Publications

Summary This paper aims to evaluate the suitability of the ECOSSE model to estimate soil heterotrophic respiration ( R h ) from arable land and short rotation coppices of poplar and willow. Between 2011 and 2013, we measured R h with automatic closed dynamic chambers on root exclusion plots at one site in the UK (willow, mixed commercial genotypes of S alix spp.) and two sites in I taly (arable and poplar, P opulus × C anadensis M oench, O udemberg genotype), and compared these measured fluxes to simulated values of R h with the ECOSSE model. Correlation coefficients ( r ) between modelled and measured monthly R h data were strong and significant, with a range between 0.81 and 0.96 for all three types of vegetation. There was no significant error and bias in the model for any site. The model was able to predict seasonal trends in R h at all three sites even though it occasionally underestimated the flux values during warm weather in spring and summer. Because of the strong correlation between the measured and modelled values, it is unlikely that underestimation of the flux is the result of missing processes in the model. Therefore, further detailed monitoring of R h is needed to modify the model. In this research, a limited set of input data was used to simulate R h at the three sites. Nevertheless, overall results of the model evaluation suggest that the ECOSSE model simulates soil R h adequately under all land uses tested and that continuous and direct measurements (such as automatic chambers installed on root‐exclusion plots) are a useful tool to test model performance to simulate R h at the site level. Highlights Model evaluation is crucial to predict soil carbon balance accurately. Modelled and measured heterotrophic respiration were compared for three land uses. The model performed well statistically for all three vegetation types. Modelled heterotrophic respiration should be evaluated by comparison to continuous measurements.

Abstract Soil heterotrophic respiration (R h ) refers to the flux of CO 2 released from soil to atmosphere as a result of organic matter decomposition by soil microbes and fauna. As one of the major fluxes in the global carbon cycle, large uncertainties still exist in the estimation of global R h , which further limits our current understanding of carbon accumulation in soils. Here, we applied a Random Forest algorithm to create a global data set of soil R h , by linking 761 field observations with both abiotic and biotic predictors. We estimated that global R h was 48.8 ± 0.9 Pg C year −1 for 1982–2018, which was 16% less than the ensemble mean (58.6 ± 9.9 Pg C year −1 ) of 16 terrestrial ecosystem models. By integrating our observational R h with independent soil carbon stock data sets, we obtained a global mean soil carbon turnover time of 38.3 ± 11 year. Using observation‐based turnover times as a constraint, we found that terrestrial ecosystem models simulated faster carbon turnovers, leading to a 30% (74 Pg C) underestimation of terrestrial ecosystem carbon accumulation for the past century, which was especially pronounced at high latitudes. This underestimation is equivalent to 45% of the total carbon emissions (164 Pg C) caused by global land‐use change at the same time. Our analyses highlight the need to constrain ecosystem models using observation‐based and locally adapted R h values to obtain reliable projections of the carbon sink capacity of terrestrial ecosystems.

Abstract Bioenergy deployment offers significant potential for climate change mitigation, but also carries considerable risks. In this review, we bring together perspectives of various communities involved in the research and regulation of bioenergy deployment in the context of climate change mitigation: Land‐use and energy experts, land‐use and integrated assessment modelers, human geographers, ecosystem researchers, climate scientists and two different strands of life‐cycle assessment experts. We summarize technological options, outline the state‐of‐the‐art knowledge on various climate effects, provide an update on estimates of technical resource potential and comprehensively identify sustainability effects. Cellulosic feedstocks, increased end‐use efficiency, improved land carbon‐stock management and residue use, and, when fully developed, BECCS appear as the most promising options, depending on development costs, implementation, learning, and risk management. Combined heat and power, efficient biomass cookstoves and small‐scale power generation for rural areas can help to promote energy access and sustainable development, along with reduced emissions. We estimate the sustainable technical potential as up to 100 EJ : high agreement; 100–300 EJ : medium agreement; above 300 EJ : low agreement. Stabilization scenarios indicate that bioenergy may supply from 10 to 245 EJ yr −1 to global primary energy supply by 2050. Models indicate that, if technological and governance preconditions are met, large‐scale deployment (>200 EJ ), together with BECCS , could help to keep global warming below 2° degrees of preindustrial levels; but such high deployment of land‐intensive bioenergy feedstocks could also lead to detrimental climate effects, negatively impact ecosystems, biodiversity and livelihoods. The integration of bioenergy systems into agriculture and forest landscapes can improve land and water use efficiency and help address concerns about environmental impacts. We conclude that the high variability in pathways, uncertainties in technological development and ambiguity in political decision render forecasts on deployment levels and climate effects very difficult. However, uncertainty about projections should not preclude pursuing beneficial bioenergy options.

This study used the ECOSSE model (v. 5.0.1) to simulate soil respiration (Rs) fluxes estimated from ecosystem respiration (Reco) for eight European permanent grassland (PG) sites with varying grass species, soils, and management. The main aim was to evaluate the strengths and weaknesses of the model in estimating Rs from grasslands, and to gain a better understanding of the terrestrial carbon cycle and how Rs is affected by natural and anthropogenic drivers. Results revealed that the current version of the ECOSSE model might not be reliable for estimating daily Rs fluxes, particularly in dry sites. The daily estimated and simulated Rs ranged from 0.95 to 3.1 g CO2-C m−2, and from 0.72 to 1.58 g CO2-C m−2, respectively. However, ECOSSE could still be a valuable tool for predicting cumulative Rs from PG. The overall annual relative deviation (RD) value between the cumulative estimated and simulated annual Rs was 11.9%. Additionally, the model demonstrated accurate simulation of Rs in response to grass cutting and slurry application practices. The sensitivity analyses and attribution tests revealed that increased soil organic carbon (SOC), soil pH, temperature, reduced precipitation, and lower water table (WT) depth could lead to increased Rs from soils. The variability of Rs fluxes across sites and years was attributed to climate, weather, soil properties, and management practices. The study suggests the need for additional development and application of the ECOSSE model, specifically in dry and low input sites, to evaluate the impacts of various land management interventions on carbon sequestration and emissions in PG.