Hourly methane and carbon dioxide fluxes from temperate ponds

Ponds are regarded as greenhouse gas (GHG) emission hot spots, but how hot are they? We examined this question by measuring methane (CH 4 ) and carbon dioxide (CO 2 ) fluxes in six forest and open land ponds on grasslands in Denmark during summer and winter. We used floating chambers with do-it-yourself sensors and automated headspace venting, allowing for 7404 hourly measurements. We found highly variable gas fluxes within ponds and between seasons and pond types. Ebullitive CH 4 fluxes were more variable than diffusive CH 4 fluxes. Ebulli-tion was absent when total CH 4 fluxes were lowest (15 µmol m −2 h −1 ), dominant (> 90%) at the highest fluxes (> 400 µmol m −2 h −1 ), and increased with water temperature. In summer, a minor daily increase in diffusive fluxes was found on days with high wind speed, while CH 4 ebullition remained constant. CO 2 fluxes paralleled the day-night balance of photosynthesis and respiration. Mean CH 4 ebullition in open and forest ponds exceeded CH 4 diffusive fluxes 4.1 and 7.1-fold in summer (avg. 22.5 °C) and 2.3 and 2.5-fold in winter (9.6 °C), respectively. CO 2 emissions were higher on a molar basis than CH 4 emissions, both in summer and winter, while their annual global warming potentials were similar. Mean annual gas emissions from open and forest ponds (1092 and 2527 g CO 2 e m −2 y −1 ) are naturally high due to extensive external input of dissolved CO 2 and organic carbon relative to pond area and volume.


Introduction
Methane (CH 4 ) and carbon dioxide (CO 2 ) fluxes are the two most important processes driving climate warming (Myhre & Shindell 2014), and lakes emerge as natural hot spots in the transmission of greenhouse gases (GHG; Rosentreter et al. 2021).Small lakes and ponds (< 5 ha) are globally abundant and have high fluxes relative to their areal cover, but these systems are poorly defined and often overlooked in investigations of GHG fluxes (Deemer et al. 2016;Richardson et al. 2022).Higher GHG fluxes in ponds, compared to larger lakes, may be due to the compounding Vol:.( 1234567890) effects of higher organic substrate concentrations and higher temperatures (Deemer & Holgerson 2021).Ponds have a higher shoreline-to-surface-area ratio and, consequently, higher inputs of terrestrial organic material relative to surface area and water volume, causing high rates of degradation and production of CO 2 and CH 4 (Kuhn et al. 2021;Sand-Jensen & Staehr 2009).Furthermore, they receive substantial inputs of dissolved CH 4 and supersaturated CO 2 through sub-surface flow from the surroundings (Abril & Borges 2019;Striegl & Michmerhuizen 1998).As a result of large lateral inputs and internal production of organic material, pond sediments produce CO 2 and CH 4 which fuels the emission of GHG (Jansen et al. 2020b).Goeckner et al. (2022) found differences in GHG emissions and carbon storage between natural and artificial ponds, with artificial ponds being carbon sources when younger (< 20 years), but carbon sinks when they become older (> 20 years).Moreover, surrounding land use and soil types may influence GHG fluxes (Peacock et al. 2021).Thus, despite only comprising 8.6% of the global lake area, small ponds (< 0.01 km 2 ) have been estimated to be responsible for approximately 40% of diffusive CH 4 emissions (i.e., 13.2-21.2Tg CH 4 y −1 ), according to early global estimates (Holgerson & Raymond 2016;Rosentreter et al. 2021).However, numbers and sizes of smaller ponds are difficult to assess using satellite imagery and include in the global GHG budget, resulting in large uncertainties of their contribution (Grinham et al. 2018).
CH 4 is formed by the degradation of organic substrates in the absence of oxygen and alternative electron acceptors (e.g., nitrate, ferric oxides and sulfate; Fenchel et al. 2012).The production is promoted by high rates of degradation at high temperatures and an ample supply of easily degradable organic substrates, when oxygen supply and alternative electron acceptors cannot metabolize the production of small organic acids (mainly acetate) and H 2 to CO 2 and H 2 O (Fenchel et al. 2012).This situation may account for the observed increase in CH 4 fluxes and the CH 4 :CO 2 flux ratio as lake size decreases, causing a higher warming potential per unit surface area (Holgerson & Raymond 2016;West et al. 2016).Hence, the higher input of organic substrates from external sources and in-lake production relative to surface area may increase CH 4 formation in small, typically warmer lakes during summer (Beaulieu et al. 2019;DelSontro et al. 2016).Although CH 4 and CO 2 emissions may be high from lakes and ponds, substantial carbon sequestration in the sediments will reduce net emission from the sites and downstream in the hydrological network (Anderson et al. 2014;Céréghino et al. 2014;Gilbert et al. 2021;Goeckner et al. 2022;Taylor et al. 2019).Thus, the debate of pond construction as a climate mitigation strategy should be evaluated as part of carbon budgets for the entire hydrological network at relevant time scales.Nonetheless, the debate of pond construction as a climate mitigation strategy remains unsettled.
The rates of microbial CH 4 production in sediments increase more with higher temperatures than CO 2 production, thereby increasing the CH 4 :CO 2 flux ratio (Yvon-Durocher et al. 2014).In eight Swedish lake sediments, CH 4 production rates measured in incubations increased 10-100-fold between 4 and 20 °C and more strongly above 10 °C, though with considerable variation between lakes (Duc et al. 2010).The influence of temperature on CH 4 fluxes from lake surfaces is even more complex because CH 4 may be produced and oxidized by different aerobic and anaerobic processes in the sediment and the water column (Bogard et al. 2019;D'Ambrosio & Harrison 2021;Donis et al. 2017).Nonetheless, we expect substantially higher CH 4 production and fluxes during summer than winter, and particularly higher CH 4 :CO 2 flux ratios as higher summer photosynthesis may reduce CO 2 emission (Jansen et al. 2020a;Wik et al. 2013Wik et al. , 2018Wik et al. , 2014)).
A recent application of automated ventilated floating chambers deployed to 80 sites on a 10-ha lake revealed high spatial variability of CH 4 and CO 2 fluxes (Sø et al. 2023a, b).The chambers provided hourly diffusive and ebullitive fluxes throughout the diel cycle because the chamber headspace is automatically replenished with atmospheric air every hour allowing new measurements of gas fluxes between water and air.Sieczko et al. (2020) found that CH 4 fluxes from Swedish lakes were 2.4 times higher in the daytime than in the nighttime.This may be due to higher daytime wind speeds causing higher water column turbulence and sediment shear stress promoting CH 4 release from water and sediment surfaces (Joyce & Jewell 2003).When estimates of CH 4 fluxes are based solely on daytime measurements, this may lead to an overestimation of diel values (Sieczko et al.Vol.: (0123456789) 2020).Floating chambers with automated headspace venting are suitable to test whether diel variations in CH 4 and CO 2 fluxes also are pronounced in ponds where wind speeds are markedly reduced due to sheltering by riparian vegetation, and short wind fetches (Vachon & Prairie 2013).CO 2 flux is likely lower during daytime due to photosynthesis and CO 2 consumption and higher during nighttime due to respiratory CO 2 release (Martinsen et al. 2022;Sand-Jensen et al. 2019).Measurements assessing variability of CO 2 and CH 4 fluxes within ponds are few.
Our goal was to determine CO 2 and CH 4 fluxes at high spatial and hourly resolution in six temperate ponds during summer and winter using automated floating chambers.To cover the two main types of small, nutrient-rich ponds, we examined three ponds located in a deciduous forest and three ponds located in open landscape nearby.We expect that forest ponds receive a higher input of organic matter that supports CO 2 and CH 4 effluxes.Ponds situated in open country receive higher summertime solar irradiance, are warmer and support higher in-pond primary production that, in turn, lowers CO 2 efflux.Overall, high input of organic matter and nutrient-rich conditions in both pond types may lead to profound CH 4 flux by diffusion and ebullition during both summer and winter.

Study sites and methods
This study was conducted during the winter and summer of 2022 in six shallow, nutrient-rich ponds located soil consisting of meltwater gravel and freshwater peat in North Zealand, Denmark (Fig. 1, Table 1).In some cases, sensors or batteries failed after a short time due to rough weather conditions; thus, data from two winter periods are included in the analyses of some ponds (Number of fluxes for each station is indicated on Fig. S2-4).Summer measurements were completed in late May or June, while winter measurements were done from January to the beginning of March.Three forest ponds (F1-F3, Table 1) and three are in open land nearby (O1-O3, Table 1) were studied.The area surrounding the forested ponds was dominated by old deciduous forest with few to no buildings.The forest is used for recreation and some timber production.The open land ponds were located in open grasslands, without buildings but in urban transitions.The ponds vary in size (878-5897 m 2 ) and all experience substantial variations in water level.The age of the ponds varied, with two of the ponds (one in a forest and one open) being natural ponds of unknown age, whereas the rest were human-made ponds created 16-26 years ago to restore the natural hydrology or increase the biodiversity (Table S1).Water depth was measured in summer at the sites of GHG measurements.Four sites were selected for each pond, two in shallower waters (30-50 cm) and two in deeper waters (50-100 cm), whenever possible.One very deep pond (O2; 274 cm) was equipped with two additional floating chambers to cover the both shallow, intermediate and deep areas (Table 1).Distance between chamber positions ranged from 10 to 48 m.

Environmental conditions
Wind speed, gust and direction were measured one meter above the water surface in the middle of one forest pond (F1; HOBO S-WSB-M003 and HOBO S-WDA-M003, Onset, USA) and at a nearby public weather station (4-6 km from the ponds; Davis Vantage Pro2, Lyngvig 2022).Wind speeds recorded at F1 were applied for the other two forest ponds, while public weather station data were used for all open ponds.Throughout the measuring period from 1 December 2021 to 30 July 2022, wind speeds in the forest remained lower than in open terrain.The barometric pressure was also acquired from the public weather station and used in Eq. 1 for all ponds.Each site was equipped with an oxygen and temperature sensor placed 50 cm above the sediment, when possible, and otherwise at the water surface.We measured temperature and oxygen in the pond water every 10 min (MiniDOT, PME, USA), while total phosphorus (TP), total nitrogen (TN) and dissolved organic carbon (DOC) were measured once during winter and summer following the methods of Kragh and Søndergaard (2004).TP and TN samples were stored at − 18 °C.Upon analyses, the samples were acidboiled in an autoclave and measured on a SKALAR autosampler.The DOC samples were filtered through a GF/F filter (Whatman, UK) and conserved using 2 M HCl.The water sample was later analyzed on a Shimadzu TOC (Shimadzu, Japan).Chlorophyll a and light absorbance of colored dissolved organic material (CDOM; Jespersen & Christoffersen 1987;Kirk 1994) were measured only during summer.Chlorophyll a was measured by ethanol extraction and spectrophotometric measurements of absorbance at 665 and 750 nm, while CDOM was measured as absorbance of 440 nm.The organic content of the upper 10 cm of sediment was measured in four cores (21 cm 2 ) for each pond in summer, with one core retrieved from each site within the 6 ponds and, thus, a total of 24 cores.The cores were split into two depth layers (0-5 cm and 5-10 cm) and the organic content was measured on duplicate samples as loss on ignition (LOI) at 550 °C.During winter, only one oxygen-temperature sensor remained in each pond.GHG measurements were not performed during the brief periods when the ponds were ice-covered which typically lasted less than one or two weeks.Submerged plant coverage in a plot of 2 × 2 m below the floating chambers was assessed visually during summer and categorized as: absent (0%), sparse (1-20%), high (20-90%), or complete coverage (90-100%).The most common submerged species present within the plot were Callitriche sp., Ceratophyllum demersum, Chara sp., Elodea canadensis, Myriophyllum spicatum, Potamogeton obtusifolius and Potamogeton pusillus.

Automated floating chambers
Floating chambers contained electronic equipment that continuously measured CH 4 and CO 2 as described in Bastviken et al. (2015), modified to perform hourly measurements enabled by automated venting of the headspace by using an air pump (surface area: 0.0615 m 2 , volume: 0.0135 m 3 ; Martinsen et al. 2018;Sø et al. 2023a, b).All measurements were done in 2022, with winter measurements performed in January and February and summer measurements between late May and June.CH 4 , relative humidity, and temperature were measured every 2 s, while CO 2 was measured every 20 s.CH 4 and CO 2 fluxes were calculated by the use of small sensors (CH 4 : NGM2611-E13, Figaro, USA, and CO 2 : K33 ELG, SenseAir, Sweden) mounted in the air headspace within floating chambers that were placed on the sites.The CH 4 sensor was originally designed for high-concentration measurements, however, Bastviken et al. (2020) showed that using a new calibration and data processing technique the sensor can be used for CH 4 fluxes in floating chambers.The CH 4 sensor reacts to changes in absolute humidity, and thus measurements of relative humidity and temperature are needed to convert the voltage signal to CH 4 concentration.Data logging and the air pump were controlled by an Arduino (Arduino, Italy) which is equipped with a 10-bit analog-to-digital converter, which limits the resolution.We used a 10-point moving average, applied five times to increase the resolution of data, as proposed by Kajiura and Tokida (2021).The sensor precision is lower than commercialized laser equipment, which might cause difficulties in measuring low rates of change; we quantified this uncertainty in our measurements to 2.1 ± 2.1 and 3.4 ± 3.5 µmol CH 4 m −2 h −1 (see Sect. "Test of setup" below).Drift in the sensor has been shown to be within only 4-6 ppb per year for long-term measurements (Eugster et al. 2020), though this was not tested in our setup.Each automated floating chamber measured CH 4 and CO 2 for a 40 min period, before a a small air pump was turned on for 20 min to ensure complete replenishment of headspace air with atmospheric air.Thus one measurement series was possible each hour (Sø et al. 2023a, b).The chamber had two hoses connected, one connected to the air pump, the other working as a pressure relief.The length and diameter (l: 1.5 m, Ø: 16 mm) of the hoses were tested to ensure no air would leave the chamber by diffusion.The air pump exchanged the air inside the chamber with atmospheric air, thus bringing the gas composition in the chambers close to that in the atmosphere and allowing continuous automated measurements of CH 4 and CO 2 fluxes.Chambers were tied to a sunk float that was held in place by an anchor, limiting the chambers to a one-meter float radius.All electronics were powered by an 18 Ah 4S LiFePO 4 battery floating next to the chamber.All sensors were turned on for a minimum of one hour before deployment, enabling the CH 4 sensor to warm up.Four chambers were distributed evenly between shallow and deep parts of each pond in areas without emergent vegetation.
Calibration of the gas sensors followed the procedure by Bastviken et al. (2020) and Sø et al. (2023a, b).All CH 4 sensors were calibrated individually, as the sensors do not react the same to changing humidity and CH 4 concentrations.During the calibration of the CH 4 sensors, a Gasmet DX4030 FTIR Gas Analyzer (Version E1.22) and an Ultraportable Greenhouse Gas Analyzer (Los Gatos Research, USA) were used.The two CH 4 analyzers were calibrated using standard CH 4 gases (100 ppm).

CH 4 and CO 2 fluxes
Each flux measurement of CH 4 was partitioned into ebullitive (F CH4-ebul ) and diffusive fluxes (F CH4-diff ), with their sum being the total flux (F CH4-total ).Thus for each flux measurement, both F CH4-ebul and F CH4-diff could be calculated.Flux of CO 2 (F CO2 ) only occurred by diffusion due to the high solubility of CO 2 in water.All fluxes (µmol m −2 h −1 ) were calculated using Eq. ( 1).
The first term (dC dt −1 ) is the rate of change (µmol mol −1 h −1 ) in the headspace of the chamber, P amb is the ambient pressure (Pa.), V is the chamber volume (m 3 ), R gas is the ideal gas constant (m 3 Pa K −1 mol −1 ), T is the ambient temperature (K), and A is the surface area (m 2 ) of the floating chamber. (1) Positive fluxes indicate the gas leaving the water (source), whereas negative fluxes indicate the gas entering the water (sink).
Partitioning of F CH4-ebul and F CH4-diff was done according to Sø et al. (2023a, b).Specifically, ebullition events were characterized as a sudden increase in the headspace CH 4 concentration (Fig. 2).Ebullitive events were identified by computing a 5-point running variance as sudden changes in concentration result in high variance, whereas F CH4-diff is characterized by low variance.We determined a cutoff value for the running variance by visually identifying ebullitive events and the running variance.All measurements which exceeded the running variance cutoff value were classified as ebullitive measurements.We determined the minimum and maximum concentration within this period and calculated F CH4-ebul as the difference between maximum and minimum concentration.All measurements one minute before and after the event were also included in the ebullitive event, to encompass the entire concentration change caused by an ebullitive event.Additionally, a diffusive threshold value was applied to exclude changes less than 1 ppm from being regarded as ebullitive events.F CH4-diff was calculated after the initial 200 measurements (6.5 min), i.e., the deadband, following the replacement of the chamber air, to avoid any initial instability, caused by large changes in humidity within the chamber which affects the CH 4 sensor measurements.Ebullition was not determined either during this period.The next 400 measuring points (13 min) were used to calculate the slope (dC/dt in Eq. 1), using a linear regression model.If ebullitive events occurred during the 400 measuring points, only the points before the ebullitive event were used.Models with a well-defined linear fit (i.e., an R 2 higher than 0.5 and more than 50 observations) were kept for calculation of F CH4-diff , while the others were discarded.All F CH4-diff were visually inspected to ensure that the headspace concentration did not approach saturation resulting in a lower slope.F CH4-ebul was calculated for the entire period after the initial 6.5 min.
F CO2 was calculated on all measuring points of each measurement period, resulting in approximately 120 measurement points over 40 min.The CO 2 concentration in the chamber during the 40 min did not show any signs of saturation.A linear model was fitted for every 40 min period for each sensor.Models with an R 2 higher than 0.5 and more than 50 observations were retained, and the rest were discarded.
In all, 1390 measurements of F CH4-diff and 2701 F CH4-ebul were collected during summer and 1569 F CH4-diff and 4588 F CH4-ebul measurements in winter.There were 1942 hourly CO 2 fluxes in summer and 1009 in winter.The number of days with flux measurements averaged 21, 51, and 21 days per pond Red lines indicate the linear model in which the slope was used to determine the diffusive exchange rates.Sudden changes in concentration were considered ebullitive events, which are shown by vertical blue lines of continuous hourly measurements of F CH4-diff , F CH4-ebul , and F CO2 , respectively.The large difference in available flux measurements is due to diffusive fluxes of CH 4 and CO 2 having R 2 values lower than 0.5 that were discarded.
The total GHG emission from the lake was calculated in terms of CO 2 -equivalents (CO 2 e) using the conversion of 27.2 as the global warming potential for non-fossil CH 4 for a time period of 100 years (IPCC 2022).The annual emissions of CO 2 and CH 4 were estimated as the mean of summer and winter fluxes.

Test of setup
The uncertainty of our setup was tested on two floating chambers, which were deployed in an open container filled with tap water at a depth of 20 cm.The container was initially equipped with two air pumps to ensure equilibrium in partial pressure of CH 4 between the atmosphere and the water.The container was open and exposed to natural weather conditions.Furthermore, to mimic higher piston velocity, a water circulation pump was turned on after 24 h.As anticipated, increasing piston velocity did not affect fluxes in this experimental situation with equilibrium in partial pressure between the water and the gas space.A total of 83 fluxes were measured with the two floating chamber sensors.Only 5% of the fluxes had an R 2 above 0.5, and 39% of the calculated fluxes were negative and 61% were positive.We assumed that no CH 4 formation would take place in the container, and expected no CH 4 flux between the water and the headspace in the chambers.The mean fluxes (± 95% confidence intervals) for the two sensors were low, close to and not significantly different from zero (i.e., 1.9 ± 2.1 and 4.8 ± 3.5 µmol m −2 h −1 ).Notably, under conditions with very high temperatures inside the chamber (> 35 °C) the CH 4 sensor may need longer time (> 15 min) to stabilize, due to rapid changes in absolute humidity.
Calculations of fluxes are difficult, even with highprecision equipment.This is further complicated by the low-resolution analog-to-digital converters used here.The signal-to-noise ratio is not proportional: the noise has a larger impact in situations with low fluxes.Thus, at low fluxes, linear estimation of the concentration change over time is difficult and may results in poor model estimates, causing a bias towards higher fluxes with accepted R 2 > 0.5 (Lammirato et al. 2017).
This phenomenon was also noticeable in our dataset, which showed a similar inverse relationship between the diffusive fluxes of CH 4 and R 2 (Fig. S1).

Statistical analysis
Linear mixed models (LME) were used to determine the variables influencing F CH4-ebul , F CH4-diff , and F CO2 .Prior to any statistical modeling, all variables were examined for intercorrelation using Spearman rank correlation.An arbitrary Spearman ρ range between − 0.39 and 0.39 was treated as uncorrelated and kept for analysis.Only one variable was kept if the ρ value was not within this range.Due to the large number of observations, the p-value was a poor measure of significance and was thus disregarded.To ease the interpretation of the model parameter estimates, all predictor variables were centered and scaled by subtracting the mean and dividing by the standard deviation, resulting in the same unit for all predictor variables.Sediment organic content (LOI) variables at 0-5 cm and 5-10 cm, DOC, and CDOM were found to be intercorrelated; LOI at 0-5 cm was retained for modeling.
Models were fitted using either hourly or average daily values of F CH4-ebul , F CH4-diff , and F CO2 as the response variable.As predictor variables, all models consisted of average water temperature, LOI, water depth, and plant coverage.Additionally, F CH4-ebul was used as a predictor variable in both models of F CH4-diff .For the modeling using daily values, all predictor variables were also averaged.Model selection was performed using a stepwise backward elimination approach, removing predictor variables that were not significant until all remaining variables were significant (using likelihood ratio tests, p < 0.05).LME were used to account for the hierarchical nesting of the data, using sites nested within a pond as the random effects.An additional LME was created to determine if meteorological variables affected the ratio of ebullitive to total fluxes.This was done using the data from the public weather station.The model consisted of daily mean pressure, air temperature, and wind speeds.However, as seasonal differences in temperature and wind speeds can be large, we also created two models examining the meteorological variables' effect for each season.As previous results have shown differences in pond GHG fluxes based on them being artificial or natural, we created three LME Vol:.( 1234567890) with F CH4-diff , F CH4-ebu , F CO2 as the dependent variables, respectively, and type (natural vs artificial) as the independent variable.To account for differences between ponds, we used pond as a random effect.For all models, the goodness of fit was assessed using the marginal (R 2 marg , fixed effects only) and conditional (R 2 cond , fixed and random effects) R 2 .We further used LMEs to determine differences in fluxes between forest and open ponds.F CH4-ebul , F CH4-diff , and F CO2 were used as dependent variables, and type (forest vs. open) and season were used as independent variables; season was included to take into account that some of the ponds had a higher number of fluxes available.
Diel variability of GHG-flux was assessed by generalized additive mixed modeling (GAMM) for each type of flux.The relationship between fluxes (response variables) and hour of the day (predictor) were modeled using cyclic cubic spline smoothing, which was evaluated for forest and open ponds during summer and winter.Moreover, ponds and sites within ponds were incorporated to account for nested data, similar to the linear mixed models.Significant diel correlation found in GAMM models was hypothesized to be due to variable wind speeds.Thus, ponds with diel variation in fluxes were modeled with wind speeds using LME.In the models, each flux was used as a response variable and wind speed from that type of pond was used as a predictor variable; we accounted for the nestedness of data like in the previous models.
Spatiotemporal variability was examined between sites within ponds and between types of ponds (forest and open) by calculating the coefficient of variation of the daily average fluxes, described as the standard deviation divided by the mean.
All data analysis and statistics were made using R (R Core Team 2018).For the linear mixed effects model, the lme4 package was used (Bates et al. 2015), while the mgcv package was used for GAMM models (Wood 2011).R 2 marg and R 2 cond were calculated using the MuMIn package (Bartoń, 2018).

Environmental conditions
All six ponds were alkaline, eutrophic, high in DOC, variable pH and with no systematic differences in water chemistry between forest and open ponds (Table 1).On average, the sediment organic contents at the 4-6 measuring sites in each pond were significantly higher in forest ponds (48.9-66.3%)than in open ponds (10.5-22.7%;t-test: df = 4, t = 6, p = 0.04).During gas flux measurements, mean water temperatures were slightly higher in open ponds than in forest ponds during both summer (23.8 and 21.1 °C, respectively) and winter (9.9 and 9.4 °C, Table 2).Two forest ponds had winter periods of hypoxia (< 10% oxygen saturation), while the other ponds were close to air saturation (Table S1).During summer, most pond sites had oxygen concentrations above air saturation during daytime and below during nighttime, but several high-plant-coverage sites in open ponds were permanently supersaturated (Table S1).One open pond (O3) had no submerged plants, while all forest ponds and one open pond (O1) had variable coverage between sites (ranging from no to high coverage) and another (O2) had full coverage (Table S1).Atmospheric pressure measurements were collected from the local weather station, showing on average a 1% difference in daily pressure from maximum to minimum, with higher average values during winter (1.3%, range: 0.2-3.1%)compared to summer (0.5%, range: 0.1-1%).The LME showed no significant relationship between F CH4-diff , F CH4-ebu , F CO2 and type (natural or artificial) of pond, indicating no differences in fluxes between natural and artificial ponds.
High F CH4-total was primarily due to ebullition, as seen in comparisons between days, sites, ponds, and seasons (Fig. 4).No ebullition took place when F CH4-total was at the lowest level (15.5 µmol m −2 h −1 ), and the percentage of ebullition in F CH4-total increased with higher F CH4-total , attaining a maximum of 94%, and an asymptote of 90% at approximately 400    5).A significant diel variation was also found during winter, in which higher fluxes were found in the evening or during the night, and lower fluxes during the day or early morning (GAMM; forest: edf = 1.9, F = 45, p < 0.001, open: edf = 1.9, F = 24, p < 0.001).

Drivers of CO 2 and CH 4 fluxes
Two models were fitted to each flux type, with the dependent variable being either hourly or daily fluxes (Table 3).Initially, all models included water temperature, oxygen concentration, sediment organic content (LOI), water depth, and plant coverage.R 2 marg was low for all hourly models (< 0.2), while R 2 cond was slightly higher (0.16-0.71), indicating that random effects accounted for some of the variation.The results from the ebullition model were particularly striking when daily rather than hourly values were used, increasing R 2 values of 0.11 and 0.34-0.17and 0.68 for R 2 marg and R 2 cond , respectively.Both R 2 marg and R 2 cond were higher in the CH 4 models that applied average daily values, but this was not the case for R 2 marg in the CO 2 model.Oxygen was positively related to fluxes in the hourly CH 4 models, but not in the CO 2 model.When water depth was introduced in the final model, it had a positive influence on the diffusive flux, while sediment organic content showed a negative correlation with daily F CH4-ebul and a positive correlation with F CH4-diff .There were no obvious differences in fluxes with age or natural vs. humanmade ponds, however, we only had a few ponds with little differences in age, thus more research is needed to evaluate the relationship.

Total greenhouse gas fluxes
On average, daily summer values of F CH4-ebul were 5.1-fold higher than F CH4-diff in forest ponds and 4.5-fold higher in open ponds (Table 2).During winter, the F CH4-ebul :F CH4-diff ratios were around 1.0 in the forest ( 1) and open ponds (1.2).
The mean F CO2 :F CH4-total ratios (expressed in terms of moles) in forest and open ponds were lower in summer (5.5) than in winter (19.8 and 13.9, Table 2).The mean F CO2 :F CH4-total ratios in forest and open ponds expressed in terms of atmospheric warming potential were below 1.0 in summer (0.55 and 0.56) and above 1 during winter (2.0 and 1.4,Table 2).Thus, the warming potential of GHG fluxes was dominated by CH 4 during summer and by CO 2 during winter.

Seasonal variations in CH 4 fluxes
Daily F CH4-total was sixfold higher during summer than winter in the small forest ponds and 3.9-fold higher in open ponds.However, winter fluxes were not negligible, while the increase of F CH4-diff from winter to summer was modest (1.5-twofold).Our findings suggest a marked increase in CH 4 formation at higher temperatures in the sediments, directly supporting ebullition, and only a relatively lower increase of diffusive CH 4 release of dissolved CH 4 in the pond water.The continued CH 4 release in winter agrees with data from more than 900 subarctic, boreal and temperate lakes and ponds showing CH 4 accumulation under winter ice despite low temperatures (< 4 °C; Denfeld et al. 2018a, b).However, despite low winter fluxes, Ollivier et al. (2019) showed that F CH4-diff in agricultural farm ponds increased 25-fold from winter to summer.An increase from winter to summer was observed in our data, but it was far less, most likely due to higher lability and amounts of the organic material entering the ponds with higher inflow in autumn and winter.
While F CH4-ebul at our sites declined substantially during winter, it remained higher than F CH4-diff , likely because winter temperatures were not particularly low (mean: 9.4-9.9°C).CH 4 bubbles are produced even at very low temperatures under winter ice in lakes (Walter Anthony et al. 2010;Wik et al. 2011).Spatial variability of CH 4 ebullition is high and localized bubbles are often present in high densities trapped in the ice (Walter Anthony et al. 2010;Wik et al. 2011).High coefficients of variation generally indicated high spatiotemporal variability of CH 4 ebullition, both in this study and in an eutrophic lake examined previously (Sø et al. 2023a, b).Ebullitive release results from anaerobic degradation in the sediment and is not affected by dissolved CH 4 in the water column, but rather by sediment dissolved CH 4 concentration, the ambient pressure and the micro-environment (organic concent, sediment texture, production depth, etc.) where the bubbles are formed (Boudreau 2012).Previous studies have shown ebullitive events being primed by sudden drops in atmospheric pressure (Mattson & Likens 1990;Wik et al. 2013).In our study, however, we only observed modest daily changes in atmospheric pressure (0.1-3 Pa.), and only slightly higher during winter, which is why we did not test for an effect of pressure changes on ebullitive fluxes.The higher spatial variability of ebullition than diffusion of CH 4 is a general pattern also observed in other studies (Bastviken et al. 2004;Sø et al. 2023a, b;Yang et al. 2020).
Measurements of F CH4-diff were occasionally slightly negative, indicating an uptake of CH 4 from the floating chambers' headspace to the water.Similar results have been reported in areas with high plant cover or high oxygen concentrations (Miller et al. 2019;Sø et al. 2023a, b).While most of our negative fluxes occurred during winter in pond O3, which had a substantial winter plant cover, most of these negative values were close to the detection limit of the floating chambers.Also, when fluxes are close to zero, the error of the regression slope of CH 4 concentration versus time increases and negative values may occur by chance.Moreover, CH 4 uptake occurs if an inward gradient from air to pond surface is formed due to elevated atmospheric CH 4 concentration just above the ponds' surfaces and, thus, an elevated initial CH 4 concentration in the chamber's headspace after flushing.It is known that a dense, very active microbial neuston community is formed in a narrow 1 − 100 µm layer immediately below pond surfaces, where CH 4 oxidation may take place (Conrad 1996).
Relationship between CO 2 and CH 4 fluxes F CH4-total decreased more than F CO2 during winter, leading to a higher CO 2 :CH 4 molar ratio during winter (open ponds = 13.9 and forest ponds = 19.8)than in summer (open = 5.5 and forest = 5.5).This may result from the lower temperature dependence (i.e., lower Q 10 values and lower activation energy) of CO 2 formation than CH 4 formation (DelSontro et al. 2016;Jansen et al. 2020b;Yvon-Durocher et al. 2014).Importantly, CH 4 formation continued at low winter temperatures and should be addressed in the annual balances (Denfeld et al. 2018a;Karlsson et al. 2013).Quantification of winter F CO2 is even more important, as summer fluxes were only marginally higher than winter fluxes in small open ponds (ratio = 1.7) and appreciably lower in forest ponds (ratio = 1.5),where CO 2 was consumed by photosynthesis during summer, but less so during winter with lower solar radiation and temperatures.In our study, daily air temperature indicated that during 2022, similar temperature conditions like the ones we measured during winter (1-7 °C) occur almost one-third of the year (summer 32%), whereas mean solar radiation similar to our winter measurements (0-80 W m −2 ) occurred almost 50% of the year (summer 48%).

Pathways of CH 4 fluxes
In summer and winter comparisons among all sites in the six ponds higher F CH4-diff was accompanied by even higher F CH4-ebul .Thus, the proportion of F CH4-ebul increased from zero at low total CH 4 release to more than 90% of F CH4-total , in accordance with our hypothesis.A likely explanation is that the diffusive CH 4 release across the sediment surface only keeps pace with a modest CH 4 production rate.However, when CH 4 production increases at high temperatures, CH 4 concentrations build up and bubbles are formed and released, bypassing possible oxidation in an oxic sediment surface and water column (Langenegger et al. 2019).This pattern was noticeable in our dataset as the summer F CH4-total were generally higher than winter total CH 4 emission, most likely a result of the increased CH 4 production at higher temperature.While bubble release is limited mainly by the rate of CH 4 production and physical restraints before being released from the sediment (Wik et al. 2013(Wik et al. , 2018)), diffusive flux is constrained by several biological and Vol:.( 1234567890) physical processes, including oxidation in surface sediments and water column, and low gas transfer velocities at the sediment-water and air-water interface (Bastviken 2009).Our initial models showed a significant relationship with meteorological variables; however, this relationship vanished when differentiating between seasons, which suggests that it was caused by coupled differences in CH 4 and meteorological variables between summer and winter.

Diel variations in CO 2 and CH 4 fluxes
The automated floating chambers measure GHG fluxes every hour and are particularly suited to detect possible diel patterns.In summer, F CH4-diff was significantly higher in the early morning than during the evening in forest ponds possibly related to convective mixing, as the ponds do not stratify and the air might become colder than the water during the night (Podgrajsek et al. 2014).In open ponds, a maximum was seen around noon, which correlated well with increasing wind speeds.Maximum F CO2 in forest ponds in summer occurred late in the morning following nighttime respiration, and the minimum was observed late in the afternoon after extended daytime photosynthesis.Our diel patterns show similar results to those of Miller et al. (2019) who found that diel fluxes of CH 4 and CO 2 from drawdown ponds can vary widely throughout the day while being dependent on water temperature, hour of the day, pH and oxygen concentration.
Overall, the diel variability of F CH4-diff was not nearly as pronounced as that observed in larger lakes, where wind exposure and gas transfer velocities are higher (Sieczko et al. 2020).If diel patterns observed in previous literature (Rudberg et al. 2021;Sieczko et al. 2020) are driven by wind shear and variable gas transfer velocities, open ponds may exert a more pronounced diel pattern as they are more susceptible to wind due to the lack of terrestrial sheltering.However, our results indicate lower CH 4 fluxes in open ponds, which could restrict the detection of diel patterns because variability was low.By contrast, F CH4-ebul is a highly stochastic process independent of wind day and night and, as anticipated, it did not vary systematically during the diel cycle.
CO 2 is consumed in the water by daytime photosynthesis and commonly changes CO 2 uptake from the atmosphere to nocturnal release (Martinsen et al. 2022).Thus, when both CO 2 and CH 4 are measured in freshwater habitats, it is essential to include measurements during both day and night as gas flux often exhibits profound diel variability related to the influence of solar radiation on photosynthesis, temperature, and ventilated gas flow of CO 2 and CH 4 through floating-leaved plants (Brix et al. 1992;Dacey & Klug 1979;Martinsen et al. 2022).

Pond greenhouse gas fluxes
Combined, F CO2 and F CH4-total showed a significant carbon loss from ponds and a high warming potential.Assuming that the mean of summer and winter fluxes is a fair measure of annual fluxes, they attained 2453 g CO 2 e m −2 y −1 from the forest ponds and less, 911 g CO 2 e m −2 y −1 , from the open ponds (Table 2).A higher mean carbon flux (i.e., 6600 g CO 2 e m −2 ) was calculated from several small ponds (< 1000 m 2 ); however, these values are tied to large uncertainties in CH 4 fluxes (Holgerson & Raymond 2016;Rosentreter et al. 2021).These ponds with very large carbon flux resemble phytoplankton primary productivity in nutrient-rich lakes (Kalff & Knoechel 1978), reflecting the high input of terrestrial and in-pond production of organic material (Mulholland & Elwood 1982).
The annual global warming potential (GWP) of released CH 4 was comparable to that of CO 2 , representing 60% of the combined GWP of both gases in the studied forest ponds, 44% in the open ponds, and, in comparison, 37% as the average of < 1000 m 2 ponds compiled by Holgerson and Raymond (2016).Previous research have shown a change in GHG emissions from ponds of different age and type (natural vs. artificial) (Abril et al. 2005;Goeckner et al. 2022;Phyoe & Wang 2019), but this was not observed for our ponds.Our ponds were mostly created to restore the natural hydrology rather than digging holes, as the artificial ponds examined by Goeckner et al. (2022) were.An initial outburst of GHG emission could be expected when sites are inundated, but our ponds are many years old and if they had experienced an initial outburst, we are likely far beyond the relevant time to measure this.On the other hand, our forest ponds might not have decreased in emissions over time due to the substantial terrestrial input they receive.
Vol.: (0123456789) In discussions of GHG, it has been pointed out, that the re-establishment of small lakes and ponds leads to a substantial release of CO 2 and CH 4 to the atmosphere (Goeckner et al. 2022;Pi et al. 2022).CO 2 and CH 4 release is a natural consequence of the high terrestrial input of organic carbon and supersaturated CO 2 relative to pond surfaces.In carbon budget evaluations, it is necessary to account for the permanent burial of organic carbon in pond sediments (Sand-Jensen & Staehr 2012).Furthermore, the origin of the carbon is important, as autochthonous carbon does not remove external carbon, which would be the case when the inputs are allochthonous (Prairie et al. 2018).However, due to the surroundings, we expect the carbon input to be primarily allochthonous in the measured ponds, although this was not tested.Annual sedimentation rates for nine small eutrophic Danish lakes as a function of maximum water depth (Anderson et al. 2014) suggest a sedimentation rate of about 550 g CO 2 e m −2 y −1 by extrapolation to a water depth of 1 m, typical of the ponds studied here.Direct measurements performed by digging out the organic material accumulated over a few years after the establishment of ponds in the English countryside yielded annual rates of 290-906 g CO 2 e m −2 y −1 (Taylor et al. 2019).Other studies have found rates varying from 29 to 1638 g CO 2 e m −2 y −1 (Goeckner et al. 2022;Ljung & Lin 2023).Though we assume that annual accumulation in small ponds is in the upper part of this range due to the high terrestrial input and the gradual expansion of littoral plants across the water surface, ultimately turning ponds into moors and bogs (Wetzel 1983), we apply a conservative mean annual sediment accumulation of 550 g CO 2 e m −2 .We compared the resulting warming potential of ponds with that of drained cropland, drained unutilized land, and rewetted soils (Tiemeyer et al. 2020) that are commonly converted to ponds to increase local biodiversity and reduce downstream eutrophication and flooding risk (Schmadel et al. 2019).If estimated organic matter deposition in our ponds and measured GHG fluxes are accounted for, the annual GWP of open ponds is low (361 g CO 2 e m −2 y −1 ; Table 4), while that of forest ponds is higher (1903 g CO 2 e m −2 y −1 ).The annual GWP of rewetted organic soils (686 g CO 2 e m −2 ) resembles that of open ponds, while drained cropland and uncultivated wetland on organic soils have much higher warming potentials (3718 and 2799 g CO 2 e m −2 ) than both our pond types.
The deep sediment cores (5-10 cm, data not shown) were expected to portray the surrounding soils, which in all forest ponds showed organic contents higher than 20%, while in open ponds organic content was from 10.1, 5.3 and 11.4%, the lowest content was in the deepest of ponds, which was artificially dug.We therefore believe that the comparison with Tiemeyer et al. (2020) is appropriate, as their estimation is based on organic-rich soils (12-18% C; IPCC 2006).
Conversion of drained cultivated and uncultivated organic soils to wetlands and ponds shifts organic carbon degradation into carbon retention and, though more CH 4 is released, the carbon, nitrogen, and phosphorus retention in pond sediment and the reduction of N 2 O fluxes when the addition of nitrogen fertilizers ceases will markedly reduce the warming potential.In conclusion, to establish a fair evaluation of conservation and management initiatives to form new ponds to replace the many lost it is essential to account for overall GHG budgets, biodiversity and downstream eutrophication and flooding risk.We propose that everyone will benefit from the conversion of drained cultivated organic soils into natural wetlands or ponds.to KSJ.Furthermore, we thank Aage V. Jensens's foundation for grants supporting the Ph.D. research of JSS.We thank the COWI foundation for funding CH 4 sensors (A-155.03)and the Carlsberg grant for funding the Ultraportable Greenhouse Table 4 Annual warming potential of CO 2 , CH 4, and N 2 O exchange from three types of wetlands in Germany (Tiemeyer et al. 2020, their

Fig. 1
Fig. 1 Arial imagery of the ponds and weather station (W).Blue areas indicate the ponds

Fig. 2
Fig. 2 Raw data from one sensor, with black dots indicating the raw data and grey areas indicating areas of flushing the chamber air.Red lines indicate the linear model in which the slope was used to determine the diffusive exchange rates.Sudden changes in concentration were considered ebullitive events, which are shown by vertical blue lines 2 h −1 .The LME containing air temperature, atmospheric pressure, and wind speeds showed a significant relationship with the daily average proportion of ebullition, with an R 2 marg of 0.23 and an R 2 cond of 0.6 when including all the data.When examining the relationship for each season, no meteorological variables had significant influence.CO 2 flux F CO2 showed substantial variation between ponds (Figs. 3 and S5), however, no significant differences were found between forest and open ponds (df = 4, t value = 0.63, p = 0.57).Summer mean values of forest ponds ranged from 1786 to 6508 µmol CO 2 m −2 h −1 , while lower fluxes in open ponds ranged from − 3440 to 4198 µmol CO 2 m −2 h −1 .One open pond (O2) with dense aquatic vegetation took up CO 2 in summer, while winter measurements revealed consistent CO 2 release from all forest ponds (1414-2471 µmol CO 2 m −2 h −1 ) and open ponds (330-3236 µmol CO 2 m −2 h −1 ).Maximum F CO2 in forest and open ponds in summer occurred at about 9:00, and the minimum at about 17:00, with both forest and open ponds showing a significant decline over the days (GAMM;

Fig. 3
Fig. 3 Average daily fluxes of ebullitive and diffusive CH 4 and CO 2 of each pond during summer and winter.The solid horizontal line indicates the median, upper and lower hinges indicate the 25th and 75th quartile

Fig. 4 Fig. 5
Fig. 4 Average F CH4-total plotted against average F CH4-ebul : F CH4-total for each individual day for all ponds during summer and winter.Black line is fitted with a local polynomial regression model

Table 1
Overview of location and physiochemical conditions in three small forest ponds (F) and three small open ponds (O).LOI is the sediment organic content (mean (± SD)

Table 2
Mean daily emissions of CH 4 (ebullitive and diffusive) and CO 2 of three forest and three open ponds in summer and winter Total annual emissions were estimated as the mean of summer and winter valuesThe ratios between emissions in summer and winter, ebullitive and diffusive CH *

Table 3
Linear mixed effect models for CH 4 and CO 2 fluxes at hourly and daily scales at 4-6 sites and 6 ponds over several days in summer and winter.Estimates are scaled and centered, and can therefore be compared within each model a Plant coverage is a factorial variable and, thus, is estimated for each of the factors Parameters which showed a tendency (0.05 < p < 0.1) * Table2) compared to that of small Danish ponds in this study Temperatures and landscapes studied in Germany resemble Danish conditions.All values in g CO 2 e m −2 y −1 .N 2 O emission is assumed to be 0 in the ponds due to very low nitrate concentrations (Table1), while sedimentation is only expected to occur in the ponds