-9326/aaaacc
Shugart, H. H., Foster, A., Wang, B., Druckenbrod, D., Ma, J., Lerdau, M., Saatchi, S., Yang, X., & Yan, X. (2020). Gap models across micro- to mega-scales of time and space: examples of Tansley’s ecosystem concept. Forest Ecosystems, 7(1), 14. https://doi.org/10.1186/s40663-020-00225-4
Line 168-176: Does this radiative transfer scheme consider solar geometry? It seems to me LAI(v) only considers the LAI right above the voxel, which means the model does not consider the diurnal and seasonal changes in solar zenith angle. This seems to be too simplistic to me, which can lead to biases in leaf physiology predictions. In addition, is *k* a fixed global parameter or species-specific?
Line 177-188: this LAI-based extrapolation of temperature and VPD seem problematic to me for at least three reasons. First, this scheme means both canopy and understory have the same diurnal temperature range based on eqn. 4 but understory will just be a few degrees cooler. In reality, canopy temperature has large diurnal variations (due to radiative heating) while understory has smaller variations (more similar to air temperature). One idea is to use radiation/air temperature to estimate crown-level temperature (e.g. following Rey-Sanchez et al. 2016). Second, such empirical extrapolation will likely violate the physical linkage between T and VPD. A better method is to interpolate relative humidity and temperature, then calculate VPD based on RH and T. Third, these empirical relationships might not hold when making future predictions under novel climate regimes. Ideally, energy balance should be tracked but I understand it might be too 'heavy' for a 3D model.
Rey-Sánchez, A. C., Slot, M., Posada, J. M., & Kitajima, K. (2016). Spatial and seasonal variation in leaf temperature within the canopy of a tropical forest. Climate Research, 71(1), 75–89. https://doi.org/10.3354/cr01427
Line 218-227. It might be better to expand equation 10 into 2, one for top layer one for other sub-surface layer.
Line 254: how is soil temperature calculated?
Line 274: it is good to see van Genuchten equation is used here!
Line 285: it is unclear to me how LA is used in the model. Is it used to calculate LAD of each voxel in the crown? If so, how?
Line 296: it seems that there is no light-associated vertical trait gradient (or, light-driven trait plasticity) in the model, but this plasticity has actually become a common feature in many vegetation models. Besides, our work with ED2 model (e.g. the method section in Xu et al. 2021 and a manuscript under review) has shown that light-driven plasticity is critical to model realistic LAD profile in tropical forests. And representing realistic environmental plasticity is recognized as a research priority (Fisher & Koven 2020; Xu & Trugman 2021)
Fisher, R. A., & Koven, C. D. (2020). Perspectives on the Future of Land Surface Models and the Challenges of Representing Complex Terrestrial Systems. Journal of Advances in Modeling Earth Systems, 12(4). https://doi.org/10.1029/2018ms001453
Xu, X., & Trugman, A. T. (2021). Trait-Based Modeling of Terrestrial Ecosystems: Advances and Challenges Under Global Change. Current Climate Change Reports, 7(1), 1–13. https://doi.org/10.1007/s40641-020-00168-6
Xu, X., Konings, A. G., Longo, M., Feldman, A., Xu, L., Saatchi, S., Wu, D., Wu, J., & Moorcroft, P. (2021). Leaf surface water, not plant water stress, drives diurnal variation in tropical forest canopy water content. New Phytologist, 231(1), 122–136. https://doi.org/10.1111/nph.17254
L 304: If allometry has a random term, how is growth in height tracked in simulations? Wouldn't it cause troubles in carbon conservation when converting NPP to woody and height growth? Or this is only used during initialization?
L 319: "the treetop grows quicker in height than the outer crown parts" reads confusing to me. How quicker? what functions are used to determine the difference? How is carbon/leaf area balance is maintained in the process? In addition, how is total leaf area calculated for each tree crown? Is any leaf area allometry used? If leaf area allometry is an emerging feature of the model, is it consistent with observations (e.g. the BAAD data base, Falster et al. 2015)
Falster, D. S., Duursma, R. A., Ishihara, M. I., Barneche, D. R., FitzJohn, R. G., Vårhammar, A., Aiba, M., Ando, M., Anten, N., Aspinwall, M. J., Baltzer, J. L., Baraloto, C., Battaglia, M., Battles, J. J., Lamberty, B. B., van Breugel, M., Camac, J., Claveau, Y., Coll, L., … York, R. A. (2015). BAAD: a Biomass And Allometry Database for woody plants. Ecology, 96(5), 1445. https://doi.org/10.1890/14-1889.1
L 357: I believe the horizontal extent of the root is usually bigger than crown. You might check Schenk et al. 2002 for some global meta-analysis of lateral root extent
Schenk, H. J., & Jackson, R. B. (2002). Rooting depths, lateral root spreads and below-ground/above-ground allometries of plants in water-limited ecosystems. Journal of Ecology, 90(3), 480–494. https://doi.org/10.1046/j.1365-2745.2002.00682.x
L. 391: Sounds like Ca has vertical gradient in the model? How is the gradient simulated in the model?
L. 420: internal leaf temperature reads confusing, does the model separates leaf surface temperature and internal temperature?
L. 426: Isn't photorespiration already accounted for in the Farquhar model? Or do you mean reduction of daytime respiration due to Kok effect?
L 479-480: for tropical forests, many recent work by M Slot has evaluated non-stomatal limitation....
L 508: In Xu et al. (2016) we applied this limitation at sub-daily scale instead of daily scale in order to better capture midday suppression in photosynthesis and stomata functioning. To be honest, I am not aware of solid physiological support for such subdaily non-stomatal effects. That being said, applying this at daily scale won't have much effects (psi_pd is usuallal much higher than psi_tlp)
L 531-582: The "leaf energy balance" from Penman-Monteith equation is interesting... I am not sure whether this is actually circular given gs as already been calculated. In my understanding, Tl and VPD are 'inferred' to match Medlyn gs with Penman-Monteith ET... This does not seem right to me. Again, figures of simulated diurnal/seasonal cycle of Tl and VPDs would be helpful.
L 610: what does "conversion factor" mean? convert to what?
L. 681: great to see woody branch turnover is included in the model! They are important!
L 702-704: interesting way to simulate increasing in new leaves and loss of old leaves in dry season but it still only assumes water is the main driver of phenology? Where does leaf age limitation comes into play in this scheme?
L 706-712: I have a hard time to interpret these parameterizations and how they influence phenology. It would be helpful to plot a diagram of the phenological cycle under different a/b/delta parameters.
L 739-740: what is the fate of the senescenced NPP? Are they lost to respiration?
L 761: what is reproduction opportunity? established 1cm sapling?
Line 879 Fig.4 panel (a) Gross not Growth, panel (d) Psi_TLP is hard to read. In addition to these figures, it would be helpful to show carbon balance at individual tree to ecosystem level (I guess only carbon conservation is tracked?)
L 973: how many CPUs (what type of CPU) are used?
L 1034: I am surprised that the C++ codes are all in a single cpp file for TROLL. From a software engineering perspective, this is not the best practice to organize the codes of 10k+ lines. I would recommend modularization in the future to facilitate model development and sharing....
Citation: https://doi.org/10.5194/egusphere-2024-3104-RC1
Viewed
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 173 | 66 | 10 | 249 | 3 | 1 |
- HTML: 173
- PDF: 66
- XML: 10
- Total: 249
- BibTeX: 3
- EndNote: 1
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1