Denitrification is an important component of the nitrogen cycle in soil, returning reactive nitrogen to the atmosphere. Denitrification activity is often concentrated spatially in anoxic microsites and temporally in ephemeral events, which presents a challenge for modelling. The anaerobic fraction of soil volume can be a useful predictor of denitrification in soils. Here, we provide a review of this soil characteristic, its controlling factors, its estimation from basic soil properties and its implementation in current denitrification models. The concept of the anaerobic soil volume and its relationship to denitrification activity has undergone several paradigm shifts that came along with the advent of new oxygen and microstructure mapping techniques. The current understanding is that hotspots of denitrification activity are partially decoupled from air distances in the wet soil matrix and are mainly associated with particulate organic matter (POM) in the form of fresh plant residues or manure. POM fragments harbor large amounts of labile carbon that promote local oxygen consumption and, as a result, these microsites differ in their aeration status from the surrounding soil matrix. Current denitrification models relate the anaerobic soil volume fraction to bulk oxygen concentration in various ways but make little use of microstructure information, such as the distance between POM and air-filled pores. Based on meta-analyses, we derive new empirical relationships to estimate the conditions for the formation of anoxia at the microscale from basic soil properties and we outline how these empirical relationships could be used in the future to improve prediction accuracy of denitrification models at the soil profile scale.

High Nitrous Oxide (N2O) emissions have been identified in hemiboreal forests in association with draining organic soils. However, the specific controlling factors that regulate the emissions remain unclear. To examine the importance of different factors affecting N2O emissions in a spruce forest on drained organic soil, a process-based model, CoupModel, was calibrated using the generalized likelihood uncertainty estimation (GLUE) method. The calibration also aims to estimate parameter density distributions, the covariance matrix of estimated parameters and the correlation between parameters and variables information, useful when applying the model on other peat soil sites and for further model improvements. The calibrated model reproduced most of the high resolution data (total net radiation, soil temperature, groundwater level, net ecosystem exchange, etc.) very well, as well as cumulative measured N2O emissions (simulated 8.7±1.1kgN2Oha−1year−1 (n=97); measured 8.7±2.7kgN2Oha−1year−1 (n=6)), but did not capture every measured peak. Parameter uncertainties were reduced after calibration, in which 16 out of 20 parameters changed from uniform distributions into normal distributions or log normal distributions. Four parameters describing bypass water flow, oxygen diffusion and soil freezing changed significantly after calibration. Inter-connections and correlations between many calibrated parameters and variables reflect the complex and interrelated nature of pedosphere, biosphere and atmosphere interactions. This also highlights the need to calibrate a number of parameters simultaneously. Model sensitivity analysis indicated that N2O emissions during growing seasons are controlled by competition between plants and microbes for nitrogen, while during the winter season snow melt periods are important. Our results also indicate that N2O is mainly produced in the capillary fringe close to the groundwater table by denitrification in the anaerobic zone. We conclude that, in afforested drained peatlands, the plants and groundwater level have important influences on soil N availability, ultimately controlling N2O emissions

This study presents the integration of the phosphorus (P) cycle into CoupModel (v6.0, referred to as Coup-CNP). The extended Coup-CNP, which explicitly considers the symbiosis between soil microbes and plant roots, enables simulations of coupled carbon (C), nitrogen (N), and P dynamics for terrestrial ecosystems. The model was evaluated against observed forest growth and measured leaf C∕P, C∕N, and N∕P ratios in four managed forest regions in Sweden. The four regions form a climatic and fertility gradient from 64^∘ N (northern Sweden) to 56^∘ N (southern Sweden), with mean annual temperature varying from 0.7–7.1 ^∘C and soil C∕N and C∕P ratios varying between 19.8–31.5 and 425–633, respectively. The growth of the southern forests was found to be P-limited, with harvested biomass representing the largest P losses over the studied rotation period. The simulated P budgets revealed that southern forests are losing P, while northern forests have balanced P budgets. Symbiotic fungi accounted for half of total plant P uptake across all four regions, which highlights the importance of fungal-tree interactions in Swedish forests. The results of a sensitivity analysis demonstrated that optimal forest growth occurs at a soil N∕P ratio between 15–20. A soil N∕P ratio above 15–20 will result in decreased soil C sequestration and P leaching, along with a significant increase in N leaching. The simulations showed that Coup-CNP could describe shifting from being mostly N-limited to mostly P-limited and vice versa. The potential P-limitation of terrestrial ecosystems highlights the need for biogeochemical ecosystem models to consider the P cycle. We conclude that the inclusion of the P cycle enabled the Coup-CNP to account for various feedback mechanisms that have a significant impact on ecosystem C sequestration and N leaching under climate change and/or elevated N deposition.

The CoupModel was used to simulate a Norway spruce forest on fertile drained peat over 60 years, from planting in 1951 until 2011, describing abiotic, biotic and greenhouse gas (GHG) emissions (CO₂ and N₂O). By calibrating the model against tree ring data a “vegetation fitted” model was obtained by which we were able to describe the fluxes and controlling factors over the 60 years. We discuss some conceptual issues relevant to improving the model in order to better understand peat soil simulations. However, the present model was able to describe the most important ecosystem dynamics such as the plant biomass development and GHG emissions. The GHG fluxes are composed of two important quantities, the spruce forest carbon (C) uptake, 413 g C m⁻² yr⁻¹ and the decomposition of peat soil, 399 g C m⁻² yr⁻¹. N₂O emissions contribute to the GHG emissions by up to 0.7 g N m⁻² yr⁻¹, corresponding to 76 g C m⁻² yr⁻¹. The 60-year old spruce forest has an accumulated biomass of 16.0 kg C m⁻² (corresponding to 60 kg CO₂ m⁻²). However, over this period, 26.4 kg C m⁻² (97 kg CO₂eq m⁻²) has been added to the atmosphere, as both CO₂ and N₂O originating from the peat soil and, indirectly, from forest thinning products, which we assume have a short lifetime. We conclude that after harvest at an age of 80 years, most of the stored biomass carbon is liable to be released, the system having captured C only temporarily and with a cost of disappeared peat, adding CO₂ to the atmosphere.