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).
During the research cruises BE03/2016 (08.03.2016), BE10/2016 (19.10.2016), BE10/2018 (23.10.2018), BE03/2019 (15.03.2019), L23-13 (13.09.2023 - 15.09.2023), Sagitta24-1 (16.09.2024), Sagitta24-2 (23.09.2024), Hai24VE2 (24.09.2024), L25-2b (09.02.2025 - 17.02.2025) and EMB374 (04.09.2025 - 13.09.2025), CTDs were deployed and sediment corers were retrieved at 99 stations in Kiel Bight in the southwestern Baltic Sea. Water column oxygen concentrations were determined using oxygen sensors attached to the CTD framework. At selected water depths, water samples were collected with Niskin bottles for the analysis of nitrate concentrations using an autoanalyzer. Short sediment cores (<50cm) were recovered using a Multicorer (MUC), Minicorer (MIC) or Rumohrlot (RL). Bottom waters were sampled from the supernatant water in the sediment cores. Solid phase sediment samples were analyzed for total organic carbon using an element analyzer. Porewater was extracted from the sediment cores using rhizones and analyzed for total alkalinity (titration), ammonium (photometer), sulfate (ion chromatography), hydrogen sulfide (photometer), dissolved iron (ICP-OES) and dissolved manganese (ICP-OES). The collected data will be used to (i) determine the spatial and temporal variability of hydrogen sulfide in bottom waters of the Kiel Bight, (ii) identify the controlling factors governing the accumulation of hydrogen sulfide at the seafloor, and (iii) establish an early warning system of sulfidic seafloor conditions for regional stakeholders in the Baltic Sea.
<p>The database of the PONDSCAPE project (Towards a sustainable management of pond diversity at the landscape level) comprises taxon occurrence data of eight different organism groups (bacteria, phytoplankton, diatoms, cladoceran, macro-invertebrates (mollusks, heteropterans and coleopterans), macrophytes, amphibians and fish) and data on physical, chemical and morphometric variables of 125 farmland ponds covering five biogeographic regions in Belgium/Luxembourg</p>
Ocean velocities were collected by a Teledyne RDI 600 kHz Workhorse Mariner ADCP that was mounted on RV HEINCKE during RV HEINCKE cruise HE667. The transducer was located at 4 m below the water line. The instrument was operated in single-ping, broadband mode with bin size of 1 m and a blanking distance of 1 m. The velocity of the ship was calculated from position fixes obtained by the Global Positioning System (GPS) received directly from RV HEINCKE. Heading, Pitch and Roll were obtained both from the MRU of RV HEINCKE and the internal ADCP gyro. Heading as well as pitch and roll data from ADCP's internal gyrocompass and the navigation and motion data were used by the data acquisition software ViSea DAS (AquaVision®) internally to convert ADCP velocities into earth coordinates. Accuracy of the ADCP velocities mainly depends on the quality of the position fixes and internal ADCP heading data. Further errors stem from a misalignment of the transducer with RV HEINCKE's centerline. ADCP data is provided at minutely sample rate. Raw data or secondly binned data are available on request.
Im Projekt "Ein kostengünstiges mechanisch gesteuertes polarimetrisches Phased-Array Doppler Wetterradar, Phase 2" entwickelt das Fraunhofer FHR in Kooperation mit dem Institut für Geowissenschaften, Abteilung Meteorologie der Uni Bonn einen Prototyp eines Phased-Array-Radars (PAR) auf Basis einer AESA-Antennenapertur (Active Electronically Scanned Array), mit dem Ziel innerhalb einer Minute eine volumetrische Wetterkarte zu erstellen. Ein PAR-Wetterradar ist optimal geeignet, um die zeitliche Auflösung durch elektronische Strahlschwenkung zu verbessern. Aus Kostengründen konnte sich diese Technologie bisher aber nicht gegenüber Reflektorsystemen durchsetzen. In Phase I wird eine neuartige Antennenlösung zur Entkopplung von Strahlschwenkung und Fokussierung untersucht, um so Komplexität und Kosten zu minimieren. Anstatt wie üblich die Apertur sowohl für die Strahlschwenkung als auch für die Fokussierung zu verwenden, fokussiert ein Parabolzylinder im Azimut, während ein kompakter PAR in seiner Brennlinie die elektronische Schwenkung sowie die Fokussierung in der Höhe ermöglicht. Zur Erzeugung polarimetrischer Momente hoher Qualität wurde eine spezielle aktive Antennenansteuerung entwickelt, um eine Unterdrückung der Kreuzpolarisation von über 40 dB in Broadside und über 30 dB bei einer Strahlneigung von 45° zu erreichen.In Phase II wird die Implementierung der Strahlschwenkung zur Fertigstellung und operationalen Bewertung des Prototyps angestrebt. Im Wesentlichen sollen Strahlbeschleunigungstechniken zur schnellen Erzeugung volumetrischer Wetterkarten untersucht werden, da die einfache Verkürzung der Verweilzeit (Dwell Time) zu größeren statistischen Unsicherheiten bei den polarimetrischen Momenten führen würde. Mit Beam Multiplexing (BMX, sequentielle Übertragung von Impulspaaren entlang verschiedener Richtungen) sollen die Dekorrelation von Stichproben erhöht und schnellere Scans bei gleichbleibender Datenqualität erzielt werden. Darüber hinaus soll mit einem herkömmlichen Step-Scan-Verfahren das BMX für die unteren Elevationen in Abhängigkeit von der spezifischen Wetterereignisstatistik adaptiv ergänzt werden. Die sorgfältige Realisierung eines solchen adaptiven Scannens wird als wesentlicher Schritt angesehen, um das Scan-Beschleunigungspotential von PARs voll auszuschöpfen und ein automatisiertes priorisiertes Tracking potenziell gefährlicher Wetterereignisse zu erreichen.Die Universität Bonn wird die Messungen der überlappenden X-Band-Forschungsradare für eine eingehende Bewertung des neuen PAR und seiner polarimetrischen Fähigkeiten nutzen. Darüber hinaus ermöglicht die neue Technologie die Überwachung der vorkonvektiven Umgebung mit einer höheren zeitlichen Auflösung, was wiederum die Fähigkeit verbessert, Wasserdampffelder aus vom Radar erfassten Änderungen des Brechungsindex abzuleiten. Wir werden die neuen Fähigkeiten bewerten und somit zum fünften Ziel des SPP beitragen, d.h. zur radarbasierten Erfassung der Konvektionsinitiierung.
The ISND07 TTAAii Data Designators decode as: T1 (I): Observational data (Binary coded) - BUFR T1T2 (IS): Surface/sea level T1T2A1 (ISN): Synoptic observations from fixed land stations at non-standard time (i.e. 0100, 0200, 0400, 0500, ... UTC) A2 (D): 90°E - 0° northern hemisphere(The bulletin collects reports from stations: 10454;Wernigerode;10458;Harzgerode;10460;Artern;10466;Halle-Kröllwitz;10471;Leipzig-Holzhausen;10474;Wittenberg;10480;Oschatz;10490;Doberlug-Kirchhain;10495;Hoyerswerda;10519;Bonn-Roleber;10520;Andernach;10526;Marienberg, Bad;10534;Hoherodskopf/Vogelsberg;10537;Neu-Ulrichstein;10540;Eisenach;10542;Hersfeld, Bad;10552;Schmücke;10557;Neuhaus am Rennweg;) (Remarks from Volume-C: SYNOP)
The SADL35 TTAAii Data Designators decode as: T1 (S): Surface data T1T2 (SA): Aviation routine reports A1A2 (DL): Germany (The bulletin collects reports from stations: EDHA;EDHI;HAMBURG-FINKENWERDER ;EDHK;KIEL-HOLTENAU ;EDJA;MEMMINGEN ALLGAU ;EDMO;OBERPFAFFENHOFEN ;EDOP;SCHWERIN PARCHIM ;EDTD;DONAUESCHINGEN-VILLINGEN ;EDTL;LAHR ;EDTY;SCHWAEBISCH HALL ;EDVE;BRAUNSCHWEIG WOLFSBURG ;EDXW;WESTERLAND SYLT ;EDZO;)
The ISCD07 TTAAii Data Designators decode as: T1 (I): Observational data (Binary coded) - BUFR T1T2 (IS): Surface/sea level T1T2A1 (ISC): Climatic observations from land stations A2 (D): 90°E - 0° northern hemisphere(The bulletin collects reports from stations: 10454;Wernigerode;10458;Harzgerode;10460;Artern;10466;Halle-Kröllwitz;10471;Leipzig-Holzhausen;10474;Wittenberg;10480;Oschatz;10490;Doberlug-Kirchhain;10495;Hoyerswerda;10519;Bonn-Roleber;10520;Andernach;10526;Marienberg, Bad;10534;Hoherodskopf/Vogelsberg;10537;Neu-Ulrichstein;10540;Eisenach;10542;Hersfeld, Bad;10552;Schmücke;10557;Neuhaus am Rennweg;)
Raw data acquired by position sensors on board RV Alkor during expedition AL644 were processed to receive a validated master track which can be used as reference of further expedition data. During AL644 data from the Seapath 330 system, the Furuno GP-170 and the Furuno GP-150 GPS receivers were used to calculate the mastertrack. Data were downloaded from DAVIS SHIP data base (https://dship.bsh.de) with a resolution of 1 sec. Processing and evaluation of the data is outlined in the data processing report. Processed data are provided as a master track with 1 sec resolution derived from the position sensors' data selected by priority and a generalized track with a reduced set of the most significant positions of the master track.
This data set comprises images of land snails that were taken for the development of Artificial Intelligence (AI)-based models for the identification of 1) European Vertigo species, and 2) land snails from Tenerife, Canary Islands. The images were taken as part of the Training Artificial Intelligence Models for Land Snail Identification (TrAILSID) project (https://tettris.eu/2024/10/11/trailsid-training-artificial-intelligence-models-for-land-snail-identification), which is part of the initiative Transforming European Taxonomy through Training, Research and Innovations (TETTRIs) funded by the European Union. The first subproject provides 1916 images of the 17 European Vertigo species and Columella edentula, Pupilla muscorum, and Sphyradium doliolum as similar species. The genus Vertigo comprises small terrestrial gastropods, which are often difficult to identify, including species listed in the EU Habitats and Species Directive. This directive requires the surveillance of these species to determine whether a favourable conservation status has been achieved. The images of Columella edentula, Pupilla muscorum, and Sphyradium doliolum, were added to the dataset for the development of the AI model for species identification so that the AI model can recognize that a specimen does not belong to Vertigo. The second subproject provides 5592 images of 106 land snail species occurring on Tenerife, Canary Islands. Endemic terrestrial gastropods in the Canary Islands, which are part of the Mediterranean biodiversity hotspot, are often under threat due to ongoing changes in land use, urbanisation, and an increase in stochastic events such as droughts or wildfires. They are also under threat due to the introduction of foreign species with high invasive potential, which are also represented in the dataset. Images of Vertigo pygmaea, which also occurs on Tenerife, were added to the Tenerife dataset from the Vertigo dataset for the development of the AI model for species identification of species from Tenerife. Note that not all figured specimens are from Tenerife. Photographs were taken of shells housed in the collections of the Zoological Museum of the Leibniz Institute for the Analysis of Biodiversity (ZMH), the Museum of Nature and Archeology Santa Cruz de Tenerife (TFMCMT), the Natural History Museum Bern (NMBE), the Natural History Museum Gothenburg (NMG), the Natural History Museum London (NHMUK), the National Museum Wales (NMW), as well as land snails from Tenerife, Canary Islands. This data set comprises images of land snails that were taken for the development of Artificial Intelligence (AI)-based models for the identification of 1) European Vertigo species, and 2) land snails from Tenerife, Canary Islands. The images were taken as part of the Training Artificial Intelligence Models for Land Snail Identification (TrAILSID) project (https://tettris.eu/2024/10/11/trailsid-training-artificial-intelligence-models-for-land-snail-identification), which is part of the initiative Transforming European Taxonomy through Training, Research and Innovations (TETTRIs) funded by the European Union. The first subproject provides 1916 images of the 17 European Vertigo species and Columella edentula, Pupilla muscorum, and Sphyradium doliolum as similar species. The genus Vertigo comprises small terrestrial gastropods, which are often difficult to identify, including species listed in the EU Habitats and Species Directive. This directive requires the surveillance of these species to determine whether a favourable conservation status has been achieved. The images of Columella edentula, Pupilla muscorum, and Sphyradium doliolum, were added to the dataset for the development of the AI model for species identification so that the AI model can recognize that a specimen does not belong to Vertigo. The second subproject provides 5592 images of 106 land snail species occurring on Tenerife, Canary Islands. Endemic terrestrial gastropods in the Canary Islands, which are part of the Mediterranean biodiversity hotspot, are often under threat due to ongoing changes in land use, urbanisation, and an increase in stochastic events such as droughts or wildfires. They are also under threat due to the introduction of foreign species with high invasive potential, which are also represented in the dataset. Images of Vertigo pygmaea, which also occurs on Tenerife, were added to the Tenerife dataset from the Vertigo dataset for the development of the AI model for species identification of species from Tenerife. Note that not all figured specimens are from Tenerife. Photographs were taken of shells housed in the collections of the Zoological Museum of the Leibniz Institute for the Analysis of Biodiversity (ZMH), the Museum of Nature and Archeology Santa Cruz de Tenerife (TFMCMT), the Natural History Museum Bern (NMBE), the Natural History Museum Gothenburg (NMG), the Natural History Museum London (NHMUK), the National Museum Wales (NMW), as well as the private research collections of Klaus Groh (KG), Stefan Meng (SM), Marco T. Neiber (MTN), and Frank Walther (FW). The photographs were taken by staff from the Malacology Section of the Zoological Museum at the Leibniz Institute for the Analysis of Biodiversity (LIB): Till Cunow, Bernhard Hausdorf, Marco T. Neiber, Elicio Tapia, and Mareike Ulrich. The AI-based models for the identification of 1) European Vertigo species, and 2) land snails from Tenerife, Canary Islands are developed by Rita Pucci and Vincent Kalkman at Naturalis, Leiden, and will be made accessible by them. The image recognition models for the European species of the genus Vertigo and the terrestrial mollusc of Tenerife were created by Rita Pucci (Naturalis Biodiversity Center/LIACS) and can be downloaded for deployment from Gitlab. The models are also deployed on ARISE: Classification model for the genus Vertigo: https://gitlab.com/arise-biodiversity/DSI/algorithms/tettris-classification-vertigo Classification model for the terrestrial mollusc of Tenerife https://gitlab.com/arise-biodiversity/DSI/algorithms/tettris-classification-tenerife
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