Waterbase is the generic name given to the EEAs databases on the status and quality of Europes rivers, lakes, groundwater bodies and transitional, coastal and marine waters, on the quantity of Europes water resources, and on the emissions to surface waters from point and diffuse sources of pollution.
Forest biomass density map at 100 m resolution for the year 2010, matching the harmonized reference statistics at national and sub-national scale in terms of forest area, biomass density and biomass stock.
This dataset corresponds to global map of built-up areas expressed in terms of a probability grid at 10 m spatial resolution derived from a Sentinel-2 global image composite (GHS_composite_S2_L1C_2017-2018_GLOBE_R2020A_UTM_10_v1_0) for reference year 2018. It builds on a new Deep Learning framework for pixel-wise large-scale classification of built-up areas named GHS-S2Net (GHS stands for Global Human Settlements, S2 refers to the Sentinel-2 satellite).
Spatial dataset consisting of annual nutrient loads and source apportionments for 2005-2012 estimated at the outlets of freshwater river basins with model GREEN (Grizzetti et al., 2012; March 2019 version). Sources of nutrients comprise agriculture (mineral and organic fertilization), nitrogen atmospheric deposition, phosphorus releases from natural areas, scattered dwellings, and point sources (domestic and industrial discharges). Nutrients are reduced by attenuation processes in land and freshwater reaches. The river basin hydrological data model is based on Catchment Characterisation and Modelling (Vogt et al., 2007; https://data.jrc.ec.europa.eu/dataset/fe1878e8-7541-4c66-8453-afdae7469221)
Spatial dataset consisting of nutrient loads and source apportionments estimated at the outlets of freshwater river basins with model GREEN (Grizzetti et al., 2012; version March 2019) according to four European nutrient (nitrogen and phosphorus) management scenarios: 1. Reference – REF: historical conditions (year 2012). 2. Business as Usual – BAU: implementation of current level of investments in water protection foreseen under Urban Waste Water Treatment Directive (UWWTD) and the Rural Development Program. 3. Nutrient Scenario – NUTR: includes a full or enhanced implementation of two EU Directives, the UWWTD for collecting and treating wastewater from urban agglomerations, and the Nitrates Directive to protect freshwater from agricultural pollution. 4. High Technically Feasible Reduction – MTFR: assumes all wastewaters in the EU are treated at the maximum level of nutrient reduction currently possible, while mineral fertilisers are applied to an optimal level. Sources of nutrients comprise agriculture (mineral and organic fertilization), nitrogen atmospheric deposition, phosphorus releases from natural areas, scattered dwellings, and point sources (domestic and industrial discharges). Nutrients are reduced by attenuation processes in land and freshwater reaches. The river basin hydrological data model is based on Catchment Characterisation and Modelling (Vogt et al., 2007; https://data.jrc.ec.europa.eu/dataset/fe1878e8-7541-4c66-8453-afdae7469221)
Accurately characterizing land surface changes with Earth Observation requires geo-localized ground truth. In the European Union (EU), a tri-annual surveyed sample of land cover and land use has been collected since 2006 under the Land Use/Cover Area frame Survey (LUCAS). A total of 1,351,293 observations at 651,780 unique locations for 117 variables along with 5.4 million photos were collected during five LUCAS surveys. Until now, these data have never been harmonised into one database, limiting full exploitation of the information. This paper describes the LUCAS point sampling/surveying methodology, including collection of standard variables such as land cover, environmental parameters, and full resolution landscape and point photos, and then describes the harmonisation process. The resulting harmonised database is the most comprehensive in-situ dataset on land cover and use in the EU. The database is valuable for geo-spatial and statistical analysis of land use and land cover change. Furthermore, its potential to provide multi-temporal in-situ data will be enhanced by recent computational advances such as deep learning.
Impact assessments for agriculture are partly based on projections delivered by models. Sectoral policies are becoming more and more interrelated. Hence, there is a need to improve the capacity of current models, connect them or redesign them to deliver on an increasing variety of policy objectives, and to explore future directions for agricultural modelling in Europe. SUPREMA (SUpport for Policy RElevant Modelling of Agriculture) is a project that has received funding from the European Union’s Horizon 2020 research and innovation programme (under grant agreement No 773499 SUPREMA) and that came to address this challenge by proposing a meta-platform that supports modelling groups linked already through various other platforms and networks. SUPREMA should help close the gaps between expectations of policy makers and the actual capacity of models to deliver relevant policy analysis. The SUPREMA model family includes a set of ‘core models’ that are already used in support of key European impact assessments in agriculture, trade, climate and bioenergy policies. One of the work-packages of the project ("Testing the SUPREMA model family") had the objective of testing the SUPREMA model family comparing model outcomes of three applications, including: (i) harmonize baseline assumptions and to the extent possible align baseline projections across models in the platform, and (ii) showcase the potential of the models in the meta-platform to respond to the upcoming and existing policy needs by means of two exploratory policy scenarios. This open dataset includes 3 components: 1 - (Baseline scenario) - the harmonized baselines (for 2030 and 2050). Please note that the baseline projections do not take into account the 2020 and possible future effects of the SARS-CoV-2 pandemic 2 - (Agricultural policy scenario) - medium-term horizon scenarios aiming comparing different models and/or model combinations, that have a large degree of ‘similarity’ such as joined indicator variables, i.e.: AGMEMOD-MITERRA (combined) modelling tool and the CAPRI model. The main focus was comparing model results in both agronomic and biophysical domains. Two variants of the agricultural policy scenario have been simulated and compared: (i) a CAP greening scenario; and (ii) a sustainable diet scenario. Both scenarios are hypothetical but have been chosen in such a way that the can provide insights in future policy issues as: (i) a further greening of the CAP fits in the policy implementation space as it is included in the ongoing policy reform of the CAP after 2020; and (ii) as increasing consumer awareness about healthy diets and their relation to meat consumption, as well as the footprint/climate consequences are highly relevant with respect to the Green Deal roadmap (December 2019) and the Farm to Fork Strategy (May 2020) documents that have been recently published. 3 - (Climate change mitigation scenario) - scenarios that quantifies the GHG mitigation potential of the EU’s agricultural sector and domestic and global impacts of the EU policy, conditional on different levels of GHG mitigation efforts in the rest of the world. These are obtained through the SUPREMA models CAPRI, GLOBIOM and MAGNET and include scenarios where the EU only takes ambitious unilateral climate action up to scenario where the 1.5 C target is pursued globally SUPREMA has been coordinated by Wageningen Research with the participation of EuroCARE, Thünen Institute, Swedish University of Agricultural Sciences (SLU), European Commission Joint Research Centre (JRC) and Research Executive Agency (REA), International Institute for Applied Systems Analysis (IIASA) and Universidad Politécnica de Madrid (UPM).
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