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Multibeam bathymetry processed data (Kongsberg EM2040 entire dataset) of SV DVOCEAN, Rhine River, Germany

BATHYMETRY, BACKSCATTER; UNCERTAINLY ABSTRACT This dataset comprises high-resolution multibeam echosounder (MBES) bathymetry and backscatter data collected in a 7.5 km segment of the River Rhine (km 747.0–754.5, Düsseldorf, Germany) between July 24th and 31st, 2024. The survey, conducted with a Kongsberg EM2040P MKII multibeam echosounder operated at a frequency of 300 kHz, achieved a point density of ≥ 160 points/m² and met the IHO Special Order standards (horizontal uncertainty ≤ 15 cm, vertical uncertainty ≤ 3 cm). Furthermore, the dataset includes 8,654 manually annotated quartzite blocks (mean area: 1.90 m², mean height above riverbed: 0.28 m), which were used to train a U-Net convolutional neural network (CNN) for an automated detection. The dataset comprises the raw (.db) bathymetric data, the applied sound velocity profiles (.csv), the pre-processed and filtered bathymetric data (0.25 m grid as .xyz and .tif) with their uncertainty metrics (.xyz, .tif), the computed and cleaned backscatter mosaics (available as 0.20 m and 0.25 m pixel size mosaics in .xyz and .tif format) and the manually digitized polygon annotations (.shp). The data are suited for hazard mapping, geomorphological studies, AI/ML model training (e.g., semantic segmentation) as well as the validation of automated boulder detection methods.

Sound velocity profiles of multibeam bathymetry processed data (Kongsberg EM2040 entire dataset) of SV DVOCEAN, Rhine River, Germany

SVP ABSTRACT This dataset comprises high-resolution multibeam echosounder (MBES) bathymetry and backscatter data collected in a 7.5 km segment of the River Rhine (km 747.0–754.5, Düsseldorf, Germany) between July 24th and 31st, 2024. The survey, conducted with a Kongsberg EM2040P MKII multibeam echosounder operated at a frequency of 300 kHz, achieved a point density of ≥ 160 points/m² and met the IHO Special Order standards (horizontal uncertainty ≤ 15 cm, vertical uncertainty ≤ 3 cm). Furthermore, the dataset includes 8,654 manually annotated quartzite blocks (mean area: 1.90 m², mean height above riverbed: 0.28 m), which were used to train a U-Net convolutional neural network (CNN) for an automated detection. The dataset comprises the raw (.db) bathymetric data, the applied sound velocity profiles (.csv), the pre-processed and filtered bathymetric data (0.25 m grid as .xyz and .tif) with their uncertainty metrics (.xyz, .tif), the computed and cleaned backscatter mosaics (available as 0.20 m and 0.25 m pixel size mosaics in .xyz and .tif format) and the manually digitized polygon annotations (.shp). The data are suited for hazard mapping, geomorphological studies, AI/ML model training (e.g., semantic segmentation) as well as the validation of automated boulder detection methods.

Multibeam bathymetry processed data (Kongsberg EM2040 entire dataset, Generic sensor format) of SV DVOCEAN, Rhine River, Germany

GSF ABSTRACT This dataset comprises high-resolution multibeam echosounder (MBES) bathymetry and backscatter data collected in a 7.5 km segment of the River Rhine (km 747.0–754.5, Düsseldorf, Germany) between July 24th and 31st, 2024. The survey, conducted with a Kongsberg EM2040P MKII multibeam echosounder operated at a frequency of 300 kHz, achieved a point density of ≥ 160 points/m² and met the IHO Special Order standards (horizontal uncertainty ≤ 15 cm, vertical uncertainty ≤ 3 cm). Furthermore, the dataset includes 8,654 manually annotated quartzite blocks (mean area: 1.90 m², mean height above riverbed: 0.28 m), which were used to train a U-Net convolutional neural network (CNN) for an automated detection. The dataset comprises the raw (.db) bathymetric data, the applied sound velocity profiles (.csv), the pre-processed and filtered bathymetric data (0.25 m grid as .xyz and .tif) with their uncertainty metrics (.xyz, .tif), the computed and cleaned backscatter mosaics (available as 0.20 m and 0.25 m pixel size mosaics in .xyz and .tif format) and the manually digitized polygon annotations (.shp). The data are suited for hazard mapping, geomorphological studies, AI/ML model training (e.g., semantic segmentation) as well as the validation of automated boulder detection methods.

Annotated boulder occurrence dataset using quartzite block detection derived from multibeam bathymetry processed data of SV DVOCEAN, Rhine River, Germany

ANNOTATED BOULDER ABSTRACT This dataset comprises high-resolution multibeam echosounder (MBES) bathymetry and backscatter data collected in a 7.5 km segment of the River Rhine (km 747.0–754.5, Düsseldorf, Germany) between July 24th and 31st, 2024. The survey, conducted with a Kongsberg EM2040P MKII multibeam echosounder operated at a frequency of 300 kHz, achieved a point density of ≥ 160 points/m² and met the IHO Special Order standards (horizontal uncertainty ≤ 15 cm, vertical uncertainty ≤ 3 cm). Furthermore, the dataset includes 8,654 manually annotated quartzite blocks (mean area: 1.90 m², mean height above riverbed: 0.28 m), which were used to train a U-Net convolutional neural network (CNN) for an automated detection. The dataset comprises the raw (.db) bathymetric data, the applied sound velocity profiles (.csv), the pre-processed and filtered bathymetric data (0.25 m grid as .xyz and .tif) with their uncertainty metrics (.xyz, .tif), the computed and cleaned backscatter mosaics (available as 0.20 m and 0.25 m pixel size mosaics in .xyz and .tif format) and the manually digitized polygon annotations (.shp). The data are suited for hazard mapping, geomorphological studies, AI/ML model training (e.g., semantic segmentation) as well as the validation of automated boulder detection methods.

Flood durations of the year 2023 on the floodplains of River Rhine and River Elbe, Germany

Floodplains are morphologically highly heterogeneous environments with dynamically in- and decreasing water levels and flows. Strong environmental filtering through floodplain inundation creates favorable conditions for highly specialized organisms leading to high biodiversity and conservation value of floodplains. With hyd1d and hydflood we provide two R packages for simplified hydrologic modelling in two large central European floodplains – along River Rhine and River Elbe in Germany. We applied the R function hydflood::flood3 on both active floodplains covering the year 2023. The datasets consists of 40 tiles along River Rhine and 49 tiles along River Elbe, resulting in a total of 89 individual raster datasets stored in GeoTiff file format. All raster have a spatial resolution of 1 m and are stored in the coordinate reference systems ETRS 1989 UTM Zone 32 N (EPSG: 25832) for River Rhine and ETRS 1989 UTM Zone 33 N (EPSG: 25833) for River Elbe.

Flood durations of the year 2022 on the floodplains of River Rhine and River Elbe, Germany

Floodplains are morphologically highly heterogeneous environments with dynamically in- and decreasing water levels and flows. Strong environmental filtering through floodplain inundation creates favorable conditions for highly specialized organisms leading to high biodiversity and conservation value of floodplains. With hyd1d and hydflood we provide two R packages for simplified hydrologic modelling in two large central European floodplains – along River Rhine and River Elbe in Germany. We applied the R function hydflood::flood3 on both active floodplains covering the year 2022. The datasets consists of 40 tiles along River Rhine and 49 tiles along River Elbe, resulting in a total of 89 individual raster datasets stored in GeoTiff file format. All raster have a spatial resolution of 1 m and are stored in the coordinate reference systems ETRS 1989 UTM Zone 32 N (EPSG: 25832) for River Rhine and ETRS 1989 UTM Zone 33 N (EPSG: 25833) for River Elbe.

Digital elevation model (DEM 1) of the River Rhine floodplain between Iffezheim and Kleve, Germany

This digital elevation model (DEM) describes the topography of the active floodplain of the freeflowing parts of River Rhine between the weir Iffezheim and the German-Dutch border near Kleve with 1 m spatial resolution in coordinate reference system "ETRS 1989 UTM Zone 32 N" and 0.01 m resolution in the German height reference system "Deutsches Haupthöhennetz 1992 (DHHN92)". The dataset was generated in four parts through aerial laser scanning (ALS) for terrestrial parts of the floodplain and echo sounding for aquatic parts of the central water course by the local waterway and navigation authorities (WSV) between 2003 and 2010. Parts not covered by any of the two data collection methods were filled through linear interpolation. A comparison between DEM and reference points confirmed a high accuracy with a mean deviation of elevations of ± 5 cm. Depending on the data source 95% of all checked points show a vertical deviation of less than 15 cm to 50 cm. Since the dataset has a large volume it was split into 40 tiles.

Ausbreitung der Noezoen in den schweizerischen Gewaessern

Das Einfuehren und Einsetzen landesfremder Arten, Rassen und Varietaeten ist nach dem BGF verboten bzw. beduerfen einer Bewilligung. Trotzdem kommen in unseren Gewaessern fremde Arten vor. Meist sind sie durch menschliche Aktivitaeten direkt in unsere Gewaesser gelangt, zudem muss aufgrund von Nachrichten aus Deutschland angenommen werden, dass weitere Arten den Rhein hoch wandern werden. Dabei handelt es sich um bei uns verschwundene Arten (z.B. Lachs, Meerneunauge), aber auch um fremde Arten (z.B. Zaehrte, Zobel, Weissflossengruendling). Projektziele: Die Liste der in der Schweiz vorkommenden Neozoen soll aktualisiert und durch jene Namen ergaenzt werden, von denen eine moegliche Einwanderung erwartet wird (v.a. im Rhein). Folgende Fragen sollten geklaert werden: Wo kommen welche Neozoen vor? Werden die neuen Arten ueberhaupt erkannt (z.B. Weissflossengruendling), welches sind die Bestimmungsmerkmale (Fotos)? Kann etwas ueber die Ausbreitungsgeschwindigkeit/das Ausbreitungspotential gesagt werden? Sind Auswirkungen auf die anwesende Fischfauna bekannt oder muessen solche erwartet werden?

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