External datasets catalogue

Global Burden of Disease Results Tool — 03 Jun 2022

Estimating irrigation water requirements in Europe — 25 May 2022

In Southern Europe, irrigated agriculture is by far the largest consumer of freshwater resources. However, consistent information on irrigation water use in the European Union is still lacking. We applied the crop growth model EPIC to calculate irrigation requirements in the EU and Switzerland, combining available regional statistics on crop distribution and crop specific irrigated area with spatial data sources on soils, land use and climate. The model was applied at a 10×10km grid using different irrigation strategies over a period of 8years. The irrigation requirements reflect the spatial distribution of irrigated areas, climatic conditions and crops. Simulated net irrigation requirements range from 53mm/yr in Denmark to 1120mm/yr in Spain, translating into estimated volumetric net irrigation requirements of 107mio.m3 and 35,919mio.m3, respectively. We estimate gross irrigation demands to be 1.3–2.5 times higher than field requirements, depending on the efficiency of transport and irrigation management. A comparison with national and regional data on water abstractions for irrigation illustrates the information deficit related to currently available reported data, as not only model limitations but also different national approaches, country-specific uncertainties (illegal or unrecorded abstractions), and restrictions of actual water use come into play. In support of European environmental and agricultural policies, this work provides a large-scale overview on irrigation water requirements in Europe applying a uniform approach with a sufficiently high spatial resolution to support identification of hot spots and regional comparisons. It will also provide a framework for national irrigation water use estimations and supports further analysis of agricultural pressures on water quantity in Europe. Keywords: Agriculture; Irrigation; Water abstractions; Crop water requirement; Europe; Large-scale modeling

Daily mean temperature, E-OBS gridded dataset version 25.0e (copyrights protected) — 20 May 2022

The E-OBS dataset consists of gridded fields created from station series throughout Europe. The dataset contains preliminary daily updates of the E-OBS dataset for daily mean temperature. Only the last 60 days are saved in this dataset, so the latest month is completely available at all times after the monthly update.

ERA5 monthly averaged data on single levels from 1950 to 1978 (preliminary version) — 19 May 2022

Global Ocean acidification - mean sea water pH time series and trend from Multi-Observations Reprocessing — 18 May 2022

Ocean acidification is quantified by decreases in pH, which is a measure of acidity: a decrease in pH value means an increase in acidity, that is, acidification. The observed decrease in ocean pH resulting from increasing concentrations of CO2 is an important indicator of global change. The estimate of global mean pH builds on a reconstruction methodology, Obtain values for alkalinity based on the so called “locally interpolated alkalinity regression (LIAR)” method after Carter et al., 2016; 2018. Build on surface ocean partial pressure of carbon dioxide (CMEMS product: MULTIOBS_GLO_BIO_CARBON_SURFACE_REP_015_008) obtained from an ensemble of Feed-Forward Neural Networks (Chau et al. 2022) which exploit sampling data gathered in the Surface Ocean CO2 Atlas (SOCAT) ( https://www.socat.info/ ) Derive a gridded field of ocean surface pH based on the van Heuven et al., (2011) CO2 system calculations using reconstructed pCO2 (MULTIOBS_GLO_BIO_CARBON_SURFACE_REP_015_008) and alkalinity. The global mean average of pH at yearly time steps is then calculated from the gridded ocean surface pH field. It is expressed in pH unit on total hydrogen ion scale. In the figure, the amplitude of the uncertainty (1σ ) of yearly mean surface sea water pH varies at a range of (0.0023, 0.0029) pH unit (see Quality Information Document for more details). The trend and uncertainty estimates amount to -0.0017±0.0004e-1 pH units per year. The indicator is derived from in situ observations of CO2 fugacity (SOCAT data base, www.socat.info , Bakker et al., 2016). These observations are still sparse in space and time. Monitoring pH at higher space and time resolutions, as well as in coastal regions will require a denser network of observations and preferably direct pH measurements. A full discussion regarding this OMI can be found in section 2.10 of the Ocean State Report 4 (Gehlen et al., 2020).

Economic accounts for agriculture - values at constant prices (2010 = 100) [aact_eaa07] — 10 May 2022

2019 Cities Adaptation Actions — 03 May 2022

Sewage sludge production and disposal [env_ww_spd] — 13 Apr 2022

Proportion of safely treated domestic wastewater flows (%) (EN_WWT_WWDS) — 13 Apr 2022

Statistical pocketbook 2021 — 13 Apr 2022

Urban Audit 2020

EuroBoundaryMap 2020 (EBM 2020), Jan. 2020 (copyrights protected) — 05 Apr 2022

JEC Well-To-Wheels report v5 — 28 Mar 2022

JRC (the Joint Research Centre of the European Commission), EUCAR and Concawe have updated their joint evaluation of the Well-to-Wheels energy use and greenhouse gas (GHG) emissions for a wide range of potential future fuel and powertrain options, first published in December 2003. As an update of the previous version, the objectives of JEC WTW v5 are to establish, in a transparent and objective manner, a consensual Well-to-Wheels energy use and GHG emissions assessment of a wide range of automotive fuels and powertrains relevant to Europe in 2025 and beyond. This versions updates the technologies investigated and applies a common methodology and data-set to estimate WTW emissions.This WTW version 5 concentrates on the evaluation of energy and GHG balances for the different combinations of fuel and powertrains, in road transport. The current version 5 investigates, for the first time, the heavy duty segment, thus expanding the scope of the previous versions of the study.

Alternative fuels fleet M1 electricity (2020) — 25 Mar 2022

Alternative fuel fleet percentage of total fleet M1 electricity (2020) — 25 Mar 2022

Alternative fuels new registrations M1 (2020) — 25 Mar 2022

Alternative fuel market share new registrations M1 (2020) — 25 Mar 2022

Summary of annual road freight transport by type of operation and type of transport (1 000 t, Mio Tkm, Mio Veh-km) [road_go_ta_tott] — 24 Mar 2022

Gross value added by A*10 industry - selected international annual data [NAIDA_10_A10__custom_2107129] — 22 Mar 2022

EU Reference Scenario 2020 (Dataset URL is not available) — 17 Mar 2022