The dataset contains temperature, salinity, oxygen saturation, chlorophyll a and turbidity data from the Helgoland MarGate underwater observatory from the year 2020 in a temporal resolution of 1 hour. The cabled observatory is located in 10m water depth and comprises single or multiple sensors for a specific parameter (see https://www.awi.de/en/science/biosciences/shelf-sea-system-ecology/main-research-focus/cosyna/underwater-node-helgoland.html). For a detailed description of the data see associated metadatafile "Metadata_description_heluwobs_data_2020.pdf"
The southern North Sea (German Bight) is influenced by the inflowing water in the Tide-Elbe area and how it is further modified. For a better understanding of the different branches of relevant events it is necessary to investigate which are the main impacting factors for the terrestrial and coastal water quality. Therefore, during different cruises between 2019 and 2022 a group of parameters was continuously traced from the Elbe River basin (Czech/German border) to Tide-Elbe and Elbe estuary (Hamburg, Germany) until the German Bight. This dataset is about the cruise "Sternfahrt 5" from August 31 to September 03 in 2020, with the objective to detect the spatial extension of the riverine influence of Elbe and Weser. Therefore, three research vessels have been involved to cover a broad area of the German Bight between Hamburg, Heligoland and Sylt. All three research vessels (Littorina, Ludwig Prandtl and Mya II) started together on the first day from Cuxhaven to Heligoland. The first day and whenever two or three of the ships met, one measuring was made together as inter-calibration station (for more details, see file description), in total four. From the second day the ship Littorina travelled between Heligoland and Büsum, one track each day. The Ludwig Prandtl headed the second day towards Wyk auf Föhr. Afterwards the crew navigated further north towards Sylt and turning back to Wyk on the same day. The last day they went back from there to Cuxhaven meeting with the vessel Littorina for a last inter-calibration station. The vessel Mya II instead was navigating on the second and third day first to Bremerhaven and on a more western track back to Heligoland. To also cover the northern part of the research area, on the last day the crew travelled to Sylt, the home harbour of the vessel. Therefore, it was not participating on the last inter-calibration station off Cuxhaven. All ships took measurements of basic hydrographic parameters (salinity, temperature, oxygen saturation, pH) and atmospheric and dissolved greenhouse gases (methane and carbon dioxide). The parameters were measured continuously on the way by specific sensors submerged in a water reservoir on deck which was supplied with surface water (about 1m depth) from the underway water supply of the vessel. Methane and carbon dioxide were measured in the surface water and in the atmosphere with the sensors "LosGatos" and "Picarro". Ferrybox systems measured physical and chemical oceanographic parameters including salinity, temperature, pH. At specific stations along the tracks additionally vertical profiles were taken as well as water samples from the surface. The respective data can be found in: Bussmann, Ingeborg, Flöser, Götz, Geißler, Felix (2021): Carbon compounds, nutrients and pigments from water samples from MOSES project's cruise Sternfahrt 5. PANGAEA, https://doi.org/10.1594/PANGAEA.934894. Further profile data can be found in the AWI O2A-Database as download. Additional information about the whole campaign and different cruises can be found in the article(s) cited.
As part of the MOSES Project, in April 2023 methane measurements were started in the north-western part of the island Heligoland in the German Bight (North Sea). The objective was to complement the measurements of the Sternfahrten to identify the carbon cycle and its flow from the start of the Elbe river into the North Sea. Therefore, a Contros methane sensor for dissolved methane was deployed under water at about 10 to 12 meter depth (depending on the tide) close to the underwater observatory (UW-OBS) MarGate (54°11' N, 7°52' E), from the COSYNA Project. To ensure correct values latter was cleaned frequently from growing organisms by scientific divers. The present data contains the data from 2024, the second year running the sensors. Based on the concentrations of dissolved methane the methane emissions (diffusive flux) was calculated.
Confronting Climate Change is one of the paramount societal challenges of our time. The main cause for global warming is the increase of anthropogenic greenhouse gases in the Earth's atmosphere. Together, carbon dioxide and methane, being the two most important greenhouse gases, globally contribute to about 81% of the anthropogenic radiative forcing. However, there are still significant deficits in the knowledge about the budgets of these two major greenhouse gases such that the ability to accurately predict our future climate remains substantially compromised. Different feedback mechanisms which are insufficiently understood have significant impact on the quality of climate projections. In order to accurately predict future climate of our planet and support observing emission targets in the framework of international agreements, the investigation of sources and sinks of the greenhouse gases and their feedback mechanisms is indispensable. In the past years, inverse modelling has emerged as a key method for obtaining quantitative information on the sources and sinks of the greenhouse gases. However, this technique requires the availability of sufficient amounts of precise and independent data on various spatial scales. Therefore, observing the atmospheric concentrations of the greenhouse gases is of significant importance for this purpose. In contrast to point measurements, airborne instruments are able to provide regional-scale data of greenhouse gases which are urgently required, though currently lacking. Providing such data from remote sensing instruments supported by the best currently available in-situ sensors, and additionally comparing the results of the greenhouse gas columns retrieved from aircraft to the network of ground-based stations is the mission goal of the HALO CoMet campaign. The overarching objective of HALO CoMet is to improve our understanding and to better quantify the carbon dioxide and methane cycles. Through analysing the CoMet data, scientists will accumulate new knowledge on the global distribution and temporal variation of the greenhouse gases. These findings will help to better understand the global carbon cycle and its influence on climate. These new findings will be utilized for predicting future climate change and assessing its impact. Within the frame of CoMet and due to the operational possibilities we will concentrate on small to sub-continental scales. This does not only allow to identify local emission sources of greenhouse gases, but also opens up the opportunity to use important remote sensing and in-situ data information for the inverse modelling approach for regional budgeting. The project also aims at developing new methodologies for greenhouse gas measurements, and promotes technological developments necessary for future Earth-observing satellites.
Insbesondere der Einfluss der Eigenschaften von Vegetationskronen und ihrer räumlich-zeitlichen Dynamik auf Rückkopplungen zwischen der Landoberfläche und der Atmosphäre (d. h. Temperatur, Niederschlag, Luftfeuchtigkeit, Eigenschaften der atmosphärischen Grenzschicht) sind nicht abschließend geklärt. Ein Hauptgrund dafür ist, dass hochauflösende Beobachtungsdatenprodukte (z. B. zur Vegetationskronenfeuchte) noch nicht mit hoher räumlicher Auflösung (Dekameter-Skala) und für mehrjährige Zeitserien verfügbar sind: weder aus der Fernerkundung noch von Modellen. Darüber hinaus können Heterogenitäten der Landoberfläche (z. B. Vielfalt in der Vegetationsbedeckung) erhebliche Auswirkungen auf Rückkopplungsprozesse zwischen Baumkronen und benachbarter Atmosphäre haben, ihre Darstellung in Modellen reicht jedoch bei hohen räumlichen Auflösungen nicht aus. Um diese Lücken zu schließen, werden auf der Fernerkundung basierende Produkte entwickelt, um einige der vielfältigen unterschiedlichen Vegetationsbedingungen zu berücksichtigen. In diesem Sinne besteht die Idee von Projekt 2 darin, eine Reihe von Eigenschaften der Vegetationskrone zu überwachen, einschließlich Wassergehalt (z. B. Boden- und Vegetationsfeuchtigkeit) und Flüsse (z. B. Evapotranspiration) sowohl vor Ort (In-situ-Daten) als auch auf regionalen (Erdsystemmodelle) Skalen. Der Ansatz nutzt die potenziellen Synergien zwischen optischen, passiven und aktiven Mikrowellensensoren, die ergänzende Informationen bieten, um Fernerkundungssignale (z. B. Mikrowellendämpfung) in biophysikalische Variablen (z. B. gravimetrische Vegetationsfeuchtigkeit, Evapotranspiration oder Vegetationsstruktur und -dichte) umzuwandeln. Diese einzigartigen und beispiellosen Datensätze der satellitengestützten Multisensor-Fernerkundung werden in Land-Atmosphäre (L-A) Modelle eingespeist, um Grenzschichteigenschaften und L-A-Rückkopplungen zu bestimmen und zu analysieren. Alle diese Landoberflächenvariablen können synergetisch dazu beitragen, den Zusammenhang zwischen Boden, Vegetation und den Prozessen der atmosphärischen Grenzschicht zu verstehen und L-A-Modelle zu initialisieren. P2 konzentriert sich direkt auf die hochauflösende (Dekameter-Skala) Bestimmung von Zuständen und räumlich-zeitlichen Dynamiken der Feuchtigkeit, Temperatur und Topographie der Vegetationskrone, um Feuchtigkeits- und Temperaturverteilungen zur Beurteilung der Transpiration und der Form sowie der 3D-Dynamik der atmosphärischen Rauheitsunterschicht nach zu verfolgen. Dies wird durch die Kombination von Multisensor-Fernerkundungsbeobachtungen (z. B. Copernicus Sentinel-Satelliten und weltraumgestützte LiDARs) erreicht. Räumlich-zeitlich dynamische Informationen dieser Vegetationsvariablen werden für die Integration in die Reihe an Land-Atmosphäre-Modellen von LAFI vorbereitet, um Grenzschichteigenschaften zu bewerten und L-A-Rückkopplungen zu verstehen.
As part of the MOSES Project, in April 2023 methane measurements were started in the north-western part of the island Heligoland in the German Bight (North Sea). The objective was to complement the measurements of the "Sternfahrten" to identify the carbon cycle and its flow from the start of the Elbe river into the North Sea. Therefore, a Contros methane sensor for dissolved methane was deployed under water at about 10 to 12 meter depth (depending on the tide) close to the underwater observatory (UW-OBS) MarGate (54°11' N, 7°52' E), from the COSYNA Project. To ensure correct values latter was cleaned frequently from growing organisms by scientific divers. The present data contains the data from 2023, the first year running the sensors. Based on the concentrations of dissolved methane the methane emissions (diffusive flux) was calculated.
Recent water trends have serious future implications. This warning once again highlights the importance of the State of Global Water Resources Report, published annually by the WMO since 2022. In its 5th edition, which was presented on 17 September 2026, it provides a semi-decadal overview of the status of global freshwater resources utilizing modelling, remote sensing and in situ observation-based data products. This 5th anniversary highlights the success and importance of the report, which helps in identification of hotspots at a global scale to inform planning and policy making and promotes monitoring and data sharing. Over five editions, the report has expanded substantially in scope, methodology, data coverage and institutional engagement. The first edition, covering 2021, focused on three variables: river discharge, total water storage and cryosphere water resources. Even back then, the river discharge analysis in the report was based on in situ data received from WMO Members represented by NMHSs, primarily obtained via the GRDC database. Over the years it has expanded to 15 hydrological variables and the 2025 edition of this report was enhanced by the addition of new chapters on lake ice extent and water quality parameters. Data on soil moisture and water quality have been provided by our partner data centres the International Soil Moisture Network (ISMN) and the Global Freshwater Quality Database GEMStat, respectively. One of the most important developments over the five editions has been the growth in observed data contributions. The number of countries providing river discharge data increased approximately 7.5-fold, from 7 in 2021 to 52 in 2025, while the number of river discharge stations rose from 14 in the first edition to 3,115 in the current one. River discharge observations are now also complemented by data on a further ~500 stations derived from space-based observations that help to address spatial and temporal gaps. This expansion has been driven by the growth in Member participation and enabled, in parallel, by progressive improvements in the methods used to request, collect, process and integrate the data. However, Africa and Asia together accounted for only about 7% of the stations included in the assessment, while South America accounted for 10%. In its semi-decadal statement, the report shows that the share of global basin area experiencing normal discharge conditions has remained below the historical mean in each of the past seven years. Across the five reporting years, recurring below-normal river discharge was observed in major parts of the Amazon and La Plata basins, North America, Central and East Africa, Central Asia and the Middle East. Overall, 2025 was a dry year for river discharge worldwide, and one of the driest in 35 years (1991–2025). Annual river discharge in 2025 was above to much above normal across South, East and South-East Asia, parts of northern South America, and selected African and north-eastern and eastern Australian basins. Below- to much-below-normal river discharge affected several major regions, including much of North America, the La Plata and São Francisco basins of South America, Eastern European basins, the Middle East and Central Asia. Africa showed contrasting river discharge conditions, with above-normal flows in the Senegal, Niger, Lake Chad, Orange and Limpopo basins, but much-below-normal discharge in the Nile, Congo and Zambezi basins. The report shows that water data availability has improved, but gaps remain especially in the areas most affected by water-related disasters such as Asia and Africa. Continued data sharing is therefore essential to improve the geographical representativeness and robustness of the global assessment, validate modelled and satellite-based information, and strengthen the evidence base for water resource management, early warning systems and climate adaptation actions. These efforts are also supporting the objectives of the Early Warnings for All (EW4All) initiative, the WMO Hydrological Observing System (WHOS) and the WMO Global Hydrological Status and Outlook System (HydroSOS). Together, these components form an end-to-end chain than ranges from observation and data exchange through operational status and outlook products to the annual global assessment provided in this report, all underpinned by the WMO Unified Data Policy. Further information and the full report are available at the website of WMO .
This dataset includes new geochemical and isotopic analyses for 46 samples from the Transminas and Pirenópolis dike swarms in southeastern and central Brazil. It comprises whole-rock major and trace element compositions, and Sr-Nd-Pb isotopes. The new analyses are presented for 46 samples using the bulk and in-situ data templates developed by EarthChem. Analytical work was conducted at the Geoanalítica Core Facility of the Instituto de Geociências, University of São Paulo, and at the Geochronology Laboratory of the University of Brasília, both in Brazil.
This in situ data set of absorption coefficients by phytoplankton at the first eight Ocean Land Colour Imager (OLCI) bands (centred at 400 nm 412.5 nm, 442.5 nm, 490 nm, 510 nm, 560 nm, 620 nm, 665 nm, abbreviated as aph(400), aph(412), aph(443), aph(490), aph(510), aph(560), aph(620), and aph(665)) consists of different data sets gathered together from in situ measurements collected in open, coastal, and inland surface waters spread around the globe and covering the time from first data delivery by OLCI on S3A in May 2016 until November 2022 which were matched to Ocean Land Colour Imager on Sentinel-3A and -3B and used in the paper by Bracher et al. (2025). We only used the absorption coefficient data derived from measurements on discrete water samples to ensure a similar method procedure followed and a similar uncertainty. It includes publicly available data and newly collected, measured and analysed data sets from the Phytooptics group at the Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research (AWI, PI: Astrid Bracher) and Hellenic Centre for Marine Research (HCMR, PI: Andrew C. Banks). This collection was matched that in situ data points had to fall within the 3x3 OLCI FR pixel box and a time window of + 12 hours which followed established community protocols (IOCCG 2018) and particularly EUMETSAT's OLCI matchup protocol (EUMETSAT 2022). Firstly, a pre-processing for quality control and a conversion of the considered in situ data to a common format following Valente et al. (2022) was performed. We flagged and disregarded the following data from the final quality-controlled data set which had (1) unrealistic or missing date or geographic coordinate fields, (2) poor quality (e.g., original flags) or method of observation that did not meet the criteria for the dataset (e.g., not defined in the community protocols (IOCCG 2018, 2019a, 2019b), and (3) spuriously high or low data. For the last item, the following limits were imposed: [0.0001–10] m−1 for aph(443). OLCI pixels were discarded when flagged with the recommended flags in (EUMETSAT 2022), and the remaining matchups were only considered valid if more than 50% of satellite pixels were available at remote sensing reflectance centred at band 560 nm (Rrs(560), e.g., 5 out of 9 for the 3x3 criterion) per an in situ data point, and a coefficient of variation <0.2. Dedicated matchup software developed by EUMETSAT was used to ensure that the validation process followed the established guidelines, ThoMaS (the Tool to generate Matchups of OC products with S3 OLCI https://gitlab.eumetsat.int/eumetlab/oceans/ocean-science-studies/ThoMaS). In situ data from Valente22 (see details on data sets below) were already provided at the nominal OLCI band 443 nm. All other aph(λ) data were provided in hyperspectral resolution (1nm, 2nm or around 3.3 nm resolution). Following Zibordi et al. (2023), these hyperspectral absorption coefficients were transformed to the nominal OLCI bands by averaging over the specific bandwidth. The OLCI matchup data, based on their associated RRS data at the first eight OLCI bands, were assigned to the specific optical water classes (OWCs) according to the Mélin & Vantrepotte (2015) classification. This contains 17 OWCs which range from very turbid to (OWC 1) oligotrophic to very clear waters (OWC 17). The OWC is also delivered for each matchup point (if the assignment fails the field contains "NaN". We provide also for OLCI the standard deviation of the OLCI matchup data to a in situ data point within the 3x3 pixels. For the in situ data we provide the estimate of the uncertainty for each matchup point further described in Bracher et al. (2025).
The dataset is about temporal variability of dissolved methane along the freshwater-sea continuum in northern Germany. Sensors were installed at fixed stations at in total three sites at different water depths. This dataset is from the station in Heligoland (54.1833 N, 7.8667 E) at about 9-12m depth (depending on the tide). The data was obtained between 27 April and 28 October in high frequency measurements (1 min) with a methane sensor from Kongsberg (4H Jena model CONTROS HydroC CH4,). Methane concentrations were calculated according to manufacturer's instructions, based on temperature and salinity values from UW-node Heligoland (Fischer, Philipp; Happel, Lea; Brand, Markus; Eickelmann, Laura; Lienkämper, Miriam; Bussmann, Ingeborg; Anselm, Norbert; Brix, Holger (2022): Hydrographical time series data of Helgoland, Southern North Sea, 2021. PANGAEA, https://doi.org/10.1594/PANGAEA.950173). A gap in the salinity data was replaced with the median value of the observed time span (31.66). For the quality control of the data a local range of 0.1 – 1000 nmol/L was set, a technical range for the pump power 2 – 8. Watt, a spike and gradient value of 1. For a more detailed description see the article cited in References.
| Organisation | Count |
|---|---|
| Bund | 106 |
| Europa | 10 |
| Global | 3 |
| Land | 4 |
| Schutzgebiete | 1 |
| Wirtschaft | 2 |
| Wissenschaft | 81 |
| Type | Count |
|---|---|
| Daten und Messstellen | 30 |
| Förderprogramm | 85 |
| Text | 18 |
| unbekannt | 23 |
| License | Count |
|---|---|
| Geschlossen | 19 |
| Offen | 126 |
| Unbekannt | 11 |
| Language | Count |
|---|---|
| Deutsch | 65 |
| Englisch | 103 |
| Resource type | Count |
|---|---|
| Archiv | 4 |
| Datei | 30 |
| Dokument | 1 |
| Keine | 84 |
| Webseite | 41 |
| Topic | Count |
|---|---|
| Boden | 136 |
| Lebewesen und Lebensräume | 134 |
| Luft | 105 |
| Mensch und Umwelt | 156 |
| Wasser | 130 |
| Weitere | 156 |