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Waterbase - Biology, 2024

Waterbase serves as the EEA’s central database for managing and disseminating data regarding the status and quality of Europe's rivers, lakes, groundwater bodies, transitional, coastal, and marine waters. It also includes information on the quantity of Europe’s water resources and the emissions from point and diffuse sources of pollution into surface waters. Specifically, Waterbase - Biology focuses on biology data from rivers, lakes, transitional and coastal waters collected annually through the Water Information System for Europe (WISE) – State of Environment (SoE) reporting framework. The data are expected to be collected within monitoring programs defined under the Water Framework Directive (WFD) and used in the classification of the ecological status or potential of rivers, lakes, transitional and coastal water bodies. These datasets provide harmonised, quality-assured biological monitoring data reported by EEA member and cooperating countries, as Ecological Quality Ratios (EQRs) from all surface water categories (rivers, lakes, transitional and coastal waters).

Noise data reported under Environmental Noise Directive (END)

The datasets includes 1) the noise exposure data, 2) the noise contours data, 3) razterized noise contours data and 4) potential quiet areas all under the terms of the Environmental Noise Directive (END). Data covers the EEA32 member countries and the United Kingdom (excluding Turkey for the third round of noise mapping in 2017).

Project OTC-Genomics: Environmental and microbial time series data from the Warnow estuary and the Baltic Sea coast

Estuaries and coasts are characterized by ecological dynamics that bridge the boundary between habitats, such as fresh and marine water bodies or the open sea and the land. Because of this, these ecosystems harbor ecosystem functions that shaped human history. At the same time, they display distinct dynamics on large and small temporal and spatial scales, impeding their study. Within the framework of the OTC-Genomics project, we compiled a data set describing the community composition as well as abiotic state of an estuary and the coastal region close to it with unprecedented spatio-temporal resolution. We sampled fifteen locations in a weekly to twice weekly rhythm for a year across the Warnow river estuary and the Baltic Sea coast. From those samples, we measured temperature, salinity, and the concentrations of Chlorophyll a, phosphate, nitrate, and nitrite (physico-chemical data); we sequenced the 16S and 18S rRNA gene to explore taxonomic community composition (sequencing data and bioinformatic processing workflow); we quantified cell abundances via flow cytometry (flow cytometry data); and we measured organic trace substances in the water (organic pollutants data). Processed data products are further available on figshare.

Synthesis data on plant community composition, plant traits and ecosystem properties from the Jena Main experiment

Data on plant communities (biomass and relative cover of all target species), plant traits (41 different traits, measured on 59 species), and 42 ecosystem properties/functions, measured between 2003 and 2012 in the Jena Main Biodiversity experiment. In floodplain grasslands of the Saale river, near Jena (Germany) 78 20x20 m grassland plots were set up, in which combinations of 1, 2, 4, 8 or 16 species were sown, from a species pool of 60. Thereby, the aim was to create a gradient in plant species richness and functional composition. In each year from 2003-2012, relative cover (in %) of each target species was estimated within 3x3 m subplots. In addition, plant biomass was measured in both spring and summer. In addition, we compiled trait data for 59 of the 60 sown species, based on a combination of existing literature, pot experiments and measurements in the Jena Main Biodiversity experiment monoculture (1-species) plots. Data on 41 traits was collected. Finally, we measured in 41 different ecosystem functions in the Jena Main Biodiversity experiment. Each ecosystem function was measured in at least 3 different years between 2003 and 2012. The "R2.model.random.text[x]" (where x is a number from 1 to 40) are secondary data files, and the outcome of statistical models. In these, 100 times a random subset of 1 to 40 (out of the 41) plant traits were analysed as predictors of the 42 ecosystem functions, in order to assess how the proportion of variance in ecosystem functioning explained by traits (R2 values) depends on the number of traits analysed.

Industrial Emissions Directive 2010/75/EU and European Pollutant Release and Transfer Register Regulation (EC) No 166/2006 - ver. 15.0 Dec. 2025 (Tabular data)

This metadata refers to the geospatial dataset representing the status of the EEA Industrial Reporting database as of 15 December 2025 (version 15). The release and emissions data cover the period 2007-2024 as result of the data reported under the E-PRTR facilities, 2017-2024 for IED installations and WI/co-WIs, and 2016-2024 for LCPs. These data are reported to EEA under Industrial Emissions Directive (IED) 2010/75/EU Commission Implementing Decision 2018/1135 and the European Pollutant Release and Transfer Register (E-PRTR) Regulation (EC) No 166/2006 Commission Implementing Decision 2019/1741. The dataset brings together data formerly reported separately under E-PRTR Regulation Art.7 and under IED Art.72. Additional reporting requirements under the IED are also included.

Climate Simulation with CLM, Scenario A1B run no.1, North Atlantic region

[ Derived from parent entry - see the respective metadata entry ] The experiment CLM_A1B_ZS contains Northern European regional climate simulations of the years 2070-2099 on a rotated grid (CLM non hydrostatic, 0.44 deg. hor. resolution, see http://www.clm-community.eu ). It is forced by the first (_1_) run of the global IPCC SRES A1B (EH5-T63L31_OM-GR1.5L40_A1B_1_6H), which describes a possible future world of very rapid economic growth, global population peaking in mid-century and rapid introduction of new and more efficient technologies with a balance across all energy sources. The model region starts at -19.36/-40.48 (lat/lon in rotated coordinates; centre of lower left corner of the domain) with rotated North Pole at 21.3/-175.0 (lat/lon). The number of grid points is 80/146 (lat/lon). The sponge zone (numerically unreliable boundary grid points) consists of 8 grid boxes at each border. EH5-T63L31_OM-GR1.5L40_A1B_1_6H were nudged during the simulations (spectral nudging,von Storch, H., A spectral nudging technique for dynamical downscaling purposes. Mon. Wea. Rev, 2000 ) The regional model variables include two-dimensional near surface fields and atmospheric fields on 6 pressure levels (200, 500, 700, 850, 925 and 1000 hPa) for zonal and meridional wind, temperature and pressure. The time interval of the output fields is 3 hours. Please contact sga"at"dkrz.de for data request details. The output format is netCDF. Experiment with CLM 2.4.6 on HPC Cluster ( blizzard ).

Global Precipitation Analysis Products of the GPCC

The Global Precipitation Climatology Centre (GPCC) has been established in 1989 on request of the World Meteorological Organization (WMO). It is operated by Deutscher Wetterdienst (DWD, National Meteorological Service of Germany) as a German contribution to the World Climate Research Programme (WCRP). Mandate of the GPCC is the global analysis of precipitation on earth’s land surface based on in situ rain gauge data. These gridded analyses provide long-term means, monthly and daily totals, quantiles and a drought index.

METOP GOME-2 - Nitrogen Dioxide (NO2) - Global

The Global Ozone Monitoring Experiment-2 (GOME-2) instrument continues the long-term monitoring of atmospheric trace gas constituents started with GOME / ERS-2 and SCIAMACHY / Envisat. Currently, there are three GOME-2 instruments operating on board EUMETSAT's Meteorological Operational satellites MetOp-A, -B, and -C, launched in October 2006, September 2012, and November 2018, respectively. GOME-2 can measure a range of atmospheric trace constituents, with the emphasis on global ozone distributions. Furthermore, cloud properties and intensities of ultraviolet radiation are retrieved. These data are crucial for monitoring the atmospheric composition and the detection of pollutants. DLR generates operational GOME-2 / MetOp level 2 products in the framework of EUMETSAT's Satellite Application Facility on Atmospheric Chemistry Monitoring (AC-SAF). GOME-2 near-real-time products are available already two hours after sensing. The operational NO2 total column products are generated using the algorithm GDP (GOME Data Processor) version 4.x integrated into the UPAS (Universal Processor for UV / VIS Atmospheric Spectrometers) processor for generating level 2 trace gas and cloud products. The total NO2 column is retrieved from GOME solar back-scattered measurements in the visible wavelength region (425-450 nm), using the Differential Optical Absorption Spectroscopy (DOAS) method. For more details please refer to relevant peer-review papers listed on the GOME and GOME-2 documentation pages: https://atmos.eoc.dlr.de/app/docs/

Schwerpunktprogramm (SPP) 1488: Planetary Magnetism (PlanetMag), Ground-truthing magnetic recording in meteorites

Whether primordial bodies in the solar system possessed internally-generated dynamos is a fundamental constraint to understand the dynamics and timing of early planetary formation. Paleointensity studies on several meteorites reveal that their host planets possessed magnetic fields within an order-of magnitude of the present Earths field. Interpretation of paleointensity data relies heavily on fundamental knowledge of the magnetic properties of the magnetic carriers, such as the single to multidomain size threshold or how the saturation magnetization varies as a function of grain size, yet very little knowledge exists about these key parameters for some of the main magnetic recorders in meteorites: the iron-nickel alloys. Moreover, most meteorites have experienced some amount of shock during their histories, yet the consequence of even very small stresses on paleointensity data is poorly known.We wish to fill these gaps by magnetically characterizing Fe-Ni alloys as a function of grain size and by determining how absolute and relative paleointensity data are biased by strain levels lower than those petrologically observable (less than 4-5 GPa). For example, our preliminary work shows that an imposed stress of 0.6 GPa will reduce absolute paleointensity estimates by 46Prozent for single domain magnetite-bearing rocks. In general, paleointensity determinations possess inherent disadvantages regarding measurement precision and the inordinate amount of human time investment. We intend to overcome these limitations by extending and improving our fully automated magnetic workstation known as the SushiBar.

Stable isotope analysis of the common periwinkle Littorina littorea depending on infection status and seasonality

Littorina littorea was collected at the study site. The foot of Littorina littorea was used for stable isotope analysis (δ15N and δ13C). The stable isotope composition of possible food sources was also determined. Samples were taken in spring, summer and autumn. For the analysis a diet tissue discrimination factor (DTDF) of 2.4 for δ15N and 1.0 for δ13C was subtracted, respectively. The data in the sheet are the raw data without the DTDF.

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