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https://doi.org/10.5194/egusphere-2024-1994
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/egusphere-2024-1994
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
15 Aug 2024
| 15 Aug 2024
Eddy covariance fluxes of CO2, CH4 and N2O on a drained peatland forest after clearcutting
Abstract. Even-aged forestry based on clearcut harvesting, planting, and one to three thinnings is currently the dominant management approach in Fennoscandia. However, our understanding of the greenhouse gas (GHG) emissions following clearcutting remains limited, particularly on drained peatland forests. In this study, we report eddy covariance-based (EC) net emissions of carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) from a boreal fertile drained peatland forest one year after the harvest. Our results show that on annual scale, the site was a net CO2 source. The CO2 emissions dominate the total annual GHG balance (23.3 t CO2-eq ha-1 a-1, 82.5 % of the total), while the role of N2O emissions (4.8 t CO2-eq ha-1 a-1, 17.1 %) was also significant. The site was a weak CH4 source (0.1 t CO2-eq ha-1 a-1, 0.4 %). A statistical model was developed to estimate surface-type-specific CH4 and N2O emissions. The model was based on air temperature and fraction of specific surface-types within the EC flux footprint. The surface-types were classified using unmanned aerial vehicle (UAV) spectral imaging and machine learning. Based on the statistical models, the highest surface-type specific CH4 emissions occurred from plant-covered ditches and exposed peat, while the surfaces dominated by living trees, dead wood, and litter along with plant-covered ditches were the main contributors to N2O emissions. Our study provides new insights into how CH4 and N2O fluxes are affected by surface-type variation across clearcutting areas in boreal forested peatlands. Our findings highlight the need for integrating surface-type-specific flux modelling, EC-based data, and chamber-based flux measurements to comprehend the GHG emissions following clearcutting. Results strengthen the accumulated evidence that recently clearcut peatland forests are significant GHG sources.
How to cite. Tikkasalo, O.-P., Peltola, O., Alekseychik, P., Heikkinen, J., Launiainen, S., Lehtonen, A., Li, Q., Martinez-García, E., Peltoniemi, M., Salovaara, P., Tuominen, V., and Mäkipää, R.: Eddy covariance fluxes of CO2, CH4 and N2O on a drained peatland forest after clearcutting, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2024-1994, 2024.
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Journal article(s) based on this preprint
06 Mar 2025
Eddy-covariance fluxes of CO2, CH4 and N2O in a drained peatland forest after clear-cutting
Olli-Pekka Tikkasalo, Olli Peltola, Pavel Alekseychik, Juha Heikkinen, Samuli Launiainen, Aleksi Lehtonen, Qian Li, Eduardo Martínez-García, Mikko Peltoniemi, Petri Salovaara, Ville Tuominen, and Raisa Mäkipää
Biogeosciences, 22, 1277–1300, https://doi.org/10.5194/bg-22-1277-2025, https://doi.org/10.5194/bg-22-1277-2025, 2025
Short summary
Short summary
The emissions of greenhouse gases (GHGs) carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) were measured from a clear-cut peatland forest site. The measurements covered the whole year of 2022, which was the second growing season after the clear-cut. The site was a strong GHG source, and the highest emissions came from CO2, followed by N2O and CH4. A statistical model that included information on different surfaces at the site was developed to unravel surface-type-specific GHG fluxes.
Interactive discussion
Status: closed
Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor
| : Report abuse
-
RC1: 'Comment on egusphere-2024-1994', Anonymous Referee #1, 17 Sep 2024
The manuscript presents results of the total GHG balance (CO2, N2O and CH4) from a clearcut stand on a fertile peatland in Finland. The manuscript uses one-year measurements of eddy covariance to quantify the strength of source from clearcutting. Combining results with a UAV-based land classification and statistical modelling the authors split the source of fluxes per land class (i.e., surface-type).
My overall assessment of the project’s objectives and approach is that this is very important and interesting work, especially when it addresses the full GHG balance which current literature fails to address adequately. However, I have some concerns/comments/suggestions regarding the methodology and the approach the authors took in this study. I will aim to first discuss my main concerns/comments.
- The authors claim that this study aims to investigate the impact of clearcutting on the GHG balance of forested peatlands. Yet in lines 136-138, they state that “stand regeneration was carried out in summer 2021 through ditch mounding and planting of Norway spruce seedlings”. So:
- This is no longer a “clearcut” site since it has been replanted. It is a restock site on its second growing season (as the authors have stated multiple times throughout the manuscript) and hence the strength of source is no longer reflective of a clearcut practise (due to GPP).
- Ditch mounding was used before planting, which suggests to me that the site was disturbed prior to measurements and hence not again representative of a clearcut site. In fact, if indeed any ditch mounding was applied after clearcutting, it means that the land classification reported is also not representative of the post-felling fluxes.
- The authors mention that this is a fertile peatland, however, they didn’t give us any further information as to how they are fertile. Was the site historically fertilised prior to planting or is because of a natural fertilisation over a number of rotations? I believe an international audience would like to know a little more information about the particulars of Finnish peatlands.
- Fluxes presented are from a single year. I understand that authors may feel compelled to present their very interesting work as soon as the first results are available, however, it is very rare, if not I dare say totally unrealistic, to draw any conclusions on the source/sink of a site with simply a single year especially when this year is not also representative of the actual effect of the forest management practice the study claims (see point 1). There is still a huge gap in our knowledge of what is the initial pulse of GHG immediately after clearcutting, and I believe the authors may have missed the opportunity here to capture a potentially significant contribution from the first few months and prior to any planting or mounding.
- The modelling, although very interesting, I don’t believe it has worked as expected particularly for methane. I believe the fact water table depth (WTD) or even soil moisture (theta) was ignored in the modelling was a major overlook since we know (and as the authors themselves demonstrated with Figure 3) both fluxes but particularly CH4 are strongly correlated. Furthermore, another pitfall was the choice of Tair over Tsoil. Volumetric heat capacity changes linearly with moisture, so for wet peatlands I would expect changes in Tsoil to have a bigger impact that those Tair. So, potentially, there was an underestimation of the flux and hence lower strength in the model. Finally, I believe the exclusion of some surface-types from the CH4 model may have resulted in reduced model efficiency, as it clearly worked for N2O. The authors claim that CH4 emissions were not surface dependent (lines 558-559), however, from a work at a Scottish peatland restoration site (Mazzola et al. 2021, European Journal of Soil Science) it was found that CH4 fluxes were significantly different with micro-topography, including water pools. Not considering any interaction of flux with water or moisture it is likely to result to a mismatch between model and data.
- I believe the uncertainty presented in Table 3 for CH4 and N2O re-enforce my opinion that the model for CH4 did not perform well (uncertainty mismatch) comparing to N2O (EC uncertainty within modelled).
- I am also surprised that N2O fluxes were not high after clearcutting. Yamulki et al. 2021 (Biogeosciences) found high N2O on an organo-mineral (30-60cm peat layer over a mineral layer). With a high fertility peatland when trees removed and WTD increases I would expect pulses of N2O. The authors demonstrated that the model was unable to capture the pulse of N2O in August. Was that pulse close to a rainfall event? If so, ignoring relationship WTD and/or theta, hindered the model’s predictive capability.
- I also found very difficult to evaluate the strength of the model’s accuracy. R-squared and RMSE although they give some indication of the model’s predictive capabilities, it was difficult to evaluate further the model, especially where little explanation was given for the LOO statistic. I understand this is a MC-based modelling approach, but I wonder whether a statistic like Akaike Information Criterion, or a significance level for the slope and intercept of the model vs data would be very useful to evaluate the explanatory capacity of the model.
- Surface-specific splitting on fluxes were performed only for CH4 and N2O, however, CO2 was ignored. Why was that? I believe it would have been a great opportunity to repeat the process for CO2.
- I would have liked to see more of an investigation not only how much of the flux is coming from each soil type, but what are the underlying processes by discussing correlation between vegetation, flux and climatological variables and topography.
- The manuscript presents the results of a footprint analysis, followed by a discussion on its potential limitations. It was unclear to me how the footprint was used in further analysis. More importantly, the manuscript is unclear whether footprint was used to either calculate the total area of for surface-type classification of even whether the fluxes were adjusted for footprint contribution once they have been split into different surface-type. This can have a potential major implication on how results are interpreted. It is expected, surface-types closer to the eddy covariance tower to have greater contribution. If for example, plant filled ditches are closer to the tower then potentially their contribution will be larger. Ignoring the combined effect of the surface-type distribution across the area can lead to bias. I suggest the authors review the methodology followed by Budishchev et al. 2014 (Biogeoscience) and revisit some of their approaches.
- The manuscript presents a section on footprint analysis and considerations with a discussion element. However, it was not clear to me how the footprint was used other than simply for presentation purposes. Was the footprint used for the classification of the surface-type?
- Following the point from above, it wasn’t clear whether the surface-type classification was for the whole of the clearcut area or for the footprint. This is potentially key to interpreting the results. Land within the footprint of the tower would have bigger contribution
- Lines 649-656, the CO2 emissions from the peatland are compared to mineral soil. The authors must understand matching fluxes in these two different soil types does not equate validity of measurements due to underlying differences in carbon stocks and respiratory processes.
Some further comments:
- The introduction only lightly touches on the importance of N2O and the current gap in knowledge.
- The introduction also did not make clear what is the uniqueness of this study. In my opinion, this is a novel approach which aims to close the total GHG balance for the boreal and specifically the Fennoscandia, but it was not explicitly highlighted.
- Figure 3 presents a correlation analysis. Are these correlations statistically significant? It was not discussed what the correlations mean for the underlying processes. Keeping the current discussion, I propose this analysis is removed. Alternatively, it can be significantly reduced to include key significant correlations which may further used in the discussion to understand processes.
- Having said that, the manuscript has a lengthy discussion on the modelling. Although, important to highlight modelling limitation and potential pitfalls, I felt there was a little less time spend discussing the underlying processes that are related to different surface-types.
- Figure 6 was very difficult to understand. The points and bars where too small for some variables and hence difficult to convey the message. I wonder whether there is an improved way to present the parameter values. A line across the zero would also have been helpful.
- I don’t understand since we have the parameter values and range in Figure 6, why we had to set the surface-type contribution to one, to “visualise” the parameters in Figure 7. Why not simply present with the estimate surface-type contribution percentage what is the total flux from each and the percentage of the total flux measured by the eddy covariance tower? I believe this information is far more useful and citable for future work.
- It was interesting that the study found N2O emission during snow cover. This is potentially a important find which the manuscript did not discussed in its full extend. Of course, the single year worth of data makes it very difficult but even so, it is important to highlight its importance and whether something similar has been reported before.
- The conclusion sections is a repetition of information already given in either the abstract or the results section. The section requires a refocus to really provide a concluding message from the study.
Citation: https://doi.org/10.5194/egusphere-2024-1994-RC1 - AC1: 'Reply on RC1', Olli-Pekka Tikkasalo, 08 Nov 2024
- The authors claim that this study aims to investigate the impact of clearcutting on the GHG balance of forested peatlands. Yet in lines 136-138, they state that “stand regeneration was carried out in summer 2021 through ditch mounding and planting of Norway spruce seedlings”. So:
-
RC2: 'Comment on egusphere-2024-1994', Anonymous Referee #2, 25 Sep 2024
- AC2: 'Reply on RC2', Olli-Pekka Tikkasalo, 08 Nov 2024
Interactive discussion
Status: closed
Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor
| : Report abuse
-
RC1: 'Comment on egusphere-2024-1994', Anonymous Referee #1, 17 Sep 2024
The manuscript presents results of the total GHG balance (CO2, N2O and CH4) from a clearcut stand on a fertile peatland in Finland. The manuscript uses one-year measurements of eddy covariance to quantify the strength of source from clearcutting. Combining results with a UAV-based land classification and statistical modelling the authors split the source of fluxes per land class (i.e., surface-type).
My overall assessment of the project’s objectives and approach is that this is very important and interesting work, especially when it addresses the full GHG balance which current literature fails to address adequately. However, I have some concerns/comments/suggestions regarding the methodology and the approach the authors took in this study. I will aim to first discuss my main concerns/comments.
- The authors claim that this study aims to investigate the impact of clearcutting on the GHG balance of forested peatlands. Yet in lines 136-138, they state that “stand regeneration was carried out in summer 2021 through ditch mounding and planting of Norway spruce seedlings”. So:
- This is no longer a “clearcut” site since it has been replanted. It is a restock site on its second growing season (as the authors have stated multiple times throughout the manuscript) and hence the strength of source is no longer reflective of a clearcut practise (due to GPP).
- Ditch mounding was used before planting, which suggests to me that the site was disturbed prior to measurements and hence not again representative of a clearcut site. In fact, if indeed any ditch mounding was applied after clearcutting, it means that the land classification reported is also not representative of the post-felling fluxes.
- The authors mention that this is a fertile peatland, however, they didn’t give us any further information as to how they are fertile. Was the site historically fertilised prior to planting or is because of a natural fertilisation over a number of rotations? I believe an international audience would like to know a little more information about the particulars of Finnish peatlands.
- Fluxes presented are from a single year. I understand that authors may feel compelled to present their very interesting work as soon as the first results are available, however, it is very rare, if not I dare say totally unrealistic, to draw any conclusions on the source/sink of a site with simply a single year especially when this year is not also representative of the actual effect of the forest management practice the study claims (see point 1). There is still a huge gap in our knowledge of what is the initial pulse of GHG immediately after clearcutting, and I believe the authors may have missed the opportunity here to capture a potentially significant contribution from the first few months and prior to any planting or mounding.
- The modelling, although very interesting, I don’t believe it has worked as expected particularly for methane. I believe the fact water table depth (WTD) or even soil moisture (theta) was ignored in the modelling was a major overlook since we know (and as the authors themselves demonstrated with Figure 3) both fluxes but particularly CH4 are strongly correlated. Furthermore, another pitfall was the choice of Tair over Tsoil. Volumetric heat capacity changes linearly with moisture, so for wet peatlands I would expect changes in Tsoil to have a bigger impact that those Tair. So, potentially, there was an underestimation of the flux and hence lower strength in the model. Finally, I believe the exclusion of some surface-types from the CH4 model may have resulted in reduced model efficiency, as it clearly worked for N2O. The authors claim that CH4 emissions were not surface dependent (lines 558-559), however, from a work at a Scottish peatland restoration site (Mazzola et al. 2021, European Journal of Soil Science) it was found that CH4 fluxes were significantly different with micro-topography, including water pools. Not considering any interaction of flux with water or moisture it is likely to result to a mismatch between model and data.
- I believe the uncertainty presented in Table 3 for CH4 and N2O re-enforce my opinion that the model for CH4 did not perform well (uncertainty mismatch) comparing to N2O (EC uncertainty within modelled).
- I am also surprised that N2O fluxes were not high after clearcutting. Yamulki et al. 2021 (Biogeosciences) found high N2O on an organo-mineral (30-60cm peat layer over a mineral layer). With a high fertility peatland when trees removed and WTD increases I would expect pulses of N2O. The authors demonstrated that the model was unable to capture the pulse of N2O in August. Was that pulse close to a rainfall event? If so, ignoring relationship WTD and/or theta, hindered the model’s predictive capability.
- I also found very difficult to evaluate the strength of the model’s accuracy. R-squared and RMSE although they give some indication of the model’s predictive capabilities, it was difficult to evaluate further the model, especially where little explanation was given for the LOO statistic. I understand this is a MC-based modelling approach, but I wonder whether a statistic like Akaike Information Criterion, or a significance level for the slope and intercept of the model vs data would be very useful to evaluate the explanatory capacity of the model.
- Surface-specific splitting on fluxes were performed only for CH4 and N2O, however, CO2 was ignored. Why was that? I believe it would have been a great opportunity to repeat the process for CO2.
- I would have liked to see more of an investigation not only how much of the flux is coming from each soil type, but what are the underlying processes by discussing correlation between vegetation, flux and climatological variables and topography.
- The manuscript presents the results of a footprint analysis, followed by a discussion on its potential limitations. It was unclear to me how the footprint was used in further analysis. More importantly, the manuscript is unclear whether footprint was used to either calculate the total area of for surface-type classification of even whether the fluxes were adjusted for footprint contribution once they have been split into different surface-type. This can have a potential major implication on how results are interpreted. It is expected, surface-types closer to the eddy covariance tower to have greater contribution. If for example, plant filled ditches are closer to the tower then potentially their contribution will be larger. Ignoring the combined effect of the surface-type distribution across the area can lead to bias. I suggest the authors review the methodology followed by Budishchev et al. 2014 (Biogeoscience) and revisit some of their approaches.
- The manuscript presents a section on footprint analysis and considerations with a discussion element. However, it was not clear to me how the footprint was used other than simply for presentation purposes. Was the footprint used for the classification of the surface-type?
- Following the point from above, it wasn’t clear whether the surface-type classification was for the whole of the clearcut area or for the footprint. This is potentially key to interpreting the results. Land within the footprint of the tower would have bigger contribution
- Lines 649-656, the CO2 emissions from the peatland are compared to mineral soil. The authors must understand matching fluxes in these two different soil types does not equate validity of measurements due to underlying differences in carbon stocks and respiratory processes.
Some further comments:
- The introduction only lightly touches on the importance of N2O and the current gap in knowledge.
- The introduction also did not make clear what is the uniqueness of this study. In my opinion, this is a novel approach which aims to close the total GHG balance for the boreal and specifically the Fennoscandia, but it was not explicitly highlighted.
- Figure 3 presents a correlation analysis. Are these correlations statistically significant? It was not discussed what the correlations mean for the underlying processes. Keeping the current discussion, I propose this analysis is removed. Alternatively, it can be significantly reduced to include key significant correlations which may further used in the discussion to understand processes.
- Having said that, the manuscript has a lengthy discussion on the modelling. Although, important to highlight modelling limitation and potential pitfalls, I felt there was a little less time spend discussing the underlying processes that are related to different surface-types.
- Figure 6 was very difficult to understand. The points and bars where too small for some variables and hence difficult to convey the message. I wonder whether there is an improved way to present the parameter values. A line across the zero would also have been helpful.
- I don’t understand since we have the parameter values and range in Figure 6, why we had to set the surface-type contribution to one, to “visualise” the parameters in Figure 7. Why not simply present with the estimate surface-type contribution percentage what is the total flux from each and the percentage of the total flux measured by the eddy covariance tower? I believe this information is far more useful and citable for future work.
- It was interesting that the study found N2O emission during snow cover. This is potentially a important find which the manuscript did not discussed in its full extend. Of course, the single year worth of data makes it very difficult but even so, it is important to highlight its importance and whether something similar has been reported before.
- The conclusion sections is a repetition of information already given in either the abstract or the results section. The section requires a refocus to really provide a concluding message from the study.
Citation: https://doi.org/10.5194/egusphere-2024-1994-RC1 - AC1: 'Reply on RC1', Olli-Pekka Tikkasalo, 08 Nov 2024
- The authors claim that this study aims to investigate the impact of clearcutting on the GHG balance of forested peatlands. Yet in lines 136-138, they state that “stand regeneration was carried out in summer 2021 through ditch mounding and planting of Norway spruce seedlings”. So:
-
RC2: 'Comment on egusphere-2024-1994', Anonymous Referee #2, 25 Sep 2024
- AC2: 'Reply on RC2', Olli-Pekka Tikkasalo, 08 Nov 2024
Peer review completion
AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
ED: Reconsider after major revisions (11 Nov 2024) by Paul Stoy
AR by Olli-Pekka Tikkasalo on behalf of the Authors (11 Nov 2024)
Author's response
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Manuscript
ED: Referee Nomination & Report Request started (15 Nov 2024) by Paul Stoy
RR by Anonymous Referee #2 (26 Nov 2024)
RR by Anonymous Referee #3 (01 Dec 2024)
ED: Publish subject to technical corrections (12 Dec 2024) by Paul Stoy
AR by Olli-Pekka Tikkasalo on behalf of the Authors (19 Dec 2024)
Author's response
Manuscript
Journal article(s) based on this preprint
06 Mar 2025
Eddy-covariance fluxes of CO2, CH4 and N2O in a drained peatland forest after clear-cutting
Olli-Pekka Tikkasalo, Olli Peltola, Pavel Alekseychik, Juha Heikkinen, Samuli Launiainen, Aleksi Lehtonen, Qian Li, Eduardo Martínez-García, Mikko Peltoniemi, Petri Salovaara, Ville Tuominen, and Raisa Mäkipää
Biogeosciences, 22, 1277–1300, https://doi.org/10.5194/bg-22-1277-2025, https://doi.org/10.5194/bg-22-1277-2025, 2025
Short summary
Short summary
The emissions of greenhouse gases (GHGs) carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) were measured from a clear-cut peatland forest site. The measurements covered the whole year of 2022, which was the second growing season after the clear-cut. The site was a strong GHG source, and the highest emissions came from CO2, followed by N2O and CH4. A statistical model that included information on different surfaces at the site was developed to unravel surface-type-specific GHG fluxes.
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Olli-Pekka Tikkasalo
CORRESPONDING AUTHOR
Natural Resources Institute Finland (Luke), 00791 Helsinki, Finland
Olli Peltola
Natural Resources Institute Finland (Luke), 00791 Helsinki, Finland
Pavel Alekseychik
Natural Resources Institute Finland (Luke), 00791 Helsinki, Finland
Authors are listed in an alphabetical order.
Juha Heikkinen
Natural Resources Institute Finland (Luke), 00791 Helsinki, Finland
Authors are listed in an alphabetical order.
Samuli Launiainen
Natural Resources Institute Finland (Luke), 00791 Helsinki, Finland
Authors are listed in an alphabetical order.
Aleksi Lehtonen
Natural Resources Institute Finland (Luke), 00791 Helsinki, Finland
Authors are listed in an alphabetical order.
Qian Li
Natural Resources Institute Finland (Luke), 00791 Helsinki, Finland
Authors are listed in an alphabetical order.
Eduardo Martinez-García
Natural Resources Institute Finland (Luke), 00791 Helsinki, Finland
Authors are listed in an alphabetical order.
Mikko Peltoniemi
Natural Resources Institute Finland (Luke), 00791 Helsinki, Finland
Authors are listed in an alphabetical order.
Petri Salovaara
Natural Resources Institute Finland (Luke), 00791 Helsinki, Finland
Authors are listed in an alphabetical order.
Ville Tuominen
Finnish Meteorological Institute (FMI), 00560 Helsinki, Finland
Authors are listed in an alphabetical order.
Raisa Mäkipää
Natural Resources Institute Finland (Luke), 00791 Helsinki, Finland
Download
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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Short summary
The emissions of greenhouse gases (GHG) carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) were measured from a clearcut peatland forest site. The measurements covered the whole year of 2022 which was the second growing season after the clearcut. The site was a strong GHG source and the highest emissions came from CO2 followed by N2O and CH4. A statistical model that included information on different surfaces in the site was developed to unravel surface-type specific GHG fluxes.
The emissions of greenhouse gases (GHG) carbon dioxide (CO2), methane (CH4) and nitrous oxide...