As part of PhytOakmeter (www.phytoakmeter.de), time-domain transmission, soil moisture and -temperature sensors with custom-made logger systems were used to measure time series of soil state variables. The aim of these investigations was to provide data on environmental properties used in a cross-disciplinary approach. The measurement device consisted of two sensors at three different depths. The dataset contains the values of time (UTC), relative permittivity, soil moisture (in % vol) derived from permittivity and soil temperature (in °C). Determination of soil moisture was done using the formula of Topp et al. (1980). As sensors, the SMT100 soil moisture sensors with integrated temperature measurement were used. All sensors were installed within the upper 50cm below ground. The exact depths for each sensor are listed in the dataset and parameter comment.
The 234Th–238U disequilibrium technique has been widely used to estimate the amount of particulate organic carbon (POC) exported from surface ocean layers to the deep sea. This method is based on determining 234Th fluxes from vertical 234Th–238U profiles in the water column and converting them into POC fluxes using POC/234Th ratios measured in sinking particles at a given calculation depth. We present here an extensive repository of POC fluxes, together with Th fluxes and POC/234Th ratios. Covering all the global ocean, classified in 13 regions, season and moment of the bloom and calculated at three different depths: i) a fixed depth (100 m) ii) the reference depth in the paper associated to the base of the euphotic zone iii) the 234Th–238U equilibrium depth. To ensure a compilation representative of the global ocean, the dataset were selected using the division areas proposed by the international network JETZON (Joint Exploration of the Twilight Zone Ocean Network); that agreed a division of the oceans in 13 regions based on their contrasted physics and biogeochemical characteristics. The stations from 234Th publications associated to each JETZON region were carefully selected according to their ability to represent regional environmental conditions. Furthermore, station selection was based on essential criteria such as data quality and accessibility, availability of time series, clear definition of export depth, measurements from established programs, e.g. GEOTRACES, and the presence of other additional relevant ancillary data. The data in the compilation are thus organized by region and include geographic coordinates, season, selected export depth, and other key factors (such as a description of the flux evaluation depth or the export depth zone). After 234Th–238U compilation, 234Th fluxes were calculated, when possible, at the three different depths, i), ii) and iii), under the assumption of steady-state conditions, following Le Moigne et al. 2013. Using POC/234Th ratios, POC fluxes are estimated from Th fluxes and both fluxes were included in the repository. POC/234Th ratios were chosen from pump samples, prioritizing particles larger than 53 μm when available. These ratios must be estimated at the flux calculation depth [i), ii) and iii)]. When they were not available at the calculation depth POC/234Th values were interpolated as described in the readme text file. The values of the ratios are included in the repository, specifying the depth at which they were determined and indicating whether they have been interpolated. Similarly, when 234Th, 238U concentrations were not available at the calculation depth, values were interpolated (see readme text file).
In order to monitor long-term variability and eventual changes in the planktonic community, a semiquantitative analysis of microplankton based on net sampling is ongoing weekly since 2020 at the Helgoland Roads station, before 9 a.m. The sampling is tightly linked to the LTER Helgoland Roads time series, thus enabling the coupling of species occurrences to the respective hydrochemical and physical conditions in the water column. These long-term data allow to quantify realized niches of frequently occurring microplankton species and to pinpoint rarely occurring species which cannot be detected in the quantitative plankton analysis.
A highly temporally and spatially resolved measurement campaign was conducted over a one-year period to analyze water samples for a total of 35 pharmaceuticals, pesticides and UV-filters. Between spring 2022 and spring 2023, surface water samples were collected twice a week at 14 sites at the Warnow estuary and its adjacent areas in Rostock, Germany, as part of the OTC-Genomics sampling campaign. The sampling area included one upstream freshwater site before the river enters the estuary, seven sampling sites along the estuary - surrounded by the urban area of Rostock - and six sites along the Baltic coastline. Sampling was conducted every Monday and Thursday, always three hours after sunrise. After enrichment with solid phase extraction, 1307 samples were analyzed using liquid chromatography–tandem mass spectrometry. Additional to the dataset included here, the same samples were analyzed for prokaryotic and eukaryotic community composition (16S and 18S rRNA gene amplicon sequencing), cell abundances (flow cytometry), and physico-chemical environmental parameters.
Die Messstelle 3064 I Waldmohr in Rheinland-Pfalz dient der Überwachung von Grundwasserstände. Zeitreihen abiotische Parameter werden derzeit nicht gemessen.
Die Messstelle 3056 Cronenberg in Rheinland-Pfalz dient der Überwachung von Grundwasserstände. Zeitreihen abiotische Parameter werden derzeit nicht gemessen.
Die Zeitreihe zeigt Luftbildaufnahmen (Digitale Orthophotos) des FHH-zugehörigen Wattenmeers, einschließlich der Inseln. Die Aufnahmen erfolgen etwa alle 6 Jahre über eine Flugzeugbefliegung und bieten mit einer Auflösung von 0,2 m eine beeindruckende Detailgenauigkeit. Die Daten stehen als Dienst (WMS-t) sowie als Downloaddateien zur Verfügung. Empfehlung: Ein Download ist nicht immer notwendig. Für viele Anwendungen reicht die Einbindung des Dienstes über ein GIS oder Geoportal völlig aus. Das ist nicht nur performanter, sondern spart auch eine Menge Speicherplatz.
Im Rahmen des Projektes WADKlim wurden mit mGROWA Projektionen auf Basis der Ergebnisse der Klimaprojektionen R26-E12-RCA, R85-CA2-CLM und R85-MI5-CLM für die Zeit von 1971 bis 2100 durchgeführt. Neben vielen anderen Größen liefern diese Projektionen auch Zeitreihen der Grundwasserneubildung (GWNB) in hoher räumlicher Auflösung. Im WADKlim Bericht werden diese Zeitreihen kurz andiskutiert und im Hinblick auf die Ausprägung zukünftiger Minimumdekaden der GWNB untersucht (siehe Kap. 2.3.10). Das Ziel der Auswertung im Projekt war es, flächendeckend für ganz Deutschland die Dekaden zu identifizieren, in denen die mittlere jährliche GWNB in den Projektionen ein Minimum erreicht. Die in den Minimumdekaden erreichte GWNB kann dann in Bezug zur jeweiligen historischen Referenzperiode 1971-2000 gesetzt werden. Es liegen teilweise verkürzte Zeitreihen bis 2095 für das Globalmodell MOHC-HadGEM2-ES vor. Das liegt daran, dass diese Modellrechnung bis zum hydrologischen Jahr 2099 lief und einige Zeitreihen innerhalb der Bias-Korrektur des Niederschlags im Rahmen von ReKliEs-De nochmals auf 2095 gekürzt wurden. Um eine höhere Dateikompression zu erzielen, wird die Jahressumme um den Faktor 10 erhöht und als Integerwert (INT2S) bereitgestellt. Nach einer Rückrechnung liegt die Jahressumme mit einer Nachkommastelle vor.
The "Germany Mosaic" is a time series of Landsat satellite images and vectorized segments covering the entirety of Germany from 1984 to 2023. The image data are divided into TK100 sheet sections (see further details: Blattschnitt der Topographischen Karte 1:100 000). The dataset provides optimized 6-band imagery for each year, representing summer (May to July) and autumn (August to October) seasons, along with vegetation indices such as NDVI (Normalized Difference Vegetation Index) and NirV (Near-Infrared Reflectance of Vegetation) for the same periods. Additionally, vectorized "zones" of approximately homogeneous pixels are available for each year. The spectral properties of the image data and the morphological characteristics of these zones are included as vector attributes (see Documentation: "Mosaic (1984–2023) - Data Description"). An overview of the coverage and quality of all sheet sections is provided as a vector layer titled D-Mosaik_Sheet-Sections within this document. The Germany Mosaic can also be considered a spatial-temporal Data Cube, enabling advanced analysis and integration into workflows requiring multi-dimensional data. This structure allows users to perform operations such as querying data across specific time periods, analyzing trends over decades, or aggregating spatial information to generate tailored insights for a wide range of research applications. In mid-latitudes, seasonal variations in vegetation—and consequently in the image data—are typically more pronounced than changes occurring over several years. The temporal segmentation of the dataset has been designed to encompass the entire vegetation period (May to October), with the division into summer and autumn periods capturing seasonal metabolic shifts in natural biotopes. This segmentation also records most agricultural changes, including sowing and harvesting activities. Depending on weather conditions, the individual image data represent either the median, mean value, or the best available image for the specified time period (see Documentation: "Mosaic (1984–2023) - Data Description). Remote sensing has become an indispensable tool for environmental research, particularly in landscape analysis. Beyond conventional applications, the Germany Mosaic supports the development of digital twins in environmental system research. By providing detailed spatial and temporal data, this dataset enables the modeling of virtual ecosystems, facilitating simulations, scenario testing, and predictive analyses for sustainable management. Moreover, the spatial and temporal trends captured by remotely sensed parameters complement traditional approaches in biological, ecological, geographical, and epidemiological research.
The "Germany Mosaic" is a time series of Landsat satellite images and vectorized segments covering the entirety of Germany from 1984 to 2023. The image data are divided into TK100 sheet sections (see further details: Blattschnitt der Topographischen Karte 1:100 000). The dataset provides optimized 6-band imagery for each year, representing summer (May to July) and autumn (August to October) seasons, along with vegetation indices such as NDVI (Normalized Difference Vegetation Index) and NirV (Near-Infrared Reflectance of Vegetation) for the same periods. Additionally, vectorized "zones" of approximately homogeneous pixels are available for each year. The spectral properties of the image data and the morphological characteristics of these zones are included as vector attributes (see Documentation: "Mosaic (1984–2023) - Data Description"). An overview of the coverage and quality of all sheet sections is provided as a vector layer titled D-Mosaik_Sheet-Sections within this document. The Germany Mosaic can also be considered a spatial-temporal Data Cube, enabling advanced analysis and integration into workflows requiring multi-dimensional data. This structure allows users to perform operations such as querying data across specific time periods, analyzing trends over decades, or aggregating spatial information to generate tailored insights for a wide range of research applications. In mid-latitudes, seasonal variations in vegetation—and consequently in the image data—are typically more pronounced than changes occurring over several years. The temporal segmentation of the dataset has been designed to encompass the entire vegetation period (May to October), with the division into summer and autumn periods capturing seasonal metabolic shifts in natural biotopes. This segmentation also records most agricultural changes, including sowing and harvesting activities. Depending on weather conditions, the individual image data represent either the median, mean value, or the best available image for the specified time period (see Documentation: "Mosaic (1984–2023) - Data Description). Remote sensing has become an indispensable tool for environmental research, particularly in landscape analysis. Beyond conventional applications, the Germany Mosaic supports the development of digital twins in environmental system research. By providing detailed spatial and temporal data, this dataset enables the modeling of virtual ecosystems, facilitating simulations, scenario testing, and predictive analyses for sustainable management. Moreover, the spatial and temporal trends captured by remotely sensed parameters complement traditional approaches in biological, ecological, geographical, and epidemiological research.
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