Preprints
https://doi.org/10.5194/gmd-2021-59
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2021-59
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Submitted as: development and technical paper 12 Apr 2021
Submitted as: development and technical paper | 12 Apr 2021
Review status: this preprint is currently under review for the journal GMD.
Using terrestrial laser scanning to constrain forest ecosystem structure and functions in the Ecosystem Demography model (ED2.2)
- 1Computational and Applied Vegetation Ecology, Department of Environment, Ghent University, Ghent, Belgium
- 2Department of Forest Sciences, University of Helsinki, Ghent, Finland
- 3School of Forest Sciences, University of Eastern Finland, Finland
- 4NPL, Climate and Earth Observation (CEO) group, National Physical Laboratory, Teddington, UK
- 5Department of Geography, UCL, Gower Street, London WC1E 6BT, UK
- 6NERC, National Centre for Earth Observation (NCEO), UCL Geography, Gower Street, London, WC1E 6BT, UK
- 7Environmental Change Institute, School of Geography and the Environement, University of Oxford, Oxford, UK
- 1Computational and Applied Vegetation Ecology, Department of Environment, Ghent University, Ghent, Belgium
- 2Department of Forest Sciences, University of Helsinki, Ghent, Finland
- 3School of Forest Sciences, University of Eastern Finland, Finland
- 4NPL, Climate and Earth Observation (CEO) group, National Physical Laboratory, Teddington, UK
- 5Department of Geography, UCL, Gower Street, London WC1E 6BT, UK
- 6NERC, National Centre for Earth Observation (NCEO), UCL Geography, Gower Street, London, WC1E 6BT, UK
- 7Environmental Change Institute, School of Geography and the Environement, University of Oxford, Oxford, UK
Abstract. Terrestrial Biosphere Modeling (TBM) is an invaluable approach for studying plant-atmosphere interactions at multiple spatial and temporal scales, as well as the global change impacts on ecosystems. Yet, TBM projections suffer from large uncertainties that limit their usefulness. A large part of this uncertainty arises from the empirical allometric (size-tomass) relationships that are used to represent forest structure in TBMs. Forest structure actually drives a large part of TBM uncertainty as it regulates key processes such as the transfer of carbon, energy, and water between the land and atmosphere, but remains challenging to measure and reliably represent. The poor representation of forest structure in TBMs results in models that are able to reproduce observed land fluxes, but which fail to realistically represent carbon pools, forest composition, and demography. Recent advances in Terrestrial Laser Scanning (TLS) techniques offer a huge opportunity to capture the three-dimensional structure of the ecosystem and transfer this information to TBMs in order to increase their accuracy. In this study, we quantified the impacts of integrating structural observations of individual trees (namely tree height, leaf area, woody biomass, and crown area) derived from TLS into the state-of-the-art Ecosystem Demography model (ED2.2) at a temperate forest site. We assessed the relative model sensitivity to initial conditions, allometric parameters, and canopy representation by changing them in turn from default configurations to site-specific, TLS-derived values. We show that forest demography and productivity as modelled by ED2.2 are sensitive to the imposed initial state, the model structural parameters, and the way canopy is represented. In particular, we show that: 1) the imposed openness of the canopy dramatically influenced the potential vegetation, the optimal ecosystem leaf area, and the vertical distribution of light in the forest, as simulated by ED2.2; 2) TLS-derived allometric parameters increased simulated leaf area index and aboveground biomass by 57 and 75 %, respectively; 3) the choice of model structure and allometric coefficient both significantly impacted the optimal set of parameters necessary to reproduce eddy covariance flux data.
How to cite. Meunier, F., Krishna Moorthy, S. M., Peaucelle, M., Calders, K., Terryn, L., Verbruggen, W., Liu, C., Saarinen, N., Origo, N., Nightingale, J., Disney, M., Malhi, Y., and Verbeeck, H.: Using terrestrial laser scanning to constrain forest ecosystem structure and functions in the Ecosystem Demography model (ED2.2), Geosci. Model Dev. Discuss. [preprint], https://doi.org/10.5194/gmd-2021-59, in review, 2021.
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Félicien Meunier et al.
Status: open (until 07 Jun 2021)
Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor
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RC1: 'Comment on gmd-2021-59', Anonymous Referee #1, 15 Apr 2021
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The concept for this study is strong, using TLS to constrain forest structure and function in the ED2.2 model, follows a decade and a half's work on using remote sensing to constrain predictions made by ecosystem models (in reducing process and initialization errors). While this idea is worth publishing, the execution is not clear, the structure of the study needs improvement, and the actual constraining of the ED2.2 model is adequately done. Concerning this last point. Essentially you want to know how well your TLS-constrained ED2.2 simulations has fared compared to Ground-based-initializaed ED2.2 simulations and compared to bare-ground simulations. To assess the improvement you need to compare all 3 of these simulations to observed data (like GPP, plot basal area changes, and/or growth and mortality dynamics). You need to do this for both TLS-structure and TLS allometric improvements.
There are many more comments in the text attachment below.
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RC2: 'Comment on gmd-2021-59', Anonymous Referee #2, 04 May 2021
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The manuscript by Meunier et al., uses TLS data to inform coefficients of an ecosystem model's allometric equations and initial conditions, quantifies its impact, as well as testing influence of TLS information on model calibration. While the study is well thought out and generally well-written and visualized, there are some issues with both modelling and calibration protocols (in terms of both technicality and clarity). Also the manuscript remains somewhat inconclusive about the superiority of TLS-informed model predictions, or at least if that wasn't the case the manuscript needs to be revised to clearly present it as such. It's a pity Table 5 was not available for the review process. Overall, I think the study would be of interest for the community and worth publishing, however, I would strongly recommend tackling the technical issues raised by both reviewers. Line numbers below refer to the author's preprint.Title: As mentioned in the general comment above, the manuscript is rather inconclusive about the reliability of TLS informed model predictions. Even the abstract reports only the sensitivity of the results to model configuration and TLS information. Hence, it feels as if the title would reflect the study more closely if it was revised to something along the lines of "Sensitivity of ED2.2 forest ecosystem simulations to TLS informed/constrained structure and functions" (as also presented by the authors on L95 and L195).L28: "imposed openness" do you mean the FC configuration here? If yes, please revise to explicitly say "model configuration that imposes finite canopy radius dramatically influenced..."L33-34: After reading the manuscript, I wasn't quite left with a conclusion about the most adequate model structure. If you identified it, why not say it in the abstract explicitly.L81: Somewhere around this paragraph I would have expected a brief introduction about other (e.g. airborne) lidar studies with TBMs as well. Especially given that studies exist directly with the ED model, Hurtt et al. 2004 (https://doi.org/10.1890/02-5317), 2019 (https://doi.org/10.1088/1748-9326/ab0bbe), Thomas et al., 2008 (https://doi.org/10.5589/m08-036). I think this could benefit the discussion as well, e.g. what did the authors build upon the previous lidar-ED2 studies? or they can draw parallels to this study.L136-140: Appreciated the length authors went with extracting the data. However, this paragraph would benefit from further information on the overall quality of the data: what the frequency of the data is (daily, sub-daily?), how it was filtered, QA/QC'd, how the GPP was derived, what the accuracy of data retrieval from Plot digitizer software is, if there are known issues with the time series that could affect the calibration and so on.L152: I'd like to point out that the authors themselves avoid using the word "validation" here, which again reinforces my comment about inconclusiveness. In case you decide to strengthen the paper's conclusions, at least consider the word "assessment" here.L164: Agreed with the other reviewer. Why was all classified as mid-successional pft in ED? I agree that each species, at least the five on Figure 1, needs reasoning as to which PFT they were mapped to and why. Please also provide citations for mappings when possible e.g. see supplementary on https://onlinelibrary.wiley.com/doi/full/10.1111/j.1365-2486.2011.02477.x where Acer is LH, Quercus is NMH. Admittedly, using multiple PFTs would complicate the reporting as authors are currently only concerned with a single set of allometric parameters, but worth exploring. Also, even if the authors decide to continue with a single PFT after revisions, they should emphasize already here that this is an over-simplification which could help prevent misuse by others referring to this study in the future.L192: Agreed with the other reviewer. Please provide more details or point to the initialization/settings files of ED2.2 specifically if you have deposited them to the repository cited at the end (you could have a supplementary table telling which initialization/settings files went with which experiment or populate the readme file on the repository) .Figure 2 is great, but I'd call Analysis III: Bayesian calibration instead of data assimilation to be more precise, or at least continue using "parameter data assimilation". Also for analysis I, did you use TLS to inform structure directly? Looking at Table 4 it's only allometries. Allometries in return affect the structure but if I saw only allometries in that box, it would have helped me follow the study better.L202-203: What do you mean by "to assess the relative importance of TLS we compared it to field observations"? Does this exercise result in Fig S1 and S2? Isn't it then better to call this ground-truthing or validation of TLS? Please clarify.L207: Could you already explain here if 100 years spinup is enough, especially considering that the actual age of the forest is much older? I know of other models running much longer spinups (e.g. 500 years), please motivate the reader if 100 years is appropriate.L214: Looking at Table 4, how about NBG-infinitely wide-TLS setup? See comment below regarding having another control for impact of TLS informed allometries.L221: Why not explicitly state in what order these changes and combinations were introduced as this might also help following the incremental effect discussion. Listing configurations for 16 runs is not that much, could be also in the supplementary.L226-228: Agreed with the other reviewer on quantification of indirect effects. I think listing all the configurations for the mentioned 16 runs will help. I assume authors performed a factorial design here but it is not clear which combinations went with which.L231: "parameter optimization by Bayesian data assimilation" -> Authors could consider using "Bayesian parameter data assimilation" here as well to be more clear. Or better yet, "Bayesian calibration of model parameters".L235: Looking at Table 4, it feels like there needs to be another intermediate setup: inventory-finite radius-default, is there a particular reason why authors omitted this configuration? Also this sentence on L231-L233 suggests this configuration was included but Table 4 does not mention this configuration: "The model configurations included a default model version (default allometric parameters, infinite crown area), and a finite crown representation (default allometric parameters, finite crown radius), *which were both initialized with field inventory data*" I believe, according to this sentence, Table 4 second to last column should read "inventory" for initial conditions, please clarify. Overall, I think if there were 4 configurations in total it would be more systematic where only one thing would change at a time, 1: inventory-infinitely wide-default 2: inventory-finite radius-default 3:tls-finite radius-default 4: tls-finite radius-tlsL237-242: As much as I liked the process-based perspective, a sensitivity analysis (running the model with varying parameter combinations drawn from their priors to see how much change they cause on model outputs) would also be warranted here to formally show these parameters are indeed constrainable by the fluxes. Also the authors might be missing some other important model parameters (although there may be many parameters that can be calibrated as authors suggest in the discussion, models are typically most sensitive to maybe a dozen or so). I.e. calibration might be pushing SLA and Vcmax to different values in the parameter space under different configurations, but in fact if other parameters were included in the calibration it may have not been the case. Besides, other aspects of a proper calibration protocol are skipped here. For example, after determining to target these two parameters, authors could vary these parameters in their prior ranges and plot a likelihood surface (if they had done a global sensitivity analysis this would have come for free). This would have revealed the trade-off (negative correlation) before the calibration and would further implicate the need for either more informative priors (see below), or even not targeting one of these parameters in the calibration. I would have understood if authors, so to speak, enforce equifinality and use TLS to resolve it, but that has not been the case in the end (authors only report differences, don't really conclude -validate- which was more accurate). Instead, authors exacerbate the equifinality issue by choosing correlated parameters and uninformative priors only to confirm low identifiability (L390) and mention TLS' potential to discriminate without actually doing so. To sum up, I have three suggestions for the authors: 1) perform a global sensitivity analysis to at least identify other important parameters, even if they decide not to calibrate them it could help discussion, 2) try to repeat the analysis with more informative priors, 3) elaborate on their calibration results (some suggestions below) and strengthen their conclusions (be less vague).L246: GPP is not measured but a derived (modeled) quantity, at least as opposed to other carbon (net ecosystem exchange) and water (latent heat) fluxes. How the uncertainties were affected in this case, how was that accounted for in the calibration?L254: Sampled how? From marginal or joint posterior distributions? Please clarify.Table 3 and Figure S3 Vcmax units are different from L144 and Fig 5, please reconcile.L255 and Table 3: Why were the priors chosen to be uniform? Are values like 5 really equally likely as 30-40 or is 60.5 impossible for Vcmax? I believe given many observations and prior knowledge about these parameters more informative priors could have been chosen, which in return could have reduced the equifinality problem. Please consider distributions other than uniform.Figure 3: Looking at the figure, hard to tell without playing with the raw data, but it almost looks like there could be two lines fitted to Acer (late hardwood) and others (mid hardwood) reinforcing the point about exploring two PFTs above.L263: If the fitted parameter values of the lines on Figure 3 are on Table 2, please already say so in this paragraph. Also Fig3 caption can refer to Table 2.L267: Although, maybe it is worth noting that both are performing badly at the tails. Please consider providing the residual plots for Figure 3 fits in the supplement.L270: I don't mind these figures being in the supplement, however, I felt like this should have been the first thing reported in the results. How well TLS do with respect to inventory, before moving on to allometries.L276: Fig 4 not 6Figure 4: It is surprising how big of a difference infinite versus finite crown configurations makes. Was this documented before in previous ED2.2 studies? Is it appropriate to spinup both configurations for 100 years? Can it be that the FC configuration needs to be run longer? Also I believe each of these bars represents a single realization from the model, is that right? I would be curious to see if slightly different NBG initializations in an ensemble mode could have provided a different picture (i.e. Fig 4 but with error bars where some configurations may manage to get into the right ballpark). Plus, NBG ensembles (with different initial conditions and Vcmax/SLA parameter combinations) could provide an additional quantification of the uncertainty reduction (their ensemble widths can be compared to their IC counterparts which were constrained by TLS). Besides, how would the tree size distribution look like if the authors have used more than just one PFT in these simulations (further point, as noted by the other reviewer, it was not clear if the seedlings were MH-only)?L281-282: This is a clear result, however, (looking at Table 4, as mentioned above) I would be curious to see a NBG-infinitely wide-TLS configuration with TLS informed allometric coefficients. Does the infinitely wide configuration not use the same allometric equations? These models can be highly non-linear, and the response of NBG-FC with and without TLS allometries could be different than the sensitivity of NBG-infinitely wide with and without TLS allometries. And if it is the same result, it would only strengthen this finding.L293, Figure 5: After referring to Fig 3, please try to make it clear here that you are back to referring to Fig 5 here. E.g. "The large variability around those mean relative changes (Fig 5, error bars) ..." Also state in Fig 5 caption what do error bars represent.L297: Yet, I'd somewhat expect larger aboveground woody biomass could also result in bigger trees -> less understory PAR. Does it imply problems in model structure?L329: Unfortunately, there is no Table 5.L331-334: IC-TLS uses both the DBH distribution and the allometric coefficients informed by TLS. So I assume it was able to capture Figure3 - leaf biomass relationship very well? Sounds like it also produced LAI values in the right ballpark. The link between leaf biomass and leaf area is through the SLA in the model (L238: SLA is used to convert the leaf biomass into leaf area), and SLA posterior of IC-TLS is agreeing with the CWM. So it almost looks like this configuration gives the right answers for the right reasons for these variables and parameters. And if IC-FC is producing the same leaf area as IC-TLS but have different SLA, it must be missing leaf biomass? I'm trying to see if authors could discriminate a bit more explicitly about performances of different configurations here, please consider elaborating as such.L343: Does this contradict or how is it related with the finding mentioned before on L281-282: when NBG is concerned model structure had a bigger impact than allometries on tree size distribution? Also although I couldn't see Table 5, I have a feeling that it will be hard to digest. I recommend authors consider a figure instead or in addition.L381-382: Could you be more specific here? The statement is too vague. NBG with TLS informed allometry didn't do any better for capturing the tree size distribution (they also did bad for the ecosystem variables L346). So informing allometry alone was not enough. Is TLS more useful when it is used for prescribing the initial conditions or what? How does this agree with studies in the literature? E.g. does this mean initial condition uncertainty is a bigger problem than allometric uncertainty?L385-386: I don't know if it is striking, but it was expected given the trade-off and uninformative priors.L388-389: "Very different" but how? Again very vague. Was a particular one any better?L395-396: But did it in this study? Does this mean the authors trust IC-TLS posteriors on Fig 7 more? Also please see my comment above for lines 331-334, and try to be more specific. If you did discriminate between equifinal model versions, say it here which did better.L407: But aren't there more formal ways to deal with this? E.g. one could start the model from the past (when flux data is available) with more uncertain IC even if they don't know about the forest structure and composition a decade ago, and then calibrate the model with past data, continue simulations in time and assimilate more recent inventory data to constrain the states? In fact, the Thomas et al 2011 paper cited by the authors already have some useful values for conditions 10 years ago. Furthermore, the Butt et al. citation implies there was a tree census in 2008? (I merely clicked the link)L412: While it is true that it would increase the overall complexity of the study, I'm not sure it sufficiently justifies simulating one PFT when at least Acer and Quercus are concerned. Could more informative priors be chosen when more distinct PFTs are used? There were also numerous occasions mentioned above where using multiple PFTs could potentially remedy some of the shortcomings. At least without demonstrating it, I'm afraid this argument remains unconvincing.L418: This statement, although true, seems rather irrelevant for the conclusion of the present study as both SLA and Vcmax are measurable. In general, until the last three sentences, the conclusion reads like an introduction and needs to be tailored towards the study more. I'd recommend starting from what you demonstrated, then telling what the implications of your findings are, how well your results aligned with your prior expectations, if your methodology was adequate, if you got new insights / new ideas for future steps and so on.L422-425: Apologies for repeating myself but I overall think the reporting was rather inconclusive as to whether the TLS informed model was indeed more reliable or able to discriminate between equifinal model versions. In other words, yes, TLS-informed results were different but were they more realistic? What was the independent validation? Which configuration got the right answers for the right reasons? Reader has to work really hard to figure it out. You could further provide your concluding recommendation regarding how TLS is best utilized.
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CEC1: 'Comment on gmd-2021-59', Juan Antonio Añel, 17 May 2021
reply
Dear authors,
We have checked your manuscript, and unfortunately, at the moment, it does not comply with our 'Code and Data Policy'. Currently, you archive the scripts that you use in Github. However, as we state in our policy and Github on its website, it is not a suitable repository for long-term archival.
Therefore, please, move your code to one of the suitable repositories that we list before the end of the Discussions period and make the necessary changes in the manuscript in potential reviewed versions. Be aware that failing to comply with these rules will prevent your manuscript from being considered for publication.https://www.geoscientific-model-development.net/policies/code_and_data_policy.html#item3
Also, you have included the link to Github of the ED-2.2 model, however, you must cite the corresponding Zenodo repository, as again Github is not a secure repository. The Zenodo repository for ED-2.2 is:
https://doi.org/10.5281/zenodo.3365659
Please, remember using the corresponding DOI to cite it in the text.
Best regards,
Juan A. Añel
Geosc. Mod. Dev. Executive Editor
Félicien Meunier et al.
Félicien Meunier et al.
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Short summary
We integrated state of the art observations of the structure of the vegetation in a temperate forest to constrain a vegetation model that aims to reproduce in silico such ecosystem. We showed that the use of this information helps to constrain the model structure, its critical parameters, as well as its initial state. This research confirms the critical importance of the representation of the vegetation structure in vegetation models and proposes a method to overcome this challenge.
We integrated state of the art observations of the structure of the vegetation in a temperate...