Methane dynamics in northern lakes : Insights from multi-scale observations

Abstract: Methane (CH4) is a potent greenhouse gas which is emitted to the atmosphere from both natural and anthropogenic sources. Current evidence indicates that lakes account for a large part of the global emissions of CH4, but their contribution is difficult to quantify because of large temporal and spatial variability in processes leading to CH4 fluxes from lakes to the atmosphere. Making sense of the complexity and variability of CH4 emissions from lakes requires observations covering the range of temporal and spatial scales at which these processes occur, both within and between lakes. Northern regions are of particular interest for such studies because they contain a larger number of lakes than any other region in the world and they are disproportionately affected by climate change, with possible consequences for future CH4 emissions.The aim of this thesis was to investigate patterns of CH4 dynamics and emissions in several lakes distributed in different climatic regions of Sweden, paying particular attention to spatial and temporal variability of CH4 fluxes and concentrations. Fluxes, concentrations, carbon stable isotope signature of CH4, and a range of commonly monitored lake characteristics were measured several times during one year at multiple locations in each lake. The measurements provided an extensive set of observations of CH4 concentrations and fluxes in lakes, together with possible environmental drivers. These observations were then used to investigate patterns of CH4 dynamics in northern lakes and to assess the ability of empirical and process-based models to predict CH4 concentrations and fluxes in lakes.The results indicate that simple empirical models, consisting of linear regressions between explanatory variables and CH4 fluxes and concentrations averaged over the lake surface and ice-free period of the year, can be useful in some specific cases (for example describing ebullitive fluxes from total phosphorus or chlorophyll a concentrations). However, it was also noted that using such models for extrapolation can lead to large errors, especially if the observations do not account for temporal and spatial variability of CH4 fluxes and concentrations. An example of high variability was seen in day-night measurements of CH4 fluxes in four lakes over several months. To try to compensate for some of the shortcomings of empirical models, an established process-based and one-dimensional lake model was used to simulate CH4 concentration in the water column of the studied lakes. Predictions were in good agreement with observations in several of the investigated lakes, considering that the model was not pre-calibrated for any of the lake specifically. However, it was also clear that there can be key processes that require specific consideration in process-based models, and some degree of simplification is needed, especially when detailed information on the modelled systems is not available. The simplifications and assumptions that need to be made can be informed by the study and observation of relevant processes in situ. For example, groundwater was found to potentially contribute a major part of CH4 stored in one small boreal lake using measurements of stable isotope signature of CH4 in littoral sediment and deep water of that lake, as well as in the groundwater in the mire next to it. Stable isotope measurements in five other lakes also revealed consistent differences in CH4 sources to the surface and deep zones of lakes when they are separated by thermal stratification of the water column. Such knowledge could be used in the design of numerical models of lakes with the objective to improve predictions of current and future emissions of CH4 from these environments.Overall, this thesis contributes to the current knowledge on assessment of CH4 emissions from lakes at several temporal and spatial scales. It also emphasizes critical aspects which must be considered to reduce bias in future empirical and process-based models of CH4 in lakes.