the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Spatial and temporal variability of methane emissions and environmental conditions in a hyper-eutrophic fishpond
Petr Znachor
Jiří Nedoma
Vojtech Kolar
Abstract. Estimations of methane (CH4) emissions are often based on point measurements using either flux chambers or a transfer coefficient method which may lead to strong underestimation of the total CH4 fluxes. In order to demonstrate more precise measurements of the CH4 fluxes from an aquaculture pond, using higher resolution sampling approach we examined the spatiotemporal variability of CH4 concentration in the water, related fluxes (diffusive and ebullitive) and relevant environmental conditions (temperature, oxygen, chlorophyll-a) during three diurnal campaigns in a hyper-eutrophic fishpond. Our data show remarkable variance spanning several orders of magnitude while diffusive fluxes accounted for only a minor fraction of total CH4 fluxes (4.1–18.5 %). Linear mixed-effects models identified water depth as the only significant predictor of CH4 fluxes. Our findings necessitate complex sampling strategies involving temporal and spatial variability for reliable estimates of the role of fishponds in a global methane budget.
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Petr Znachor et al.
Status: final response (author comments only)
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RC1: 'Comment on bg-2023-4', Anonymous Referee #1, 10 Apr 2023
The authors describe the determination of methane emissions in a fishpond with special focus on controlling environmental conditions in the aquatic system and also in the atmosphere. Their work reveals the strong spatial and temporal variability, which has to be taken into account for several cases: for instance for the fishpond management, for the determination of regional carbon budget and the upscaling activities in climate modelling. I strongly support the statements regarding the consideration of this heterogeneity. However, as stated by the authors themselves, the amount of data is low in order to make a detailed consideration of the temporal variability (which is based on three dates in summer with three different sampling times). But all those who use such methods in the field know how much work is involved.
General comments:
I really appreciate the approach to consider the meteorological and atmospheric conditions as a controlling factor for greenhouse gas emissions. With this in mind, I recommend for future work the involvement of meteorological standard measurements into the monitoring program (such as air temperature, humidity and air pressure (especially to investigate the relation to ebullition fluxes); wind velocity and wind direction was already measured). Furthermore, also GHG concentration in ambient air can be a useful indicator to understand the temporal characteristics of emission processes in a better way.
At the end of the manuscript I expected a paragraph regarding recommendations for improved monitoring strategies for better estimation of methane emissions. The paper would gain a lot if all the recommendations made in the text were consolidated in one place in terms of best practice actions: how to measure, where, who often, which additional information is needed, ….
Some detailed comments:
Line 48-49 – Sanseverio et al., 2013 – in reference list 2012?
Line 70 – Jansen at al., 2020 – in reference list 2019?
Line 94 – 96 – you measure the values at the deepest point (at each sampling point or the deepest point of the pond?). It ist not clear to me at this point of the manuscript, later in text it becomes more clear. However, it would be good to mark this point in Fig. 1c as a prominent sampling point.
Line 101 – bbe_Moldaenke, Kiel
Line 202, Fig 2: As a comment – maybe the CV is not so well suited to show spatial heterogeneity of CH4 concentration. The mean values for July and August are very low with high SD, resulting in high CV values?
Line 479-481 – Beaulieu et al. not mentioned in main text
Line 577-579 – Ostrovsky et al. not mentioned in main text
Line 623-628 – Tranvik et al. not mentioned in main text
Citation: https://doi.org/10.5194/bg-2023-4-RC1 -
AC1: 'Reply on RC1', Anna Matousu, 22 May 2023
Dear Referee,
we are thankful for your stimulating comments, suggestions, and recommendations. Below is our detailed, step-by-step response, your comments are in bold, followed by our responses.
At the end of the manuscript, I expected a paragraph regarding recommendations for improved monitoring strategies for better estimation of methane emissions. The paper would gain a lot if all the recommendations made in the text were consolidated in one place in terms of best practice actions: how to measure, where, who often, which additional information is needed, ….
As suggested, we added a separate paragraph with our recommendations at the end. For clarity, we inserted it right here.
“For improved monitoring strategies, however, a continuous measurement approach like eddy covariance is generally more efficient than traditional sampling at regular intervals. Eddy covariance accounts for temporal variability and provides high temporal resolution data by continuously measuring wind speed, gas concentration, and vertical turbulent fluxes to estimate methane emissions (Erkillä et al., 2018). More importantly, it also offers spatially integrated measurements, averaging emissions over a larger area and therefore accounts for pond spatial heterogeneity. However, it's worth noting that the choice of monitoring approach depends on various factors, including the specific objectives, available resources, and the characteristics of the emission sources. To accurately capture both short-term variability and lake spatial heterogeneity of methane ebullition and diffusion fluxes, the most efficient approach was found to be a combination of continuous measurements with traditional methods including floating chambers, anchored funnels and boundary model calculations (Schubert et al., 2012, Podgrajsek et al., 2014, Erkillä et al., 2018). This integrated approach would provide a comprehensive understanding of methane emissions, enabling better estimation and more effective mitigation efforts.”
Some detailed comments:
Line 48-49 – Sanseverio et al., 2013 – in reference list 2012?
Corrected to 2012.
Line 70 – Jansen at al., 2020 – in reference list 2019?
Corrected to 2019.
Line 94 – 96 – you measure the values at the deepest point (at each sampling point or the deepest point of the pond?). It is not clear to me at this point of the manuscript, later in text it becomes more clear. However, it would be good to mark this point in Fig. 1c as a prominent sampling point.
Done, see a new version of Fig. 1c and the description.
"Figure 1. Location (a, b; copyright www.d-maps.com; https://d-maps.com/carte.php?num_car=2232&lang=en and https://d-maps.com/carte.php?num_car=265046&lang=en; modified) and bathymetric map (c; credit Jiří Jarošík) of the sampled Dehtář fishpond: Blue lines indicate hydrological connections; red dots representing the sampling points. Highlighted sampling point at the dam depicts the deepest site where vertical profiles were measured. Numbers indicate isobath depth."
Line 101 – bbe_Moldaenke, Kiel
Corrected.
Line 202, Fig 2: As a comment – maybe the CV is not so well suited to show spatial heterogeneity of CH4 concentration. The mean values for July and August are very low with high SD, resulting in high CV values?
We used the CV as a commonly used simple and easily interpretable parameter describing variability. As the value of CV is normalized for mean (CV=100 x standard deviation / mean) it allows comparing of different variables which may attain different numerical (mean) values. It reflects RELATIVE variability of values observed at different sampling points. Values in July and August are generally low but unequal, so CV is high. In September, values are high but more evenly distributed so CV is lower. Therefore, in our opinion, the used of CV is OK.
Line 479-481 – Beaulieu et al. not mentioned in main text
Deleted from the reference list.
Line 577-579 – Ostrovsky et al. not mentioned in main text
Deleted from the reference list.
Line 623-628 – Tranvik et al. not mentioned in main text
Deleted from the reference list.
Citation: https://doi.org/10.5194/bg-2023-4-AC1
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AC1: 'Reply on RC1', Anna Matousu, 22 May 2023
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RC2: 'Comment on bg-2023-4', Anonymous Referee #2, 02 May 2023
The work of Znachor and co-authors describes the spatial and temporal variability of methane emissions from a large fish-pond.
This subject is certainly very important as the influence of small water bodies on the global budget is often not well constrained. Also, the problem of temporal variability is seldom assessed. However, there are some draw backs in the study:
Introduction: clarification which parameters react on which time scale…. And why these parameters are important for the spatio temporal variability…?
L 107 ff: it would be interesting to see the variations at one fixed station during the day..!
How long did it take to set up all the sampling sites?
The correction of the parameters for temporal variations is based on the assumption that the fluctuations are linear between the measured time points? But I would assume that at least oxygen (or parameters related to photosynthesis) vary with a sinus curve….
If all data were corrected for this temporal variation, how can you assess this influence later on with the statistics, as daytime variation?
The sampling scheme to assess the temporal and spatial variations are not well explained, and confusing…? May be a table would help here, with the deep station compared with the other stations.
L 125 ff, The Calculation of the diffusive flux is missing some crucial parameters: how was k determined? Which atmospheric CH4 concentration was used?
Discussion:
L310 ff: Could you give an estimate how many stations would be needed for your lake to get a good coverage of the variability? Could you calculate and plot the CV for n = 3, n=4…. n=16 samples
The results are well related to other studies, however explanations or reasons for the obtained results in this study are missing.
L316 ff: Why was the highest CH4 flux in your lake at the deepest station? Could you give any ideas?
L352: why did you find highest ebullition rates in September in your lake?
Further minor comments can be found in the attached pdf file.
- AC2: 'Reply on RC2', Anna Matousu, 22 May 2023
Petr Znachor et al.
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Data sets
Dehtar 2019 data set Znachor et al. https://zenodo.org/badge/latestdoi/587640213
Petr Znachor et al.
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