ALFAwetlands extrapolates the knowledge about restoration effects on ecosystem services (ES) from living labs to similar conditions across larger regions and Europe. With this, project is aiming to quantify the potential contribution of wetland restoration and rehabilitation to the EU climate mitigation efforts. To do so, ALFAwetlands employs:
- Modelling at experimental sites to build capabilities for GHG flux and other ES predictions in different kinds of pristine, degraded and restored wetlands.
- EU-scale, state-of-the-art modelling toolbox projecting GHG fluxes and other ES in the EU land-based sectors. This includes forests, cropland, grasslands, and wetlands. The large-scale toolbox integrates and uses the innovations from site modelling in living labs into the EU-scale scenario assessment.
EU-scale models
In our setup, site modelling builds the capability to assess restoration effects in different kinds of existing and degraded wetlands, while the EU-scale models:
- deliver ES for mitigation options in other Land Use, Land-Use Change and Forestry (LULUCF) land
- bring up-to-date spatial information on land use and management across Europe, and
- guarantee the consistency of simulated reference scenario with the pivotal EU databases and assessments used for EU policy support. Among which are EUROSTAT, UNFCCC.
Besides, the toolbox allows accounting for exogenous scenarios of socio-economic drivers, climate change, costs of mitigation, and policy instruments. Well-established biophysical, land use and economic models are the backbone of these efforts.
Site-scale models
Site-scale models simulate the main biophysical processes determining ecosystem services, including plant growth, water, carbon, and nitrogen cycles. They predict CO2, N2O and CH4 fluxes, carbon stocks, and commodity production in different kinds of land as regulated by management and climate change. Importantly, site models can simulate ecosystem services provided by wetlands consistently with other LULUCF land.
JSBACH
JSBACH is a land surface model of the Max Planck Institute for Meteorology Earth System Model (MPI-ESM) simulating terrestrial energy, hydrology, and carbon fluxes (Reick et al., 2013). Vegetation diversity is introduced via different plant functional types. JSBACH was coupled with the soil carbon model YASSO (Goll et al., 2015), the methane model HIMMELI (Raivonen et al., 2017) and the groundwater table dynamics to simulate GHG fluxes in peatlands. Tree growth processes were added to account for forestry management in the site version of JSBACH-YASSO-HIMMELI. The model predicts carbon sequestration, vegetation and soil respiration, foliage, woody and root biomass, litter flux, carbon storages in several soil carbon pools, forest growth and harvest, evapotranspiration, soil moisture and temperature profiles, snow cover and other variables in half-hourly to multidecadal timescales.
3PGmix
3PGmix is a process-based forest growth model that predicts stand productivity based on the absorbed photosynthetically active radiation, leaf area index, and canopy quantum efficiency. The canopy quantum efficiency is constrained by site conditions. It includes vapour pressure deficit, soil water availability, air temperature, and soil fertility (Almeida et al. 2004). 3PGmix simulates gross and net primary productivity, and carbon allocation in roots, foliage and stem biomass. Following the carbon allocation, the model also computes stand structural parameters relevant for management. Besides, it integrates mortality and soil water balance routines and allows the simulation of silvicultural operations. 3PGmix has been coupled with the YASSO-peat module to facilitate the upscaling process.
EPIC
The Environmental Policy Integrated Climate (EPIC) model is a process-based model of crop and grass growth, water, nutrient and carbon cycling, soil temperature and erosion on agricultural land (Williams, 1995). It simulates plant biomass using radiation-use efficiency and growth stresses caused by extreme temperature, water and nutrient deficit, or inadequate aeration due to water logging. The organic C and N module (Izaurralde et al., 2006) calculates heterotrophic respiration (CO2) and transformations of organic compartments as regulated by soil moisture, temperature, oxygen, tillage mixing and lignin content. Also, EPIC calculates carbon leaching and atmospheric emissions of N2O from the soil profile (Izaurralde et al., 2017). It allows the simulation of water table dynamics and management options. EPIC includs tillage, fertilization, irrigation, crop rotations, residue management, erosion control, grazing, mowing, or drainage.
References
Almeida, A.C., Landsberg, J.J., Sands, P.J., 2004. Parameterisation of 3-PG model for fast-growing Eucalyptus grandis plantations. For. Ecol. Manag. 193, 179–195. https://doi.org/10.1016/j.foreco.2004.01.029
Goll, D.S., Brovkin, V., Liski, J. et al., 2015. Strong dependence of CO2 emissions from anthropogenic land cover change on initial land cover and soil carbon parametrization. Glob. Biogeochem. Cycles 29, 1511–1523. https://doi.org/10.1002/2014GB004988
Izaurralde, R.C., McGill, W.B., Williams, J.R. et al., 2017. Simulating microbial denitrification with EPIC: Model description and evaluation. Ecol. Model. 359, 349–362. https://doi.org/10.1016/j.ecolmodel.2017.06.007
Izaurralde, R.C., Williams, J.R., McGill, W.B. et al., 2006. Simulating soil C dynamics with EPIC: Model description and testing against long-term data. Ecol. Model. 192, 362–384. https://doi.org/10.1016/j.ecolmodel.2005.07.010
Raivonen, M., Smolander, S., Backman, L. et al., 2017. HIMMELI v1.0: HelsinkI Model of MEthane buiLd-up and emIssion for peatlands. Geosci. Model Dev. 10, 4665–4691. https://doi.org/10.5194/gmd-10-4665-2017
Reick, C.H., Raddatz, T., Brovkin, V., Gayler, V., 2013. Representation of natural and anthropogenic land cover change in MPI‐ESM. J. Adv. Model. Earth Syst. 5, 459–482. https://doi.org/10.1002/jame.20022
Williams, J.R., 1995. The EPIC model, in: Singh, V.P. (Ed.), Computer models of watershed hydrology. Water resources publisher, Colorado, pp. 909–1000.