Introduction

Methane (CH4) is the second most important greenhouse gas in the atmosphere with a global warming potential (i.e., ability to trap heat) 32 to 45 times greater than carbon dioxide over a 100-year time period (IPCC 2013; Neubauer and Megonigal 2015). Wetlands are the largest natural source of atmospheric methane and contribute approximately 164 Tg methane yr.−1, albeit estimates range from 80 to 280 Tg methane yr.−1 among studies (Bridgham et al. 2013). The high degree of uncertainty among emission estimates is due to our incomplete understanding of the mechanistic abiotic and biotic controls of methane flux. These limitations are attributable, in part, to the methodological challenges associated with measuring and modeling the extremely high spatiotemporal variability of methane flux rates. Of all the sources of variability in both space and time, diurnal variation is particularly dynamic (Chanton et al. 1993; Chen et al. 2010; Henneberger et al. 2017; Xu et al. 2017). A better understanding of how methane flux rates change throughout diel cycles could help elucidate short-term, mechanistic drivers of flux, improve methane emissions estimates from wetlands, and improve prediction of how future conditions could affect methane flux dynamics.

Temporal and spatial variation at any scale is driven by both abiotic and biotic mechanisms (Bubier and Moore 1994; Armstrong et al. 2015). Temperature and hydrology (e.g., sediment saturation, period of inundation, water depth) are the most common abiotic factors that control methane flux in wetland systems (Moore and Dalva 1993; Le Mer and Roger 2001; Bubier et al. 2005; Bansal et al. 2016). These abiotic factors have direct effects on the microbial communities that produce and consume methane (Zinder et al. 1984; Mohanty et al. 2007; Andersen et al. 2013; Yvon-Durocher et al. 2014). The short-term environmental conditions that favor methanogenesis versus methane oxidation may have considerable impact on the long-term, net methane emissions of a wetland (Oremland and Culbertson 1992; Mikkelä et al. 1995). Plant community composition, total biomass, and net primary productivity also have been found to influence methane emissions (Sebacher et al. 1985; Whiting and Chanton 1993; Phillips and Beeri 2008; Lawrence et al. 2017). Plants provide the initial carbon substrates from root decay and exudates that support belowground microbial processes and influence community composition (Cui et al. 2015). In addition, methane is often routed through aerenchyma of plants during the day, by-passing the slow process of diffusion through the water column (Laanbroek 2010).

Most studies of diurnal methane flux have been conducted using either eddy covariance (EC) towers or static chambers (Bridgham et al. 2013). The EC method has considerable potential to capture variation in diurnal flux because of the continuous nature of the data (Morin et al. 2014; Koebsch et al. 2015; van den Berg et al. 2016). However, the EC method is less practical for identifying mechanisms of diurnal flux due to the large measurement footprint that may cover a range of environmental conditions. Alternatively, chamber-based methods are ideal for understanding mechanistic controls of diurnal flux, but challenging due to extensive time and labor requirements (which typically require overnight measurements). Therefore, the vast majority of chamber-based studies on diurnal variation conduct sampling over only 2–4 diel cycles (Crill et al. 1988; Mikkelä et al. 1995; Nakano et al. 2000; Wang and Han 2005; Chen et al. 2010; Tong et al. 2013; Henneberger et al. 2017; Martin and Moseman-Valtierra 2017; Xu et al. 2017). Most wetland-related methane studies rely on periodic chamber-based measurements that typically occur only once during the day (often midday), and are then upscaled to the entire 24-h diel cycle. If these midday measurements are not representative of the average flux rate throughout a diel cycle, then up-scaled estimates in space and time of methane emissions would not reflect actual emissions (Pennock et al. 2010). Consequently, it is essential to measure or model diurnal flux for accurate assessments of total emissions.

The Prairie Pothole Region (PPR) is the largest wetland ecosystem in North America (770,000 km2) and is home to millions of palustrine and lacustrine, mineral-soil wetlands (Beeri and Phillips 2007; Dahl 2014). These wetlands are typically small (<0.01 km2) and are known to be hotspots (i.e., elevated flux) of methane emission (Badiou et al. 2011; Creed et al. 2013; Tangen et al. 2015). The vast majority of these wetlands are seasonally ponded, meaning they hold surface water for 1–3 months and then dry by late summer/fall. A study on diurnal variation in methane flux was conducted using an automated chamber system to collect gas samples every 2.5 to 4 h over three growing seasons in a seasonal PPR wetland. A total of 1138 flux measurements were made over 205 diel cycles, resulting in one of the most comprehensive chamber-based flux datasets in existence. This large dataset allowed for exploration and modeling of flux rates throughout the diel cycle under a range of conditions. This work will provide a better understanding of 1) diurnal methane flux dynamics in wetlands, 2) the environmental factors that drive short-term flux dynamics, and 3) methodologies to account for diurnal variation in methane flux.

Methods

Study Site

The study was conducted at Cottonwood Lake Study Area (CLSA) in Stutsman County, North Dakota, USA, which is a long-term (1967–present) prairie ecology research site located on the Missouri Coteau of the PPR. The 30-year mean temperature and precipitation at CLSA are 4.9 °C and 474 mm, respectively, and the growing season generally begins in April and ends in October. Data were collected over 3 years (May–Nov. 2013; June–Nov. 2014; July–Sept. 2015) near the center of a relatively small (1360 m2), seasonal wetland in the CLSA known as T5 (Fig. 1). During 2013 and 2014, T5 started the growing season in a ponded state with water from snowmelt/spring rainfall and the soil gradually dried from evapotranspiration as the growing season progressed. In 2015, T5 was relatively dry compared to the 2013 and 2014 seasons due to less snow pack and spring rainfall, and did not have ponded water at any point during gas sampling. The primary vegetation during the flooded conditions was Polygonum amphibium L. (pond smartweed), with a gradual shift to Carex atherodes Spreng. (slough sedge) as the wetland dried throughout the growing season. The top 15 cm of soil had organic carbon of 8.9%, bulk density of 0.6 g cm−3 and pH of 5.7 (Tangen and Bansal 2017).

Fig. 1
figure 1

Aerial image of seasonal wetland T5 (area = 1360 m2) at the Cottonwood Lake Study Area in North Dakota during ponded (a) and dry (b) saturation states. The automated gas sampling system was positioned towards the middle of the wetland (white circle)

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Gas Sampling and Abiotic Covariates

Gas samples were collected throughout the diel cycle using an automated gas-flux chamber system (Venture Technology, Baton Rouge, LA; Fig. 2). The chamber system was designed to place an opaque gas collection chamber made of polyvinyl chloride (20 cm diameter, 20 cm height) onto either the water surface (during ponded conditions) or onto a base (during semi-saturated and dry conditions). During ponded conditions, the automated system was adjusted to the water level as needed so the chamber would land on the water surface in order to maintain a relatively consistent chamber air volume. The base used during dry conditions was fixed in the soil to a depth of approximately 10 cm, and was moved to a new location each year within 1 m distance of the previous year. The water or the base formed a seal with the chamber for 30-min. At the end of a 30-min collection period, approximately 50-mls of chamber headspace gases were suctioned out of the chamber using a vacuum pump and forcibly injected into pre-evacuated exetainer 10-ml vials. Ambient air samples were similarly, automatically collected prior to chamber measurements as an estimate of gas concentrations at the beginning of the sample period. New automated flux measurements were initiated every 2.5 to 4 h, transferred to the lab within 4 days, and analyzed within three weeks using a gas chromatograph equipped with a flame ionization detector (SRI 8610C, SRI Instruments, Torrance, CA). Headspace height was measured every fourth day during vial collection. Methane flux rates (mg CH4 m−2 h−1) were calculated using the change in concentration of methane, length of time that the chamber was set, molecular mass of methane, collection chamber area and volume, air temperature during sampling, and the Ideal Gas Law (Finocchiaro et al. 2014). Water depth was intermittently monitored and had a maximum depth of 30 cm. Vegetation inside the base was periodically clipped to avoid interfering with placement of the chamber onto the base. Clipping of vegetation may have influenced total flux by affecting plant-mediated flux (Bhullar et al. 2013), although the goal of our study was to observe diurnal patterns in methane flux and not absolute values (the potential impacts of vegetation and its removal on diurnal patterns are addressed in the Discussion).

Fig. 2
figure 2

The automated gas sampling system (a) and the exetainer collection box (b) used to sample methane every 2.5 to 4 h during 2013, 2014 and 2015 growing seasons in a seasonal wetland in the Prairie Pothole Region of North Dakota. Picture taken during a ‘dry’ saturation state

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Abiotic covariates were monitored in conjunction with the gas samples. Surface volumetric soil water content (upper 10 cm) was automatically measured and recorded every 30 min (ThetaProbe ML2x, DL-6, Dynamax Inc., Houston, TX, USA). Water-filled pore space (WFPS) was calculated by dividing soil water content by soil porosity following methods of Tangen et al. (2015). Due to equipment malfunction in 2015, automated soil moisture was not measured with every gas sample. Periodic manual measurements of soil moisture (EC-5, Decagon Devices, Pullman, WA) were conducted throughout the study period to confirm saturation status at the automated chamber. Soil or water temperature (upper 10 cm) was measured using a variety of temperature sensors (ThetaProbe ML2x; iButton, Maxim Integrated Products, Inc., San Jose, CA; WT-HR 1500, TruTrack Ltd., Wainoni, Christchurch, New Zealand). Air temperature, barometric pressure, wind speed, and relative humidity was obtained using local weather stations (Watchdog 2900ET, Spectrum Technologies, Aurora, IL, USA; NDAWN [https://ndawn.ndsu.nodak.edu/]) and sensors (iButton; WT-HR 1500). Photosynthetically active radiation (PAR) was measured using quantum light sensors associated with the local weather station (Watchdog 2900 ET) and an adjacent automated flux system (Li-Cor 8100, Lincoln, NE).

Data Analysis

The gas flux data (see Bansal and Tangen 2018 for data) were compiled into measurement intervals of continuous data without any interruption in the sampling regime. Intervals were an average of 7.6 (±1.1) days in length of continuous data. Each measurement interval was categorized into a saturation state (ponded, semi-saturated, dry) based on mean WFPS during the measurement interval. ‘Dry’ was distinguished from ‘semi-saturated’ saturation state using a cutoff value of 40% WFPS, which has been shown as a lower limit for significant positive methane flux in similar wetlands (Finocchiaro et al. 2014). The methane flux data were divided into ‘day’ and ‘night’ using daily sunrise and sunset information. Each day and night was further split into thirds, so that each diel cycle was divided into six distinct time periods (pre-dawn, early day, midday, late day, early night, midnight). Analysis of variance (ANOVA) was used to compare methane flux rates among the six time periods within each saturation state. The Least Significant Difference test was used to compare means among time periods with saturation states.

Correlation and multiple regression analyses (R version 3.0.1; R Core Development Team, Vienna) were performed to evaluate if variation in methane flux rates was related to abiotic covariates commonly associated with wetland methane flux. Covariates included air and soil temperature, PAR, WFPS, barometric pressure, relative humidity, and wind speed. Pearson correlation coefficients were used to determine if correlations were significantly different than zero within each saturation state (cor.test function in “Hmisc” R package) (Harrell 2018). For the multiple regression analysis, methane flux values were modeled using linear models (with Gaussian error distributions) with an autoregressive term to account for autocorrelation between consecutive samples. All possible combinations of abiotic factors and their interactions were evaluated and ranked in order of AICc scores to determine the most parsimonious model with the lowest AICc score (dredge function in “MuMIn” R package) (Bartoń 2013). Only 2013 and 2014 data were used for modeling due to missing values for WFPS in 2015. Methane flux rates were natural log transformed with a nominal value added to make all values positive for regression and correlation analyses.

Results

Three distinctive diurnal flux patterns were observed in the methane flux data (see Bansal and Tangen 2018 for data), which were associated with the saturation status of the wetland soils (i.e., ‘ponded’, ‘semi-saturated’ or ‘dry’). Water-filled pore space for ponded, semi-saturated, and dry conditions were 100%, 45% and 30%, respectively (Fig. 3).

Fig. 3
figure 3

Mean (±SE) water-filled pore space (WFPS) for each of the three saturation states observed during repeated gas-sampling measurements during 2013, 2014 and 2015 growing seasons in a seasonal wetland in the Prairie Pothole Region of North Dakota

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Ponded

When wetland T5 was ponded, methane flux rates were primarily positive and varied among the six diel time periods (F5,310 = 5.64, p < 0.0001; Fig. 4a). Flux rates followed similar diurnal patterns as air (e.g., Fig. 5a) and soil temperature, with relatively high rates during the late day and low rates at pre-dawn/early day (Table 1). The mean midday flux rate (10.7 ± 1.6 mg CH4 m−2 h−1) was relatively close to the overall mean flux rate during ponded conditions over the course of the study (10.1 ± 0.8 mg CH4 m−2 h−1) (Fig. 4a). Values for air temperature, soil temperature and PAR were positively correlated with flux rates (Fig. 6a; Table 1). The correlations with air (Fig. 6a) and soil temperatures exhibited a threshold in which flux rates decreased with temperature down to zero, but generally did not continue to decrease even with cooler temperatures.

Fig. 4
figure 4

Diurnal variation in methane (CH4) flux rates from a seasonal wetland in the Prairie Pothole Region of North Dakota. Mean (±SE) values are presented for six distinct time periods throughout the diel cycle within each of the three saturation states: ponded (a), semi-saturated (b), and dry (c). Different upper case letters indicate significant differences among the time periods within each saturation state. The horizontal, dashed lines represent mean flux rates for each saturation state

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Fig. 5
figure 5

Illustrative examples of time series of methane (CH4) flux rates (solid red lines with circles) and corresponding air temperature (blue dashed lines with triangles) during daytime (open symbols) and nighttime (closed symbols) in a seasonal wetland in the Prairie Pothole Region of North Dakota. Data represent examples of flux dynamics during ponded (a), semi-saturated (b), and dry (b) saturation states in 2013

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Fig. 6
figure 6

Relationship between methane (CH4) flux rates (natural log [ln]) and air temperature in a seasonal wetland in the Prairie Pothole Region of North Dakota during ponded (a), semi-saturated (b), and dry (c) saturation states. Data are from gas flux samples collected during the growing seasons of 2013, 2014 and 2015

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Table 1 Pearson’s correlation coefficients between abiotic covariates and the natural log of methane flux rates for each saturation state: ponded, semi-saturated and dry, for N = 145–616 pairwise samples
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Semi-Saturated

When wetland T5 was in a semi-saturated state, methane flux rates oscillated between positive and negative values within individual diel cycles and there were no significant differences in flux rates across the six distinct time periods (F5,637 = 0.74, p = 0.59; Fig. 4b); the mean flux rate during semi-saturated conditions (−0.006 ± .003 mg CH4 m−2 h−1) was marginally lower than zero. Flux did not follow patterns in air temperature (e.g., Figs. 5b, 6b) and was uncorrelated to any abiotic covariate (Table 1).

Dry

When soils of wetland T5 were relatively dry, methane flux rates were predominantly negative and varied across the diel time periods (F5,367 = 5.10, p = 0.0002); fluxes were highest (i.e., least negative) at pre-dawn and lowest during midday/late day (Fig. 4c). The mean flux rate for dry conditions was negative (−0.05 ± 0.002 mg CH4 m−2 h−1), indicating net oxidation of atmospheric methane. Methane flux rates inversely followed diurnal patterns in air temperature (e.g., Figs. 5c, 6c), with low temperatures corresponding with relatively high, sometimes positive flux rates. Values for air and soil temperature and PAR were negatively correlated to flux during dry conditions (Table 1).

In the multiple regression analyses, air and soil temperature, PAR, WFPS, and all two-way interactions (except soil temperature × PAR and WFPS × PAR) were selected in the final model based on AICc scores. Values of barometric pressure, wind speed and relative humidity were not selected in the final model, and had minimal or no significant correlations with flux rates under any of the saturation states (Table 1). The model explained 72% of variation in methane flux rates (F9,394 = 118.7, p < 0.0001, r2 = 0.72; slope = 0.73; Figs. 7 and 8). The model tended to underestimate flux rates, particularly during ponded conditions when flux rates remained relatively high at night despite cooler temperatures (e.g., Fig. 7).

Fig. 7
figure 7

Illustrative examples of time series of actual (solid red lines with circles) and modeled (blue dashed lines with triangles) methane (CH4) flux rates during ponded saturation states in daytime (open symbols) and nighttime (closed symbols) in a seasonal wetland in the Prairie Pothole Region of North Dakota. Flux rates were modeled as a function of air and soil temperature, photosynthetically active radiation and water-filled pore space using data collected during the growing seasons of 2013 and 2014

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Fig. 8
figure 8

Relationship between actual and modeled methane (CH4) flux in a seasonal wetland in the Prairie Pothole Region of North Dakota. Flux rates were modeled as a function of air and soil temperature, photosynthetically active radiation and water-filled pore space, for N = 403 samples collected in 2013 and 2014. The solid blue line represents best-fit values and blue shading represent 95% confidence interval; the black, dashed line indicates the 1:1 ratio of actual to modeled flux rates. Symbols and colors represent different saturation states

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Discussion

Depressional wetlands of the PPR can be hotspots for methane emissions (Badiou et al. 2011; Tangen et al. 2015). Through automated, repeated sampling of fluxes, we were able to identify time periods during the diel cycle that were hot moments in methane flux, as well as the environmental conditions that were associated those moments.

Diurnal variation in methane flux from wetlands has been observed in a number of wetland ecosystems over the last 3 decades, including limited data from PPR wetlands in Canada (Pennock et al. 2010). Most, but not all, of these studies have demonstrated diurnal variation in methane flux, albeit the extent and timing of maximum and minimum flux rates vary considerably among studies. Throughout the diel cycle, the highest methane flux rates have been observed in the early morning (Chanton et al. 1993; Van Der Nat et al. 1998; Ding et al. 2004), mid-day (Käki et al. 2001; Duan et al. 2005; Pennock et al. 2010; Tong et al. 2013; Morin et al. 2014; Martin and Moseman-Valtierra 2017), late afternoon (Wang and Han 2005; Chen et al. 2010), at night (Mikkelä et al. 1995; Xu et al. 2017), or not at all (Zhang and Ding 2011; Moseman-Valtierra et al. 2016; Henneberger et al. 2017; Milberg et al. 2017) depending on the ecosystem, plant community, hydrological status, or other associated environmental conditions. This observed variability highlights the need for local and region-specific studies on diurnal flux of methane. In a PPR wetland examined during this study, methane flux rates exhibited strong diurnal variation during the growing season, with the highest rates occurring during the day, at night, or not at all depending on environmental conditions (see next section below). These observed patterns have implications for studies on methane emissions with regard to mechanistic drivers and methodological considerations.

Mechanisms

When controlled for soil moisture conditions, temperature was a primary predictor of methane flux. During ponded conditions, fluxes were positively related to temperature, and thus peak fluxes occurred during the day. In contrast, during dry conditions, fluxes were negatively related to temperature and were highest at night when it was relatively cool. Under ponded conditions, it is likely that the positive effect of temperature on methane flux rates is causative due to the thermal stimulation of methanogenesis and methane emissions, which have been demonstrated in wetlands at numerous scales from microbe to ecosystem (Zinder et al. 1984; Mohanty et al. 2007; Yvon-Durocher et al. 2014). Under drier conditions, microbial methane oxidation may have also been stimulated by warmer temperatures, thus the most negative fluxes were observed during the daytime (Schütz et al. 1990; Mikkelä et al. 1995). Given that wetlands typically experience a range of saturation states over time and space, it is likely that diurnal methane flux dynamics are highly specific to local conditions, contributing to the high degree of spatiotemporal variability reported in the majority of studies on methane flux. Moreover, even though we found strong diurnal trends, there were many exceptions in which flux rates did not follow expected patterns (e.g., high flux rates at night during ponded conditions possibly due to ebullition), demonstrating the need for high temporal replication over several diurnal cycles to determine generalized diel patterns.

The majority of PPR wetlands have seasonal water regimes (Dahl 2014), alternating between ponded and dry saturation states, meaning most wetlands oscillate between being methane sources and sinks throughout the growing season (Bansal et al. 2016). This oscillating flux behavior has been observed at seasonal scales in other wetland ecosystems as well, such as the Great Dismal Swamp (Harriss et al. 1982) and Sphagnum-dominated mires (Mikkelä et al. 1995). In the current study, oscillating flux behavior was regularly observed at diel scales throughout the growing season, regardless of saturation state (e.g., Fig. 5). However, this behavior was most prevalent during semi-saturated conditions. When soils are moist but not ponded, warmer temperatures appear to stimulate both methanogenesis and methane oxidation to a balance point resulting in net neutral methane emissions. This nuanced, yet straightforward interaction of hydrology and temperature on flux demonstrates how diel-scale observations can be used to understand the underlying, mechanistic controls on methane emissions from wetlands.

Regarding biotic drivers, nearly every study on diurnal variation in methane flux reports an influence of local plants as providers of carbon substrates to fuel methanogenesis and as conduits for methane gas through the sediment-water-atmosphere continuum (Sebacher et al. 1985; Schütz et al. 1990; Thomas et al. 1996; Wang and Han 2005; Laanbroek 2010; Cui et al. 2015; see Martin and Moseman-Valtierra 2017 for no conduit effect). In wetland T5, the dominant plant species during the ponded state was P. amphibium, which has aerenchyma in stems and leaves (Gaberscik 1993) and is functionally similar to other floating-leaved vegetation (e.g., Nymphaea mexicana [yellow waterlily]) that are known to mediate methane flux (Dacey and Klug 1979; Dacey 1981). For this study, plants were clipped from the sampling chambers so transport of methane from sediment to atmosphere may have occurred through a pressurized gas flow system that moves air from younger, healthy leaves of plants outside the chamber to belowground rhizomes and then out through dead/broken/clipped stems inside the chamber (Armstrong 1980). The diel cycles in plant processes such as photosynthesis (Bansal and Germino 2008), carbon assimilation and transport (Bansal and Germino 2009), and root exudation (Allen et al. 2003) of the plants just outside of our measurement chamber may also have indirectly contributed to our observed diurnal patterns in methane flux by supplying carbon substrates for microbial activity. Indeed, air temperature and PAR were relatively strong predictors of methane flux, which are both important to plant activity (Lambers et al. 1998) (Table 1). Given that PPR and other wetlands have a range of vegetation zones and species diversity (Stewart and Kantrud 1971), it is likely that diurnal trends may vary considerably within any given wetland. Changes in vegetation that may occur from natural wet-dry cycles, invasion by non-native species, or through active vegetation management could have a considerable impact on methane flux dynamics from wetlands.

Methodological Implications

The question of ‘when to sample during the day’ is a common dilemma for greenhouse gas studies using chamber-based methods. Under an ideal research scenario, continuously running, automated chamber systems would be deployed at all locations for the duration of a study. Currently, this ideal scenario is unrealistic for most scientists due to prohibitive costs associated with automation. Another option is to model diurnal fluxes using environmental covariates such as temperature, WFPS and PAR. The third option, which is the most common, is to choose a narrow window in time during the day for gas sampling to capture average or peak flux rates that are comparable among replicates. The large diurnal amplitude in methane flux observed during this study has important implications for choosing the appropriate window in time.

In this study, we found that mid-day samples captured average flux rates during ponded and dry conditions, while samples collected closer to sunrise or sunset better represented maximum or minimum flux rates (Fig. 4). During semi-saturated conditions of the growing season, flux rates were similar (and very low) throughout the day, thus allowing for greater flexibility in sampling protocol. Confining sampling to narrow windows in time often presents logistical challenges for researchers, as does conducting multiple measurements throughout the diel cycle. Given the extremely large, potential magnitude of variation in diurnal flux, a limited number of periodic diurnal measurements could be used to 1) determine the times during the diel cycle that corresponds with average flux, or 2) develop strong time-flux relationships to establish correction factors to offset over- or under-estimates of average flux (Pennock et al. 2010), and 3) identify environmental conditions that correspond with greater diurnal amplitudes in flux which may require more stringent sampling regimes.

Modeling diurnal flux in methane is especially important for studies that are quantifying total emissions due to the large error that can occur from linear interpolations between periodic samples (Gana et al. 2016). Prior to the current study, low temporal replication of most chamber-based diurnal studies limited modeling to simple bivariate correlations with variables such as air temperature (e.g., Zhang and Ding 2011). In our study, we were able to demonstrate that diurnal fluxes could be modeled relatively well using commonly measured environmental covariates (e.g., air and soil temperature, PAR, WFPS). However, our model results often underestimated observed flux, indicating that other covariates may have improved the model (e.g., vegetation data). Moreover, even with exceptionally high temporal replication, our automated flux chamber was only placed at a single location in a single wetland, thus the model developed during this study was site-specific. Even so, the concept of modeling diurnal variation using standard environmental covariates is likely true across wetlands ecosystems. Overall, continued advancements in automation technologies will dramatically improve both our mechanistic understanding of methane flux dynamics as well as our abilities to accurately estimate current and future methane emissions from wetlands.