the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 20 Jun 2022
Mechanistic modelling of the variability of methane emissions from an artificial reservoir
Abstract. We present the first mechanistic model LAKE2.3 for prediction of methane emissions from artificial reservoirs. Estimates of CH4 emissions from the Mozhaysk reservoir (Moscow region) surface provided by the model are demonstrated. The annual emission value was 361 tC-CH4 yr-1, the average flux 37.7 mgC-CH4 m-2 day-1. The methane emission according to the model were shown to be in good agreement with the observation data. The ebullition makes the largest contribution to the total emission (up to 95 %). During the heating period, an increase of methane emission is observed both in the model and empirical data with a maximum before the onset of the autumn overturn. Effective parameter for calibrating the diffusion component of methane flux in the model is the potential rate of methane oxidation in the Michaelis-Menten reaction, and the same for ebullition is the temperature dependence parameter for methane production in bottom sediments – q10.
This preprint has been withdrawn.
-
Withdrawal notice
This preprint has been withdrawn.
-
Preprint
(2767 KB)
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2022-329', Anonymous Referee #1, 27 Jul 2022
The manuscript applies the LAKE model, version 2.3, to the Mozhaysk reservoir to simulate temperature, oxygen and methane dynamics over multiple years, discusses the fit with observed field data, and provides next steps for lake model development. The averaging over the horizontal axis of the 3D equations in a set of 1D equations and the resulting good fit of biogeochemical dynamics and fluxes is very exciting, and without a doubt of high interest to modelers and limnologists. Although I think the study is methodologically sound and all results support the conclusions, the overall writing feels rushed with style and grammar errors appearing more frequently in the latter part of the paper. I hope the authors can revise the manuscript accordingly to publish an otherwise very exciting and interesting manuscript.
Major points:
- L580: It is still unclear to me if Vmax and q10 were also used in calibrating the model to Mozhaysk? It is not mentioned in the methods but only in L 593. Was the model calibrated with these variables after a sensitivity analysis? Please provide more details.
Minor points:
- L12: I doubt that LAKE2.3 is the “[…] first mechanistic model […] for the prediction of methane emissions from artificial reservoirs” as e.g., models like GLM-AED2 or ALBM mechanically simulate carbon dynamics and methane fluxes.
- L34: Could you please provide an explanation of the terms hydrogenotrophic and acetoclastic here?
- L44: Wouldn’t any organic carbon input provide CH4 production by decomposition, is there a specific reason to focus on littoral macrophytes here?
- L78: “[…] to the remaining (unexplored) reservoirs, […]”
- L138: remove “which worked”
- L158: I recommend replacing space scanners with satellites
- L203: Equation number one is missing (manuscript starts with (1))
- L207: F_f is stated in the equation explicitly as an eddy diffusivity approach, I’d focus here on explaining K_t and k_m
- L222: The relationship between salinity and mineralization is unclear to me
- L226 and 227: I’d remove “atoms”
- L235: I’m confused here as maybe I’m misunderstanding something, but wouldn’t this cause Eq. 2 to have two identical terms for advection?
- L239: I’d write imbalance instead inequality
- L242: Isn’t the continuity equation number 4?
- L268: exudation
- L281: What do you mean by liquid moisture?
- L291: ‘Data were corrected for the modelling period 2015-2019”
- L307: Is k_BD added to k_t and k_m then for depths equal thermocline?
- L318: enhancing sounds here like artificial engineering solutions to provide more methane, would increasing be better suited
- L331: So, there were seven total measurements in 3 years?
- L347: “was assumed to be 0.4”
- L369: “do not allow”
- L381: “Neglecting the total emission estimations, […]”
- 6: I am wondering why that additional diffusion k_BD still resulted in that strong difference of thermocline depth temperatures (roughly 10 m) here
- L397: transport
- L399: “(equation 2)”
- L402: Why was the seiche parameterisation switched off when the model has shortcomings in reproducing vertical mixing and thermocline oscillations?
- L403: “reproduced the averaged”
- L403: I think you mean spatially averaged here, not to confuse the reader with temporally averaged
- L406: “are visualized in Fig. 7”
- L419: “In the model, oxygen concentrations are in close […]”
- L419: Why should atmospheric exchange cause an underestimation? This is very unclear to me
- L425 “are too high for limiting the methanotrophic […]”
- L427: Is there a specific reference of a k_1/2 of 0.33?
- L431: “which is important”
- L435: “data are available”
- 8: I would argue the model does a not sufficient job in replication the oxygen drawdown at 14 m in summer 2017 and 2018, as the modeled DO consumption happens too slowly
- L436: “were not calculated”
- L438: Why is it not feasible to do a statistical approach with 7 measurements as the regressions in Fig. 5a had also only 7 measurements?
- L440: “[…], modeled concentrations were lower by 2.08 mg L-1”
- 9: Why is so much of summer 2017 missing?
- L450: “The process-based modeled underestimated observed surface oxygen concentrations, but there is a strong agreement between modeled bottom water temperature to observed data.
- L456: “This can be partially related to the spatial […]”
- L464: “[…] methane is accumulating less than what was observed […]”
- L468: So, the important linkage to organic matter decomposition is not included yet, could the model then have a bias for simulating diffusive and ebullitive flux?
- Fig10: It looks like methane is peaking during ice conditions here instead of spring
- L479: “were attributed to methane degassing from turbines”
- L483: Can you show a plot highlighting reservoir level change to methane fluxes?
- L503: “annual emission of methane, […]”
- L507: Which figure are you referring here to for the period in 2018 and 2019?
- L522: “except for 2018. Most possible this is […]”
- L523: “the rate exceeds the”
- L527: “for reservoirs in the temperate zone”
- L532: Isn’t it “during the autumn convection”?
- L558: “get oxidized”
- L560: Any degree over 10 deg C would directly multiply the methane flux, or was there a multiplier for a multitude of 10 deg C, e.g., 0, 10, 20, n+10?
- L561: “are shown in Fig. 14”
- L563: “measurements were carried”
- L585: criteria
- L587: Wouldn’t a seiche parameterization be a perfect candidate for this?
- L597: “it contributes 95 % of the total flux”
- L598: “, respectively”
- L662: “better estimates of annual emission”
Citation: https://doi.org/10.5194/egusphere-2022-329-RC1 -
RC2: 'Comment on egusphere-2022-329', Anonymous Referee #2, 31 Jul 2022
Methane is one of the most important greenhouse gases and its concentration increase in the atmosphere is of large concern to the society due to methane's strong warming potential. Previous reports showed that methane emissions from reservoirs are an important natural methane source. However, few models can capture the dynamics of methane emissions from reservoirs. In this study, Lomov et al. describe a process-based lake model that includes representation of processes that are important for reservoir methane dynamics and present a new set of valuable data in Mozhaysk reservoir, Russia to validate the model. I find that this study is interesting. However, its scientific and presentation quality have not reached the standard of this journal.
First, I agree with the first reviewer that the manuscript looks like a rough draft rather than one ready for publication. There are many grammar errors and typos. Sometimes, the writing is difficult to follow. Thus, a thorough proofreading is needed.
Second, the model description is in a poor format. For example, the manuscript lacks an overall description of each term in the righ-hand side of Eq. 2 for their physical meanings. In addition, the description of each term should be better organized. The whole section 2.4 in the current form is very difficult to follow. Also, as the model development puts methane dynamics in the central focus, a detailed description of methane biogeochemical processes is necessary. For example, the authors need to provide details on the parameterization of methane production, oxidation and transport. Importantly, the authors should only introduce the processes that actually have been turned on in the numerical experiments. For example, internal wave induced diffusivity and detritus sedimentation are described in section 2.4 but were not used in the simulations.
Third, the upscaling of methane flux observations from station IV to the whole reservoir surface is very arbitrary. As shown in Fig. 2b, the statisitical relationship between riverbed methane emission at station IV and that from floodplain is not significant. As such, using this probably erronous total methane emission estimates to validate the model does not make sense to me. Instead, the authors should only use the observational data for model validation. For example, the authors can compare the modeled and measured methane emissions for each station. In my view, although the model is presented in an longitudinal integrated form, the methane emissions over the riverbed portion of each station can be easily isolated. In the end, once the model is validated, the authors can use it to estimate the total methane emission for the reservoir.
Forth, although it is fine that the model fails to reproduce observations in several cases, it is important to dive deeply into these model errors. In my view, the clear explanation of the successes and failures of the model validation is the main value of a model development study. It will inspire other model developers to improve the representation of related processes and help interested users choose appropriate models for their applications. However, most of the analyses in section 3.2 are only qualitative and much spectulative. For example, how does the error of simulated bottom water temperature shown in Fig. 6 affect the simulated oxygen and methane dynamics? Are methane production and oxidation rates correctly simulated? From the large bias in simulated bottom methane concentrations, it seems to be not the case. Also, from Fig. 12, I cannot tell what the model does right and what the model does wrong. I am also not confident that the model get the right result for the right reason.
Specific comments
L44-45: This sentence and Figure 1 give me an impression that the introduced model will represent methane flux through macrophytes. But I cannot find it in the manuscript.
Table 1: What do mean and maximum width refer to? From Figure 2, I cannot see the width of the reservoir is so small.
L143: replace "was" by "can be"
Table 2: Could you use a more standard date format, such as MM/DD
L149: were evaluated
L219: from all the descriptions, I still do not know how the horizontal heterogeneity of some variables, such as oxygen and methane, are represented in the model but the authors emphasized that the longitudinal difference is substantial.
L239: replace "inequality" by "imbalance"
L244-247: please specify this background diffusion. How was it used in the simulations?
L251-253: This is too vague. What does this momentum flux represent?
Figure 4: Could you also show observed diffusion and ebullition?
L321: thermal stratification
L322-323: Do you have simulation results to support this claim?
Figure 5: The use of R value is misleading for statistical significance. Please use R2 instead.
Tables 5-7: Please use bias (difference in mean), root mean squared error (RMSE) and NSE for model performance metrics. Pearson R is not a good metric for model validation.
L423-424: But the error of simulated oxygen in the middle layers, such as metalimnion, would be important for methane dynamics. Could you show the comparison?
Figure 9: Where are the surface oxygen comparison for 2019 and bottom oxygen comparison for 2016 and 2017?
L481: Do you represent the emission of ice-trapped bubbles in the model? If not, I cannot understand why high ebullition occurred in early spring.
Figure 11. As you spectulated that the drop of hydrostatic pressure is responsible for many of these high ebullition, could you present its dynamics in the figure?
Figure 12. Could you also compare the model with observations for diffusion and ebullition?
Citation: https://doi.org/10.5194/egusphere-2022-329-RC2
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2022-329', Anonymous Referee #1, 27 Jul 2022
The manuscript applies the LAKE model, version 2.3, to the Mozhaysk reservoir to simulate temperature, oxygen and methane dynamics over multiple years, discusses the fit with observed field data, and provides next steps for lake model development. The averaging over the horizontal axis of the 3D equations in a set of 1D equations and the resulting good fit of biogeochemical dynamics and fluxes is very exciting, and without a doubt of high interest to modelers and limnologists. Although I think the study is methodologically sound and all results support the conclusions, the overall writing feels rushed with style and grammar errors appearing more frequently in the latter part of the paper. I hope the authors can revise the manuscript accordingly to publish an otherwise very exciting and interesting manuscript.
Major points:
- L580: It is still unclear to me if Vmax and q10 were also used in calibrating the model to Mozhaysk? It is not mentioned in the methods but only in L 593. Was the model calibrated with these variables after a sensitivity analysis? Please provide more details.
Minor points:
- L12: I doubt that LAKE2.3 is the “[…] first mechanistic model […] for the prediction of methane emissions from artificial reservoirs” as e.g., models like GLM-AED2 or ALBM mechanically simulate carbon dynamics and methane fluxes.
- L34: Could you please provide an explanation of the terms hydrogenotrophic and acetoclastic here?
- L44: Wouldn’t any organic carbon input provide CH4 production by decomposition, is there a specific reason to focus on littoral macrophytes here?
- L78: “[…] to the remaining (unexplored) reservoirs, […]”
- L138: remove “which worked”
- L158: I recommend replacing space scanners with satellites
- L203: Equation number one is missing (manuscript starts with (1))
- L207: F_f is stated in the equation explicitly as an eddy diffusivity approach, I’d focus here on explaining K_t and k_m
- L222: The relationship between salinity and mineralization is unclear to me
- L226 and 227: I’d remove “atoms”
- L235: I’m confused here as maybe I’m misunderstanding something, but wouldn’t this cause Eq. 2 to have two identical terms for advection?
- L239: I’d write imbalance instead inequality
- L242: Isn’t the continuity equation number 4?
- L268: exudation
- L281: What do you mean by liquid moisture?
- L291: ‘Data were corrected for the modelling period 2015-2019”
- L307: Is k_BD added to k_t and k_m then for depths equal thermocline?
- L318: enhancing sounds here like artificial engineering solutions to provide more methane, would increasing be better suited
- L331: So, there were seven total measurements in 3 years?
- L347: “was assumed to be 0.4”
- L369: “do not allow”
- L381: “Neglecting the total emission estimations, […]”
- 6: I am wondering why that additional diffusion k_BD still resulted in that strong difference of thermocline depth temperatures (roughly 10 m) here
- L397: transport
- L399: “(equation 2)”
- L402: Why was the seiche parameterisation switched off when the model has shortcomings in reproducing vertical mixing and thermocline oscillations?
- L403: “reproduced the averaged”
- L403: I think you mean spatially averaged here, not to confuse the reader with temporally averaged
- L406: “are visualized in Fig. 7”
- L419: “In the model, oxygen concentrations are in close […]”
- L419: Why should atmospheric exchange cause an underestimation? This is very unclear to me
- L425 “are too high for limiting the methanotrophic […]”
- L427: Is there a specific reference of a k_1/2 of 0.33?
- L431: “which is important”
- L435: “data are available”
- 8: I would argue the model does a not sufficient job in replication the oxygen drawdown at 14 m in summer 2017 and 2018, as the modeled DO consumption happens too slowly
- L436: “were not calculated”
- L438: Why is it not feasible to do a statistical approach with 7 measurements as the regressions in Fig. 5a had also only 7 measurements?
- L440: “[…], modeled concentrations were lower by 2.08 mg L-1”
- 9: Why is so much of summer 2017 missing?
- L450: “The process-based modeled underestimated observed surface oxygen concentrations, but there is a strong agreement between modeled bottom water temperature to observed data.
- L456: “This can be partially related to the spatial […]”
- L464: “[…] methane is accumulating less than what was observed […]”
- L468: So, the important linkage to organic matter decomposition is not included yet, could the model then have a bias for simulating diffusive and ebullitive flux?
- Fig10: It looks like methane is peaking during ice conditions here instead of spring
- L479: “were attributed to methane degassing from turbines”
- L483: Can you show a plot highlighting reservoir level change to methane fluxes?
- L503: “annual emission of methane, […]”
- L507: Which figure are you referring here to for the period in 2018 and 2019?
- L522: “except for 2018. Most possible this is […]”
- L523: “the rate exceeds the”
- L527: “for reservoirs in the temperate zone”
- L532: Isn’t it “during the autumn convection”?
- L558: “get oxidized”
- L560: Any degree over 10 deg C would directly multiply the methane flux, or was there a multiplier for a multitude of 10 deg C, e.g., 0, 10, 20, n+10?
- L561: “are shown in Fig. 14”
- L563: “measurements were carried”
- L585: criteria
- L587: Wouldn’t a seiche parameterization be a perfect candidate for this?
- L597: “it contributes 95 % of the total flux”
- L598: “, respectively”
- L662: “better estimates of annual emission”
Citation: https://doi.org/10.5194/egusphere-2022-329-RC1 -
RC2: 'Comment on egusphere-2022-329', Anonymous Referee #2, 31 Jul 2022
Methane is one of the most important greenhouse gases and its concentration increase in the atmosphere is of large concern to the society due to methane's strong warming potential. Previous reports showed that methane emissions from reservoirs are an important natural methane source. However, few models can capture the dynamics of methane emissions from reservoirs. In this study, Lomov et al. describe a process-based lake model that includes representation of processes that are important for reservoir methane dynamics and present a new set of valuable data in Mozhaysk reservoir, Russia to validate the model. I find that this study is interesting. However, its scientific and presentation quality have not reached the standard of this journal.
First, I agree with the first reviewer that the manuscript looks like a rough draft rather than one ready for publication. There are many grammar errors and typos. Sometimes, the writing is difficult to follow. Thus, a thorough proofreading is needed.
Second, the model description is in a poor format. For example, the manuscript lacks an overall description of each term in the righ-hand side of Eq. 2 for their physical meanings. In addition, the description of each term should be better organized. The whole section 2.4 in the current form is very difficult to follow. Also, as the model development puts methane dynamics in the central focus, a detailed description of methane biogeochemical processes is necessary. For example, the authors need to provide details on the parameterization of methane production, oxidation and transport. Importantly, the authors should only introduce the processes that actually have been turned on in the numerical experiments. For example, internal wave induced diffusivity and detritus sedimentation are described in section 2.4 but were not used in the simulations.
Third, the upscaling of methane flux observations from station IV to the whole reservoir surface is very arbitrary. As shown in Fig. 2b, the statisitical relationship between riverbed methane emission at station IV and that from floodplain is not significant. As such, using this probably erronous total methane emission estimates to validate the model does not make sense to me. Instead, the authors should only use the observational data for model validation. For example, the authors can compare the modeled and measured methane emissions for each station. In my view, although the model is presented in an longitudinal integrated form, the methane emissions over the riverbed portion of each station can be easily isolated. In the end, once the model is validated, the authors can use it to estimate the total methane emission for the reservoir.
Forth, although it is fine that the model fails to reproduce observations in several cases, it is important to dive deeply into these model errors. In my view, the clear explanation of the successes and failures of the model validation is the main value of a model development study. It will inspire other model developers to improve the representation of related processes and help interested users choose appropriate models for their applications. However, most of the analyses in section 3.2 are only qualitative and much spectulative. For example, how does the error of simulated bottom water temperature shown in Fig. 6 affect the simulated oxygen and methane dynamics? Are methane production and oxidation rates correctly simulated? From the large bias in simulated bottom methane concentrations, it seems to be not the case. Also, from Fig. 12, I cannot tell what the model does right and what the model does wrong. I am also not confident that the model get the right result for the right reason.
Specific comments
L44-45: This sentence and Figure 1 give me an impression that the introduced model will represent methane flux through macrophytes. But I cannot find it in the manuscript.
Table 1: What do mean and maximum width refer to? From Figure 2, I cannot see the width of the reservoir is so small.
L143: replace "was" by "can be"
Table 2: Could you use a more standard date format, such as MM/DD
L149: were evaluated
L219: from all the descriptions, I still do not know how the horizontal heterogeneity of some variables, such as oxygen and methane, are represented in the model but the authors emphasized that the longitudinal difference is substantial.
L239: replace "inequality" by "imbalance"
L244-247: please specify this background diffusion. How was it used in the simulations?
L251-253: This is too vague. What does this momentum flux represent?
Figure 4: Could you also show observed diffusion and ebullition?
L321: thermal stratification
L322-323: Do you have simulation results to support this claim?
Figure 5: The use of R value is misleading for statistical significance. Please use R2 instead.
Tables 5-7: Please use bias (difference in mean), root mean squared error (RMSE) and NSE for model performance metrics. Pearson R is not a good metric for model validation.
L423-424: But the error of simulated oxygen in the middle layers, such as metalimnion, would be important for methane dynamics. Could you show the comparison?
Figure 9: Where are the surface oxygen comparison for 2019 and bottom oxygen comparison for 2016 and 2017?
L481: Do you represent the emission of ice-trapped bubbles in the model? If not, I cannot understand why high ebullition occurred in early spring.
Figure 11. As you spectulated that the drop of hydrostatic pressure is responsible for many of these high ebullition, could you present its dynamics in the figure?
Figure 12. Could you also compare the model with observations for diffusion and ebullition?
Citation: https://doi.org/10.5194/egusphere-2022-329-RC2
Viewed
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 411 | 174 | 25 | 610 | 21 | 21 |
- HTML: 411
- PDF: 174
- XML: 25
- Total: 610
- BibTeX: 21
- EndNote: 21
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1