Catchment properties as predictors of greenhouse gas concentrations across a gradient of boreal lakes

Abstract

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 covering 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.

Drivers and variability of CO2:O2 saturation along a gradient from boreal to Arctic lakes

“…In-lake DOM mineralization together with catchment derived (allochthonous) DIC inputs make most lakes worldwide supersaturated with-and net emitters of CO 2 to the atmosphere 1,37 .There are conflicting reports of whether CO 2 produced in aquatic environments via DOM mineralization or exported from terrestrial environments is the main regulator of lake CO 2 flux 36,[38][39][40] . Some of these contradictions likely depend on climate, local hydrology, catchment slopes, water retention time, and not the least catchment properties like lake size or fraction and type of forest, bogs and wetlands 41,42 .…”

Technical Note: Preventing CO2 overestimation from mercuric or copper (II) chloride preservation of dissolved greenhouse gases in freshwater samples

“…The use of chemical inhibitors may not be the best approach. Alternatives exist, such as directly measuring gas concentrations in situ with sensors, or sampling the headspace out in the field, and bringing back gas samples (e.g., Cole et al, 1994;Karlsson et al, 2013;Kling et al, 1991;Valiente et al, 2022), rather than water samples, to the lab for gas chromatography analyses. However, care must be taken to know the exact equilibration temperature (Koschorreck et al, 2021).…”

Disaggregating the Carbon Exchange of Degrading Permafrost Peatlands Using Bayesian Deep Learning

“…We used a Li-7810 gas analyzer (Li-Cor, USA), with a plexiglass chamber covering 25 × 25 cm 2 , and followed Pedersen et al (2010) to estimate fluxes from the measured concentration sequences. Dissolved concentrations of CO 2 and CH 4 were measured in the surface waters in three ponds (not including the largest pond) with the acidified headspace technique (Valiente et al, 2022) at five occasions during the snow-free season, ranging between 40 and 520 μmol L 1 for CO 2 , and between 1.1 and 26 μmol L 1 for CH 4 . Pond fluxes were estimated from these dissolved gas concentrations following the methodology in Clayer et al ( 2021), using the surface renewal gas exchange model by MacIntyre et al ( 2010) for the gas transfer velocity, accounting for the small pond sizes (Vachon & Prairie, 2013) and the typically low wind speeds at Iškoras (Crusius & Wanninkhof, 2003).…”