Effect of climate change and nitrogen deposition on central-European forests: Regional-scale simulation for South Bohemia

The simulation of forest production until 2100 under different environmental scenarios and current management practices was performed using a process-based model BIOME-BGC previously parameterized for the main Central-European tree species: spruce, pine, beech and oak and adapted to include forest management practices. Climatic scenario HadCM3 used in the simulations was taken from the IPCC database created within the 3rd Assessment Report. It was combined with a scenario of CO₂ concentration development and a scenario of N deposition. The control scenario considered no changes of climatic characteristics, CO₂ concentration and N deposition. Simulation experiment was performed for the test region - South Bohemia - using a 1 km × 1 km grid. The actual data on the regional forest cover were aggregated for each grid cell in such a way that each cell represented an even-aged single-dominant species stand or non-forested area, and a standard management scenario depending on the stand age and species was applied to each cell. The effect of environmental variables was estimated as the difference of simulated carbon pools and fluxes in 2050 under environmental changes and under control scenario.The model simulation for the period to 2050 with only climate change under constant CO₂ concentration and N deposition indicated a small decrease of NPP (median values by species reached −0.9 to −1.7% for different species), NBP (−0.3 to −1.7%) and vegetation carbon (−0.3 to −0.7%), whereas soil C slightly increased. Separate increase of N deposition gave small positive effect on carbon pools (0.8-2.9% for wood C and about 0.5% for soil C) and more expressed effect on carbon fluxes (1.8-4.3% for NPP and 1.0-9.7% for NBP). Separate increase of CO₂ concentration lead to 0.6-2.4% increase of wood C pool and 0.1-0.5% increase of soil C. The positive effects of CO₂ concentration and N deposition were more pronounced for coniferous than for deciduous stands.Replacement of 0.5% of coniferous plantations every year by natural broadleaved stands evoked 10.5% of increase of wood carbon pool due to higher wood density of beech and oak compared to spruce and pine, but slightly decreased soil and litter carbon pools.     

Assessment and up-scaling of CO2 exchange by patches of the herbaceous vegetation mosaic in a Portuguese cork oak woodland

Long-term eddy covariance measurements over a montado oak woodland in southern Portugal have documented a vulnerability to predicted decreases in springtime rainfall, since water availability during spring limits annual CO₂ gain, the growth of fodder for animals, and the production of cork by Quercus suber. The current study examined CO₂ exchange of three different herbaceous vegetation components distributed over montado landscapes and within the footprint of long-term landscape eddy covariance monitoring studies. Simultaneous measurements with eddy covariance at two sites and with manually operated chambers at multiple locations revealed that slow drainage of shallow basins, the onset of drying at higher sites and a high release of CO₂ below tree canopies significantly influenced the overall course of montado ecosystem gas exchange during the spring.Hyperbolic light response models were employed to up-scale and compare herbaceous gas exchange with landscape net ecosystem CO₂ flux. The up-scaling demonstrates the importance of the herbaceous understory in determining annual carbon balance of the montado and suggests a relatively small additional CO₂ uptake by the tree canopies and boles, i.e., by the aboveground tree compartment, during springtime. Annual flux totals obtained during the extremely dry year 2005 and a normal precipitation year 2006 for the oak woodland and a nearby grassland were essentially the same, indicating that both ecosystems similarly exploit available resources. Based on comparisons with additional temperate grasslands, we can visualize the montado herbaceous cover as a typical European grassland canopy, but where temperature fluctuations in winter control uptake, and where total production depends on springtime rainfall as it controls phenological events and eventually dieback of the vegetation. On the other hand, tree canopies remain active longer during late spring and early summer, modifying the montado response from that of grassland. Uncertainties in flux estimates via both chamber and eddy covariance methodologies currently prevent a full understanding of vegetation/atmosphere coupling, of the recycling of CO₂ between the understory communities and trees, and of relationships between exchange rates of individual components of the vegetation mosaic and overall carbon and water balances in montado landscapes.     

Uncertainties in up-scaling N2O flux from field to 1°×1° scale: A case study for Inner Mongolian grasslands in China

Since N₂O emissions cannot be measured easily at large scales, global emission estimates inevitably involve problems with scaling. To date, up-scaling processes depend highly on the models and database. Because of the limitation in resolution of the databases, which provide input parameters to drive the model's regional simulations, the uncertainties generated from the up-scaling processes must be quantified. In this paper, the uncertainties in up-scaling N₂O emissions from the field scale (∼1 km²) to 1°×1° scale (∼10,000 km²) were quantified in a case study from the Xilin River basin of Inner Mongolia, China. A revised process-based DNDC model was applied in the study for quantifying N₂O fluxes with a high-resolution (1 km²) soil database constructed with remote sensing data and GIS technique. The results showed that the uncertainties coming from spatial scaling effect is 63.6%, and from the partitioning of sensitive model parameter (SOC) is 86.4%. We found that inclusion of spatial heterogeneity of soil factors resulted in lower regional N₂O emission estimates. Utilization of the spatial structural information based on soil type was more effective for reducing the spatial scaling effect in comparison with the variability information calculated from Monte Carlo method.     

Modelling productivity and resource use efficiency for grassland ecosystems in the UK

Estimating spatially resolved grassland productivity is essential for benchmarking the total UK productive potential to assess food, feed and fuel trade-offs in the context of whole systems analyses. Our objectives were to adapt and evaluate a well-known process-based model (PBM) and estimate productivity of improved (permanent, temporary) and semi-natural grassland systems using meta-models (MM) trained by extensive PBM scenario simulations. Observed dry matter (DM) yields in multi-site nitrogen (N) response (0, 150 and 300 kg N ha⁻¹) experiments were well emulated describing the average productivity of rough grazing, permanent and temporary grassland (3.1, 7.4 and 9.8 t DM ha⁻¹, respectively). Cross-validated with independent and long-term data (Park Grass Experiment), the PBM explained more variation when considering all systems combined (81%) than across all improved grasslands (61%) but little for rough grazing (26%). The PBM-trained MMs explained 48, 72 and 70% of the simulated yield variation in the grasslands of increasing management intensity, and 43 and 75% of observed variation in the combined improved and all three grassland systems, respectively. Considering the assessment of ecosystem services, like drainage and water productivity, PBM scenario simulations are essential. Compared to improved grassland rough grazing will result in 40% more groundwater recharge due to its lower simulated water use and water productivity (12 versus 25 and 43 kg ha⁻¹ mm⁻¹ for permanent and temporary grassland, respectively).