The range of measured surface concentrations of the dataset (0.00–5.89 µmol L−1) was of the same order of magnitude as boreal or other Alpine lakes (0.03–4.00 µmol L−1) (Bastviken et al. 2008; Juutinen et al. 2009; Schubert et al. 2010; Diem et al. 2012; Tang et al. 2014; Natchimuthu et al. 2014). Except for one (Schlegeis Speicher, reservoir), all the lakes were supersaturated in surface dissolved CH4 with respect to the atmospheric concentration, despite the well-oxygenated water surface layer (Table S1). These results are in accordance with Tang et al. (2014) and Grossart et al. (2011), who also found a CH4 supersaturation at the surface of lake Stechlin (Germany), however, at a lower range of concentrations (0.09–0.76 µmol L−1) compared to the results of this study. They also showed that an oversaturation of CH4 was overlaying a well-oxygenated mid-water layer. Similar to lake Stechlin, a supersaturation of CH4 was observed in the mesotrophic lake Hallwil (Donis et al. 2017), and the stratified lake Constance (Schulz et al. 2001). The CH4 paradox regarding CH4 supersaturation, its origin and its contribution to surface concentrations is still under debate. According to Wang et al. (2017), this CH4 oversaturation layer would be produced by phototrophs, together with oxygen tolerant methanogens, leading to a pelagic methane-enriched zone. Whereas, Fernàndez et al. (2016) stated that CH4 surface concentrations would mainly come from shallow zones, where water is rich in methane. In order to verify if Alpine lakes also exhibit this oversaturated layer of CH4 overlaying a well-oxygenated water layer, CH4 samples along the water column would be needed. In addition, ebullition, methane-rich air bubbles rising from the lake bottom, may be contributing to the observed super-saturation of surface waters (McGinnis et al. 2006; Tang et al. 2014; Deshmukh et al. 2014).
Lake surface temperature is closely related to air temperature (Livingstone and Lotter 1998; Livingstone and Dokulil 2001). As CH4 production is temperature dependent (Zeikus and Winfrey 1976; Dunfield et al. 1993; Duc et al. 2010), we hypothesized (H2) a positive relationship between surface dissolved CH4 concentrations and lake temperature (r = 0.39, p < 0.001). Commonly, elevation is seen as a proxy for temperature due to the decrease of air temperature with elevation. Accordingly, we further expected a decrease of dissolved CH4 concentration with increasing elevation. Indeed, we observed a negative correlation between lake temperature and elevation and a significant positive relationship between lake temperature and dissolved methane concentrations, however, no significant relationship between lake elevation and dissolved methane concentrations was found (Table 2). This suggests that lake elevation is a poor proxy for capturing the relationship between lake temperature and dissolved CH4 concentrations, possibly because other factors, e.g. lake depth and/or surface area, are confounding the relationship with elevation. Similarly, no relationship was observed between CH4 concentration and latitude, which suggests that the latitudinal gradient between the sampled lakes was either too small to result in a measurable trend in terms of surface dissolved CH4 and/or confounded by other factors.
Abril et al. (2007) showed that turbidity was negatively correlated to CH4 concentrations in river environments, whereas Oswald et al. (2015) showed that light was promoting the CH4 oxidation by CH4 oxidizing bacteria which was also confirmed by Dumestre et al. (1999), for both natural and artificial lakes. While turbidity was not measured in the present study, some of the highest methane concentrations were measured in lakes that were characterised by dark brownish colours (e.g. Möserer See, Levico, Caldaro), corresponding with the above-mentioned studies.
The minimum adequate model indicated that CH4 could be predicted through a positive relationship with water surface temperature and a negative relationship with depth, which is consistent with previous studies demonstrating the relationship between CH4 and temperature (Schütz et al. 1989; Rasilo et al. 2014).
The near-surface dissolved CH4 concentration decreased with depth of the lakes (Fig. 4). In deep lakes, the oxidation of CH4 into CO2 prevails during the diffusion of CH4 molecules upwards through the water column (Bastviken et al. 2004). The deeper a lake is, the less dissolved CH4 is measured at the surface. This is consistent with results of this dataset, where the lowest dissolved CH4 concentrations were measured for the deepest lakes. Similar results were also found for boreal lakes, where CH4 was negatively correlated with lake depth and surface CH4 concentrations measured were the highest in shallow lakes (Juutinen et al. 2009). Juutinen et al. (2009) and Borges et al. (2011) also found a negative correlation with lake surface area, as observed for this dataset (Table 2). The low concentrations measured at the surface can also be explained by the stratification created in the water column of deep lakes, which supports the accumulation and isolation of GHG at the bottom of the lakes (Salmaso and Mosello 2010). In addition, surface sediments may warm faster in shallow lakes, resulting in a larger methane production, which contributes to higher concentrations in shallow lakes (Thebrath et al. 1993).
Reservoirs CH4 concentrations were comparable with the results that Diem et al. (2012) found for Swiss hydropower reservoirs within a similar range of elevation. Similarly to this study, Diem et al. (2012) measured surface CH4 concentrations just at or above supersaturation. The results of the present study for the reservoirs are also within the same order of magnitude found by Duchemin et al. (1995) for two hydroelectric reservoirs situated in the Canadian boreal region. This, compared to natural lakes, is typically much shorter residence time of surface waters, which affects carbon input, processing, and output (Adrian et al. 2009; Venkiteswaran et al. 2013), may further contribute to the observed lower dissolved CH4 concentrations in reservoirs, and explain the difference in term of dissolved CH4 (and CO2) between natural lakes and reservoirs.
The significant correlation between lake depth and surface area (Table 2) found for Alpine water bodies is also supported by Kankaala et al. (2013) and Juutinen et al. (2009) for boreal water bodies, showing that lake depth and surface area were positively correlated. They also reported a negative correlation between CH4 surface concentration and lake surface area. As observed for boreal regions (e.g. Bastviken et al. 2004), lakes situated in the Alpine area were also characterized by a negative correlation between CH4 surface concentration and lake surface area.
Carbon dioxide concentration
The range of dissolved CO2 concentration for both natural lakes and reservoirs was in the same order of magnitude as the one found for Swiss reservoirs, which were in the same range of elevations (Diem et al. 2012), and for other lakes (Casper et al. 2000; Sobek et al. 2003; Lazzarino et al. 2009; Panneer Selvam et al. 2014).
As observed for CH4, all lakes were supersaturated in surface dissolved CO2 (average 36 µmol L−1) compared to the CO2 atmospheric concentration (13.74 µmol L−1). These results are consistent and agree with the findings of Cole et al. (1994), who analyzed a worldwide set of lakes in which 87% of them were supersaturated, on average by a factor of three compared to the atmospheric concentration. This is also consistent with Sobek et al. (2005), who showed that most of the world’s lakes were supersaturated in CO2, without following a latitudinal pattern, and that temperature was not a good predictor for CO2 partial pressure.
The minimum adequate linear model for CO2 included a negative relationship with elevation and dissolved oxygen. The low value of the adjusted r2 and the relatively high value of the RMSE and the fact that no single variable was significantly correlated with CO2 (Table 2), suggest that other explanatory variables are required to establish a robust linear model for CO2, like dissolved organic carbon, chlorophyll a, or ion contents. As shown by Xenopoulos et al. (2003), the concentration of dissolved organic carbon decreases with elevation, the inclusion of elevation in the minimum adequate model may thus, partially and indirectly, account for differences in dissolved organic carbon contents between lakes. According to Kosten et al. (2010), CO2 partial pressure (pCO2) could be partially explained by water temperature, together with other variables such as chlorophyll a, humic substances inflow or evaporation. CO2 concentration is then driven by a group of variables, which could explain why temperature, by itself, did not explain CO2 variability for this dataset.
As for dissolved CH4, similar trends, albeit not significant, were observed for dissolved CO2 as a function of lake depth and elevation: higher concentrations of dissolved CO2 were found for shallow natural lakes, compared to reservoirs which all, except two of them, were deeper than 100 m. The range of CO2 concentrations for the reservoirs in this dataset was lower than the range found by Diem et al. (2012), and lower than boreal reservoirs (Duchemin et al. 1995) and tropical reservoirs (Abril et al. 2006). Compared to the reservoirs studied by Duchemin et al. (1995) and Abril et al. (2006), reservoirs for this study were mainly at high elevation, explaining the low values of CO2 measured. However, for the same range of elevation in the study of Diem et al. (2012) and this one, the results found for Swiss hydropower reservoirs were up to five times higher than our dataset. No clear explanation could be found to explain the difference between the two sets of reservoirs, however, this could be due to the age of the reservoirs (Abril et al. 2005). Another factor explaining the near-surface concentration differences between lakes is the presence of stratification, which typically goes along with an accumulation of CO2 (and CH4) in bottom waters. As shown by Kortelainen et al. (2006), there is a positive relationship between CO2 concentrations in surface waters and in water layers above the sediment that can lead to supersaturation. Future studies should aim at supplementing near-surface water concentrations with samples from bottom waters and/or determine oxygen profiles.
Kankaala et al. (2013), found a negative correlation between the surface CO2 concentration and surface area of boreal lakes. Such a correlation was not found for our dataset (Table 2), presumably because, as shown by the linear model, surface dissolved CO2 in the alpine area is explained by a combination of variables.
When testing correlations between CO2 and CH4 concentrations for each cluster, it appeared that none was present for Cluster 4 (data not shown here). This cluster was composed by the largest and the deepest lakes—mostly reservoirs—with supposed small allochthonous input. CO2 can be produced at the surface of the water by respiration, but can also originate from the oxidation of CH4, produced in the anoxic layer by methanogenesis, while reaching the epilimnion of the lake. These differences could explain the absence of a correlation in Cluster 4. Distinguishing natural lakes from reservoirs, as implied in H1, was not a relevant criterion, since reservoirs were found in two of the four clusters (Fig. 2).
On the other hand, Cluster 1, was characterized by the highest values for dissolved CH4 and CO2 and the highest surface water temperature, corroborating the relationship between CH4 and water temperature (Yvon-Durocher et al. 2014) and our hypothesis (H1). Lakes in this cluster were also the smallest and the shallowest, and dissolved CH4 and CO2 were correlated to each other (r2 = 0.60) suggesting that both compounds would have the same origin, and that part of CO2 would come from the oxidation of CH4, and where temperature would enhance GHG production.
The results of the cluster analysis can be used to guide site selection of future studies. In particular for experimental approaches that are more time consuming compared to the sampling in this study and thus not practical at multiple lakes (e.g. direct lake-atmosphere flux measurements using the eddy covariance method; Eugster et al. 2011), the cluster analysis may help to select the most appropriate study site with respect to the study objectives and experimental limitations (e.g. flux detection limit).