the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Assimilating Multi-site Eddy-Covariance Data to Calibrate the CH4 Wetland Emission Module in a Terrestrial Ecosystem Model
Abstract. In this study, we use a data assimilation framework based on the Adaptive Markov Chain Monte Carlo (MCMC) algorithm to constrain process parameters in LPJ-GUESS using CH4 eddy covariance flux observations from 14 different natural boreal and temperate wetlands. The objective is to derive a single set of calibrated parameter values. These parameters are then used in the model to validate its CH4 flux output against 5 different types of natural wetlands situated in different locations, assessing their generality for simulating CH4 fluxes from different boreal and temperate wetlands. The results show that the MCMC framework has substantially reduced the cost function (measuring the misfit between simulated and observed CH4 fluxes) and facilitated detailed characterisation of the posterior distribution. A reduction of around 95 % in the cost function and approximately 50 % in RMSE were observed. The validation experiment results indicate that four out of 5 sites successfully reduced RMSE, demonstrating the effectiveness of the framework for estimating CH4 emissions from wetlands not included in the study.
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RC1: 'Comment on egusphere-2024-373', Anonymous Referee #1, 11 Jun 2024
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Overview
The manuscript by Kallingal et al. presents a data assimilation framework and its application to the calibration of model parameters for the wetland methane module in the LPJ-GUESS, a global terrestrial ecosystem model. The data assimilation framework is based on an adaptive Markov Chain Monte Carlo (MCMC) algorithm that is computationally intensive. Such a calibration approach can be very useful to the scientific community in general and wetland methane modellers in particular, because the choice of model parameter values is sometimes done "manually" in a less systematic way than what the authors described in their manuscript.According to the authors, the objective of the manuscript is to derive a single set of calibrated parameter values for the wetland methane model in LPJ-GUESS (Abstract; Pg 1, Ln 3). Different aims are associated with that objective (Pg 2, Ln 56-59): (1) to investigate the capacity of the data assimilation framework at multiple sites in the northern high-latitudes; (2) to optimise selected model parameters [10 out 11 parameters]; (3) to examine to what extent this optimisation improves the model’s ability to simulate the seasonal cycle of methane from different wetlands over northern latitudes above 40◦ N.
The current version of the manuscript does not convince me with regards to these study aims. In particular, the manuscript falls short with respect to the second and third aims. As a modeller with experience in wetland methane processes, this work sounds incomplete because the authors focus on reducing the RMSE (e.g. Figure 4) but not on improving the simulation of emissions relative to observations (e.g. Figure 6b). Moreover, considering the potential of the LPJ-GUESS as a global model (Pg 2, Ln 40), it is surprising to see that the authors do not show whether/how their calibrated parameters improve the model performance with respect to large-scale methane emissions from northern wetlands (e.g. total methane emissions from wetlands north of 40◦ N) at least. Furthermore, given that their calibration focuses on methane emissions from northern wetlands, it is disappointing to see how poor their posterior-based estimates are for seasonal methane fluxes in general and cold-season methane fluxes in particular.
Considering the above concerns, I find that the current version of the manuscript would need to be revised before it can be considered for publication.
General comments
1) Manuscript structure - Methodology:
The manuscript is quite technical and its current structure makes it hard for readers to digest the presented results (Section 3). You should re-structure the manuscript in order to improve its logical flow. For instance, all formulas, key metrics and other technical aspects should be described prior to the results section (in the methodology section for instance). It would be appropriate to add a new subsection under the methodology section (let's say - Section 2.6: Statistical metrics) in which you would need to include equations 5 and 6 as well as descriptions for the RMSE, the chi-square test, and other statistics in the context of this study.2) Manuscript structure - Missing discussion:
Although Section 3 is entitled "Results and discussion", I find that there is no authentic discussion in the manuscript. I recommend that you add a discussion (as a section or subsection), in which you should talk about limitations of your study, algorithm, model, etc. by taking into account prior research.3) Method generalization:
The authors seem to be satisfied with a computationally-intensive algorithm that reduces the so-called cost function and RMSE (Figures 4 and 9) without necessarily improving the ability of the model to simulate wetland methane emissions at the same sites where the algorithm was tested (Figure 6b). So, what's the added value for such an algorithm in terms of model predictions? How would that convince someone interested in applying the algorithm with the end goal of improving their simulations of wetland methane emissions across spatial scales (from the site scale to the global scale)?4) Method scalability:
Given that LPJ-GUESS is a global model, it would be more convincing to show whether/how the calibrated parameters improve the simulation of wetland methane emissions at large scales. Why not show that for - at least - the magnitude of methane emissions from northern wetlands?5) Seasonal cycle and cold-season methane emissions:
As your study focuses on northern sites, not capturing cold-season methane flux is a major weakness of your model. In my opinion, this weakness is primarily due to the fact that your methane fluxes are modeled as a function of air temperature but not soil temperature. Various simpler models are able to simulate non-zero methane emissions in winter months (e.g. https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023GL103037; https://gmd.copernicus.org/articles/14/6215/2021). You should talk about this limitation in your manuscript (results and discussion).6) Algorithm validation versus Study hypothesis:
You describe your study hypothesis (Pg 2, Ln 53-55) and your plan to do a validation to verify the hypothesis (Pg 2, Ln 64). Does your validation verify the hypothesis? That's not clearly stated in the manuscript.Also, considering your results on the validation of wetland methane emissions (Figure C1), would you say that your calibration is worth the effort?
Specific comments
Pg 2, Ln 28-29:
Could you be more specific about relevant "stresses"? Alternatively, you can use another word for more clarity.Pg 3, Ln 59-60:
Please rephrase the part that says "the model process and parameter correlations and uncertainties". It is not clear what correlations you are talking about. Whether there is a link between the words "process" and "uncertainties" in that sentence.Pg 3, Ln 77:
No comma needed between "including" and "estimation of peat..."Pg 3, Ln 81:
I suggest you say "...an acrotelm with a thickness of 0.3 m..."Pg 3, Ln 82:
Not clear what "its " stands for here? Is that the peat composition or the layer composition? Please rephrase for more clarification.Pg 3, Ln 84:
No need to add "PFT" after "lichen moss".Pg 4, Ln 86-87:
That's a long sentence. Please break it into two sentences that are easy to digest.Pg 4, Ln 86:
You write that "The carbon in the soil is transformed to CH4 or CO2 depending on the hydrological conditions". Isn't that supposed to be A PORTION of the soil carbon that gets transformed to CH4 or CO2? The way you say it makes it seem that all soil carbon within LPJ-GUESS is either CH4 or CO2. But I presume that there must be some sort of organic matter that do not get access by microbes for their metabolism (i.e. soil carbon preserved from microbial activity).Pg 4, Ln 97:
Please describe what w_tiller represents to make it easier for readers who will not check Kallingal et al. (2023).Pg 5, Ln 110:
Don't you need a dash between measurement and years (i.e. measurement-years) for consistency?Pg 5, Ln 112:
I suggest you add "for CH4 fluxes" right after "gap-filled data".Pg 6, Ln 127-128:
I find your description of terms from equation (2) to be confusing. I suggest you talk about M_i and R_i for the i-th site, the same way you did it for Y_i.Pg 7, Ln 135:
How does P(x) relate to J(x)? In other words, how do you relate the functions in equations (2) and (3)?Pg 7, Ln 141:
I suggest you insert "CH4 flux" between "actual" and "observations". That is, saying "actual CH4 flux observations".Pg 8, Ln 155:
What does "model fat" mean?Pg 9, Ln 185-197:
Please remove the entire paragraph. It has no added value to your Section 3 (which can start right away with Section 3.1).Pg 10, Ln 220:
Isn't "Rmoist" supposed to be in mathematical typing format similar to "Rmoist_an"?Pg 15, Ln 311:
You need to use a minor/small "c" letter for "contradiction".Pg 18, Ln 328:
Please add "of CH4 flux" right after "888 gCm-2" for more clarity.Pg 20, Ln 359:
The acronyms MAS and MAV are not needed in this sentence. Please remove.Pg 23, Ln 405:
No need to use "observed" here. Please remove.Pg 23, Ln 415-416:
What's the point of saying that it takes around 480 computational hours to complete the 100,000 iterations on an AMD Ryzen Threadripper processor here? I suggest you move that whole sentence to the methodology section.Tables
Table 3:
I suggest that you re-arrange the table columns as follows: Move column 3 to column 5; Move column 4 to column 3; Move column 5 to column 4.Table 4:
Please add "Posterior" in front of "std" in the third row.Table 5:
Why do you have totals just under the first 7 sites? Do you need these totals in the first place? Same point for Table B.1Table 6:
"Hemi-boreal" or "Semi-boreal"?Table B.1:
Why do you have totals just under the first 7 sites? Do you need these totals in the first place? Same point for Table 5.Figures
Figure 2:
I suggest that you write "Probability distribution functions (PDFs)", instead of just the acronym.Figure 5:
The figure caption misses a description of the horizontal green lines. Are these lines representing back-filled values? If so, please describe that in the caption.Figure 6:
Panel a: Why do you only show time series for four sites out 14?
Panel b: My interpretation of this figure is that only a few sites compare well with observations even with posterior estimates. Isn't that a major conclusion to be drawn from the figure?Citation: https://doi.org/10.5194/egusphere-2024-373-RC1
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