Catchment properties as predictors of greenhouse gas concentrations across a gradient of boreal lakes - Valiente, Nicolas; Eiler, Alexander; Allesson, Lina; Andersen, Tom; Clayer, François; Crapart, Camille; Dörsch, Peter; Fontaine, Laurent; Heuschele, Jan; Vogt, Rolf D.; Wei, Jing; de Wit, Heleen A.; et al. (Frontiers in Environmental Science, 2022)
Boreal lakes are the most abundant lakes on Earth. Changes in acid rain deposition, climate, and catchment land use have increased lateral fluxes of terrestrial dissolved organic matter (DOM), resulting in a widespread browning of boreal freshwaters. This browning affects the aqueous communities and ecosystem processes, and boost emissions of the greenhouse gases (GHG) CH4, CO2, and N2O. In this study, we predicted biotic saturation of GHGs in boreal lakes by using a set of chemical, hydrological, climate, and land use parameters. For this purpose, concentrations of GHGs and nutrients (organic C, -P, and -N) were determined in surface water samples from 73 lakes in south-eastern Norway cover wide ranges in DOM and nutrient concentrations, as well as catchment properties and land use. The spatial variation in saturation of each GHG is related to explanatory variables. Catchment characteristics (hydrological and climate parameters) such as lake size and summer precipitation, as well as NDVI were key determinants when fitting GAM models for CH4 and CO2 saturation (explaining 71% and 54%, respectively), while summer precipitation and land use data were the best predictors for the N2O saturation, explaining almost 50% of deviance. Our results suggest that lake size, precipitation, and terrestrial primary production in the watershed control the saturation of GHG in boreal lakes. These predictions based on the 73-lake dataset was validated against an independent dataset from 46 lakes in the same region. Together, this provides an improved understanding of drivers and spatial variation in GHG saturation in boreal lakes across wide gradients of lake and catchment properties. The assessment highlights the need to incorporate multiple explanatory parameters in prediction models of GHGs for extrapolation across the boreal biome.