Arising from M. Günthel et al. Nature Communications https://doi.org/10.1038/s41467-019-13320-0 (2019)
Methane emissions from lakes are a major natural source in the global budget of atmospheric methane. A large fraction of these emissions result from diffusive CH4 emissions, i.e., the diffusive transport of oversaturated methane from the surface waters to the atmosphere. CH4 is typically produced in anoxic sediments and oxidized in oxic waters1 but can also be produced in oxic waters2,3. Schmidt and Conrad4 suggested that the oversaturation of CH4 in surface waters of lakes results from two processes: CH4 release from littoral sediments in combination with horizontal transport to the open water and in situ net production of CH4 in oxic surface water. The relative importance of the two processes for diffusive emissions of CH4 from lakes is the focus of this discussion.
Günthel et al.5 claim that oxic methanogenesis contributes the main fraction, i.e., up to 100%, of the CH4 emitted from lakes with surface area >1 km2. Their conclusion is based on their Fig. 45, which combines results from re-analyses of Donis et al.6 and DelSontro et al.7 and from CH4 mass balances for Lake Stechlin. We demonstrate below that the analyses of Günthel et al.5 contain several errors. Without these errors, the data do not support their main conclusion but suggest that CH4 fluxes from littoral zones are the dominant source of diffusive CH4 emissions from all lakes independent of their size. The latter is consistent with the analysis of DelSontro et al.7 and the conclusions of Encinas Fernandez et al.8 and Peeters et al.9.
In the following, we first explain our approach estimating the contribution of net oxic methane production to the total diffusive CH4 emissions, NOMC (net oxic methane production contribution). We then clarify several errors and inconsistencies in the analyses of Günthel et al.5 and summarize the corrected results in Fig. 1. These new results on NOMC are discussed in relation to other studies commenting also on the limitations of mass balance and of other approaches to estimate NOMC.
Determination of the contribution of oxic methanogenesis to diffusive CH4 emissions
Net production of methane in oxic waters (NOM) in the surface mixed layer (SML) is estimated as the difference between the total diffusive CH4 emissions from the lake surface, Fsurf,tot, and the total flux from the sediments in the SML, Fsed,tot, i.e., NOM = Fsurf,tot − Fsed,tot. This procedure neglects processes contributing to the mass balance, e.g., vertical transport of CH4 into the SML, but allows for a consistent comparison of the observations from Lake Hallwil and Lake Stechlin with the results derived from data of DelSontro et al.7. Note that the neglected processes are typically small5 or uncertain (e.g., estimates of turbulent diffusivities in the thermocline have large uncertainty; the amount and dissolution of microbubbles were not measured in the studies considered by ref. 5) and are sources of CH4 in Lake Hallwil and Lake Stechlin5. Our estimates are therefore upper limits of NOM. The contribution of NOM to overall diffusive CH4 emission is defined as: NOMC = NOM/Fsurf,tot. The advantage of using NOM instead of gross production of CH4 is explained in Supplementary Note 1.
In the mass balance of the SML extending from 0 to 5 m water depth5,6, Günthel et al.5 used an average sediment flux of Fsed = 1.75 mmol m−2 day−1, averaging flux estimates of Donis et al.6 from two sediment cores, one collected at 3 m and the other at 7 m water depth. The δ13C of the CH4 in the pore water of these two cores differ substantially6, indicating differences in production and oxidation of CH4 between the sediments in the SML and at 7 m water depth. The estimate of Fsed in the SML should therefore be based on the core collected at 3 m water depth. Using the approach of Donis et al.6, the correct Fsed derived from the data of this core is Fsed = 2.8 mmol m−2 day−1 (Peeters et al.9, see Supplementary Note 2.1 for details).
Günthel et al.5 and Donis et al.6 apparently have erroneously used gas transfer coefficients instead of proper CH4 fluxes to calculate emissions. This conclusion is demonstrated by the perfect agreement between the values published erroneously as CH4 fluxes, Fsurf, by Günthel et al.5 and the values of the gas transfer coefficients of CH4 at 20 °C, kCH4, calculated by us (Table 1). The values published by Donis et al.6 as CH4 fluxes are very similar to these kCH4 and therefore also do not represent CH4 fluxes but gas transfer coefficients (for details, see Supplementary Note 2.2).
The gas transfer coefficient of CH4 must be multiplied by the difference between the surface concentration (0.3 mmol m−3, ref. 6) and the atmospheric equilibrium concentration of CH4 (CH4,equ = 0.003 mmol m−3 at 20 °C9), i.e. by ~0.3 mmol m−3, to obtain Fsurf. Fsurf is therefore ~3.3 times smaller than the values of the gas transfer coefficients erroneously taken by Günthel et al.5 and Donis et al.6 as CH4 fluxes (Table 1 and details in Supplementary Note 2.2).
Donis et al.6 and Günthel et al.5 used values obtained from measurements with floating chambers to calculate emissions, but these values claimed to represent Fsurf appear to be in fact values for transfer coefficients, suggesting the same mistake as in the case of the wind models. Donis et al.6 stated: “Average flux (April–August 2016) is equal to 0.8 ± 0.2 mmol m−2 d−1 from MacIntyre relationship for positive buoyancy and to 0.6 ± 0.3 mmol m−2 d−1 from chamber measurements. The latter, not significantly different from the wind-based relationship, was used for the mass balance”. Günthel et al.5, co-authored by D. Donis, claim that the “MacIntyre relationship for positive buoyancy”10 provides an average value of 0.7 for Fsurf, but in fact 0.7 is the average value for kCH4 in unit m day−1 (0.7 m d−1, see Table 1) and Fsurf for this model is 3.3 times smaller (0.21 mmol m−2 d−1, see Table 1). The value by Donis et al.6 for the MacIntyre relationship10 is even slightly larger than 0.7 and therefore clearly incompatible with Fsurf but is rather a gas transfer coefficient as is obvious in the case of Günthel et al.5. The good agreement between the value for the gas transfer coefficient obtained from the MacIntyre model for positive buoyancy flux10 and the values from the chamber measurements suggests that the values from the chamber measurements are not gas fluxes but also gas transfer coefficients (see Supplementary Note 2.2 for more details).
Donis et al.6 derived from their chamber measurements the wind-based model “Hallwil relationship” specifically for Lake Hallwil. The establishment of this Hallwil relationship required that Donis et al.6 calculated gas transfer coefficients from their chamber measurements. In their Supplementary Fig. 4, Donis et al.6 show that the values from their chamber measurements agree well with those from the Hallwil relationship (Supplementary Fig. 2 and Supplementary Note 2.2). Note, however, that the values for the Hallwil relationship are in fact gas transfer coefficients and not Fsurf, supporting that also the values from the chamber measurements represent gas transfer coefficients and not Fsurf (Supplementary Fig. 2 and Supplementary Note 2.2 for more details). This conclusion implies that the values from the chamber measurements by Donis et al.6 must be multiplied by ~0.3 mmol m−3 to give proper CH4 fluxes, which are then ~3.3 times smaller than the CH4 fluxes used in the mass balances of refs. 5,6.
Because there are only four chamber measurements available for 2016 and one of them was exceptionally low (see ref. 6 and Supplementary Note 2.2), the Hallwil relationship is considered here to provide the most reliable estimate of the average k600 in Lake Hallwil and therefore applied to calculate the average surface CH4 flux for April to August 2016, i.e., Fsurf = 0.24 mmol m−2 d−1 (see Table 1 and Supplementary Note 2.2). The reliability of the Hallwil relationship was confirmed by Günthel et al.5 and by Hartmann et al.11 comparing different estimates of surface fluxes in the South Basin of Lake Stechlin.
With Fsed = 2.8 mmol m2 day−1 and Fsurf = 0.24 mmol m2 day−1, NOM = 416 mol day−1 and the contribution of NOM to total emissions is NOMC = 17% (Supplementary Table 1 in Supplementary Note 2.3 includes also additional estimates of NOMC). The low value of NOMC suggests that most of CH4 in the SML originates from the sediments, which is consistent with the δ13C isotopic composition of CH4 in Lake Hallwil9. The uppermost CH4 in the sediment core from the SML is characterized by δ13C about –59‰, which corresponds very closely to the δ13C of the CH4 in the open water of the SML ranging from −62‰ to −58‰ (Figs. 4 and 5 both in ref. 6). Thus the δ13C values suggest that the CH4 from the uppermost pore water in the sediment of the SML is the source of the CH4 in the open water and do not indicate a reduction of the δ13C expected in case of substantial CH4 production.
Re-evaluation of the analysis of Günthel et al.5: data from Lake Stechlin
Günthel et al.5 underestimated the sediment flux and overestimated the emissions in Lake Stechlin and thus overestimated NOM (see below). We therefore re-evaluated the mass balances from Lake Stechlin to provide NOMC that are presented in Fig. 1 (see also Supplementary Table 3 in Supplementary Note 3.2). Oxic methanogenesis in Lake Stechlin was determined by Günthel et al.5 using the same mass balance approach as in Lake Hallwil, but the sediment flux was estimated from a mesocosm experiment that involved two mesocosms. CH4 surface fluxes from the two mesocosms were utilized to calculate CH4 production within the mesocosms. Assuming that CH4 production in the SML of the lake is the same as in the mesoscosm, Fsed was determined by closing the mass balance of the SML in the lake. However, Fsurf from the mesocosms was overestimated because the gas transfer coefficient k600 (transfer coefficient of CO2 at 20 °C) determined for the open water of the lake was also used for the mesocosms5. The turbulence in the mesocosm is substantially lower than in the open water, i.e., in the uppermost 1 m of measurements the energy dissipation ε in the lake is 5–10 times larger than in the mesocosm (for details, see Supplementary Note 3.1; values on energy dissipation ε are from the data source of Supplementary Fig. 8 in Günthel et al.5). Because k600 ~ ε¼ 12,13,14, the difference in energy dissipation between lake and mesocosm suggest that k600 in the mesocosm should be scaled by 5−¼ to 10−¼ and is therefore only 67 or 56%, respectively, of the k600 in the lake (see Supplementary Note 3.1). Hence, Fsurf in the mesocosm is only 67 or 56% of the value used by Günthel et al.5 and the lower and upper bounds of the sediment flux become Fsed = 1.8 and 2.0 mmol m−2 day−1, respectively (Supplementary Table 2 and Supplementary Note 3.1) and thus are substantially larger than Fsed = 1.4 mmol m−2 day−1 used by Günthel et al.5.
The sediment flux derived from the mesocosm experiments conducted in the South Basin of Lake Stechlin in 2014 was also used for the stratified periods in 2016 and 2018 and in both basins of Lake Stechlin5. Hence the underestimation of the sediment flux in 2014 resulted in an overestimation of net production of CH4 in all results of Günthel et al.5.
NOMC calculated from the mass balance in the South and North Basin of Lake Stechlin, using the sediment fluxes corrected for the difference in turbulence between lake and mesocosms, are lower than 40% and agree well between 2014 and 2016 and between the basins (Supplementary Table 3 and detailed analysis in Supplementary Note 3.2).
Hartmann et al.11 collected in 2017 one sediment core from the SML in the South Basin of Lake Stechlin and provided another wind model for k600. Re-analysis of the CH4 pore water of the sediment core provides a sediment flux into the water of 0.08–0.26 mmol m−2 day−1 (Supplementary Note 3.3) This flux is exceptionally low and incompatible with the sediment flux derived from the mesocosm experiments for the same basin, suggesting that the flux estimate based on a single sediment core is not representative for the average Fsed in the SML. NOMC derived from this sediment flux and the model of k600 of ref. 11 is exceptionally high (Fig. 1, Supplementary Table 4, and Supplementary Note 3.3).
However, in addition to our re-analysis of the data of ref. 5 for Lake Stechlin, there is further evidence that NOMC is typically not very large in Lake Stechlin. According to Fig. 3 in Günthel et al.5, oxic CH4 production was small in 2018 and even negative in the South Basin, implying NOMC < 0. Apparently, net oxidation instead of net production of CH4 was the dominant process in the South Basin in 2018.
Furthermore, in the central mesocosm (central reservoir) in Lake Stechlin, which was disconnected from the littoral CH4 source for a very long time period, CH4 concentrations were very low and close to atmospheric saturation5. Emissions from this mesocosm were therefore very small5 showing no indication of significant in situ production of CH4. The mesocosms used for estimating oxic methanogenesis in Lake Stechlin were measured within 10 days after their filling and possibly had not reached steady state.
Günthel et al.5 re-analyzed data from 7 lakes originally investigated by DelSontro et al.7 and claim that in these lakes oxic methane production contributes between 82 and 100% of the total CH4 emissions. However, these values are incompatible with the average net production of 25% stated by DelSontro et al.7 for their systems with positive net production. Furthermore, according to DelSontro et al.7 net production was negative in 30% of their lakes suggesting that in these lakes 100% of the emitted CH4 was provided by CH4 fluxes from the littoral zone. One of these lakes with negative net production was Lake Champlain7, but Günthel et al.5 claim that in this lake 100% of the emissions originate from oxic methane production.
It is unclear how Günthel et al.5 performed the analysis of the data of DelSontro et al.7 (see Supplementary Note 4.1 for details). We therefore determined NOMC for all lakes studied by DelSontro et al.7 (Figs. 1, S4, and S5; Tables 2 and S5, and details in Supplementary Note 4.1).
DelSontro et al.7 compared observations of the spatial distribution of CH4 and δ13C of CH4 in the SML of lakes with results from numerical simulations and provided estimates on the contribution of net oxic methane production in relation to a reference condition without biological processes. Their numerical model7 assumes steady state and includes as sources for emissions only the CH4 flux from the littoral and net oxic CH4 production. The total emission for the reference condition therefore corresponds to the total flux from the littoral, Flitt,tot. Del Sontro et al.7 analyzed the impact of biological processes as fractional increase or decrease, fbiol, of the CH4 concentrations relative to the reference condition without oxidation. Figure 4 and Supplementary Table 8 of ref. 7 provide values on fbiol + 1, denoted here as RCH4 (see Supplementary Note 4.1). Assuming that CH4,equ is negligible compared to the CH4 concentrations in the SML, RCH4 can be interpreted as the ratio of the total CH4 emission Fsurf,tot to the emission under reference conditions Flitt,tot, thus RCH4 ≈ Fsurf,tot/Flitt,tot = (NOM + Flitt,tot)/Flitt,tot = NOM/Flitt,tot + 1. The ratio of NOM to Fsurf,tot is given by NOMC = (RCH4 − 1)/RCH4 (for details and further analyses, see Supplementary Note 4.1).
DelSontro et al.7 provided RCH4 for six of the seven lakes investigated by Günthel et al.5. In these 6 lakes, NOMC is <20% and is negative in Lake Champlain indicating dominance of oxidation in this lake, which is consistent with ref. 7 (Table 2).
Interpretation of the results of our re-analyses
The results on NOMC suggest that net production of CH4 is not the dominant source of the CH4 emissions from the lakes investigated but fluxes of CH4 from shallow water regions (Fig. 1). NOMC is <50% in all lakes except in the South Basin of Lake Stechlin in 2017 where NOMC is unrealistically high because the average Fsed in the SML is most likely substantially underestimated (the average Fsed used by, e.g., Günthel et al.5 for this basin in 2014, 2016, and 2018 was 5–18 times larger than Fsed used for 2017, see Supplementary Note 3.3). On average NOMC is 10% (mean of all lakes using for Lake Stechlin the upper limits of 2016, i.e., 37 and 33%, for South and North Basin, respectively) and is 16% for the 6 lakes with lowest Ased/VSML. NOMC does not significantly increase with Asurf or Ased/VSML (correlation using the same data as for the mean of all lakes: R2 = 0.005, p = 0.8, and R2 = 0.07, p = 0.3, respectively). Hence, there is no support for the hypothesis that net oxic CH4 production contributes a major fraction of the CH4 emitted from the lakes investigated or increases in importance with increasing lake size or decreasing Ased/VSML. The latter even suggests that NOMC is negligible.
However, the results of our analysis cannot be taken as proof that NOM is negligible or, in contrast, as a confirmation that NOM contributes up to 37% to CH4 emissions, because the uncertainty of the estimated NOMC is high: assessing the relevance of oxic methanogenesis from mass balance approaches involves the difference of comparative large contributions, i.e., surface emissions and sediments fluxes, which both have a large uncertainty. In particular, basin-wide average sediment fluxes from the littoral are not well constraint by estimates based on a single sediment core as in Lake Hallwil and Lake Stechlin, which becomes obvious from the comparison of sediment fluxes in the South Basin of Lake Stechlin derived from mesocosm experiments and the CH4 pore water technique applied to a single core (Fig. 1, data from ref. 11, and Supplementary Note 3.3). Furthermore, closing mass balances of CH4 requires that the components of the mass balance are measured at the same temperature, because CH4 fluxes are temperature dependent15, and at the same time and not several months or even years apart as in Donis et al.6 and Günthel et al.5, respectively.
The investigations of Encinas Fernadez et al.8, DelSontro et al.7, and Peeters et al.9 are based on the spatial distribution of CH4 and consistently show that the observed horizontal distribution patterns of CH4 in the SML require a large source of CH4 in the shallow water region to explain the typically enriched concentrations in near shore zones. Spatially averaged CH4 concentrations in the SML are not correlated with Asurf but with Ased/Asurf8 implying that total emissions are proportional to Ased in the SML and that the littoral zone must therefore be an important source of CH4 emissions8. The seasonal change in the horizontal distribution pattern of CH4 in the SML and of the overall emission of CH4 can be explained by a temperature-dependent sediment flux9. However, the quantification of sediment fluxes and of net CH4 production using inverse modeling of spatial distributions of CH47,9 requires estimates of horizontal turbulent diffusion coefficients, which are unfortunately highly uncertain. Combining inverse modeling of spatial distributions, isotope measurements, and full mass balance approaches is not only a promising avenue to further constrain the relevance of oxic methanogenesis in lakes but also requires information on sediment fluxes, which appear to be the most uncertain component in the studies so far.
Global emissions from lakes are dominated by emissions from small- and medium-sized lakes. Lakes with Asurf < 1 km2 contribute ~84% and lakes with Asurf > 1 km2 only ~16% of the global diffusive CH4 emissions from lakes (Supplementary Table 1.2. in ref. 16). The contribution of oxic methanogenesis to global diffusive CH4 emissions therefore depends on NOMC in small- and medium-sized lakes rather than on NOMC in large lakes. There seems to be consensus that diffusive emissions from lakes <1 km2 are dominated by fluxes from littoral zones. Hence, improving the understanding and quantification of the sources of CH4 in littoral zones appears to be particularly important for predicting the impact of changing conditions in lakes on the global CH4 budget.
All relevant data are available from the tables in the supplement and the data sources cited but can also be requested from the authors.
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H.H. was financially supported by the Ministry of Science, Research and the Arts of the Federal State Baden-Württemberg, Germany (grant: Water Research Network project: Challenges of Reservoir Management – Meeting Environmental and Social Requirements).
The authors declare no competing interests.
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Peeters, F., Hofmann, H. Oxic methanogenesis is only a minor source of lake-wide diffusive CH4 emissions from lakes. Nat Commun 12, 1206 (2021). https://doi.org/10.1038/s41467-021-21215-2