Search for: All records

To understand patterns in CO2 partial pressure (PCO2) over time in wetlands’ surface water and porewater, we examined the relationship between PCO2 and land–atmosphere flux of CO2 at the ecosystem scale at 22 Northern Hemisphere wetland sites synthesized through an open call. Sites spanned 6 major wetland types (tidal, alpine, fen, bog, marsh, and prairie pothole/karst), 7 Köppen climates, and 16 different years. Ecosystem respiration (Reco) and gross primary production (GPP), components of vertical CO2 flux, were compared to PCO2, a component of lateral CO2 flux, to determine if photosynthetic rates and soil respiration consistently influence wetland surface and porewater CO2 concentrations across wetlands. Similar to drivers of primary productivity at the ecosystem scale, PCO2 was strongly positively correlated with air temperature (Tair) at most sites. Monthly average PCO2 tended to peak towards the middle of the year and was more strongly related to Reco than GPP. Our results suggest Reco may be related to biologically driven PCO2 in wetlands, but the relationship is site-specific and could be an artifact of differently timed seasonal cycles or other factors. Higher levels of discharge do not consistently alter the relationship between Reco and temperature normalized PCO2. This work synthesizes relevant data and identifies key knowledge gaps in drivers of wetland respiration. 

Abstract. Peatlands have often been neglected in Earth system models (ESMs).Where they are included, they are usually represented via a separate, prescribed grid cell fraction that is given the physical characteristics of a peat (highly organic) soil. However, in reality soils vary on a spectrum between purely mineral soil (no organic material) and purely organicsoil, typically with an organic layer of variable thickness overlying mineral soil below. They are also dynamic, with organic layer thickness and its properties changing over time. Neither the spectrumof soil types nor their dynamic nature can be captured by current ESMs. Here we present a new version of an ESM land surface scheme (Joint UK Land Environment Simulator, JULES) where soil organic matter accumulation – and thus peatland formation, degradation and stability – is integratedin the vertically resolved soil carbon scheme. We also introduce the capacity to track soil carbon age as a function of depth in JULES and compare this to measured peat age–depth profiles. The new scheme is tested and evaluated at northern and temperate sites. This scheme simulates dynamic feedbacks between the soil organic material and its thermal and hydraulic characteristics. We show that draining the peatlands can lead to significant carbon loss, soil compaction and changes in peat properties. However, negative feedbacks can lead to the potential for peatlands to rewet themselves following drainage.These ecohydrological feedbacks can also lead to peatlands maintaining themselves in climates where peat formation would not otherwise initiate in the model, i.e. displaying some degree of resilience. The new model produces similar results to the original model for mineral soils and realistic profiles of soil organic carbon for peatlands.We evaluate the model against typical peat profiles based on 216 northern and temperate sites from a global dataset of peat cores.The root-mean-squared error (RMSE) in the soil carbon profile is reduced by 35 %–80 % in the best-performing JULES-Peat simulationscompared with the standard JULES configuration. The RMSE in these JULES-Peat simulations is 7.7–16.7 kg C m−3 depending on climate zone, which is considerably smaller than the soil carbon itself (around 30–60 kg C m−3). The RMSE at mineral soil sites is also reducedin JULES-Peat compared with the original JULES configuration (reduced by ∼ 30 %–50 %). Thus, JULES-Peat can be used as a complete scheme that simulates both organic and mineral soils. It does not requireany additional input data and introduces minimal additional variables to the model. This provides a new approach for improving the simulation of organic and peatland soils andassociated carbon-cycle feedbacks in ESMs. 

Abstract. In the global methane budget, the largest natural sourceis attributed to wetlands, which encompass all ecosystems composed ofwaterlogged or inundated ground, capable of methane production. Among them,northern peatlands that store large amounts of soil organic carbon have beenfunctioning, since the end of the last glaciation period, as long-termsources of methane (CH4) and are one of the most significant methanesources among wetlands. To reduce uncertainty of quantifying methane flux in theglobal methane budget, it is of significance to understand the underlyingprocesses for methane production and fluxes in northern peatlands. A methanemodel that features methane production and transport by plants, ebullitionprocess and diffusion in soil, oxidation to CO2, and CH4 fluxes tothe atmosphere has been embedded in the ORCHIDEE-PEAT land surface modelthat includes an explicit representation of northern peatlands.ORCHIDEE-PCH4 was calibrated and evaluated on 14 peatland sites distributedon both the Eurasian and American continents in the northern boreal andtemperate regions. Data assimilation approaches were employed to optimizedparameters at each site and at all sites simultaneously. Results show thatmethanogenesis is sensitive to temperature and substrate availability overthe top 75 cm of soil depth. Methane emissions estimated using single siteoptimization (SSO) of model parameters are underestimated by 9 g CH4 m−2 yr−1 on average (i.e., 50 % higher than the site average ofyearly methane emissions). While using the multi-site optimization (MSO),methane emissions are overestimated by 5 g CH4 m−2 yr−1 onaverage across all investigated sites (i.e., 37 % lower than the siteaverage of yearly methane emissions). 

Abstract. Natural wetlands constitute the largest and most uncertain sourceof methane (CH4) to the atmosphere and a large fraction of them are found in the northern latitudes. These emissions are typically estimated using process (“bottom-up”) or inversion (“top-down”) models. However, estimates from these two types of models are not independent of each other since the top-down estimates usually rely on the a priori estimation of these emissions obtained with process models. Hence, independent spatially explicit validation data are needed. Here we utilize a random forest (RF) machine-learning technique to upscale CH4 eddy covariance flux measurements from 25 sites to estimate CH4 wetland emissions from the northern latitudes (north of 45∘ N). Eddy covariance datafrom 2005 to 2016 are used for model development. The model is then used to predict emissions during 2013 and 2014. The predictive performance of the RF model is evaluated using a leave-one-site-out cross-validation scheme. The performance (Nash–Sutcliffe model efficiency =0.47) is comparable to previous studies upscaling net ecosystem exchange of carbon dioxide and studies comparing process model output against site-level CH4 emission data. The global distribution of wetlands is one major source of uncertainty for upscaling CH4. Thus, three wetland distribution maps are utilized in the upscaling. Depending on the wetland distribution map, the annual emissions for the northern wetlands yield 32 (22.3–41.2, 95 % confidence interval calculated from a RF model ensemble), 31 (21.4–39.9) or 38 (25.9–49.5) Tg(CH4) yr−1. To further evaluate the uncertainties of the upscaled CH4 flux data products we also compared them against output from two process models (LPX-Bern and WetCHARTs), and methodological issues related to CH4 flux upscaling are discussed. The monthly upscaled CH4 flux data products are available athttps://doi.org/10.5281/zenodo.2560163 (Peltola et al., 2019).