The Urban Waste Water Treatment Directive concerns the collection, treatment and discharge of urban waste water and the treatment and discharge of waste water from certain industrial sectors. The objective of the Directive is to protect the environment from the adverse effects of the above mentioned waste water discharges. This series contains time series of spatial and tabular data covering Agglomerations, Discharge Points, and Treatment Plants.
Das Ziel des Projekts ist die Klimaneutralität der der Weleda Unternehmensgruppe (mit Standorten in Arlesheim (Schweiz), Schwäbisch Gmünd (Deutschland) und Huningue (Frankreich)). Folgende Arbeitsschritte sind vorgesehen: 1. Bilanz und Ist-Analyse (optional: Erstellung von Zeitreihen mit Berücksichtigung der Effekte von in der Vergangenheit getroffenen Maßnahmen), 2. Bewertung von Vermeidungs- und Verringerungsmaßnahmen, 3. Bewertung von Kompensationsmaßnahmen, 4. Zusammenstellung der Ergebnisse, technische Dokumentation und Leitfaden zur kontinuierlichen Weiterführung. Als optionaler fünfter Arbeitsschritt sollen neben den Treibhausgasemissionen der Verbrauch an Energie und Wasser bilanziert werden.
Dieser Datensatz umfasst Monitoringdaten und Modellergebnisse (Hydronumerisches Modell) für das Weserästuar, Nordsee. Die Daten wurden für quantitative Analysen in dem Manuskript „Surges control Salt Flux Variability in a partially-mixed Estuary“ verwendet, das im Journal of Geophysical Research: Oceans zur Veröffentlichung eingereicht wurde. Die Modellergebnisse, wie im Manuskript beschrieben, umfassen Salzflüsse, die aus den simulierten Strömungsgeschwindigkeiten und Salzgehalten abgeleitet wurden. Die vier Salzflusskomponenten [kg s-1] enthalten ein barotrope Komponente (barotropic flux, Fbt), Tidal Pumping (tidal oscillatory salt flux, Fto), den Beitrag durch die ästuarine Austauschströmung (exchange flow contribution, Fex) und eine weitere Komponente, die durch intratidal veränderliche Scherraten (tidal oscillatory shear, Ftos) bestimmt wird. Die Salzflüsse wurden jeweils, entlang der Zeitreihe, für die Dauer eines Tidetages bestimmt. Jeder Schritt beginnt mit einem Stauwasser. Die zeitliche Auflösung beträgt daher eine Halbtide. Die Zeitreihe umfasst ein hydrologisches Jahr. Die Auflösung entlang des Ästuars beträgt 1 km. Zusätzlich zu den Salzflüssen wurden fünf weitere Parameter abgeleitet: die Tideasymmetrie (Strömungsgeschwindigkeit), der gezeitengemittelte Salzgehalt, der Tidenhub [m] sowie die gezeitengemittelte Schichtung (potenzielle Energieanomalie) [J m-3]. Diese Parameter legen auf dem Gitter der Salzflusskomponenten vor. Die Salzintrusion ist in Flusskilometern angegeben und entspricht der Lage der Isohaline der Salinität von 2 PSU (gezeitengemittelt, bestimmt aus Modellergebnissen). Die übrigen Parameter im Datensatz wurden aus Monitoringdaten ermittelt. Hierzu zählen Zeitreihen der Windkomponente des Wasserstands [m], der Windgeschwindigkeit [m s-1] sowie der Windrichtung [°], abgeleitet von Messungen am Leuchtturm Alte Weser. Die spezifischen Methoden für jeden Monitoringparameter sind im Manuskript beschrieben. Dazu kommt noch der Abfluss (Intschede) [m3 s-1]. Alle Daten liegen als selbsterklärende Textdateien mit Kopfzeile vor. This dataset contains monitoring data and numerical model results for the Weser estuary, North Sea, used for quantitative analyses in the paper “Surges control Salt Flux Variability in a partially-mixed Estuary”, which was submitted to the Journal of Geophysical Research: Oceans. Model results comprise salt fluxes, derived from simulated velocity and salinity, as described in the paper. The four salt flux contributions [kg s-1] are the barotropic flux (Fbt), the tidal oscillatory salt flux (Fto), the exchange flow contribution (Fex) and the flux due to tidal oscillatory shear (Ftos). Salt fluxes were determined for the duration of one tidal day, moving stepwise through the timeseries. Each step starts with one slack water. Therefore, the temporal resolution is one value per tidal phase, as described in detail in the manuscript. The time series covers one hydrological year. The along-channel resolution is 1 km. Five additional parameters are derived from model results: the tidal velocity asymmetry, tidally averaged salinity, tidal range [m], and tidally averaged stratification (potential energy anomaly [J m-3]), all stored on the same spatiotemporal grid as the salt flux contributions. The salt intrusion is obtained from subtidal salinity using the location of the isohaline 2 (PSU). The salt intrusion is stored in terms of river km. The remaining parameters in the dataset are derived from monitoring data. These are time series of discharge [m3 s-1], surge [m], tidal range [m], wind speed [m s-1] and wind direction [°], all provided on the same temporal grid as the salt fluxes. The specific methods are described in the paper, for each of the monitoring parameters. All data are stored as self-explanatory, character-delimited text files with header lines.
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.
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.
Temperature and heating-induced temperature difference profiles were measured through the atmosphere, sea ice, and ocean using a SIMBA-type sea ice mass balance buoy equipped with a several meter long thermistor chain. The present dataset was recorded by SIMBA 2022T97 (original name NPOL_0803) installed on drifting sea ice in the Arctic Ocean during the expedition Kronprins Haakon AO22 in 2022. Data is available between 2022-08-06 10:38:00 and 2022-11-22 03:02:00. The thermistor chain was Variable 5 m long and included 241 sensors with a regular spacing of 2 cm. The resulting time series includes the evolution of temperature and temperature differences at 30 s and 120 s during a heating cycle of 120 s as a function of location, depth and time. The sampling intervals were usually between hourly and daily, but were most frequently configured to 6 hours for temperature, and 24 hours for temperature differences. In addition to temperatures and geographic location, barometric pressure, ~1 m air temperature, instrument tilt, and compass heading were measured. The present dataset was processed as follows: obvious inconsistencies (missing values) and unrealistic values of GPS position have been removed. This instrument was deployed as part of the project Arctic Passion.
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.
Cosmic ray neutron sensing (CRNS) sensor, is an innovative technique used to measure soil moisture and snow water equivalent over large areas (Zreda et al., 2012). The advantage of CRNS over traditional point-based sensors is its ability to cover a large footprint of typically 10–20 ha (Köhli et al., 2015), making it ideal for monitoring hydrological processes at the field to landscape scales. In the present study, one CRNS Sensor, Model SP, made by StyX Neutronica GmbH was deployed in a stationary position to provide continuous, non-invasive measurements at the KITcube site near Villingen-Schwenningen.
Dieser Web Map Service (WMS) zeigt detaillierte Luftbilder (DOP), die während der belaubten Jahreszeit in Hamburg aufgenommen wurden. Die dargestellten Geodaten beinhalten zusätzlich eine zeitliche Dimension (WMS-Time). Zur genaueren Beschreibung der Daten und Datenverantwortung nutzen Sie bitte den Verweis zur Datensatzbeschreibung.
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.
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