Accepted Article
Received Date : 09-Apr-2014 Accepted Date : 25-Aug-2014 Article type
: Primary Research Articles
Large-scale patterns in summer diffusive CH4 fluxes across boreal lakes, and contribution to diffusive C emissions
Summer CH4 emissions in boreal lakes
Terhi Rasilo, Yves T. Prairie, and Paul A. del Giorgio
Groupe de Recherche Interuniversitaire en Limnologie (GRIL), Département des sciences biologiques, Université du Quebéc à Montréal, Québec, Canada.
Corresponding author : T. Rasilo, Département des sciences biologiques, Université du Québec à Montréal, Case postale 8888, succ. Centre-Ville, Montréal, QC, Canada. ([email protected])
Tel : +1-514-987-3000, ext. 2927 Fax : +1-514-987-3000, ext. 4647
Key words: aquatic ecology, greenhouse gases (GHG), methane (CH4), boreal landscape, northern lakes, climate change, carbon emissions, large-scale
This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as doi: 10.1111/gcb.12741 This article is protected by copyright. All rights reserved.
Accepted Article
Original Research: Primary Research Article
Abstract Lakes are a major component of boreal landscapes, and whereas lake CO2 emissions are recognized as a major component of regional C budgets, there is still much uncertainty associated to lake CH4 fluxes. Here we present a large-scale study of the magnitude and regulation of boreal lake summer diffusive CH4 fluxes, and their contribution to total lake carbon (C) emissions, based on in situ measurements of concentration and fluxes of CH4 and CO2 in 224 lakes across a wide range of lake type and environmental gradients in Québec. The diffusive CH4 flux was highly variable (mean 11.6 ± 26.4 s.d. mg m-2 d-1), and it was positively correlated to temperature and lake nutrient status, and negatively correlated to lake area and colored dissolved organic matter (CDOM). The relationship between CH4 and CO2 concentrations fluxes was weak, suggesting major differences in their respective sources and/or regulation. For example, increasing water temperature leads to higher CH4 flux but does not significantly affect CO2 flux, whereas increasing CDOM concentration leads to higher CO2 flux but lower CH4 flux. CH4 contributed to 8 ± 23 % to the total lake C emissions (CH4 + CO2), but 18 ± 25 % to the total flux in terms of atmospheric warming potential, expressed as CO2-equivalents. The incorporation of ebullition and plant mediated CH4 fluxes would further increase the importance of lake CH4. The average Q10 of CH4 flux was 3.7, once other covarying factors were accounted for, but this apparent Q10 varied with lake morphometry and was higher for shallow lakes. We conclude that global climate change and the resulting shifts in temperature will strongly influence lake CH4 fluxes across the boreal biome, but these climate effects may be altered by regional patterns in lake morphometry, nutrient status and browning.
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Introduction Freshwaters are now recognized as important components of the continental carbon cycle (Tranvik et al., 2009; Bastviken et al., 2011). Whereas surface freshwaters cover on average only 3% of the globe’s continental area (Downing et al., 2006), they represent a much higher portion of boreal landscape, comprising on average 10% of the territory (Brandt, 2009). Lakes and rivers in the boreal zone are in general supersaturated in carbon (C) gases, releasing carbon fixed in terrestrial ecosystems back into the atmosphere mostly in the form of carbon dioxide (CO2) but also as methane (CH4) (e.g. Ojala et al., 2011; Lopez Bellido et al., 2011). While there has been substantial research on CO2 dynamics in these aquatic systems (e.g. Kortelainen et al., 2006; Tranvik et al., 2009; Roehm et al., 2009; Huotari et al., 2011), CH4 dynamics and fluxes from lakes are still poorly studied, and probably form one of the largest gaps in northern C budgets (Bousquet et al., 2006; Ortiz-Llorento & Alvarez-Cobelas, 2012). There is increasing evidence that continental waters are a globally significant source of
CH4, but global estimates are scarce and still extremely uncertain, ranging from 8 to over 100 Tg CH4 y-1 (Bastviken et al., 2004; Walter et al., 2006; Bastviken et al., 2009, 2011; Lundin et al., 2013). The estimates of the contributions of lakes and rivers, which were traditionally assumed to be very modest, have consistently increased in recent years as new data have emerged (Bastviken et al., 2011; Lundin et al., 2013). Part of the difficulty in estimating the contribution of northern lakes to regional and global CH4 budgets is that the existing data is not only very fragmented in terms of lake type and region covered, but also that the existing data suggest a wide range of lake CH4 fluxes, from less than one to the thousands of mg m-2 d-1 (Smith & Lewis, 1992; Huttunen et al., 2003; Bastviken et al., 2004; Juutinen et al., 2009; López Bellido et al., 2011; Natchimuthu et al., 2014). Extrapolating from current average estimates is thus extremely uncertain, and in addition, there is a lack of robust relationships between lake CH4 and its potential drivers that This article is protected by copyright. All rights reserved.
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could facilitate temporal prediction and spatial extrapolation. There is thus an urgent need to better understand CH4 regulation in northern lakes. Several studies have explored the cross-lake variation in CH4 concentrations and fluxes
(Bastviken et al., 2003; Huttunen et al., 2003; Juutinen et al., 2009; Kankaala et al., 2013), and have linked it to lake depth and size, organic C loading, lake nutrient status and temperature. It is thus clear that whereas CH4 may have a strong influence on climate, climate also has a strong influence on CH4 production in and fluxes from lakes. Predicted temperature increases, eutrophication, and shifts in organic C loading to lakes may affect CH4 fluxes (IPCC 2007; Juutinen et al., 2009; Linnaluoma, 2012; Larsen et al., 2011; Marotta et al., 2014), but how these factors will interact to shape future lake CH4 emissions is still uncertain. Climatic changes may not only influence lake CH4 fluxes, but also CO2 fluxes, which
together form the net C emission from lakes. The relative contribution of CH4 to total C emissions, and how this contribution may change under future environmental scenarios remain poorly constrained. There have only been a handful of studies that assessed lake CH4 and CO2 simultaneously, and these have mostly focused on individual systems and small-scale variability (e.g. Striegl & Michmerhuizen, 1998; Riera et al., 1999; López Bellido et al., 2011; Ojala et al., 2011). The largest studies are from Finland and report that the ratio of CH4 to CO2 fluxes (as mmol m-2 d-1) was on average low (0.15), but extremely variable both temporally within lakes and also across lakes (Huttunen et al. 2003; Kankaala et al., 2013). As a result of this lack of concurrent measurements of CH4 and CO2 we still do not understand their links and potential coupling in aquatic environments or how the contribution of CH4 to total C emissions may vary across lakes under scenarios of environmental and climate change. The sources and pathways that influence the ambient concentrations of these two key
greenhouse gases are very different. Unlike CO2 production, methanogenesis proceeds only in This article is protected by copyright. All rights reserved.
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anoxic environments (but see Grossart et al., 2011; Tang et al., 2014), and the role of lateral terrestrial or riverine inputs is less clear for CH4 than for CO2 (Nakamura et al., 1999; Ojala et al., 2011). In addition, the transport pathways of CH4 resulting in emissions into the atmosphere are complex. CH4 can be oxidized during diffusion and transport towards the surface, or it can be released directly to the atmosphere by ebullition (Casper et al., 2000; Bastviken et al., 2004; Walter Anthony et al., 2012) or plant-mediated transport (Juutinen et al., 2003; Bergström et al., 2007). During the spring and autumn turnover storage flux of accumulated CH4 can also contribute significantly to the total flux (Michmerhuizen et al., 1996; Phelps et al., 1998; López Bellido et al., 2009). Therefore, in addition to biological processes that may be influenced by the availability of organic C and by temperature (Kelly & Chynoweth, 1981; Linnaluoma, 2012; Lofton et al., 2013), CH4 fluxes into the atmosphere are further dependent on physical factors such as lake morphometry and water column physics (Bastviken et al., 2004; López Bellido et al., 2009). Other factors may further uncouple CH4 from CO2 dynamics in lakes. For example, high
primary production may lead to CO2 undersaturation (del Giorgio et al., 1999) but enhance CH4 fluxes by increasing the downward flux of organic C and promoting anoxia in deep waters (Huttunen et al., 2003; Bastviken et al., 2004; DelSontro et al., 2011; Linnaluoma, 2012). In addition, CH4 production is considerably more temperature sensitive than the processes underlying CO2 fluxes (Sobek et al., 2005; Yvon-Durocher et al., 2012, 2014), and therefore changes in water column or sediment temperature may result in shifts in the relative contribution of CH4 to total C emissions from lakes (Marotta et al., 2014). Although the change of CH4/CO2 flux ratio might have only minor effect on total carbon fluxes, its influence on warming potential of lakes may be significant.
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In this paper, we expand on previous studies that have explored the magnitude and regulation of CH4 dynamics from northern lakes (Smith & Lewis, 1992; Riera et al., 1999; Huttunen et al., 2003; Bastviken et al., 2004, 2008; Juutinen et al., 2009; Ojala et al., 2011): we explore the magnitude and factors controlling the CH4 concentrations and diffusive fluxes across 224 lakes distributed throughout the boreal landscapes of Québec during summer stratification period, and we explicitly address the relationship between CH4 and CO2 fluxes. We examine the factors that influence the contribution of CH4 to total diffusive C emissions and to the global warming potential of these lakes, and discuss the possible effects of climate change on these patterns of lake emission.
Material and Methods Study sites This large-scale study of gas dynamics and environmental conditions covered 224 different lakes across 8 different regions of boreal Québec (Fig 1): Laurentians (46°N, 74°W), Chicoutimi (48°N, 71°W), Abitibi (48.5°N, 79°W), Chibougamau (49.6°N, 74°W), James Bay (49.7°N, 79°W), Côte-Nord (50.4°N, 67°W), Eastmain (52° N, 75°W), and Schefferville (55°N, 67°W). The study regions are located several hundreds of kilometers from each other whereas lakes in each region are generally within 50 km from each other. Each region represents different climate characteristics (mean annual temperature, precipitation, latitude, Table 1), vegetation coverage and catchment morphometry. The vegetation changes from the Laurentians’ deciduous forests (dominated by Populus balsamifera L., Populus tremuloides Michx. and Betula papyrifera Marsh.) through mixed forests (dominated by Abies balsamea (L.) Mill. and Betula papyrifera Marsh.) in Abitibi and Chicoutimi to coniferous (dominated by Picea mariana (Mill.) BSP) forests in Eastmain, Chibougamau, James Bay and Côte-Nord. The vegetation in Schefferville This article is protected by copyright. All rights reserved.
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varies from thick to sparse coniferous forests in lower areas to often exposed bedrock in upper ridges. Abitibi and James Bay are situated on the Abitibi clay belt and thus topography is rather flat and lakes are shallow and turbid, but also kettle lakes exist in those regions. The catchment slopes are steeper and elevation varies more in the Laurentians and Chicoutimi region. Soils in Schefferville are typically glacial deposits formed over sedimentary rocks. Situated close to the Gulf of St Laurence, Côte-Nord experiences a more maritime climate. Lakes in the Laurentians are mainly clear water lakes, while in Chicoutimi, Chibougamau, Côte-Nord and Eastmain they are mainly humic, and in Schefferville they are oligotrophic and clear (Table 1). Abitibi, James Bay and Chicoutimi are also characterized by a high density of beavers (Castor Canadensis, Kuhl), which alter the hydrology by building dams. However, none of the study lakes was a beaver pond.
Sampling The study regions were visited in different years (Eastmain 2007–2009, Laurentians, Abitibi and James Bay in 2010, Chibougamau and Chicoutimi in 2011, Schefferville in 2012, and Côte-Nord in 2013), and here we only report measurements made during the late spring and summer stratification period, typically between May 27 and August 18. Most of the lakes were visited once but some (35 lakes) were visited twice during one summer, and 16 lakes in Eastmain were sampled several times during consecutive years (Table 1). Lakes were accessed by truck and sampled by boat when possible, but as some of the study regions were remote many of the lakes were accessed by hydroplane. Samples were always taken at the deepest measured spot of the lake, although in the absence of hypsographic maps for most of these lakes, this may not coincide with the absolute deepest point. A complete depth profile (from 0.5 m depth to bottom at 0.5 m or 1.0 m interval) of temperature, dissolved oxygen, pH and conductivity was carried out with a YSI This article is protected by copyright. All rights reserved.
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probe (600XLV2-M, Yellow Springs Instruments, OH). Floating chambers were deployed to measure gas fluxes, as described in sections below, and water for gas samples and chemical analyses was collected at 0.5 m using a peristaltic pump, and kept refrigerated in the dark until returned to the lab.
Surface water gas concentrations Surface water CO2 partial pressure (pCO2, in µatm) was determined in situ by pumping lake water through a MiniModule® equilibrating module (Membrana, Wuppertal, Germany), and the equilibrated air was re-circulated directly through an EGM-4 (PP Systems, Amesbury, MA) infrared CO2 analyzer. The headspace method was used to determine the surface water CH4 partial pressure (pCH4). We collected 30 ml of lake water in a 60 ml polypropylene syringe, and created a 30-ml headspace of ambient air. Triplicate syringes were vigorously shaken for 1 minute, in order to equilibrate the gases in water and air (Bastviken et al., 2010). The resulting 30 ml headspace was injected into 30-ml glass vials equipped with rubber stoppers (20 mm diameter, red bromobutyl) filled with saturated saline solution, and kept inverted until analysis. In the lab, the gas in the headspace of the vials was injected into a gas chromatograph (GC-8A/GC2014, Shimadzu, Kyoto, Japan) equipped with a FID (flame ionization detector) to determine its CH4 concentration. The original surface water pCH4 was then calculated according to the headspace ratio, in situ temperature and assuming a constant ambient air pCH4 of 1.77 µatm.
Flux measurements CH4 and CO2 fluxes were measured by the floating chamber technique (Kling et al., 1992; Duchemin et al., 1999; Vachon et al., 2010). The round (darkened) plastic chamber (0.34 m diameter, 0.18 m height) was equipped with a thermometer to control for changes in the This article is protected by copyright. All rights reserved.
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temperature of the chamber headspace. The chamber headspace was connected in a closed loop to an infrared CO2 analyzer (EGM-4, PP-systems, Amesbury, MA) that measured the changes in pCO2 in the chamber headspace every minutes for 10 minutes. The chamber also had a sampling port that allowed the withdrawal of air samples via a syringe for CH4 analysis, which was done every 10 minutes for 30 minutes. The collected gas was injected into 30-ml vials containing saturated saline solution, and processed as described above for ambient pCH4 samples.
Calculations and data analysis
Gas fluxes were calculated on the basis of the rates of change in pCO2 and pCH4 in the chamber headspace with time. More than 95% of the chamber measurements had linear increases with r2 > 0.95, and we did not observe signs of decelerating fluxes due to the accumulating gas. A small certain fraction (6%) of measurements were rejected based on erratic time courses that had low r2. For CH4, in samples where change in the chamber was positive but very close to the detection limit of the GC analysis (6% of all measurements), we used a fixed flux value of 0.003 mmol m-2 d-1, which is the minimum rate that can be resolved given our analytical error. The rates of change in pCO2 and in pCH4 in the chamber were used to estimate CH4 and CO2 fluxes (fCH4 and fCO2, mmol m-2 d-1) with the following equation: (1)
where (s) is the rate of change of the gas in the chamber (µatm min-1), (V) is the volume of the chamber in liters (L), (S) is the surface area of the chamber (m2), (Vm) is the molar volume (L mol1
) adjusted for in situ temperature, and (t) is a conversion factor from minutes to a day and from
mol to mmol. Fluxes were further converted to mg C m-2 d-1 by multiplying by the molar mass of C (12.01 g mol-1) and to CO2-equivalent fluxes (g C-CO2-eq m-2 d-1) by assuming that one gram of This article is protected by copyright. All rights reserved.
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CH4 corresponds to 25 g of CO2 in terms of warming potential over the next 100 years (IPCC 2007). We further calculated the gas transfer coefficients (k, m d-1) based on CO2, using the
measured pCO2, CO2 flux and the temperature dependent solubility of the gas following: (2)
where ƒ is flux of CO2 (mmol m−2 d−1), Kh is the temperature-corrected Henry’s constant (Lide, 1992), and ∆gas is the difference in partial pressures between the air and the water phases for CO2. To facilitate comparison, k values were further normalized to k600 values with a Schmidt number of 600 using: (3)
where Sc is the Schmidt number of a gas at a given temperature (Wanninkhof, 1992). We used n = ⅔ for wind speed 3.7 m s−1 (Guérin et al., 2007). We modeled the temperature dependence of CH4 and CO2 fluxes as a log-linear
relationship with temperature (logFlux = a + b x temperature), and then derived a Q10 value from the slope of this log-linear relationship, as: (4)
where b is the slope of the linear relationship between the log10-transformed flux versus temperature, or the corresponding regression coefficient in the case of multiple regression models. This model assumes that the relative increase in fluxes as a function of temperature is constant across the temperature range.
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Chemical analyses Dissolved organic carbon (DOC) concentration was measured with a TOC analyzer (OI 1010, OI Analytical, College Station, TX) after filtering (0.45 µm PES cartridge, Sarstedt AG & co, Nümbrecht, Germany) and sodium persulfate digestion. Total phosphorus (TP) and total nitrogen (TN) concentration were analyzed after persulfate and alkaline persulfate digestions, respectively. TP was then measured as orthophosphate using the spectrophotometric molybdenum blue technique (Wetzel & Likens, 2000; 890 nm, Ultrospec 2100 pro, Biochrom Ltd., Cambridge, UK) and TN as NO3- with an Alpkem FlowSolution IV autoanalyzer (O I Analytical, College Station, TX). Chlorophyll a (Chla) samples were filtered (GF/F, Whatman, Kent, UK), the filters were extracted with hot ethanol (90%) via sonication and analyzed spectrophotometrically with an acidification step to correct for phaeophytin (665 and 750 nm, Ultrospec 2100 pro, Thermo Fisher Scientific Inc., Waltham, MA). Colored dissolved organic matter (CDOM) was measured as absorbance at 440 nm using an Ultrospec 3100 spectrophotometer (Thermo Fisher Scientific Inc., Waltham, MA).
Geographical and statistical analyses We determined the lake and catchment areas, as well as elevation of the sampled lakes, using the ArcMap 10 and ArcGIS V10 software (ESRI Inc., Redland, CA) applied on the DEM derived from (1:50000) maps. The data were log10-transformed to assure normality, and regional differences in basic limnological factors and gas fluxes were compared using ANOVA. We built regression models for two objectives: simple models based on easily obtained variables for predictive and upscaling purposes, and multiple regression models based on the ensemble of measured variables to identify the factors affecting partial pressures and fluxes. Synoptic sampling such as that used in this study attempts to maximize the information gained from a This article is protected by copyright. All rights reserved.
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greater diversity of ecosystems at the expense of obtaining a precise determination of the average behavior of individual systems. The resulting relationship are often unduly noisy, depending on the relative magnitude of intra- versus inter-system variability, and the resulting empirical models may be weaker than would have been obtained from multiple sampling of fewer systems, although the model parameters should remain unbiased. Therefore, to identify the shape of the relationships more clearly, we also performed regression analysis on the means obtained after binning the data into groups. The untransformed data were first grouped into lake area bins of 0.5 logarithmic (base-10) intervals, arithmetic means were calculated for each bin and these were subsequently log-transformed for further statistical analyses. Similarly, when exploring the effect of temperature, the data at 0.5 m depth were organized into 2°C temperature bins, and when exploring CDOM, the untransformed data were organized into 0.3 bins of log10-transformed CDOM data, and the average per bin was log-transformed and used for subsequent analyses. To ensure that the binning procedure was not artificially overestimating the precision of the resulting relationships, we assessed the statistical robustness of the binned relationship parameters using 1000 bootstrap samples. Results from the bootstrapping analysis have shown the relationship based on the binning adequately reflected the uncertainty of the model parameters. This is illustrated in figures 3 a to d but applies to all the other binned relationships. All statistical analyses were carried out using R 3.0.0 software (R Core Team, 2013).
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Results General lake characteristics The studied lakes represent a wide gradient in key environmental and morphometric factors, with large differences not only between studied regions but also within them (Table 1). Summer (late May–August) surface water temperatures varied greatly among regions: they were on average lowest in Schefferville (mean ± s.d. 15.1 ± 1.1°C) and highest in the Laurentians (22.7 ± 0.8°C). There was a wide range in lake size (from 0.002 to over 2000 km2) (Table 1). Most of the lakes were thermally stratified during the sampling period, except for the shallowest lakes (< 2-3 m deep, i.e., 25% of all lakes), which remained mixed throughout the study period. The mean DOC concentration varied from 3.8 ± 1.5 mg L-1 in Schefferville to 12.6 ± 7.0 mg L-1 in James Bay. Both TN and TP mean concentrations were highest in Abitibi (389 ± 170 µg L-1 and 28.7 ± 31.5 µg L-1, respectively) and lowest in Chibougamau (175 ± 33 µg L-1) for TN and in Schefferville (6.9 ± 3.7 µg L-1) for TP. There were clear differences in Chla concentrations between regions and the regional means ranged from 0.76 (± 0.56 s.d.) µg L-1 in Schefferville to 5.6 (± 6.5 s.d.) µg L-1 in Abitibi. Conductivity was the lowest in Eastmain (2.6 ± 5.2 s.d. µS m-1) and highest in Abitibi (105.7 ± 160.0 µS m-1).
Lake CH4 concentrations and fluxes All of our measurements were done during the summer stratification period and thus this study excludes possible high values during early spring or fall turnover. Surface water pCH4 varied from 6 to 3612 µatm across all lakes (Fig. 2a; Table 2). In general, pCH4 was highest in the southernmost regions and decreased with increasing latitude (Fig. 2a). However, the highest single pCH4 measurement was found in Abitibi (3612 µatm). Regional pCH4 minima converged
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to 6–17 µatm in all regions except in the Laurentians, where it was much higher (75 µatm). There were significant regional differences in pCH4 (ANOVA F=20.23, p
Received Date : 09-Apr-2014 Accepted Date : 25-Aug-2014 Article type
: Primary Research Articles
Large-scale patterns in summer diffusive CH4 fluxes across boreal lakes, and contribution to diffusive C emissions
Summer CH4 emissions in boreal lakes
Terhi Rasilo, Yves T. Prairie, and Paul A. del Giorgio
Groupe de Recherche Interuniversitaire en Limnologie (GRIL), Département des sciences biologiques, Université du Quebéc à Montréal, Québec, Canada.
Corresponding author : T. Rasilo, Département des sciences biologiques, Université du Québec à Montréal, Case postale 8888, succ. Centre-Ville, Montréal, QC, Canada. ([email protected])
Tel : +1-514-987-3000, ext. 2927 Fax : +1-514-987-3000, ext. 4647
Key words: aquatic ecology, greenhouse gases (GHG), methane (CH4), boreal landscape, northern lakes, climate change, carbon emissions, large-scale
This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as doi: 10.1111/gcb.12741 This article is protected by copyright. All rights reserved.
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Original Research: Primary Research Article
Abstract Lakes are a major component of boreal landscapes, and whereas lake CO2 emissions are recognized as a major component of regional C budgets, there is still much uncertainty associated to lake CH4 fluxes. Here we present a large-scale study of the magnitude and regulation of boreal lake summer diffusive CH4 fluxes, and their contribution to total lake carbon (C) emissions, based on in situ measurements of concentration and fluxes of CH4 and CO2 in 224 lakes across a wide range of lake type and environmental gradients in Québec. The diffusive CH4 flux was highly variable (mean 11.6 ± 26.4 s.d. mg m-2 d-1), and it was positively correlated to temperature and lake nutrient status, and negatively correlated to lake area and colored dissolved organic matter (CDOM). The relationship between CH4 and CO2 concentrations fluxes was weak, suggesting major differences in their respective sources and/or regulation. For example, increasing water temperature leads to higher CH4 flux but does not significantly affect CO2 flux, whereas increasing CDOM concentration leads to higher CO2 flux but lower CH4 flux. CH4 contributed to 8 ± 23 % to the total lake C emissions (CH4 + CO2), but 18 ± 25 % to the total flux in terms of atmospheric warming potential, expressed as CO2-equivalents. The incorporation of ebullition and plant mediated CH4 fluxes would further increase the importance of lake CH4. The average Q10 of CH4 flux was 3.7, once other covarying factors were accounted for, but this apparent Q10 varied with lake morphometry and was higher for shallow lakes. We conclude that global climate change and the resulting shifts in temperature will strongly influence lake CH4 fluxes across the boreal biome, but these climate effects may be altered by regional patterns in lake morphometry, nutrient status and browning.
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Introduction Freshwaters are now recognized as important components of the continental carbon cycle (Tranvik et al., 2009; Bastviken et al., 2011). Whereas surface freshwaters cover on average only 3% of the globe’s continental area (Downing et al., 2006), they represent a much higher portion of boreal landscape, comprising on average 10% of the territory (Brandt, 2009). Lakes and rivers in the boreal zone are in general supersaturated in carbon (C) gases, releasing carbon fixed in terrestrial ecosystems back into the atmosphere mostly in the form of carbon dioxide (CO2) but also as methane (CH4) (e.g. Ojala et al., 2011; Lopez Bellido et al., 2011). While there has been substantial research on CO2 dynamics in these aquatic systems (e.g. Kortelainen et al., 2006; Tranvik et al., 2009; Roehm et al., 2009; Huotari et al., 2011), CH4 dynamics and fluxes from lakes are still poorly studied, and probably form one of the largest gaps in northern C budgets (Bousquet et al., 2006; Ortiz-Llorento & Alvarez-Cobelas, 2012). There is increasing evidence that continental waters are a globally significant source of
CH4, but global estimates are scarce and still extremely uncertain, ranging from 8 to over 100 Tg CH4 y-1 (Bastviken et al., 2004; Walter et al., 2006; Bastviken et al., 2009, 2011; Lundin et al., 2013). The estimates of the contributions of lakes and rivers, which were traditionally assumed to be very modest, have consistently increased in recent years as new data have emerged (Bastviken et al., 2011; Lundin et al., 2013). Part of the difficulty in estimating the contribution of northern lakes to regional and global CH4 budgets is that the existing data is not only very fragmented in terms of lake type and region covered, but also that the existing data suggest a wide range of lake CH4 fluxes, from less than one to the thousands of mg m-2 d-1 (Smith & Lewis, 1992; Huttunen et al., 2003; Bastviken et al., 2004; Juutinen et al., 2009; López Bellido et al., 2011; Natchimuthu et al., 2014). Extrapolating from current average estimates is thus extremely uncertain, and in addition, there is a lack of robust relationships between lake CH4 and its potential drivers that This article is protected by copyright. All rights reserved.
Accepted Article
could facilitate temporal prediction and spatial extrapolation. There is thus an urgent need to better understand CH4 regulation in northern lakes. Several studies have explored the cross-lake variation in CH4 concentrations and fluxes
(Bastviken et al., 2003; Huttunen et al., 2003; Juutinen et al., 2009; Kankaala et al., 2013), and have linked it to lake depth and size, organic C loading, lake nutrient status and temperature. It is thus clear that whereas CH4 may have a strong influence on climate, climate also has a strong influence on CH4 production in and fluxes from lakes. Predicted temperature increases, eutrophication, and shifts in organic C loading to lakes may affect CH4 fluxes (IPCC 2007; Juutinen et al., 2009; Linnaluoma, 2012; Larsen et al., 2011; Marotta et al., 2014), but how these factors will interact to shape future lake CH4 emissions is still uncertain. Climatic changes may not only influence lake CH4 fluxes, but also CO2 fluxes, which
together form the net C emission from lakes. The relative contribution of CH4 to total C emissions, and how this contribution may change under future environmental scenarios remain poorly constrained. There have only been a handful of studies that assessed lake CH4 and CO2 simultaneously, and these have mostly focused on individual systems and small-scale variability (e.g. Striegl & Michmerhuizen, 1998; Riera et al., 1999; López Bellido et al., 2011; Ojala et al., 2011). The largest studies are from Finland and report that the ratio of CH4 to CO2 fluxes (as mmol m-2 d-1) was on average low (0.15), but extremely variable both temporally within lakes and also across lakes (Huttunen et al. 2003; Kankaala et al., 2013). As a result of this lack of concurrent measurements of CH4 and CO2 we still do not understand their links and potential coupling in aquatic environments or how the contribution of CH4 to total C emissions may vary across lakes under scenarios of environmental and climate change. The sources and pathways that influence the ambient concentrations of these two key
greenhouse gases are very different. Unlike CO2 production, methanogenesis proceeds only in This article is protected by copyright. All rights reserved.
Accepted Article
anoxic environments (but see Grossart et al., 2011; Tang et al., 2014), and the role of lateral terrestrial or riverine inputs is less clear for CH4 than for CO2 (Nakamura et al., 1999; Ojala et al., 2011). In addition, the transport pathways of CH4 resulting in emissions into the atmosphere are complex. CH4 can be oxidized during diffusion and transport towards the surface, or it can be released directly to the atmosphere by ebullition (Casper et al., 2000; Bastviken et al., 2004; Walter Anthony et al., 2012) or plant-mediated transport (Juutinen et al., 2003; Bergström et al., 2007). During the spring and autumn turnover storage flux of accumulated CH4 can also contribute significantly to the total flux (Michmerhuizen et al., 1996; Phelps et al., 1998; López Bellido et al., 2009). Therefore, in addition to biological processes that may be influenced by the availability of organic C and by temperature (Kelly & Chynoweth, 1981; Linnaluoma, 2012; Lofton et al., 2013), CH4 fluxes into the atmosphere are further dependent on physical factors such as lake morphometry and water column physics (Bastviken et al., 2004; López Bellido et al., 2009). Other factors may further uncouple CH4 from CO2 dynamics in lakes. For example, high
primary production may lead to CO2 undersaturation (del Giorgio et al., 1999) but enhance CH4 fluxes by increasing the downward flux of organic C and promoting anoxia in deep waters (Huttunen et al., 2003; Bastviken et al., 2004; DelSontro et al., 2011; Linnaluoma, 2012). In addition, CH4 production is considerably more temperature sensitive than the processes underlying CO2 fluxes (Sobek et al., 2005; Yvon-Durocher et al., 2012, 2014), and therefore changes in water column or sediment temperature may result in shifts in the relative contribution of CH4 to total C emissions from lakes (Marotta et al., 2014). Although the change of CH4/CO2 flux ratio might have only minor effect on total carbon fluxes, its influence on warming potential of lakes may be significant.
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In this paper, we expand on previous studies that have explored the magnitude and regulation of CH4 dynamics from northern lakes (Smith & Lewis, 1992; Riera et al., 1999; Huttunen et al., 2003; Bastviken et al., 2004, 2008; Juutinen et al., 2009; Ojala et al., 2011): we explore the magnitude and factors controlling the CH4 concentrations and diffusive fluxes across 224 lakes distributed throughout the boreal landscapes of Québec during summer stratification period, and we explicitly address the relationship between CH4 and CO2 fluxes. We examine the factors that influence the contribution of CH4 to total diffusive C emissions and to the global warming potential of these lakes, and discuss the possible effects of climate change on these patterns of lake emission.
Material and Methods Study sites This large-scale study of gas dynamics and environmental conditions covered 224 different lakes across 8 different regions of boreal Québec (Fig 1): Laurentians (46°N, 74°W), Chicoutimi (48°N, 71°W), Abitibi (48.5°N, 79°W), Chibougamau (49.6°N, 74°W), James Bay (49.7°N, 79°W), Côte-Nord (50.4°N, 67°W), Eastmain (52° N, 75°W), and Schefferville (55°N, 67°W). The study regions are located several hundreds of kilometers from each other whereas lakes in each region are generally within 50 km from each other. Each region represents different climate characteristics (mean annual temperature, precipitation, latitude, Table 1), vegetation coverage and catchment morphometry. The vegetation changes from the Laurentians’ deciduous forests (dominated by Populus balsamifera L., Populus tremuloides Michx. and Betula papyrifera Marsh.) through mixed forests (dominated by Abies balsamea (L.) Mill. and Betula papyrifera Marsh.) in Abitibi and Chicoutimi to coniferous (dominated by Picea mariana (Mill.) BSP) forests in Eastmain, Chibougamau, James Bay and Côte-Nord. The vegetation in Schefferville This article is protected by copyright. All rights reserved.
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varies from thick to sparse coniferous forests in lower areas to often exposed bedrock in upper ridges. Abitibi and James Bay are situated on the Abitibi clay belt and thus topography is rather flat and lakes are shallow and turbid, but also kettle lakes exist in those regions. The catchment slopes are steeper and elevation varies more in the Laurentians and Chicoutimi region. Soils in Schefferville are typically glacial deposits formed over sedimentary rocks. Situated close to the Gulf of St Laurence, Côte-Nord experiences a more maritime climate. Lakes in the Laurentians are mainly clear water lakes, while in Chicoutimi, Chibougamau, Côte-Nord and Eastmain they are mainly humic, and in Schefferville they are oligotrophic and clear (Table 1). Abitibi, James Bay and Chicoutimi are also characterized by a high density of beavers (Castor Canadensis, Kuhl), which alter the hydrology by building dams. However, none of the study lakes was a beaver pond.
Sampling The study regions were visited in different years (Eastmain 2007–2009, Laurentians, Abitibi and James Bay in 2010, Chibougamau and Chicoutimi in 2011, Schefferville in 2012, and Côte-Nord in 2013), and here we only report measurements made during the late spring and summer stratification period, typically between May 27 and August 18. Most of the lakes were visited once but some (35 lakes) were visited twice during one summer, and 16 lakes in Eastmain were sampled several times during consecutive years (Table 1). Lakes were accessed by truck and sampled by boat when possible, but as some of the study regions were remote many of the lakes were accessed by hydroplane. Samples were always taken at the deepest measured spot of the lake, although in the absence of hypsographic maps for most of these lakes, this may not coincide with the absolute deepest point. A complete depth profile (from 0.5 m depth to bottom at 0.5 m or 1.0 m interval) of temperature, dissolved oxygen, pH and conductivity was carried out with a YSI This article is protected by copyright. All rights reserved.
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probe (600XLV2-M, Yellow Springs Instruments, OH). Floating chambers were deployed to measure gas fluxes, as described in sections below, and water for gas samples and chemical analyses was collected at 0.5 m using a peristaltic pump, and kept refrigerated in the dark until returned to the lab.
Surface water gas concentrations Surface water CO2 partial pressure (pCO2, in µatm) was determined in situ by pumping lake water through a MiniModule® equilibrating module (Membrana, Wuppertal, Germany), and the equilibrated air was re-circulated directly through an EGM-4 (PP Systems, Amesbury, MA) infrared CO2 analyzer. The headspace method was used to determine the surface water CH4 partial pressure (pCH4). We collected 30 ml of lake water in a 60 ml polypropylene syringe, and created a 30-ml headspace of ambient air. Triplicate syringes were vigorously shaken for 1 minute, in order to equilibrate the gases in water and air (Bastviken et al., 2010). The resulting 30 ml headspace was injected into 30-ml glass vials equipped with rubber stoppers (20 mm diameter, red bromobutyl) filled with saturated saline solution, and kept inverted until analysis. In the lab, the gas in the headspace of the vials was injected into a gas chromatograph (GC-8A/GC2014, Shimadzu, Kyoto, Japan) equipped with a FID (flame ionization detector) to determine its CH4 concentration. The original surface water pCH4 was then calculated according to the headspace ratio, in situ temperature and assuming a constant ambient air pCH4 of 1.77 µatm.
Flux measurements CH4 and CO2 fluxes were measured by the floating chamber technique (Kling et al., 1992; Duchemin et al., 1999; Vachon et al., 2010). The round (darkened) plastic chamber (0.34 m diameter, 0.18 m height) was equipped with a thermometer to control for changes in the This article is protected by copyright. All rights reserved.
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temperature of the chamber headspace. The chamber headspace was connected in a closed loop to an infrared CO2 analyzer (EGM-4, PP-systems, Amesbury, MA) that measured the changes in pCO2 in the chamber headspace every minutes for 10 minutes. The chamber also had a sampling port that allowed the withdrawal of air samples via a syringe for CH4 analysis, which was done every 10 minutes for 30 minutes. The collected gas was injected into 30-ml vials containing saturated saline solution, and processed as described above for ambient pCH4 samples.
Calculations and data analysis
Gas fluxes were calculated on the basis of the rates of change in pCO2 and pCH4 in the chamber headspace with time. More than 95% of the chamber measurements had linear increases with r2 > 0.95, and we did not observe signs of decelerating fluxes due to the accumulating gas. A small certain fraction (6%) of measurements were rejected based on erratic time courses that had low r2. For CH4, in samples where change in the chamber was positive but very close to the detection limit of the GC analysis (6% of all measurements), we used a fixed flux value of 0.003 mmol m-2 d-1, which is the minimum rate that can be resolved given our analytical error. The rates of change in pCO2 and in pCH4 in the chamber were used to estimate CH4 and CO2 fluxes (fCH4 and fCO2, mmol m-2 d-1) with the following equation: (1)
where (s) is the rate of change of the gas in the chamber (µatm min-1), (V) is the volume of the chamber in liters (L), (S) is the surface area of the chamber (m2), (Vm) is the molar volume (L mol1
) adjusted for in situ temperature, and (t) is a conversion factor from minutes to a day and from
mol to mmol. Fluxes were further converted to mg C m-2 d-1 by multiplying by the molar mass of C (12.01 g mol-1) and to CO2-equivalent fluxes (g C-CO2-eq m-2 d-1) by assuming that one gram of This article is protected by copyright. All rights reserved.
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CH4 corresponds to 25 g of CO2 in terms of warming potential over the next 100 years (IPCC 2007). We further calculated the gas transfer coefficients (k, m d-1) based on CO2, using the
measured pCO2, CO2 flux and the temperature dependent solubility of the gas following: (2)
where ƒ is flux of CO2 (mmol m−2 d−1), Kh is the temperature-corrected Henry’s constant (Lide, 1992), and ∆gas is the difference in partial pressures between the air and the water phases for CO2. To facilitate comparison, k values were further normalized to k600 values with a Schmidt number of 600 using: (3)
where Sc is the Schmidt number of a gas at a given temperature (Wanninkhof, 1992). We used n = ⅔ for wind speed 3.7 m s−1 (Guérin et al., 2007). We modeled the temperature dependence of CH4 and CO2 fluxes as a log-linear
relationship with temperature (logFlux = a + b x temperature), and then derived a Q10 value from the slope of this log-linear relationship, as: (4)
where b is the slope of the linear relationship between the log10-transformed flux versus temperature, or the corresponding regression coefficient in the case of multiple regression models. This model assumes that the relative increase in fluxes as a function of temperature is constant across the temperature range.
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Chemical analyses Dissolved organic carbon (DOC) concentration was measured with a TOC analyzer (OI 1010, OI Analytical, College Station, TX) after filtering (0.45 µm PES cartridge, Sarstedt AG & co, Nümbrecht, Germany) and sodium persulfate digestion. Total phosphorus (TP) and total nitrogen (TN) concentration were analyzed after persulfate and alkaline persulfate digestions, respectively. TP was then measured as orthophosphate using the spectrophotometric molybdenum blue technique (Wetzel & Likens, 2000; 890 nm, Ultrospec 2100 pro, Biochrom Ltd., Cambridge, UK) and TN as NO3- with an Alpkem FlowSolution IV autoanalyzer (O I Analytical, College Station, TX). Chlorophyll a (Chla) samples were filtered (GF/F, Whatman, Kent, UK), the filters were extracted with hot ethanol (90%) via sonication and analyzed spectrophotometrically with an acidification step to correct for phaeophytin (665 and 750 nm, Ultrospec 2100 pro, Thermo Fisher Scientific Inc., Waltham, MA). Colored dissolved organic matter (CDOM) was measured as absorbance at 440 nm using an Ultrospec 3100 spectrophotometer (Thermo Fisher Scientific Inc., Waltham, MA).
Geographical and statistical analyses We determined the lake and catchment areas, as well as elevation of the sampled lakes, using the ArcMap 10 and ArcGIS V10 software (ESRI Inc., Redland, CA) applied on the DEM derived from (1:50000) maps. The data were log10-transformed to assure normality, and regional differences in basic limnological factors and gas fluxes were compared using ANOVA. We built regression models for two objectives: simple models based on easily obtained variables for predictive and upscaling purposes, and multiple regression models based on the ensemble of measured variables to identify the factors affecting partial pressures and fluxes. Synoptic sampling such as that used in this study attempts to maximize the information gained from a This article is protected by copyright. All rights reserved.
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greater diversity of ecosystems at the expense of obtaining a precise determination of the average behavior of individual systems. The resulting relationship are often unduly noisy, depending on the relative magnitude of intra- versus inter-system variability, and the resulting empirical models may be weaker than would have been obtained from multiple sampling of fewer systems, although the model parameters should remain unbiased. Therefore, to identify the shape of the relationships more clearly, we also performed regression analysis on the means obtained after binning the data into groups. The untransformed data were first grouped into lake area bins of 0.5 logarithmic (base-10) intervals, arithmetic means were calculated for each bin and these were subsequently log-transformed for further statistical analyses. Similarly, when exploring the effect of temperature, the data at 0.5 m depth were organized into 2°C temperature bins, and when exploring CDOM, the untransformed data were organized into 0.3 bins of log10-transformed CDOM data, and the average per bin was log-transformed and used for subsequent analyses. To ensure that the binning procedure was not artificially overestimating the precision of the resulting relationships, we assessed the statistical robustness of the binned relationship parameters using 1000 bootstrap samples. Results from the bootstrapping analysis have shown the relationship based on the binning adequately reflected the uncertainty of the model parameters. This is illustrated in figures 3 a to d but applies to all the other binned relationships. All statistical analyses were carried out using R 3.0.0 software (R Core Team, 2013).
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Results General lake characteristics The studied lakes represent a wide gradient in key environmental and morphometric factors, with large differences not only between studied regions but also within them (Table 1). Summer (late May–August) surface water temperatures varied greatly among regions: they were on average lowest in Schefferville (mean ± s.d. 15.1 ± 1.1°C) and highest in the Laurentians (22.7 ± 0.8°C). There was a wide range in lake size (from 0.002 to over 2000 km2) (Table 1). Most of the lakes were thermally stratified during the sampling period, except for the shallowest lakes (< 2-3 m deep, i.e., 25% of all lakes), which remained mixed throughout the study period. The mean DOC concentration varied from 3.8 ± 1.5 mg L-1 in Schefferville to 12.6 ± 7.0 mg L-1 in James Bay. Both TN and TP mean concentrations were highest in Abitibi (389 ± 170 µg L-1 and 28.7 ± 31.5 µg L-1, respectively) and lowest in Chibougamau (175 ± 33 µg L-1) for TN and in Schefferville (6.9 ± 3.7 µg L-1) for TP. There were clear differences in Chla concentrations between regions and the regional means ranged from 0.76 (± 0.56 s.d.) µg L-1 in Schefferville to 5.6 (± 6.5 s.d.) µg L-1 in Abitibi. Conductivity was the lowest in Eastmain (2.6 ± 5.2 s.d. µS m-1) and highest in Abitibi (105.7 ± 160.0 µS m-1).
Lake CH4 concentrations and fluxes All of our measurements were done during the summer stratification period and thus this study excludes possible high values during early spring or fall turnover. Surface water pCH4 varied from 6 to 3612 µatm across all lakes (Fig. 2a; Table 2). In general, pCH4 was highest in the southernmost regions and decreased with increasing latitude (Fig. 2a). However, the highest single pCH4 measurement was found in Abitibi (3612 µatm). Regional pCH4 minima converged
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to 6–17 µatm in all regions except in the Laurentians, where it was much higher (75 µatm). There were significant regional differences in pCH4 (ANOVA F=20.23, p
