CO2 fluxes and ecosystem dynamics at five European treeless peatlands – merging data and process oriented modeling

The carbon dioxide (CO2) exchange of five different peatland systems across Europe with a wide gradient in land use intensity, water table depth, soil fertility and climate was simulated with the process oriented CoupModel. The aim of the study was to find out whether CO2 fluxes, measured at different sites, can be explained by common processes and parameters or to what extend a site specific configuration is needed. The model was calibrated to fit measured CO2 fluxes, soil temperature, snow depth and leaf area index (LAI) and resulting differences in model parameters were analyzed. Finding site independent model parameters would mean that differences in the measured fluxes could be explained solely by model input data: water table, meteorological data, management and soil inventory data. Seasonal variability in the major fluxes was well captured, when a site independent configuration was utilized for most of the parameters. Parameters that differed between sites included the rate of soil organic decomposition, photosynthetic efficiency, and regulation of the mobile carbon (C) pool from senescence to shooting in the next year. The largest difference between sites was the rate coefficient for heterotrophic respiration. Setting it to a common value would lead to underestimation of mean total respiration by a factor of 2.8 up to an overestimation by a factor of 4. Despite testing a wide range of different responses to soil water and temperature, rate coefficients for heterotrophic respiration were consistently the lowest on formerly drained sites and the highest on the managed sites. Substrate decomposability, pH and vegetation characteristics are possible explanations for the differences in decomposition rates. Specific parameter values for the timing of plant shooting and senescence, the photosynthesis response to temperature, litter fall and plant respiration rates, leaf morphology and allocation fractions of new assimilates, were not needed, even though the gradient in site latitude ranged from 48° N (southern Germany) to 68° N (northern Finland) differed largely in their vegetation. This was also true for common parameters defining the moisture and temperature response for decomposition, leading to the conclusion that a site specific interpretation of these processes is not necessary. In contrast, the rate of soil organic decomposition, photosynthetic efficiency, and the regulation of the mobile carbon pool need to be estimated from available information on specific soil conditions, vegetation and management of the ecosystems, to be able to describe CO2 fluxes under different condition

The seasonal cycle of the greenhouse gas balance of a continental tundra site in the Indigirka lowlands, NE Siberia

International audienceCarbon dioxide and methane fluxes were measured at a tundra site near Chokurdakh, in the lowlands of the Indigirka river in north-east Siberia. This site is one of the few stations on Russian tundra and it is different from most other tundra flux stations in its continentality. A suite of methods was applied to determine the fluxes of NEE, GPP, Reco and methane, including eddy covariance, chambers and leaf cuvettes. Net carbon dioxide fluxes were unusually high, compared with other tundra sites, with NEE=?92 g C m?2 yr?1, which is composed of an Reco=+141 g C m?2 yr?1 and GPP=?232 g C m?2 yr?1. This large carbon dioxide sink may be explained by the continental climate, that is reflected in low winter soil temperatures (?14°C), reducing the respiration rates, and short, relatively warm summers, stimulating high photosynthesis rates. Interannual variability in GPP was dominated by the frequency of light limitation (Rg ?2), whereas Reco depends most directly on soil temperature and time in the growing season, which serves as a proxy of the combined effects of active layer depth, leaf area index, soil moisture and substrate availability. The methane flux, in units of global warming potential, was +28 g C-CO2e m?2 yr?1, so that the greenhouse gas balance was ?64 g C-CO2e m?2 yr?1. Methane fluxes depended only slightly on soil temperature and were highly sensitive to hydrological conditions and vegetation composition

Studying the spatial variability of methane flux with five eddy covariance towers of varying height

In this study, the spatial representativeness of eddy covariance (EC) methane (CH4) measurements was examined by comparing parallel CH4 fluxes from three short (6 m) towers separated by a few kilometres and from two higher levels (20 m and 60 m) at one location. The measurement campaign was held on an intensively managed grassland on peat soil in the Netherlands. The land use and land cover types are to a large degree homogeneous in the area. The CH4 fluxes exhibited significant variability between the sites on 30-min scale. The spatial coefficient of variation (CVspa) between the three short towers was 56% and it was of similar magnitude as the temporal variability, unlike for the other fluxes (friction velocity, sensible heat flux) for which the temporal variability was considerably larger than the spatial variability. The CVspa decreased with temporal averaging, although less than what could be expected for a purely random process View the MathML source(1/N), and it was 14% for 26-day means of CH4 flux. This reflects the underlying heterogeneity of CH4 flux in the studied landscape at spatial scales ranging from 1 ha (flux footprint) to 10 km2 (area bounded by the short towers). This heterogeneity should be taken into account when interpreting and comparing EC measurements. On an annual scale, the flux spatial variability contributed up to 50% of the uncertainty in CH4 emissions. It was further tested whether EC flux measurements at higher levels could be used to acquire a more accurate estimate of the spatially integrated CH4 emissions. Contrarily to what was expected, flux intensity was found to both increase and decrease depending on measurement height. Using footprint modelling, 56% of the variation between 6 m and 60 m CH4 fluxes was attributed to emissions from local anthropogenic hotspots (farms). Furthermore, morning hours proved to be demanding for the tall tower EC where fluxes at 60 m were up to four-fold those at lower heights. These differences were connected with the onset of convective mixing during the morning period

Vegetation Type Dominates the Spatial Variability in CH<inf>4</inf> Emissions Across Multiple Arctic Tundra Landscapes

Methane (CH4) emissions from Arctic tundra are an important feedback to global climate. Currently, modelling and predicting CH4 fluxes at broader scales are limited by the challenge of upscaling plot-scale measurements in spatially heterogeneous landscapes, and by uncertainties regarding key controls of CH4 emissions. In this study, CH4 and CO2 fluxes were measured together with a range of environmental variables and detailed vegetation analysis at four sites spanning 300 km latitude from Barrow to Ivotuk (Alaska). We used multiple regression modelling to identify drivers of CH4 flux, and to examine relationships between gross primary productivity (GPP), dissolved organic carbon (DOC) and CH4 fluxes. We found that a highly simplified vegetation classification consisting of just three vegetation types (wet sedge, tussock sedge and other) explained 54% of the variation in CH4 fluxes across the entire transect, performing almost as well as a more complex model including water table, sedge height and soil moisture (explaining 58% of the variation in CH4 fluxes). Substantial CH4 emissions were recorded from tussock sedges in locations even when the water table was lower than 40 cm below the surface, demonstrating the importance of plant-mediated transport. We also found no relationship between instantaneous GPP and CH4 fluxes, suggesting that models should be cautious in assuming a direct relationship between primary production and CH4 emissions. Our findings demonstrate the importance of vegetation as an integrator of processes controlling CH4 emissions in Arctic ecosystems, and provide a simplified framework for upscaling plot scale CH4 flux measurements from Arctic ecosystems

Author Correction: The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data

The following authors were omitted from the original version of this Data Descriptor: Markus Reichstein and Nicolas Vuichard. Both contributed to the code development and N. Vuichard contributed to the processing of the ERA-Interim data downscaling. Furthermore, the contribution of the co-author Frank Tiedemann was re-evaluated relative to the colleague Corinna Rebmann, both working at the same sites, and based on this re-evaluation a substitution in the co-author list is implemented (with Rebmann replacing Tiedemann). Finally, two affiliations were listed incorrectly and are corrected here (entries 190 and 193). The author list and affiliations have been amended to address these omissions in both the HTML and PDF versions

The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data.

The FLUXNET2015 dataset provides ecosystem-scale data on CO2, water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible