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This dataset contains ascii text files of latitude, longitude, and water depth data which were collected using a pole-mounted multibeam echosounder system from the R/V Ukpik in July-August, 2021. Dr. Emily Eidam was the team lead and Dan Duncan was the multibeam operator. The data were collected along discrete tracklines across Harrison Bay. The general study was seaward of the Colville Delta between Cape Halkett to the west and Oliktok Point to the east, with a maximum seaward extent to water depths of approximately 30 meters (m) (about half to three-quarters of the way across the shelf from the shoreline). The dataset also contains a netcdf file of bathymetric change which was computed as the difference between the combined 2021 and 2022 data contained in this archive and a 1950s dataset which was recently corrected and is publicly available through Zimmerman et al., 2022 (doi.org/10.1016/j.csr.2022.104745). The multibeam data provide information about a rich diversity of seabed features including large and small ice-keel scours, sand waves, strudel scour pits, and unusual scoured substrates. A detailed description of these datasets is provided in an in-preparation manuscript (Eidam et al., Seafloor sediments and morphologic features of Harrison Bay in the Alaskan Beaufort Sea). The bathymetric change data illustrates erosion of the inner and inner-middle shelf over the past ~70 years, including erosion of up to ~3 m near Cape Halkett and on the Colville Delta front. These changes are addressed in detail in Heath, 2024 (Oregon State University Master of Science Thesis, "Sedimentation and Erosion on an Arctic Continental Shelf: Harrison Bay and Colville River Delta, Alaska"). 

This project documents flood-induced geo-structural damage and geomorphological change due to the flooding in the Ahr Valley in Germany during the 2021 Western European floods. It contains detailed, multi-instrument measurements both within the river channel and along the river banks at five carefully selected sites. 

Geotechnical data are increasingly utilized to aid investigations of coastal erosion and the development of coastal morphological models; however, measurement techniques are still challenged by environmental conditions and accessibility in coastal areas, and particularly, by nearshore conditions. These challenges are exacerbated for Arctic coastal environments. This article reviews existing and emerging data collection methods in the context of geotechnical investigations of Arctic coastal erosion and nearshore change. Specifically, the use of cone penetration testing (CPT), which can provide key data for the mapping of soil and ice layers as well as for the assessment of slope and block failures, and the use of free-fall penetrometers (FFPs) for rapid mapping of seabed surface conditions, are discussed. Because of limitations in the spatial coverage and number of available in situ point measurements by penetrometers, data fusion with geophysical and remotely sensed data is considered. Offshore and nearshore, the combination of acoustic surveying with geotechnical testing can optimize large-scale seabed characterization, while onshore most recent developments in satellite-based and unmanned-aerial-vehicle-based data collection offer new opportunities to enhance spatial coverage and collect information on bathymetry and topography, amongst others. Emphasis is given to easily deployable and rugged techniques and strategies that can offer near-term opportunities to fill current gaps in data availability. This review suggests that data fusion of geotechnical in situ testing, using CPT to provide soil information at deeper depths and even in the presence of ice and using FFPs to offer rapid and large-coverage geotechnical testing of surface sediments (i.e., in the upper tens of centimeters to meters of sediment depth), combined with acoustic seabed surveying and emerging remote sensing tools, has the potential to provide essential data to improve the prediction of Arctic coastal erosion, particularly where climate-driven changes in soil conditions may bias the use of historic observations of erosion for future prediction.