the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 26 Jun 2024
Temporal dynamics and environmental controls of carbon dioxide and methane fluxes measured by the eddy covariance method over a boreal river
Abstract. Boreal rivers and streams are significant sources of carbon dioxide (CO2) and methane (CH4) to the atmosphere. Yet the controls and the magnitude of these emissions remain highly uncertain, as current estimates are mostly based on indirect and discrete flux measurements. In this study, we present and analyse the longest CO2 and the first ever CH4 flux dataset measured by the eddy covariance (EC) technique over a river. The field campaign (KITEX) was carried out during June–October 2018 over the River Kitinen, a large regulated river with a mean annual discharge of 103 m3 s−1 located in northern Finland. The EC system was installed on a floating platform, where the river was 180 m wide and with a maximum depth of 7 m. The river was on average a source of CO2 and CH4 to the atmosphere. The mean CO2 flux was 0.36 ± 0.31 μmol m−2 s−1 and the highest monthly flux occurred in July. The mean CH4 flux was 3.8 ± 4.1 nmol m−2 s−1 and it was also highest in July. During midday hours in June, the river acted occasionally as a net CO2 sink. In June–August, the nocturnal CO2 flux was higher than the daytime flux. The CH4 flux did not show any statistically significant diurnal variation. Results from a multiple regression analysis show that pattern of daily and weekly mean fluxes of CO2 are largely explained by partial pressure of CO2 in water (pCO2w), photosynthetically active radiation (PAR), water flow velocity and wind speed. Water surface temperature and wind speed were found to be the main drivers of CH4 fluxes.
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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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RC1: 'Comment on egusphere-2024-1644', Mingxi Yang, 03 Jul 2024
This paper reports a fairly long and potentially valuable time series of CO2 and CH4 fluxes measured directly by the eddy covariance (EC) method over a boreal river. EC method certainly offers many advantages over other methods (chamber, tracer) in terms of temporal and spatial scales. At the same time, careful processing and filtering of EC data are critical for the data to be trust worthy. I have some reservations about the authors’ flux processing/filter methods. Please see detailed comments below.
Concurrent observations of pCO2w, water flow speed and discharge, in addition to winds, seem to offer the ideal setup for examining the drivers for the transfer velocity of CO2 (flux/dC). Yet puzzlingly, the authors have chosen to bypass this calculation entirely. The multi-linear regression approach has some usefulness for investigating the drivers of pCO2w. But it’s less suitable for investigating the drivers of flux in my opinion. As it stands, I don't feel like I've learned much new from this paper.
That the CO2 flux measurements were made from an undried Licor raises question marks, as it’s well known by now that H2O can cause a substantial interference in CO2 flux over water. If the Picarro also measured CO2, the authors can compare CO2 flux from the two instruments.
I have a strong suspicion that the flux footprint overlapped onto the land upwind at times, which possibly contributed to the observed diurnal cycle in CO2 flux. Please check carefully.
Detailed comments:
Line 44-46. Not sure if this brief section warrants mentioning at this stage.
Line 106. Lack of drying is a concern for the Licor7200. Several works have now shown that for air-sea flux measurements, not drying the air will result in biased CO2 flux measurements due to imperfect H2O correction within the Licor (e.g. www.atmos-chem-phys.net/14/3361/2014/ ).
The H2O issue may be less severe for the Picarro (e.g. www.atmos-meas-tech.net/9/5509/2016/), perhaps due to the very sharp laser frequency (compared to Licor’s broadband).
Does the Picarro not measure CO2 as well? It would be insightful to compare CO2 flux from the Picarro vs. the Licor7200.
Line 122: lag of 0.34 s was used for CO2 and 7.0 s for CH4
Looking at the indicated inlet dimensions and flow rates, I compute a delay time of <0.1 s for CO2 and <0.6 s for CH4. These look to be very different from the estimated delay time from the maximum covariance method, especially for CH4. Did the Picarro subsample from the 10 m inlet? Or were too many outlier delay estimates included in the mean calculation? The computed flux will be biased low if the delay time is incorrect.
Line 132. I don’t see this low frequency ‘attenuation’ correction being applied very often. I understand that by linear detrending, an implicit decision is made such that variability below a certain frequency is not considered to be flux in the EC calculation. A typical averaging time scale of 30 min is never going to statistically capture those very slow varying eddies. But this low frequency signal (e.g. due to convection within the boundary layer) could have either a positive or negative contribution of the flux, right? Then in that case is it really an ‘attenuation’?
More generally, it’d be useful to see the mean CO2 and CH4 cospectra.
Line 158. ‘removed’ or ‘retained’? I would’ve thought that the low variability data should be retained.
Line 161. Z0 of 0.01 m seems large for a water body. Furthermore, the flux footprint is very sensitive to the stability of the atmosphere. Stable conditions lead to much larger footprints and in this case more overlap with upwind land. From the measured sonic flux, the authors can probably screen out cases when the atmosphere is stable. I’m guessing that for a lot of the daytime measurements, Ta > Tsurf and it was fairly stable.
Fig 6a. that CO2 flux goes negative in midday in June, to me, suggests that the EC flux footprint is overlapping onto land further upwind. (As shown in Figure 3, pCO2w >> pCO2a during the entire campaign). This is probably because of the stable atmosphere when Ta > Tsurf. I suggest the authors check their flux filtering more carefully before commenting further on the apparent diurnal cycle in CO2 flux.
Section 4.1 see this paper on filtering of CO2 and CH4 fluxes from a coastal site, which may be of use: www.atmos-chem-phys.net/16/5745/2016/
In particular, the authors could use dC/dt and <u’C’> as additional filtering criteria for longer distance transport.
Also, I’m not sure if section 4.1 is most suitable for discussion. Might be more suitable for the methods section.
Table 5. I understand that pCO2a didn’t vary hugely and thus variability in dpCO2 is more driven by variability in pCO2w. Still, shouldn’t be the air-water concentration difference in pCO2, rather than pCO2w that’s most relevant here?
Also, why don’t the authors compute the CO2 transfer velocity (flux/dC), and then explore how the transfer velocity may be affected by these secondary terms? It seems strange to have multi-linear relationships where flux is parametrized as e.g.: a * pCO2w + b*U. Surely something like flux = (a + b * U + c*Uw) * dpCO2w would be better (I’ve neglected temperature dependencies for simplicity).
Line 351. Is there a diurnal cycle in measured pCO2w? That’d help to address this inconsistency.
Line 364. Were there previous pCH4w measurements at this site? Or pCH4 from similar rivers? What sort of CH4 supersaturation and flux are we expecting roughly?
Citation: https://doi.org/10.5194/egusphere-2024-1644-RC1 -
RC3: 'Reply on RC1', Alex Zavarsky, 09 Jul 2024
"Concurrent observations of pCO2w, water flow speed and discharge, in addition to winds, seem to offer the ideal setup for examining the drivers for the transfer velocity of CO2 (flux/dC). Yet puzzlingly, the authors have chosen to bypass this calculation entirely. The multi-linear regression approach has some usefulness for investigating the drivers of pCO2w. But it’s less suitable for investigating the drivers of flux in my opinion. As it stands, I don't feel like I've learned much new from this paper."
definitively true, but as there not that many data records of River EC I still think the paper is valuable. Some journals offer the type "measurement report", which to some extent could fit here.
"I have a strong suspicion that the flux footprint overlapped onto the land upwind at times, which possibly contributed to the observed diurnal cycle in CO2 flux. Please check carefully."
True, but reading the cited papers, they have done EC measurements on larger lakes with less vegetation and still, however smaller, had a diurnal cycle. I personally can't make a clear decision about the inhomogeneities at rivers. The variable CO2air values must always originate from somewhere, so it its at first hand not important if they come from land vegetation or somewhere else (at the ocean they also come from somewhere). The question is have you reached that level of homogeneity over water that the lower or higher CO2 levels are influencing the flux and are a signal of the flux.
All your concerns and questions are valid, just wanted to throw in my two cents
Citation: https://doi.org/10.5194/egusphere-2024-1644-RC3 -
AC2: 'Reply on RC3', Aki Vähä, 29 Aug 2024
"Concurrent observations of pCO2w, water flow speed and discharge, in addition to winds, seem to offer the ideal setup for examining the drivers for the transfer velocity of CO2 (flux/dC). Yet puzzlingly, the authors have chosen to bypass this calculation entirely. The multi-linear regression approach has some usefulness for investigating the drivers of pCO2w. But it’s less suitable for investigating the drivers of flux in my opinion. As it stands, I don't feel like I've learned much new from this paper."
definitively true, but as there not that many data records of River EC I still think the paper is valuable. Some journals offer the type "measurement report", which to some extent could fit here.
"I have a strong suspicion that the flux footprint overlapped onto the land upwind at times, which possibly contributed to the observed diurnal cycle in CO2 flux. Please check carefully."
True, but reading the cited papers, they have done EC measurements on larger lakes with less vegetation and still, however smaller, had a diurnal cycle. I personally can't make a clear decision about the inhomogeneities at rivers. The variable CO2air values must always originate from somewhere, so it its at first hand not important if they come from land vegetation or somewhere else (at the ocean they also come from somewhere). The question is have you reached that level of homogeneity over water that the lower or higher CO2 levels are influencing the flux and are a signal of the flux.
All your concerns and questions are valid, just wanted to throw in my two cents
- We address these questions in detail in our replies to RC1 and RC2. We believe that our flux data is processed in an appropriate way and our fluxes do not contain significant amounts of signal from land.
Citation: https://doi.org/10.5194/egusphere-2024-1644-AC2
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AC2: 'Reply on RC3', Aki Vähä, 29 Aug 2024
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AC1: 'Reply on RC1', Aki Vähä, 29 Aug 2024
This paper reports a fairly long and potentially valuable time series of CO2 and CH4 fluxes measured directly by the eddy covariance (EC) method over a boreal river. EC method certainly offers many advantages over other methods (chamber, tracer) in terms of temporal and spatial scales. At the same time, careful processing and filtering of EC data are critical for the data to be trust worthy. I have some reservations about the authors’ flux processing/filter methods. Please see detailed comments below.
Concurrent observations of pCO2w, water flow speed and discharge, in addition to winds, seem to offer the ideal setup for examining the drivers for the transfer velocity of CO2 (flux/dC). Yet puzzlingly, the authors have chosen to bypass this calculation entirely. The multi-linear regression approach has some usefulness for investigating the drivers of pCO2w. But it’s less suitable for investigating the drivers of flux in my opinion. As it stands, I don't feel like I've learned much new from this paper.
That the CO2 flux measurements were made from an undried Licor raises question marks, as it’s well known by now that H2O can cause a substantial interference in CO2 flux over water. If the Picarro also measured CO2, the authors can compare CO2 flux from the two instruments.
I have a strong suspicion that the flux footprint overlapped onto the land upwind at times, which possibly contributed to the observed diurnal cycle in CO2 flux. Please check carefully.
- We thank Dr. Yang for his insightful and detailed review. We address these questions as well as all the detailed comments below. Please see our response below.
Detailed comments:
Line 44-46. Not sure if this brief section warrants mentioning at this stage.
- We have reworded the latter part of the sentence to make it less bold of a claim.
Line 106. Lack of drying is a concern for the Licor7200. Several works have now shown that for air-sea flux measurements, not drying the air will result in biased CO2 flux measurements due to imperfect H2O correction within the Licor (e.g. www.atmos-chem-phys.net/14/3361/2014/ ).
The H2O issue may be less severe for the Picarro (e.g. www.atmos-meas-tech.net/9/5509/2016/), perhaps due to the very sharp laser frequency (compared to Licor’s broadband).
- According to Nilsson et al. (2018) (https://doi.org/10.1175/JTECH-D-17-0072.1) who studied the effect of H2O on CO2 fluxes measured with Li-7200, the effect was largely due to sea salt contamination on the optical windows of the gas analyser. A similar setup without a dryer has been used in other freshwater studies, e.g. by Huotari et al. (2013), Erkkilä et al. (2018) and Spank et al. (2020) (references in the manuscript). It is a standard setup that follows the ICOS protocol (Sabbatini et al. 2018) (https://doi.org/10.1515/intag-2017-0043) and the data processing is well-established. The volume effect of H2O on CO2 and CH4 fluxes has been taken care of by utilising dry mole fractions of CO2 and CH4.
Does the Picarro not measure CO2 as well? It would be insightful to compare CO2 flux from the Picarro vs. the Licor7200.
- Our Picarro did not measure CO2. It would indeed have revealed important insight of the ecosystem and the measurement setup.
Line 122: lag of 0.34 s was used for CO2 and 7.0 s for CH4
Looking at the indicated inlet dimensions and flow rates, I compute a delay time of <0.1 s for CO2 and <0.6 s for CH4. These look to be very different from the estimated delay time from the maximum covariance method, especially for CH4. Did the Picarro subsample from the 10 m inlet? Or were too many outlier delay estimates included in the mean calculation? The computed flux will be biased low if the delay time is incorrect.
- The lag time calculation with the maximum cross-covariance method was limited to a certain time window and there were no significant outliers in the lag times. We recalculated the lag times, and the results were the same as in the manuscript. The lag times of both gas analysers are increased a little from the theoretical value by the sample line filters. One reason between the discrepancy in the case of Picarro could be clock synchronisation between the sonic anemometer and the Picarro. We added a figure in the manuscript showing examples of the cross-covariance functions for both CO2 and CH4. The figure is also below.
- Figure 1. Example of the time lag determination for (a) CO2 and (b) CH4. The solid line shows the cross-covariance function of the vertical wind w and the gas mixing ratio χ. The vertical dashed lines mark the windows within which the lag times were searched. In this example, the location of the cross-covariance peak, i.e. the time lag for these averaging periods, was 0.3 s for CO2 and 7.1 s for CH4.
Line 132. I don’t see this low frequency ‘attenuation’ correction being applied very often. I understand that by linear detrending, an implicit decision is made such that variability below a certain frequency is not considered to be flux in the EC calculation. A typical averaging time scale of 30 min is never going to statistically capture those very slow varying eddies. But this low frequency signal (e.g. due to convection within the boundary layer) could have either a positive or negative contribution of the flux, right? Then in that case is it really an ‘attenuation’?
- The low-frequency correction is a standard procedure and implemented according to the ICOS protocol (Sabbatini et al. 2018). In the form that it is implemented, it aims to correct the flux attenuation from excluding the largest eddies. However, for our site the correction is very small (1–2%) because of the rather low measurement height and the absence of strong convection (on average z<<-L, where L is the Obukhov length).
More generally, it’d be useful to see the mean CO2 and CH4 cospectra.
- They are now added to the manuscript.
Line 158. ‘removed’ or ‘retained’? I would’ve thought that the low variability data should be retained.
- The correct word is ‘retained’. The error is now fixed.
Line 161. z0 of 0.01 m seems large for a water body. Furthermore, the flux footprint is very sensitive to the stability of the atmosphere. Stable conditions lead to much larger footprints and in this case more overlap with upwind land. From the measured sonic flux, the authors can probably screen out cases when the atmosphere is stable. I’m guessing that for a lot of the daytime measurements, Ta > Tsurf and it was fairly stable.
- We had another eddy covariance system on the platform measuring fluxes at 1 m height adjacent to the system described in the manuscript. Calculating z0 from the two wind measurements resulted in z0 ≈ 0.01 m. The straight section in the river and the fetch being close to or exceeding 1 km enabled rather large wind-generated waves, especially when the wind was from the south, i.e. against the current. At highest, the waves were approximately 0.5 m tall and 0.1–0.2 m waves were common. In this sense, z0 = 0.01 m is not unreasonable.
- In footprint calculations, all meteorological characteristics are considered. The acceptable wind directions regarding the fetch are following the calculation results. In addition, we desired to be conservative and only directions which are more along the river were selected, although footprints would have indicated acceptable fetch for directions more towards the bank. Note also that the footprint probability density function is weighted near the measurement location. Thus, even stable cases allow for acceptable data as the measurement height was small.
Fig 6a. that CO2 flux goes negative in midday in June, to me, suggests that the EC flux footprint is overlapping onto land further upwind. (As shown in Figure 3, pCO2w >> pCO2a during the entire campaign). This is probably because of the stable atmosphere when Ta > Tsurf. I suggest the authors check their flux filtering more carefully before commenting further on the apparent diurnal cycle in CO2 flux.
- See the previous answer. Even though we intended to be careful in data filtering regarding the fetch, it is true that some flux signal – beyond the footprint extent of 90% – originates from land. This creates scatter in the data but it cannot change the sign of the flux. The mismatch between the flux sign and pCO2w/pCO2 ratio may result from several reasons, like that the concentration measurement is not fully representing the larger flux footprint.
Section 4.1 see this paper on filtering of CO2 and CH4 fluxes from a coastal site, which may be of use: www.atmos-chem-phys.net/16/5745/2016/
In particular, the authors could use dC/dt and <u’C’> as additional filtering criteria for longer distance transport.
- We have closely followed the ICOS protocol (Sabbatini et al., 2018) in filtering the fluxes. Additionally, we have utilised the approach of further calculating the mean wind direction in 5-minute intervals and using those wind directions as additional filtering criteria (Section 2.3, lines 166–179). According to Blomquist et al. (2012) (https://doi.org/10.5194/amt-5-3069-2012) who used dC/dt and <u’C’> as criteria for filtering fluxes and who are cited in the paper above, these criteria are used for ensuring the stationarity in the time series. However, we have already utilised the flux stationarity criterion by Foken and Wichura (1996). dC/dt and <u’C’> might be suitable for assessing the effect of lateral advection in the fluxes, but we have implemented the gas mixing ratio standard deviation filtering for excluding cases with strong advection.
Also, I’m not sure if section 4.1 is most suitable for discussion. Might be more suitable for the methods section.
- This section contains discussion about applying the eddy covariance method over water bodies rather than describing the method itself. For example, the section contains discussion about the 5-minute wind direction method. To expand the discussion, we have added a reference to the paper by Blomquist et al. (2012) to Section 4.1.
Table 5. I understand that pCO2a didn’t vary hugely and thus variability in dpCO2 is more driven by variability in pCO2w. Still, shouldn’t be the air-water concentration difference in pCO2, rather than pCO2w that’s most relevant here?
- This is a valid point. The multivariate model calculations were redone using dpCO2 instead of pCO2w. The resulting models and their ranking are almost identical to the ones in the preprint. The R2 and p values changed only slightly. The tables in the manuscript are now updated accordingly. The emergent picture of the flux drivers did not change and thus the discussion remains the same.
Also, why don’t the authors compute the CO2 transfer velocity (flux/dC), and then explore how the transfer velocity may be affected by these secondary terms? It seems strange to have multi-linear relationships where flux is parametrized as e.g.: a * pCO2w + b*U. Surely something like flux = (a + b * U + c*Uw) * dpCO2w would be better (I’ve neglected temperature dependencies for simplicity).
- We are writing another manuscript related to the gas transfer velocity using the same dataset and where we will analyse in detail the dependence of the transfer velocity on e.g. flow speed. The purpose of our multivariate analysis in this manuscript with daily and weekly means is to gain a basic understanding of flux drivers.
Line 351. Is there a diurnal cycle in measured pCO2w? That’d help to address this inconsistency.
- There is a small diurnal cycle in the measured pCO2w. The maximum mean difference in pCO2w is 46 ppm. A figure is of the mean diurnal pCO2w is now added in the manuscript.
Line 364. Were there previous pCH4w measurements at this site? Or pCH4 from similar rivers? What sort of CH4 supersaturation and flux are we expecting roughly?
- There are no previous measurements of pCH4w at this site. We have added a paragraph in section 4.2 where we summarise earlier studies of pCH4w and FCH4 from similar rivers.
Citation: https://doi.org/10.5194/egusphere-2024-1644-AC1
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RC3: 'Reply on RC1', Alex Zavarsky, 09 Jul 2024
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RC2: 'Comment on egusphere-2024-1644', Alex Zavarsky, 09 Jul 2024
The preprint provides an interesting dataset of eddy covariance flux measurements at a river site in Finland and as the paper states eddy covariance measurements of CO2 and CH4 at rivers have only been done once before. Such measurements are especially difficult due to the inhomogeneous surroundings at small to medium rivers and have to be analysed carefully:
Scientific questions:
Line 40: “The River Kitinen is a regulated river and, as such, provides information on e.g. how the anthropogenic modification of the flow affects gas exchange”
Have you extensively addressed this later in the paper? Later there is only a short sentence about this.Line 159: You are using the Kljun et al 2015 footprint calculations. Did you put in the whole time series of wind speed, stability etc and the calculate the footprint climatology? Did you exclude certain “good directions” (parallel to the river) EC data because of an extensive footprint? What does “the river bank directions are excluded” mean? Did you exclude them anyways or was the footprint calculation stopped there?
Line 253: You chose two timespans for day and night-time. Do you have a total radiation data set to back this up or is it a feature of your EC data set? You measured PAR in the water but some questions (also by Mr Yang) refer to land vegetation. A total radiation dataset would be helpful. Wasn’t there a meteorological mast nearby? Was there such a sensor mounted?
You have correlated fluxes, PAR and, pCO2w on a daily scale. What about hourly/2 or 3 hourly scale fluxes with respect to PAR or total radiation? This could also help interpreting your diel cycles.Line 327: Good point. Sections across the river with pCO2w values would be interesting.
Line 383: there a plenty of gridded precipitation reanalysis products. One could combine that with watershed geographical data and get a better picture of the upstream precipitation.
Technical Questions:
Line 227: “The 30-minute average air temperature rose from 10-20°C …. to 30°C. “Didn’t it just rise from 10°C to 30°C.
Figure 3: I would recommend changing the water temperature heat plot to a line plot with maybe surface, depth at x m and bottom. I couldn’t figure out any temperature features over depth. It is mostly variable over time.
Additionally, a total radiation dataset, as mentioned above, would be helpful.
Line 269: In this paragraph you state the you present the five best models. You could make them more visible and distinguishable: like “a)….b)…c)…”
Line 273: “Some of best daily models Include U”. Can you be more precise.
Line 366: This paragraph covers a lot. Maybe some subsections or partitions would be helpful.
Line 397: “…on only some of the best models”. Can you be more precise.
Citation: https://doi.org/10.5194/egusphere-2024-1644-RC2 -
AC3: 'Reply on RC2', Aki Vähä, 29 Aug 2024
The preprint provides an interesting dataset of eddy covariance flux measurements at a river site in Finland and as the paper states eddy covariance measurements of CO2 and CH4 at rivers have only been done once before. Such measurements are especially difficult due to the inhomogeneous surroundings at small to medium rivers and have to be analysed carefully.
- We thank Dr. Zavarsky for his thoughtful and thorough remarks. Please see our responses below.
Scientific questions:
Line 40: “The River Kitinen is a regulated river and, as such, provides information on e.g. how the anthropogenic modification of the flow affects gas exchange”
Have you extensively addressed this later in the paper? Later there is only a short sentence about this.- We decided to remove this sentence from this manuscript and will address this question in the second manuscript focusing on the gas transfer coefficient.
Line 159: You are using the Kljun et al 2015 footprint calculations. Did you put in the whole time series of wind speed, stability etc and the calculate the footprint climatology? Did you exclude certain “good directions” (parallel to the river) EC data because of an extensive footprint? What does “the river bank directions are excluded” mean? Did you exclude them anyways or was the footprint calculation stopped there?
- The full measured dataset of the mentioned variables was used in the calculation and periods when the wind was from the river bank sectors are excluded from further flux analysis. We rephrased this paragraph to better explain the calculation process. We want to add here that the data filtering is a compromise between data statistics and the safest fetch. Narrowing even more the acceptable wind directions would result into a very low amount of data points. See the comments above related to Dr. Yang’s remarks about the footprint.
Line 253: You chose two timespans for day and night-time. Do you have a total radiation data set to back this up or is it a feature of your EC data set? You measured PAR in the water but some questions (also by Mr Yang) refer to land vegetation. A total radiation dataset would be helpful. Wasn’t there a meteorological mast nearby? Was there such a sensor mounted?
- This rudimentary classification was selected to get an idea of diurnal differences. We are not using the classification for any further analysis. We indeed have a full radiation dataset, but for avoiding land contamination in the fluxes, we have already implemented several methods: flux stationarity criterion (Foken and Wichura, 1996), limiting the standard deviations of the gas mixing ratios and the new method of observing the 5-minute mean wind directions.
You have correlated fluxes, PAR and, pCO2w on a daily scale. What about hourly/2 or 3 hourly scale fluxes with respect to PAR or total radiation? This could also help interpreting your diel cycles.
- We have conducted an additional multivariate analysis on average diel cycles of the same variables as in the daily and weekly analyses in the manuscript (excluding precipitation, as we only had daily precipitation rates) and will add those results and discussion in the manuscript. According to this analysis, pCO2 correlates the most with Tsurf and Uw. For FCO2, the main drivers are pCO2 and U and for FCH4, they are Tsurf and pCO2.
Line 327: Good point. Sections across the river with pCO2w values would be interesting.
- We agree, however, we were measuring pCO2w in only one location during the campaign.
Line 383: there a plenty of gridded precipitation reanalysis products. One could combine that with watershed geographical data and get a better picture of the upstream precipitation.
- This would require a modelling approach and extensive research on the river discharges in the watershed, flushing of carbon from soils etc. and is beyond the scope of this manuscript. Our approach focuses instead on simple environmental variables that are relatively easy to obtain.
Technical Questions:
Line 227: “The 30-minute average air temperature rose from 10-20°C …. to 30°C. “Didn’t it just rise from 10°C to 30°C.
- This sentence is reworded to make it clearer to the reader.
Figure 3: I would recommend changing the water temperature heat plot to a line plot with maybe surface, depth at x m and bottom. I couldn’t figure out any temperature features over depth. It is mostly variable over time.
- We have changed the heat plot to a line plot.
Additionally, a total radiation dataset, as mentioned above, would be helpful.
- We would like to show in the manuscript only those variables that are used in the analysis but a full radiation dataset can be added in the supplementary information.
Line 269: In this paragraph you state the you present the five best models. You could make them more visible and distinguishable: like “a)….b)…c)…”
- The letters are now added to Tables 4, 5 and 6.
Line 273: “Some of best daily models include U”. Can you be more precise.
- We now mention the exact number of models of the five best ones than contain U as a driving variable.
Line 366: This paragraph covers a lot. Maybe some subsections or partitions would be helpful.
- We now divide this section into drivers of pCO2 and drivers of fluxes to make it more readable.
Line 397: “…on only some of the best models”. Can you be more precise.
- Similarly as in the comment above, we now mention the exact number of models containing U.
Citation: https://doi.org/10.5194/egusphere-2024-1644-AC3
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AC3: 'Reply on RC2', Aki Vähä, 29 Aug 2024
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2024-1644', Mingxi Yang, 03 Jul 2024
This paper reports a fairly long and potentially valuable time series of CO2 and CH4 fluxes measured directly by the eddy covariance (EC) method over a boreal river. EC method certainly offers many advantages over other methods (chamber, tracer) in terms of temporal and spatial scales. At the same time, careful processing and filtering of EC data are critical for the data to be trust worthy. I have some reservations about the authors’ flux processing/filter methods. Please see detailed comments below.
Concurrent observations of pCO2w, water flow speed and discharge, in addition to winds, seem to offer the ideal setup for examining the drivers for the transfer velocity of CO2 (flux/dC). Yet puzzlingly, the authors have chosen to bypass this calculation entirely. The multi-linear regression approach has some usefulness for investigating the drivers of pCO2w. But it’s less suitable for investigating the drivers of flux in my opinion. As it stands, I don't feel like I've learned much new from this paper.
That the CO2 flux measurements were made from an undried Licor raises question marks, as it’s well known by now that H2O can cause a substantial interference in CO2 flux over water. If the Picarro also measured CO2, the authors can compare CO2 flux from the two instruments.
I have a strong suspicion that the flux footprint overlapped onto the land upwind at times, which possibly contributed to the observed diurnal cycle in CO2 flux. Please check carefully.
Detailed comments:
Line 44-46. Not sure if this brief section warrants mentioning at this stage.
Line 106. Lack of drying is a concern for the Licor7200. Several works have now shown that for air-sea flux measurements, not drying the air will result in biased CO2 flux measurements due to imperfect H2O correction within the Licor (e.g. www.atmos-chem-phys.net/14/3361/2014/ ).
The H2O issue may be less severe for the Picarro (e.g. www.atmos-meas-tech.net/9/5509/2016/), perhaps due to the very sharp laser frequency (compared to Licor’s broadband).
Does the Picarro not measure CO2 as well? It would be insightful to compare CO2 flux from the Picarro vs. the Licor7200.
Line 122: lag of 0.34 s was used for CO2 and 7.0 s for CH4
Looking at the indicated inlet dimensions and flow rates, I compute a delay time of <0.1 s for CO2 and <0.6 s for CH4. These look to be very different from the estimated delay time from the maximum covariance method, especially for CH4. Did the Picarro subsample from the 10 m inlet? Or were too many outlier delay estimates included in the mean calculation? The computed flux will be biased low if the delay time is incorrect.
Line 132. I don’t see this low frequency ‘attenuation’ correction being applied very often. I understand that by linear detrending, an implicit decision is made such that variability below a certain frequency is not considered to be flux in the EC calculation. A typical averaging time scale of 30 min is never going to statistically capture those very slow varying eddies. But this low frequency signal (e.g. due to convection within the boundary layer) could have either a positive or negative contribution of the flux, right? Then in that case is it really an ‘attenuation’?
More generally, it’d be useful to see the mean CO2 and CH4 cospectra.
Line 158. ‘removed’ or ‘retained’? I would’ve thought that the low variability data should be retained.
Line 161. Z0 of 0.01 m seems large for a water body. Furthermore, the flux footprint is very sensitive to the stability of the atmosphere. Stable conditions lead to much larger footprints and in this case more overlap with upwind land. From the measured sonic flux, the authors can probably screen out cases when the atmosphere is stable. I’m guessing that for a lot of the daytime measurements, Ta > Tsurf and it was fairly stable.
Fig 6a. that CO2 flux goes negative in midday in June, to me, suggests that the EC flux footprint is overlapping onto land further upwind. (As shown in Figure 3, pCO2w >> pCO2a during the entire campaign). This is probably because of the stable atmosphere when Ta > Tsurf. I suggest the authors check their flux filtering more carefully before commenting further on the apparent diurnal cycle in CO2 flux.
Section 4.1 see this paper on filtering of CO2 and CH4 fluxes from a coastal site, which may be of use: www.atmos-chem-phys.net/16/5745/2016/
In particular, the authors could use dC/dt and <u’C’> as additional filtering criteria for longer distance transport.
Also, I’m not sure if section 4.1 is most suitable for discussion. Might be more suitable for the methods section.
Table 5. I understand that pCO2a didn’t vary hugely and thus variability in dpCO2 is more driven by variability in pCO2w. Still, shouldn’t be the air-water concentration difference in pCO2, rather than pCO2w that’s most relevant here?
Also, why don’t the authors compute the CO2 transfer velocity (flux/dC), and then explore how the transfer velocity may be affected by these secondary terms? It seems strange to have multi-linear relationships where flux is parametrized as e.g.: a * pCO2w + b*U. Surely something like flux = (a + b * U + c*Uw) * dpCO2w would be better (I’ve neglected temperature dependencies for simplicity).
Line 351. Is there a diurnal cycle in measured pCO2w? That’d help to address this inconsistency.
Line 364. Were there previous pCH4w measurements at this site? Or pCH4 from similar rivers? What sort of CH4 supersaturation and flux are we expecting roughly?
Citation: https://doi.org/10.5194/egusphere-2024-1644-RC1 -
RC3: 'Reply on RC1', Alex Zavarsky, 09 Jul 2024
"Concurrent observations of pCO2w, water flow speed and discharge, in addition to winds, seem to offer the ideal setup for examining the drivers for the transfer velocity of CO2 (flux/dC). Yet puzzlingly, the authors have chosen to bypass this calculation entirely. The multi-linear regression approach has some usefulness for investigating the drivers of pCO2w. But it’s less suitable for investigating the drivers of flux in my opinion. As it stands, I don't feel like I've learned much new from this paper."
definitively true, but as there not that many data records of River EC I still think the paper is valuable. Some journals offer the type "measurement report", which to some extent could fit here.
"I have a strong suspicion that the flux footprint overlapped onto the land upwind at times, which possibly contributed to the observed diurnal cycle in CO2 flux. Please check carefully."
True, but reading the cited papers, they have done EC measurements on larger lakes with less vegetation and still, however smaller, had a diurnal cycle. I personally can't make a clear decision about the inhomogeneities at rivers. The variable CO2air values must always originate from somewhere, so it its at first hand not important if they come from land vegetation or somewhere else (at the ocean they also come from somewhere). The question is have you reached that level of homogeneity over water that the lower or higher CO2 levels are influencing the flux and are a signal of the flux.
All your concerns and questions are valid, just wanted to throw in my two cents
Citation: https://doi.org/10.5194/egusphere-2024-1644-RC3 -
AC2: 'Reply on RC3', Aki Vähä, 29 Aug 2024
"Concurrent observations of pCO2w, water flow speed and discharge, in addition to winds, seem to offer the ideal setup for examining the drivers for the transfer velocity of CO2 (flux/dC). Yet puzzlingly, the authors have chosen to bypass this calculation entirely. The multi-linear regression approach has some usefulness for investigating the drivers of pCO2w. But it’s less suitable for investigating the drivers of flux in my opinion. As it stands, I don't feel like I've learned much new from this paper."
definitively true, but as there not that many data records of River EC I still think the paper is valuable. Some journals offer the type "measurement report", which to some extent could fit here.
"I have a strong suspicion that the flux footprint overlapped onto the land upwind at times, which possibly contributed to the observed diurnal cycle in CO2 flux. Please check carefully."
True, but reading the cited papers, they have done EC measurements on larger lakes with less vegetation and still, however smaller, had a diurnal cycle. I personally can't make a clear decision about the inhomogeneities at rivers. The variable CO2air values must always originate from somewhere, so it its at first hand not important if they come from land vegetation or somewhere else (at the ocean they also come from somewhere). The question is have you reached that level of homogeneity over water that the lower or higher CO2 levels are influencing the flux and are a signal of the flux.
All your concerns and questions are valid, just wanted to throw in my two cents
- We address these questions in detail in our replies to RC1 and RC2. We believe that our flux data is processed in an appropriate way and our fluxes do not contain significant amounts of signal from land.
Citation: https://doi.org/10.5194/egusphere-2024-1644-AC2
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AC2: 'Reply on RC3', Aki Vähä, 29 Aug 2024
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AC1: 'Reply on RC1', Aki Vähä, 29 Aug 2024
This paper reports a fairly long and potentially valuable time series of CO2 and CH4 fluxes measured directly by the eddy covariance (EC) method over a boreal river. EC method certainly offers many advantages over other methods (chamber, tracer) in terms of temporal and spatial scales. At the same time, careful processing and filtering of EC data are critical for the data to be trust worthy. I have some reservations about the authors’ flux processing/filter methods. Please see detailed comments below.
Concurrent observations of pCO2w, water flow speed and discharge, in addition to winds, seem to offer the ideal setup for examining the drivers for the transfer velocity of CO2 (flux/dC). Yet puzzlingly, the authors have chosen to bypass this calculation entirely. The multi-linear regression approach has some usefulness for investigating the drivers of pCO2w. But it’s less suitable for investigating the drivers of flux in my opinion. As it stands, I don't feel like I've learned much new from this paper.
That the CO2 flux measurements were made from an undried Licor raises question marks, as it’s well known by now that H2O can cause a substantial interference in CO2 flux over water. If the Picarro also measured CO2, the authors can compare CO2 flux from the two instruments.
I have a strong suspicion that the flux footprint overlapped onto the land upwind at times, which possibly contributed to the observed diurnal cycle in CO2 flux. Please check carefully.
- We thank Dr. Yang for his insightful and detailed review. We address these questions as well as all the detailed comments below. Please see our response below.
Detailed comments:
Line 44-46. Not sure if this brief section warrants mentioning at this stage.
- We have reworded the latter part of the sentence to make it less bold of a claim.
Line 106. Lack of drying is a concern for the Licor7200. Several works have now shown that for air-sea flux measurements, not drying the air will result in biased CO2 flux measurements due to imperfect H2O correction within the Licor (e.g. www.atmos-chem-phys.net/14/3361/2014/ ).
The H2O issue may be less severe for the Picarro (e.g. www.atmos-meas-tech.net/9/5509/2016/), perhaps due to the very sharp laser frequency (compared to Licor’s broadband).
- According to Nilsson et al. (2018) (https://doi.org/10.1175/JTECH-D-17-0072.1) who studied the effect of H2O on CO2 fluxes measured with Li-7200, the effect was largely due to sea salt contamination on the optical windows of the gas analyser. A similar setup without a dryer has been used in other freshwater studies, e.g. by Huotari et al. (2013), Erkkilä et al. (2018) and Spank et al. (2020) (references in the manuscript). It is a standard setup that follows the ICOS protocol (Sabbatini et al. 2018) (https://doi.org/10.1515/intag-2017-0043) and the data processing is well-established. The volume effect of H2O on CO2 and CH4 fluxes has been taken care of by utilising dry mole fractions of CO2 and CH4.
Does the Picarro not measure CO2 as well? It would be insightful to compare CO2 flux from the Picarro vs. the Licor7200.
- Our Picarro did not measure CO2. It would indeed have revealed important insight of the ecosystem and the measurement setup.
Line 122: lag of 0.34 s was used for CO2 and 7.0 s for CH4
Looking at the indicated inlet dimensions and flow rates, I compute a delay time of <0.1 s for CO2 and <0.6 s for CH4. These look to be very different from the estimated delay time from the maximum covariance method, especially for CH4. Did the Picarro subsample from the 10 m inlet? Or were too many outlier delay estimates included in the mean calculation? The computed flux will be biased low if the delay time is incorrect.
- The lag time calculation with the maximum cross-covariance method was limited to a certain time window and there were no significant outliers in the lag times. We recalculated the lag times, and the results were the same as in the manuscript. The lag times of both gas analysers are increased a little from the theoretical value by the sample line filters. One reason between the discrepancy in the case of Picarro could be clock synchronisation between the sonic anemometer and the Picarro. We added a figure in the manuscript showing examples of the cross-covariance functions for both CO2 and CH4. The figure is also below.
- Figure 1. Example of the time lag determination for (a) CO2 and (b) CH4. The solid line shows the cross-covariance function of the vertical wind w and the gas mixing ratio χ. The vertical dashed lines mark the windows within which the lag times were searched. In this example, the location of the cross-covariance peak, i.e. the time lag for these averaging periods, was 0.3 s for CO2 and 7.1 s for CH4.
Line 132. I don’t see this low frequency ‘attenuation’ correction being applied very often. I understand that by linear detrending, an implicit decision is made such that variability below a certain frequency is not considered to be flux in the EC calculation. A typical averaging time scale of 30 min is never going to statistically capture those very slow varying eddies. But this low frequency signal (e.g. due to convection within the boundary layer) could have either a positive or negative contribution of the flux, right? Then in that case is it really an ‘attenuation’?
- The low-frequency correction is a standard procedure and implemented according to the ICOS protocol (Sabbatini et al. 2018). In the form that it is implemented, it aims to correct the flux attenuation from excluding the largest eddies. However, for our site the correction is very small (1–2%) because of the rather low measurement height and the absence of strong convection (on average z<<-L, where L is the Obukhov length).
More generally, it’d be useful to see the mean CO2 and CH4 cospectra.
- They are now added to the manuscript.
Line 158. ‘removed’ or ‘retained’? I would’ve thought that the low variability data should be retained.
- The correct word is ‘retained’. The error is now fixed.
Line 161. z0 of 0.01 m seems large for a water body. Furthermore, the flux footprint is very sensitive to the stability of the atmosphere. Stable conditions lead to much larger footprints and in this case more overlap with upwind land. From the measured sonic flux, the authors can probably screen out cases when the atmosphere is stable. I’m guessing that for a lot of the daytime measurements, Ta > Tsurf and it was fairly stable.
- We had another eddy covariance system on the platform measuring fluxes at 1 m height adjacent to the system described in the manuscript. Calculating z0 from the two wind measurements resulted in z0 ≈ 0.01 m. The straight section in the river and the fetch being close to or exceeding 1 km enabled rather large wind-generated waves, especially when the wind was from the south, i.e. against the current. At highest, the waves were approximately 0.5 m tall and 0.1–0.2 m waves were common. In this sense, z0 = 0.01 m is not unreasonable.
- In footprint calculations, all meteorological characteristics are considered. The acceptable wind directions regarding the fetch are following the calculation results. In addition, we desired to be conservative and only directions which are more along the river were selected, although footprints would have indicated acceptable fetch for directions more towards the bank. Note also that the footprint probability density function is weighted near the measurement location. Thus, even stable cases allow for acceptable data as the measurement height was small.
Fig 6a. that CO2 flux goes negative in midday in June, to me, suggests that the EC flux footprint is overlapping onto land further upwind. (As shown in Figure 3, pCO2w >> pCO2a during the entire campaign). This is probably because of the stable atmosphere when Ta > Tsurf. I suggest the authors check their flux filtering more carefully before commenting further on the apparent diurnal cycle in CO2 flux.
- See the previous answer. Even though we intended to be careful in data filtering regarding the fetch, it is true that some flux signal – beyond the footprint extent of 90% – originates from land. This creates scatter in the data but it cannot change the sign of the flux. The mismatch between the flux sign and pCO2w/pCO2 ratio may result from several reasons, like that the concentration measurement is not fully representing the larger flux footprint.
Section 4.1 see this paper on filtering of CO2 and CH4 fluxes from a coastal site, which may be of use: www.atmos-chem-phys.net/16/5745/2016/
In particular, the authors could use dC/dt and <u’C’> as additional filtering criteria for longer distance transport.
- We have closely followed the ICOS protocol (Sabbatini et al., 2018) in filtering the fluxes. Additionally, we have utilised the approach of further calculating the mean wind direction in 5-minute intervals and using those wind directions as additional filtering criteria (Section 2.3, lines 166–179). According to Blomquist et al. (2012) (https://doi.org/10.5194/amt-5-3069-2012) who used dC/dt and <u’C’> as criteria for filtering fluxes and who are cited in the paper above, these criteria are used for ensuring the stationarity in the time series. However, we have already utilised the flux stationarity criterion by Foken and Wichura (1996). dC/dt and <u’C’> might be suitable for assessing the effect of lateral advection in the fluxes, but we have implemented the gas mixing ratio standard deviation filtering for excluding cases with strong advection.
Also, I’m not sure if section 4.1 is most suitable for discussion. Might be more suitable for the methods section.
- This section contains discussion about applying the eddy covariance method over water bodies rather than describing the method itself. For example, the section contains discussion about the 5-minute wind direction method. To expand the discussion, we have added a reference to the paper by Blomquist et al. (2012) to Section 4.1.
Table 5. I understand that pCO2a didn’t vary hugely and thus variability in dpCO2 is more driven by variability in pCO2w. Still, shouldn’t be the air-water concentration difference in pCO2, rather than pCO2w that’s most relevant here?
- This is a valid point. The multivariate model calculations were redone using dpCO2 instead of pCO2w. The resulting models and their ranking are almost identical to the ones in the preprint. The R2 and p values changed only slightly. The tables in the manuscript are now updated accordingly. The emergent picture of the flux drivers did not change and thus the discussion remains the same.
Also, why don’t the authors compute the CO2 transfer velocity (flux/dC), and then explore how the transfer velocity may be affected by these secondary terms? It seems strange to have multi-linear relationships where flux is parametrized as e.g.: a * pCO2w + b*U. Surely something like flux = (a + b * U + c*Uw) * dpCO2w would be better (I’ve neglected temperature dependencies for simplicity).
- We are writing another manuscript related to the gas transfer velocity using the same dataset and where we will analyse in detail the dependence of the transfer velocity on e.g. flow speed. The purpose of our multivariate analysis in this manuscript with daily and weekly means is to gain a basic understanding of flux drivers.
Line 351. Is there a diurnal cycle in measured pCO2w? That’d help to address this inconsistency.
- There is a small diurnal cycle in the measured pCO2w. The maximum mean difference in pCO2w is 46 ppm. A figure is of the mean diurnal pCO2w is now added in the manuscript.
Line 364. Were there previous pCH4w measurements at this site? Or pCH4 from similar rivers? What sort of CH4 supersaturation and flux are we expecting roughly?
- There are no previous measurements of pCH4w at this site. We have added a paragraph in section 4.2 where we summarise earlier studies of pCH4w and FCH4 from similar rivers.
Citation: https://doi.org/10.5194/egusphere-2024-1644-AC1
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RC3: 'Reply on RC1', Alex Zavarsky, 09 Jul 2024
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RC2: 'Comment on egusphere-2024-1644', Alex Zavarsky, 09 Jul 2024
The preprint provides an interesting dataset of eddy covariance flux measurements at a river site in Finland and as the paper states eddy covariance measurements of CO2 and CH4 at rivers have only been done once before. Such measurements are especially difficult due to the inhomogeneous surroundings at small to medium rivers and have to be analysed carefully:
Scientific questions:
Line 40: “The River Kitinen is a regulated river and, as such, provides information on e.g. how the anthropogenic modification of the flow affects gas exchange”
Have you extensively addressed this later in the paper? Later there is only a short sentence about this.Line 159: You are using the Kljun et al 2015 footprint calculations. Did you put in the whole time series of wind speed, stability etc and the calculate the footprint climatology? Did you exclude certain “good directions” (parallel to the river) EC data because of an extensive footprint? What does “the river bank directions are excluded” mean? Did you exclude them anyways or was the footprint calculation stopped there?
Line 253: You chose two timespans for day and night-time. Do you have a total radiation data set to back this up or is it a feature of your EC data set? You measured PAR in the water but some questions (also by Mr Yang) refer to land vegetation. A total radiation dataset would be helpful. Wasn’t there a meteorological mast nearby? Was there such a sensor mounted?
You have correlated fluxes, PAR and, pCO2w on a daily scale. What about hourly/2 or 3 hourly scale fluxes with respect to PAR or total radiation? This could also help interpreting your diel cycles.Line 327: Good point. Sections across the river with pCO2w values would be interesting.
Line 383: there a plenty of gridded precipitation reanalysis products. One could combine that with watershed geographical data and get a better picture of the upstream precipitation.
Technical Questions:
Line 227: “The 30-minute average air temperature rose from 10-20°C …. to 30°C. “Didn’t it just rise from 10°C to 30°C.
Figure 3: I would recommend changing the water temperature heat plot to a line plot with maybe surface, depth at x m and bottom. I couldn’t figure out any temperature features over depth. It is mostly variable over time.
Additionally, a total radiation dataset, as mentioned above, would be helpful.
Line 269: In this paragraph you state the you present the five best models. You could make them more visible and distinguishable: like “a)….b)…c)…”
Line 273: “Some of best daily models Include U”. Can you be more precise.
Line 366: This paragraph covers a lot. Maybe some subsections or partitions would be helpful.
Line 397: “…on only some of the best models”. Can you be more precise.
Citation: https://doi.org/10.5194/egusphere-2024-1644-RC2 -
AC3: 'Reply on RC2', Aki Vähä, 29 Aug 2024
The preprint provides an interesting dataset of eddy covariance flux measurements at a river site in Finland and as the paper states eddy covariance measurements of CO2 and CH4 at rivers have only been done once before. Such measurements are especially difficult due to the inhomogeneous surroundings at small to medium rivers and have to be analysed carefully.
- We thank Dr. Zavarsky for his thoughtful and thorough remarks. Please see our responses below.
Scientific questions:
Line 40: “The River Kitinen is a regulated river and, as such, provides information on e.g. how the anthropogenic modification of the flow affects gas exchange”
Have you extensively addressed this later in the paper? Later there is only a short sentence about this.- We decided to remove this sentence from this manuscript and will address this question in the second manuscript focusing on the gas transfer coefficient.
Line 159: You are using the Kljun et al 2015 footprint calculations. Did you put in the whole time series of wind speed, stability etc and the calculate the footprint climatology? Did you exclude certain “good directions” (parallel to the river) EC data because of an extensive footprint? What does “the river bank directions are excluded” mean? Did you exclude them anyways or was the footprint calculation stopped there?
- The full measured dataset of the mentioned variables was used in the calculation and periods when the wind was from the river bank sectors are excluded from further flux analysis. We rephrased this paragraph to better explain the calculation process. We want to add here that the data filtering is a compromise between data statistics and the safest fetch. Narrowing even more the acceptable wind directions would result into a very low amount of data points. See the comments above related to Dr. Yang’s remarks about the footprint.
Line 253: You chose two timespans for day and night-time. Do you have a total radiation data set to back this up or is it a feature of your EC data set? You measured PAR in the water but some questions (also by Mr Yang) refer to land vegetation. A total radiation dataset would be helpful. Wasn’t there a meteorological mast nearby? Was there such a sensor mounted?
- This rudimentary classification was selected to get an idea of diurnal differences. We are not using the classification for any further analysis. We indeed have a full radiation dataset, but for avoiding land contamination in the fluxes, we have already implemented several methods: flux stationarity criterion (Foken and Wichura, 1996), limiting the standard deviations of the gas mixing ratios and the new method of observing the 5-minute mean wind directions.
You have correlated fluxes, PAR and, pCO2w on a daily scale. What about hourly/2 or 3 hourly scale fluxes with respect to PAR or total radiation? This could also help interpreting your diel cycles.
- We have conducted an additional multivariate analysis on average diel cycles of the same variables as in the daily and weekly analyses in the manuscript (excluding precipitation, as we only had daily precipitation rates) and will add those results and discussion in the manuscript. According to this analysis, pCO2 correlates the most with Tsurf and Uw. For FCO2, the main drivers are pCO2 and U and for FCH4, they are Tsurf and pCO2.
Line 327: Good point. Sections across the river with pCO2w values would be interesting.
- We agree, however, we were measuring pCO2w in only one location during the campaign.
Line 383: there a plenty of gridded precipitation reanalysis products. One could combine that with watershed geographical data and get a better picture of the upstream precipitation.
- This would require a modelling approach and extensive research on the river discharges in the watershed, flushing of carbon from soils etc. and is beyond the scope of this manuscript. Our approach focuses instead on simple environmental variables that are relatively easy to obtain.
Technical Questions:
Line 227: “The 30-minute average air temperature rose from 10-20°C …. to 30°C. “Didn’t it just rise from 10°C to 30°C.
- This sentence is reworded to make it clearer to the reader.
Figure 3: I would recommend changing the water temperature heat plot to a line plot with maybe surface, depth at x m and bottom. I couldn’t figure out any temperature features over depth. It is mostly variable over time.
- We have changed the heat plot to a line plot.
Additionally, a total radiation dataset, as mentioned above, would be helpful.
- We would like to show in the manuscript only those variables that are used in the analysis but a full radiation dataset can be added in the supplementary information.
Line 269: In this paragraph you state the you present the five best models. You could make them more visible and distinguishable: like “a)….b)…c)…”
- The letters are now added to Tables 4, 5 and 6.
Line 273: “Some of best daily models include U”. Can you be more precise.
- We now mention the exact number of models of the five best ones than contain U as a driving variable.
Line 366: This paragraph covers a lot. Maybe some subsections or partitions would be helpful.
- We now divide this section into drivers of pCO2 and drivers of fluxes to make it more readable.
Line 397: “…on only some of the best models”. Can you be more precise.
- Similarly as in the comment above, we now mention the exact number of models containing U.
Citation: https://doi.org/10.5194/egusphere-2024-1644-AC3
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AC3: 'Reply on RC2', Aki Vähä, 29 Aug 2024
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Aki Vähä
Timo Vesala
Sofya Guseva
Anders Lindroth
Andreas Lorke
Sally MacIntyre
Ivan Mammarella
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
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Boreal rivers are significant sources of carbon dioxide (CO2) and methane (CH4) to the atmosphere but the controls of these emissions are uncertain. We measured four months of CO2 and CH4 exchange between a regulated boreal river and the atmosphere with eddy covariance. We found statistical relationships between the gas exchange and several environmental variables, the most important of which were dissolved CO2 partial pressure in water, wind speed, and water temperature.
Boreal rivers are significant sources of carbon dioxide (CO2) and methane (CH4) to the...