The EU modelling toolbox integrates models used in living lab experimental plots with large-scale input data gridded over Europe. The biophysical and land use models are already trained for a long list of mitigation options in agriculture, grasslands, and forests, while the wetland options have been built based on the ALFAwetlands site modelling. In each grid cell, the respective models are run to project GHG fluxes and other ES for the referential land use and management scenario, along with climate change and restoration or rehabilitation options where appropriate. In the referential scenario, the modelling toolbox has been calibrated to represent the present-day EU forestry and agricultural sectors and consistently reproduce the pivotal sectoral statistics, including those from EUROSTAT for land cover and commodity production and UNFCCC for GHG emissions and removals. Furthermore, toolbox harmonises the most up-to-date EU datasets, including the European Wetlands Map.
- Pan-European gridded crop modelling system EPIC-IIASA was built by coupling the field-scale model EPIC with EU-scale data on environmental conditions, farming practices, and grassland management intensity (Balkovič et al., 2013) and has already been used in the EU-scale assessments of CO2 fluxes in cropland and grassland (McGrath et al., 2023).
- The Wetland Soils Denitrification Model WSDM is a parsimonious process-based model for estimating nitrogen denitrification fluxes in wetland ecosystems (Martínez-Espinosa et al., 2022). WSDM simulates denitrification processes in wetland soils, influenced by three main physical parameters: soil moisture, temperature and nitrate availability. The WSDM model simplifies the denitrification process by forcing available daily spatial data to enable large-scale applications.
- The Global Forest Model G4M (Kindermann et al., 2008) simulates the properties and evolution of forests on global, continental, and national scales. The model calculates forest productivity following climate and soil parameters, and inputs from other models such as 3PGmix. This productivity is combined with an empirical description of forest growth to simulate the long-term evolution of forests under different management regimes and climate change. G4M forecasts land-use change, carbon sequestration and emissions in forests. G4M also estimates the net income derived from forests.
- Land-use Global Biosphere Management Model GLOBIOM is an economic global recursive dynamic partial equilibrium model (Havlík et al. 2014) integrating the agricultural, bioenergy, and forestry sectors to provide EU policy assessments concerning land use competition. The model runs in a spatially explicit setting following gridded biophysical (e.g., EPIC-IIASA, G4M) and technical cost information. Its spatial equilibrium modelling approach represents bilateral trade based on cost competitiveness, while commodity markets and international trade are modelled at the level of aggregate economic regions. GLOBIOM was updated with comprehensive LULUCF coverage, allowing for a full account of the main agriculture, forestry, and now wetland GHG sources. GHG reduction, productivity changes, and economic costs are used to characterize the mitigation options in GLOBIOM land use projections. All GHG emissions calculations are based on IPCC guidelines. GLOBIOM has been successfully applied for estimating LULUCF emission pathways and impact assessments in the EU. For example, EU Reference Scenario 2020, assessment of indirect LUC effects of EU biofuel targets, policy scenarios for delivering the EGD, EU Climate Target Plan impact assessment, EU Long-Term Strategy, review of the ambition to 55%, several legislative proposals of the Fit for 55 package.
(a) Net biome productivity (nbp) of CO2-C fluxes simulated for cropland in the Reference EU scenario using the ALFAwetlands modelling toolbox (EPIC-IIASA), and
(b) comparison of the simulated nbp fluxes (blue data points and intervals) in the Reference EU scenario with the values calculated from UNFCCC CL NGHGI for exemplary European countries (magenta data points). Negative fluxes represent a source.
References
Balkovič, J., van der Velde, M., Schmid, E. et al., 2013. Pan-European crop modelling with EPIC: Implementation, up-scaling and regional crop yield validation. Agric. Syst. 120, 61–75. https://doi.org/10.1016/j.agsy.2013.05.008
Havlík, P., Valin, H., Herrero, M. et al., 2014. Climate change mitigation through livestock system transitions. Proc. Natl. Acad. Sci. 111, 3709–3714. https://doi.org/10.1073/pnas.1308044111
Kindermann, G., Obersteiner, M., Sohngen, B. et al., 2008. Global cost estimates of reducing carbon emissions through avoided deforestation. Proc. Natl. Acad. Sci. 105, 10302–10307. https://doi.org/10.1073/pnas.0710616105
Martínez-Espinosa, C., Sauvage, S., Al Bitar, A., Sánchez Pérez, J.M., 2022. A dynamic model for assessing soil denitrification in large-scale natural wetlands driven by Earth Observations. Environ. Model. Softw. 158, 105557. https://doi.org/10.1016/j.envsoft.2022.105557
McGrath, M.J., Petrescu, A.M.R., Peylin, P. et al., 2023. The consolidated European synthesis of CO 2 emissions and removals for the European Union and United Kingdom: 1990–2020. Earth Syst. Sci. Data 15, 4295–4370. https://doi.org/10.5194/essd-15-4295-2023
If you are interested in more details regarding modelling, you can contact International Institute for Applied Systems Analysis (IIASA) and Finnish Meteorological Institute (FMI) and ALFAwetlands team.