Spatial and temporal variability of methane emissions and environmental conditions in a hyper-eutrophic fishpond
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.
Petr Znachor et al.
Status: final response (author comments only)
RC1: 'Comment on bg-2023-4', Anonymous Referee #1, 10 Apr 2023
- AC1: 'Reply on RC1', Anna Matousu, 22 May 2023
RC2: 'Comment on bg-2023-4', Anonymous Referee #2, 02 May 2023
- AC2: 'Reply on RC2', Anna Matousu, 22 May 2023
Petr Znachor et al.
Dehtar 2019 data set https://zenodo.org/badge/latestdoi/587640213
Petr Znachor et al.
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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.
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