Interactive comment on “ A simple calculation algorithm to separate high-resolution CH 4 flux measurements into ebullition and diffusion-derived components

The authors present the validation and application of a data processing algorithm, which allows to distinguish and quantify erratic (ebullition) and diffusive CH4 emissions detected in an automatic chamber time series. This approach facilitates automatic data analysis of chamber measurements, which is an important step towards an improved comparability of these measurements. Secondly, it allows to identify erratic events of CH4 release from wetland ecosystems and to quantify the magnitude of


Introduction
Wetlands and freshwaters are among the main sources for methane (CH 4 ), which is one of the major greenhouse gases (Dengel et al., 2013;Bastviken et al., 2011;IPCC, 2013).In wetland ecosystems, CH 4 is released via three main pathways: (i) diffusion (including "storage flux", in terms of rapid diffusive release from methane stored in the water column), (ii) ebullition and (iii) plant-mediated transport (e.g.Goodrich et al., 2011;Bastviken et al., 2004;Van der Nat and Middelburg, 2000;Whiting and Chanton, 1996).The magnitude of CH 4 released via the different pathways is subject to variable environmental drivers and conditions such as water level, atmospheric pressure, temperature gradients, wind velocity and the presence of macrophytes (Lai et al., 2012;Tokida et al., 2007;Chanton and Whiting, 1995).As particularly ebullition varies in time and space (Maeck et al., 2014;Walter et al., 2006), total CH 4 emissions feature an extremely high spatial and temporal variability (Koch et al., 2014;Repo et al., 2007;Bastviken et al., 2004).Hence, attempts to model CH 4 emissions based on individual environmental drivers are highly complex.To identify relevant environmental drivers of CH 4 emissions, the separation of measured CH 4 emissions into the individual pathway-associated components is crucial (Bastviken et al., 2011(Bastviken et al., , 2004)).Moreover, the understanding of the complex processes determining the temporal and spa-tial patterns of CH 4 emissions is a prerequisite for upscaling field-measured CH 4 emissions to the landscape or regional scale, and thus for adequately quantifying the contribution of wetland CH 4 emissions to global greenhouse gas (GHG) budgets (Walter et al., 2015;Koebsch et al., 2015;Lai et al., 2012;Limpens et al., 2008).
However, field studies measuring CH 4 release above shallow aquatic environments or flooded peatlands generally measure total CH 4 emissions as a mixed signal of individual CH 4 emission components, released via all possible pathways (i.e.diffusion, ebullition and plant-mediated transport).Studies separately measuring temporal and spatial patterns of CH 4 emissions resulting only from either ebullition or diffusion are rare.Measurements of CH 4 ebullition can be performed using manual or automatic gas traps, as well as optical and hydro-acoustic methods (Wik et al., 2013(Wik et al., , 2011;;Maeck et al., 2014;Walter et al., 2008;Ostrovsky et al., 2008;Huttunen et al., 2001;Chanton and Whiting, 1995), often requiring considerable instrumentation within the studied system.Diffusive CH 4 fluxes are commonly either derived indirectly as the difference between total CH 4 emissions and measured ebullition, or directly obtained based on the use of bubble shields or gradient measurements of CH 4 concentration differences (DelSontro et al., 2011;Bastviken et al., 2010Bastviken et al., , 2004)).A graphical method to separate diffusion, steady ebullition and episodic ebullition fluxes from the total CH 4 flux was presented by Yu et al. (2014), using a flow-through chamber system.However, performed at the laboratory scale for a peat monolith, measurement results as well as the applied method were lacking direct field applicability.A first simple mathematical approach for field measurements to separate ebullition from the sum of diffusion and plant-mediated transport was introduced by Miller and Oremland (1988), who used low-resolution static chamber measurements.Goodrich et al. (2011) specified the approach using piecewise linear fits for single ebullition events.However, the static threshold to determine ebullition events, as well as low-resolution measurements, limited the approach on estimating ebullition events which were characterized by a sudden concentration increase of ≥ 8 nmol mol −1 s −1 .This prevents a complete and clear flux separation.Moreover, CH 4 flux separation approaches based on manual chamber measurements with rather low temporal resolution fail to capture the rapidly changing absolute and relative contributions of the pathway-associated flux components both in time and space (Maeck et al., 2014;Walter et al., 2006).
Hence, there is a need for a non-intrusive method for separating pathway-associated CH 4 flux components.Improvements in measurement techniques, particularly by using high-resolution gas analysers (e.g.eddy covariance (EC) measurements), allow for high-temporal-resolution records of CH 4 emissions (Schrier-Uijl et al., 2011;Wille et al., 2008).Recently, a growing number of experimental GHG studies have employed automatic chambers (ACs) (Koskinen et al., 2014;Lai et al., 2014;Ramos et al., 2006), which can provide flux data with an enhanced temporal resolution and capture short-term temporal (e.g.diurnal) dynamics.In addition, AC measurements can also represent small-scale spatial variability, and thus identify potential hot spots of CH 4 emissions (Koskinen et al., 2014;Lai et al., 2014).AC systems therefore combine the advantages of chamber measurements and micrometeorological methods with respect to the quantification of spatial as well as temporal dynamics of CH 4 emissions (Savage et al., 2014;Lai et al., 2012).
Combined with a high-resolution gas analyser (e.g.cavity ring-down spectroscopy), AC measurements provide opportunities for (i) detecting even minor ebullition events, and (ii) developing a statistically based flux separation approach.This study presents a new calculation algorithm for separating open-water CH 4 fluxes into its ebullition-and diffusionderived components based on ebullition-related sudden concentration changes during chamber closure.A variable ebullition filter is applied using the lower and upper quartile and the interquartile range (IQR) of measured concentration changes.Data processing is based on the R script developed by Hoffmann et al. (2015).The script was modified for the purpose of CH 4 flux calculation and separation, thus including the advantages of automated and standardized flux estimation.We hypothesize that the presented flux calculation and separation algorithm together with the presented AC system can reveal concealed spatial and temporal dynamics in ebullition-and diffusion-associated CH 4 fluxes.This will facilitate the identification of relevant environmental drivers.

Automatic chamber system
In April 2013, an exemplary measurement site was equipped with an AC system and a nearby climate station (Fig. 1).The AC system consists of four rectangular transparent chambers, installed along a transect from the shoreline into the lake.Chambers are made of Lexan polycarbonate with a thickness of 2 mm and reinforced with an aluminium frame.Each chamber (volume of 1.5 m 3 ; base area 1 m 2 ) is mounted in a steel profile, secured by wires, and lifted/lowered by an electronically controlled cable winch located at the top of the steel profile.All chambers are equipped with a water sensor (capacitive limit switch KB 5004, efector150) at the bottom, which allows steady immersion (5 cm) of the chambers into the water surface.Hence, airtight sealing and constant chamber volume are ensured during the study period, despite possible changes of the water level.All chambers are connected by two tubes and a multiplexer to a single Los Gatos fast greenhouse gas analyser (911-0010, Los Gatos; gas flow rate: 5 L m −1 ), which measures the air concentration of carbon dioxide (CO 2 ), methane (CH 4 ) and water vapour (H 2 O).To ensure consistent air pressure and mixture during measurements, chambers are ventilated by a fan and sampled air is transferred back into the chamber headspace.However, due to the large chamber volume, mixture of the chamber headspace took up to 30 s.As a result of this, most peaks due to ebullition events were directly followed by a smaller decrease in measured CH 4 concentration.This indicates a short-term overestimation of the ebullition event (peak), which was compensated after the chamber headspace was mixed properly (decrease).This signal in the observed data is hereafter referred to as overcompensation.Concentration measurements are performed in sequence, sampling each chamber for 10 min with a 15 s frequency once per hour.When switching from one chamber to another, the tubes were vented for 2 min using the air of the open chamber to be measured next.Between two measurements at the same chamber position, each chamber was vented using the internal fan throughout the entire 50 min.A wooden boardwalk north of the measurement site allows for maintenance access, while avoiding disturbances of the water body and peat surface.

Flux calculation and separation algorithm
CH 4 flux calculation and separation was performed based on an adaptation of a standardized R script (Hoffmann et al., 2015).Figure 2 shows a flow chart of the flux calculation algorithm and the principle of the performed CH 4 flux separation.To estimate the relative contribution of diffusion and ebullition to total CH 4 emissions, flux calculation was performed twice (Fig. 3), once for the total CH 4 flux (CH 4 total ) and once for the diffusive component of CH 4 total (CH 4 diffusion ), by adjusting selected user-defined parameter setups of the used R script.First of all, a death band of 25 % (user defined) was applied to the beginning of each flux measurement, thus excluding measurement artefacts triggered by the process of closing the chamber.On the remaining flux measurement data sets a variable moving window (MW) with a minimum size of 5 (CH 4 diffusion ; user defined) and 30 consecutive data points (CH 4 total ; user defined) was applied.This generated several data subsets per flux measurement for CH 4 diffusion and one data subset for CH 4 total .Subsequently, CH 4 fluxes were calculated for all data subsets per flux measurement using Eq. ( 1), where M is the molar mass of CH 4, A and V denote the basal area and chamber volume, respectively, and T and P represent the inside air temperature and air pressure.R is a constant (8.3143 m 3 Pa K −1 mol −1 ).
In the case of CH 4 total , δν is calculated as the difference between the start and end CH 4 concentration of the enlarged MW (30 consecutive data points; 7.5 min).To avoid measurement artefacts (e.g.overcompensation), being taken into account as start or end concentration, measurement points representing an inherent concentration change smaller or larger than the upper and lower quartile ±0.25 times IQR (user defined) were discarded prior to calculation of CH 4 total .
In the case of diffusion, δν is the slope of a linear regression fitted to each data subset.The resulting numerous CH 4 diffusion fluxes calculated per measurement (based on the moving window data subsets) were further evaluated according to different exclusion criteria: (i) range of within-chamber air temperature not larger than ±1.5 K; (ii) significant regression slope (p ≤ 0.1); and (iii) non-significant tests (p > 0.1) for normality (Lilliefors' adaption of the Kolmogorov-Smirnov test), homoscedasticity (Breusch-Pagan test) and linearity.
In addition (iv) abrupt concentration changes within each Unfilled dots outside the dashed lines display ebullition events (see also Goodrich et al., 2011;Miller and Oremland, 1988).Grey shaded areas indicate the applied death band at the beginning of each measurement (25 %).Negative CH 4 values indicate a overcompensation due to (temporally) insufficient headspace mixing.
MW data subset were identified by a rigid outlier test, which discarded fluxes with an inherent concentration change outside of the range between the upper and lower quartile ±0.25 times (user defined) the interquartile range (IQR).Calculated CH 4 diffusion fluxes which did not meet all exclusion criteria were discarded.In the case of more than one flux per measurement meeting all exclusion criteria, the CH 4 diffusion flux with a starting CH 4 concentration being closest to the atmospheric CH 4 concentration was chosen.Finally, the proportion of the total CH 4 emission released via ebullition was estimated by subtracting identified CH 4 diffusion from the calculated CH 4 total following Eq.(2).
Since no emergent macrophytes were present below the automatic chambers, plant-mediated transport of CH 4 was assumed to be zero.The same accounts for negative estimates of CH 4 released through ebullition.The used R script, a manual and test data set are available at https://zenodo.org/record/53168.

Verification of applied flux separation algorithm
A laboratory experiment was performed under controlled conditions to verify the used flux separation algorithm.In order to artificially simulate ebullition events, distinct amounts (5, 10, 20, 30 and 50 mL) of a gaseous mixture (25 000 ppm CH 4 in artificial air; Linde, Germany) were inserted by a syringe through a pipe into a water-filled tub (12 L) covered with a closed chamber (headspace V = 0.114 m 3 ; A = 0.145 m 2 ).The water within the tub was not replaced during the laboratory experiment, thus ensuring CH 4 saturation after the first simulations of ebullition events.Airtight sealing was achieved by a water-filled frame, connecting tub and chamber.The chamber was ventilated by a fan and connected via pipes to a Los Gatos greenhouse gas analyser (911-0010, Los Gatos), measuring CH 4 concentrations inside the chamber with a 1 Hz frequency (Fig. 4).To ensure comparability between in vitro and in situ measurements, data processing was performed based on 0.066 Hz records.The expected concentration changes within the chamber headspace as the result of injected CH 4 were calculated as the mixing ratio between the amount of inserted gaseous mixture (25 000 ppm) and the air-filled chamber volume (2 ppm).

Exemplary field study
Ecosystem  iments formed during inundation.Prior to rewetting, the vegetation was dominated by reed canary grass (Phalaris arundinacea), which disappeared after rewetting due to permanent inundation.At present, the water surface is partially covered with duckweed (Lemnoideae), while broadleaf cattail (Typha latifolia) and reed mannagrass (Glyceria maxima) are present next to the shoreline (Franz et al., 2016;Hahn-Schöffl et al., 2011).However, below the chambers, no emergent macrophytes were present throughout the study period.
Temperatures were recorded in the water (5 cm above sediment surface) and different sediment depths (2, 5 and 10 cm below the sediment-water interface), using thermocouples (T107, Campbell Scientific).Additionally, air temperature at 20 and 200 cm height, wind speed, wind direction, precipitation, relative humidity and air pressure were measured by a nearby climate station (WXT52C, Vaisala).Water table depth was measured by a pressure probe (PDCR1830, Campbell Scientific).All parameters were continuously recorded at 30 min intervals and stored by a data logger (CR 1000, Campbell Scientific) connected to a GPRS radio modem.

Verification of the flux separation algorithm
A good overall agreement was found during the laboratory experiment between CH 4 ebullition fluxes calculated for the simulated ebullition events and the amount of injected CH 4 .This supports the assumption of using sudden changes in chamber-based CH 4 concentration measurements to separate diffusion and ebullition flux components and shows the accuracy of the presented algorithm (Fig. 5).However, when applied under field conditions, flux separation might be biased due to a steady flux originating from other processes than diffusion through peat and water layers, such as the steady ebullition of microbubbles (Prairie and del Giorgio, 2013; Goodrich et al., 2011).To minimize the potential impact of the steady ebullition of microbubbles on calculated CH 4 diffusion , the concentration measurement frequency during chamber closure should be enhanced.This allows identifying and filtering small-scale differences within measured concentration changes using the variable IQR criterion, which thereby reduces the detection limit of ebullition events.

Application to an exemplary field study
Time series of measured CH 4 total fluxes, integrated over the four chambers of the transect, as well as the respective contributions of ebullition and diffusion, are shown in Fig. 6.Apart from short-term measurement gaps, a considerable loss of data occurred between 27 July and 7 August 2013 due to malfunction of the measurement equipment.CH 4 total fluxes observed by the AC system and calculated with the presented algorithm were comparable to CH 4 emissions measured during the study period by a nearby eddy covariance system (Franz et al., 2016).This indicates the general accuracy of the used measurement system and calculation algorithm.
Observed CH 4 total fluxes showed distinct seasonal patterns following the temperature regime at 10 cm sediment depth.This is in accordance with Christensen et al. (2005) and Bastviken et al. (2004), who showed that biochemical processes driving CH 4 production are closely related to temperature regimes, determining the CH 4 production within the sediment.In addition to seasonality, CH 4 total also featured diurnal dynamics, with lower fluxes during daytime and higher fluxes during nighttime, which were most pronounced during July and early September (Fig. 6).During August, the diurnal variability was superimposed by short-term emission events and high amplitudes in recorded CH 4 total .Similar to CH 4 total , diffusive fluxes also showed a www.atmos-meas-tech.net/10/109/2017/Atmos.Meas.Tech., 10, 109-118, 2017 distinct temperature-driven seasonality as well as clear diurnal patterns throughout the entire study period (Fig. 7).However, compared to the diurnal variability of CH 4 total fluxes, a pronounced shift of maximum emissions from early morning to nighttime hours was revealed for CH 4 diffusion during August 2013 (Figs. 6,7).While maximum CH 4 diffusion fluxes during July were recorded during early morning hours (approx.03:00 to 06:00 CET), a shift to the nighttime was observed for August (max.from 21:00 to 00:00 CET).During September maximum fluxes shifted back to the early morning, with maximum fluxes between 00:00 and 09:00 CET (Fig. 6).This could be explained by differences in turbulent mixing due to changing water temperature gradients.During daytime, the surface water is warmed, thus preventing an exchange with the CH 4 -enriched water near the sediment, which results in lower fluxes for CH 4 diffusion .During nighttime, when the upper water layer cools down and mixing is undisturbed, enhanced CH 4 diffusion fluxes can be detected.These dynamics are more pronounced during warm days, explaining the seasonal shift, and concealed during periods with a high wind velocity.The obtained diurnal trend is in accordance with findings of Sahlée et al. (2014) and Lai et al. (2012), who reported higher nighttime and lower daytime CH 4 emissions for a lake site in Sweden and an ombrotrophic bog in Canada, respectively.However, an opposing tendency was found by Deshmukh et al. (2014), who reported higher daytime and lower nighttime CH 4 emissions from a newly flooded subtropical freshwater hydroelectric reservoir within the Nam Theun river valley, Laos.In contrast to diurnal trends obtained for CH 4 total and CH 4 diffusion , estimated ebullition events occurred erratically and showed neither clear seasonal nor diurnal dynamics.Nonetheless, periods characterized by more pronounced ebullition seemed to roughly follow the sediment temperature-driven CH 4 production within the sediment as, for example, reported by Bastviken et al. (2004) (Fig. 5).This is confirmed by a distinct correlation between daily mean sediment temperatures and corresponding sums of measured ebullition fluxes (r 2 : 2 cm = 0.63; 5 cm = 0.63; 10 cm = 0.62).Moreover, fewer and smaller ebullition events were detected in times of reduced wind velocity and high relative humidity (RH) (e.g.10-11 September and 18-19 September 2013).However, at the level of single flux measurements, no significant dependency was found between the recorded environmental drivers and CH 4 release via ebullition.The relative contributions of diffusion and ebullition were 55 % (min.33 to max.70 %) and 46 % (min.30 to max.67 %), respectively.This is in accordance with values reported by Bastviken et al. (2011), who compiled CH 4 emission estimates from 474 freshwater ecosystems with clearly defined emission pathways.A similar ratio was also found by Tokida et al. (2007), who investigated the role of decreasing atmospheric pressure as a trigger for CH 4 ebullition events in peatlands.Comparison of flux data among the four chambers reveals considerable spatial heterogeneity within the measured transect (data not shown).Monthly averages of diffusive, ebullition and total CH 4 emissions for all four chambers of the established transect as well as statistics showing the explanatory power of different environmental variables are summarized in Table 1.With respect to total CH 4 emissions, neighbouring chambers generally featured high differences in CH 4 fluxes, with no obvious trend along the transect.The same holds true for derived ebullition and diffusive CH 4 flux components.After separation into diffusion and ebullition, fluxcomponent-specific dependencies on different environmental drivers were revealed (Table 1).

Overall performance
Compared to direct measurements of diffusion or ebullition (e.g.Bastviken et al., 2010Bastviken et al., , 2004) ) the presented calculation algorithm features two major advantages.On the one hand it allows deriving ebullition and diffusion flux components based on the same measurement and spatial entity, which prevents an interfering influence of spatial heterogeneity on observed flux components.This is not the case for flux separation based on a combination of different measurement devices, such as automatic chambers and bubble traps, which need a sufficient number of repetitions and degree in data aggregation to reduce the bias, emerging from the spatiotemporal heterogeneity of erratically occurring ebullition events.On the other hand, the solely data-processing-based flux separations approach allows for an application when the use of direct measurement systems for either ebullition (gas traps, funnels) or diffusion (bubble shields) might be limited.This Table 1.Monthly averages ±1 standard deviation of hourly CH 4 emissions (mg m −2 h −1 ) for the chamber transect (from chamber I-IV, starting near the shoreline).Average standardized (beta) coefficients and Nash-Sutcliffe efficiency (NSE) based on linear regressions and multiple linear regressions between different environmental drivers and daily subsets of calculated CH 4 emissions are shown below.Monthly averages as well as statistics are separated according to diffusion, ebullition and total CH 4 flux.Superscript letters indicate significant differences between chambers and the p values of applied linear and multiple linear regressions (MLRs).Taking into account that the presented measurement site is characterized by rather large CH 4 emissions (Franz et al., 2016) and frequently occurring ebullition events (Fig. 3), this limitation seems to be negligible.
Compared to other data-processing-based approaches for CH 4 flux separation (e.g.Goodrich et al., 2011;Miller and Oremland, 1988), the presented algorithm calculates an integrated ebullition flux component.This ensures a reliable flux separation, despite potential measurement artefacts such as overcompensation or incomplete ebullition records.
Accounting for the few prerequisites (high-resolution closed chamber measurements) as well as mentioned advantages, an application of the presented approach to open-water areas of a broad range of wetland ecosystems and automatic closed chamber systems is stated.

Conclusions
The results of the laboratory experiment as well as the estimated relative contributions of ebullition and diffusion during the field study indicate that the presented algorithm for CH 4 flux calculation and separation into diffusion and ebullition delivers reasonable and robust results.Temporal dynamics, spatial patterns and relations to environmental parameters well established in the scientific literature, such as sediment temperature, water temperature gradients and wind velocity, became more pronounced when analysed separately for diffusive CH 4 emissions and ebullition.The presented algorithm will be applicable as long as the underlying closed chamber measurements deliver continuous high-resolution records of CH 4 concentrations and air temperature.However, steady ebullition of microbubbles might yield an overestimation of the diffusive flux component, whereas continuously strong ebullition events might totally prevent flux separation.Hence, the application and adaptation of the presented algorithm for different wetland ecosystems and automatic chamber designs is needed.Obtained results should be further validated against direct flux measurements using, for example, bubble traps or barriers.This will allow evaluating the generalizability and applicability to other freshwater and wetland ecosystems as well as chamber designs.
Despite the mentioned shortcomings, the presented calculation algorithm for separating CH 4 emissions increases the amount of information about the periodicity of CH 4 release and may help to reveal the influence of potential drivers as well as to explain temporal and spatial variability within both separated flux components.In future, the implementation of CH 4 released through plant-mediated transport into the flux separation algorithm should be addressed.This could be realized by complete chamber measurements with CH 4 concentrations measured in different water and/or sediment depth, which will allow the direct derivation of CH 4 diffusion .In a next step, the remaining two flux components could be separated using the presented algorithm.

Data availability
The presented simple calculation algorithm, a test data set and manual, as well as all raw data sets of automatic chamber flux measurements shown in this study, are available at https: //zenodo.org(Hoffmann and Jurisch, 2016;Hoffmann et al., 2017).

Figure 1 .
Figure 1.Transect of automatic chambers (ACs) established at the measurement site.The arrow indicates the position of the climate station near chamber II.

Figure 2 .
Figure 2. Flow chart showing the principles of the calculation of CH 4 diffusion and CH 4 total (dashed boxes) as well as subsequent CH 4 flux separation (dotted box).

Figure 3 .
Figure 3.Time series plot of recorded concentrations (ppm) within the chamber headspace for (a) a simulated ebullition event and (b) an exemplary field study CH 4 measurement.Time spans dominated by diffusive CH 4 release are marked by (c, d) black dots, enclosed by the 25 and 75 % quantiles ±0.25 IQR of obtained concentration changes, shown as black dashed lines.Unfilled dots outside the dashed lines display ebullition events (see alsoGoodrich et al., 2011;Miller and Oremland, 1988).Grey shaded areas indicate the applied death band at the beginning of each measurement (25 %).Negative CH 4 values indicate a overcompensation due to (temporally) insufficient headspace mixing.

Figure 4 .
Figure 4. Scheme of experimental setup used for the simulation and determination of ebullition events with a Los Gatos fast greenhouse gas (FGG) analyser (911-0010, Los Gatos).The crimped area represents water-filled tub.

Figure 5 .
Figure 5. Scatter plot of the amount of injected CH 4 and the corresponding calculated CH 4 ebullition event.The solid black line indicates the 1 : 1 agreement.The linear fit between the displayed values is represented by the black dashed line, surrounded by the 95 % confidence interval (grey shaded area).

Figure 6 .
Figure 6.Time series of (a) total CH 4 emissions with proportions of ebullition (grey bar) and diffusion flux components (black bar) during the study period from July until September 2013.Figure 6b and c show the separated flux components (b ebullition and c diffusion), together with the development of important environmental parameters, which are assumed to explain their specific dynamics (a water level, b RH and wind speed and c sediment (solid line) and water temperature (dashed line)).Pie charts represent the biweekly pooled diurnal cycle of measured CH 4 fluxes.Slices are applied clockwise, creating a 24 h clock, with black and light grey slices indicating hours with CH 4 flux above and below the daily mean, respectively.

Figure 7 .
Figure 7. Monthly averaged diurnal cycle of diffusive CH 4 fluxes indicating differences in magnitude and amplitude as well as a shift in minimum and maximum daily CH 4 fluxes over the course of the study period.
Franz et al. (2016)easured from beginning of July to end of September 2013 at a flooded former fen grassland site, located within the Peene river valley in Mecklenburg-West Pomerania, northeast Germany (53 • 52 N, 12 • 52 E).The long-term annual precipitation is 570 mm.The mean annual air temperature is 8.7 • C (DWD, Anklam).The study site was particularly influenced by a complex melioration and drainage programme between 1960 and 1990, characterized by intensive agriculture.As a consequence, the peat layer was degraded and the soil surface was lowered by subsidence.Being included in the Mecklenburg-West Pomerania mire restoration programme, the study site was rewetted in the beginning of 2005.As a result, the water level rose above the soil surface, thus transforming the site into a shallow lake.Exceptionally high CH 4 emissions at the measurement site were reported byFranz et al. (2016), who measured CO 2 and CH 4 emissions using an eddy covariance system, andHahn-Schöffl et al. (2011), who investigated sed- in an overestimation of CH 4 diffusion and underestimation of CH 4 ebullition , an effect that might be reduced by enhancing the measurement frequency and thus the sensitivity of the variable IQR filter.Compared to that, frequently occurring strong ebullition events might disable the calculation of CH 4 diffusion , which hampers flux separation for the corresponding measurement.Out of 14 828 valid automatic chamber measurements during the exemplary field study, the algorithm failed to calculate CH 4 diffusion during 170 measurements.This equals 1.15 % of all measurements.