The "EMODnet Digital Bathymetry (DTM)- 2022" is a multilayer bathymetric product for Europe’s sea basins covering: • the Greater North Sea, including the Kattegat and stretches of water such as Fair Isle, Cromarty, Forth, Forties,Dover, Wight, and Portland • the English Channel and Celtic Seas
• Western Mediterranean, the Ionian Sea and the Central Mediterranean Sea • Iberian Coast and Bay of Biscay (Atlantic Ocean) • Adriatic Sea (Mediterranean) • Aegean - Levantine Sea (Mediterranean). • Madeira and Azores (Macaronesia) • Baltic Sea • Black Sea • Norwegian and Icelandic Seas • Canary Islands (Macaronesia) • Arctic region and Barentz Sea

The DTM is based upon 21937 bathymetric survey data sets and Composite DTMs that have been gathered from 64 data providers from 28 countries riparian to European seas and beyond. Also Satellite Derived Bathymetry data products have been included fro Landsat 8 and Sentinel satellite images. Areas not covered by observations are completed by integrating GEBCO 2022 and IBCAO V4.

The source reference layer in the portal viewing service gives metadata of the data sets used with their data providers; the metadata also acknowledges the data originators. The incorporated survey data sets itself can be discovered and requested for access through the Common Data Index (CDI) data discovery and access service that in December 2022 contained > 41.000survey data sets from European data providers for global waters. The Composite DTMs can be discovered through the Sextant Catalogue service. Both discovery services make use of SeaDataNet standards and services and have been integrated in the EMODnet portal (https://emodnet.ec.europa.eu/en/bathymetry#bathymetry-services ). In addition, the EMODnet Map Viewer (https://emodnet.ec.europa.eu/geoviewer/ ) gives users wide functionality for viewing and downloading the EMODnet digital bathymetry such as: • water depth (refering to the Lowest Astronomical Tide Datum - LAT) in gridded form on a DTM grid of 1/16 * 1/16 arc minute of longitude and latitude (ca 115 * 115 meters). • option to view depth parameters of individual DTM cells and references to source data • option to download DTM in 58 tiles in different formats: ESRI ASCII, XYZ, EMODnet CSV, NetCDF (CF), GeoTiff and SD • option to visualize the DTM in 3D in the browser without plug-in • layer with a number of high resolution DTMs for coastal regions • layer with wrecks from the UKHO Wrecks database.

The EMODnet DTM is also available by means of OGC web services (WMS, WFS, WCS, WMTS), which are specified at the EMODnet Bathymetry portal.
The original datasets themselves are not distributed but described in the metadata services, giving clear information about the background survey data used for the DTM, their access restrictions, originators and distributors and facilitating requests by users to originator.

Bathymetry data were collected in the Wainwright Inlet, the mouth of the Kuk River, and in the nearshore region off Wainwright, Alaska, in August of 2009. Bathymetry was measured with a single-beam echo-sounder (10 Hz 144 ODOM Echotrac CV-100) mounted on the stern of a small vessel and synchronized to a 145 Real-Time Kinematic (RTK) Global Positioning Systems (GPS). The depth is measured relative to approximate Mean Sea Level (see attribute accuracy report in this file for further details on the MSL), and depth values are meters below the water surface. The GPS sampling rate was 1 Hz with vehicle speeds maintained at less than 15 km/hour, resulting in a sample point resolution of 3-5 meters. Data are available in a single csv file.

Dataset abstract

Depth of the seabed along the five-minute averaged cruise track (Landwehr et al., 2020; DOI: 10.5281/zenodo.3752691) of the Antarctic Circumnavigation Expedition (ACE) was calculated from the the General Bathymetric Chart of the Oceans (GEBCO; GEBCO Compilation Group, 2019) 2019 gridded 30-arc second bathymetry data. The nearest gridded value from the bathymetry dataset was used to find the depth at the averaged position.

Provided within this dataset is the average position of the vessel during a five-minute time period (where the time given is the middle time of the averaging interval).

Dataset contents

cruise_track_gebco2019_depth_5min.csv, data file, comma-separated values

data_file_header.txt, metadata, text

README.txt, metadata, text

ace_cruise_track_gebco2019_depth_5min_change_log.txt, metadata, text

Change log

v1.1 - Added additional data coverage and therefore track coverage from 2016-11-17 - 2016-11-22 inclusive. Updated README.txt with information about data coverage. Added change_log file.

v1.0 - Initial release of depth along cruise track data set.

Dataset license

This GEBCO sea floor depth along the cruise track dataset from ACE is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/

On October 5-6, 2022, a combination of Acoustic Doppler Current Profiler (ADCP) and Global Navigation Satellite System (GNSS) surveying was used to collect bathymetric data of the Lummi Sea Pond near Bellingham, WA. Distributed XYZ points were interpolated to create a continuous bathymetric map of the sea pond. The final product is two-meter raster in NAD88 (2011) UTM 10N coordinates, with elevations referenced to NAVD88 (GEOID18a). Details of the process used to construct the surface are provided in this metadata. Supporting datasets included in the zip file include raw ADCP data, ADCP bathymetry data in CSV format, and GNSS data in CSV format.

Farm ponds are a common feature of agricultural landscapes for irrigation of crops. Yet small water bodies have been ignored as reservoirs and carbon balance features despite ubiquity in the global landscape. These ponds contain surface water from precipitation and runoff, but in South Georgia, USA, groundwater supplementation is required to maintain a supply for irrigation. As part of a project to characterize water balance and quality of irrigation ponds in this landscape, data were collected describing terrain, bathymetry, inputs and outputs, and water quality from October 2021 through October 2023. The pond described in this study was located on a farm near Ty Ty, GA (Ty Ty Cooperator Farm, TCF; 31.5086980, -83.6167862). The TCF is a rotational cropping system, alternating among corn, cotton, and peanuts. During the 2022 growing season, the fields adjacent to the pond were planted with corn. Datasets include georeferenced image files (.tif) and tabular files (.csv). Image data were obtained on 27 SEP 2022 at approximately 0915 local time using a commercial, off-the-shelf unmanned aircraft system (UAS; DJI Mavic 2 Pro L1P) with an integrated digital RGB camera (Hasselblad L1D-20c_10.3 RGB with 20MP 1" CMOS sensor). The UAS was flown at 107 meters above ground level with 75% front and side overlap to capture an area of 12.99 ha. The images were used to produce an orthorectified mosaic (RMS error = 0.001 m) with 2.5 cm average ground sample distance (GSD), and a digital terrain model (DTM) with 12.5 cm GSD using 5 surveyed ground control points. A derived topobathymetric surface (12.5 cm GSD) was created by fusing bathymetric and topographic survey data. All geospatial data were projected in WGS84 / UTMZone 17N (EGM96 Geoid). Tabular data include GPS survey data points of the bathymetry and shoreline, and log files of daily water pumping into and out of the pond (acre-inches), 5-minute staff gauge levels within the pond (US feet), and 1-minute precipitation data (inches) at the farm. Water pumping, pond level and precipitation data were measured using instrumentation and data loggers operated by the USDA ARS Southeast Watershed Research Laboratory. Relevant water chemistry data were also recorded for this study, are flagged (Flag = 4) and are published separately by Pisani et al (2025) in the USDA Ag Data Commons repository, as "Water chemistry data for three agricultural ponds in the southern Coastal Plain of Georgia, USA".Datasets provided here include:1. A georeferenced orthomosaic (.tif) of aerial imagery of the study site from 27SEP2022 (1_TyTyCooperatorFarm_RGB_mosaic_20220927.tif)2. A digital terrain model (.tif) of the study site, produced from provided orthomosaic from 27SEP2022 (2_TyTyCooperatorFarm_DTM_20220927.tif)3. A fused topobathymetric surface model (.tif) of the study site (3_TyTyCooperatorFarm_DTM_20220927.tif)4. Survey data points from a bathymetric survey providing global positioning system (GPS) outputs (4_TyTyCooperatorFarm_Bathymetry_Survey_20220923_20220927.csv) NOTE: Field values for Tables 4 and 6 are nearly identical. Refer to Table 4 for values in Table 6.5. Water amounts pumped into and out of the study site pond in 2022 (5_TyTyCooperatorFarm_InOut_Pumping_Data_2022.csv)6. Survey data points from a shoreline survey providing global positioning system (GPS) outputs (6_TyTyCooperatorFarm_Shoreline_Survey_20220922.csv) NOTE: Field values for Tables 4 and 6 are nearly identical. Refer to Table 4 for values in Table 6.7. Water elevation readings from a staff gauge in the pond at the study site 2021 through 2023 (7_TyTyCooperatorFarm_Staff_Gauge_Pressure_Readings_2021_2023.csv)8. Rainfall precipitation measured at the study site during 2022 (8_TyTyCooperatorFarm_Precipitation_Rainfall_2022.csv)9. Dataset dictionary: Tabular data field values for tables 4 – 9 (9_TyTyCooperatorFarm_Data_Dictionary_Pond_Study_2022.xlsx). NOTE: Field values for Tables 4 and 6 are nearly identical. Refer to Table 4 for values in Table 6.al. Refer to Table 4 for values in Table 6.

These data sets were derived from surveys at the Missouri River and Papillion Creek Water Resource Recovery Facility outfalls on the Missouri River. Each site had three transects, one at the outfall, one 45.7 meters upstream of the outfall and one 45.7 meters downstream of the outfall. Velocity and depth data were collected using an acoustic Doppler current profiler at each transect. Elevations of water surface, bank topography, and shallow channel bed elevations were collected at each transect using survey grade Global Navigation and Satellite System instruments connected to the High Precision Real-Time Kinematic (HPRTK) correction service (https://hprtk.net). Data were processed using Velocity Mapping Toolbox: a processing and visualization suite for moving-vessel acoustic Doppler current profiler measurements, Parsons and others, 2013. Data from the surveyed cross-section at the outfall of each site were also processed to estimate discharge during the time of the survey. The discharge during data collection at the Papillion Creek Water Resource Recovery Facility outfall was 1,005 cubic meters per second (35,500 cubic feet per second) at around 4:00 pm central standard time and at the Missouri River Water Resource Recovery Facility outfall was 1,020 cubic meters per second (36,000 cubic feet per second) at around 6:00 pm central standard time. This data release contains two comma separated value files. The file named Missouri_River_Papillion_Creek_WRRF_BedElevations&Topopoints_UTM15M.csv contains Universal Transverse Mercator zone 15 Northing, Easting, and elevation information for all survey points including bathymetry and Global Navigation and Satellite System data and the Water Resource Recovery Facility where the data were collected. The file named Missouri_River_Papillion_Creek_WRRF_Velocity_UTM15M.csv contains velocity specific positioning data and depth averaged velocity data.

These data are single-beam bathymetry points compiled in comma separated values (CSV) file format, generated from a hydrographic survey of the northern portion of Lake Calumet in Cook County, Illinois. Hydrographic data were collected July 18-19, 2023, using a single-beam echosounder (SBES) integrated with a Global Navigation Satellite System (GNSS) mounted on a marine survey vessel. Surface water elevation data were collected July 18 utilizing a single-base real-time kinematic (RTK)/GNSS unit. Bathymetric data points were collected as the vessel traversed the northern portions of the lake along overlapping survey lines. The SBES internally collected and stored the depth data from the echosounder and the horizontal and vertical position data of the vessel from the GNSS in real time. Data processing required specialized computer software to export bathymetry data from the raw data files. A Python script was written to calculate the lakebed elevations and identify outliers in the dataset. These data are provided in comma separated values (CSV) format as LakeCalumet_SBES_20230718.csv. Data points are stored as a series of x (longitude), y (latitude), and z (elevation or depth) points along with variable length records specific to the data transects.

On August 25, 2020, the U.S. Geological Survey conducted a bathymetry survey of a 550 meter long reach of the Black River near Great Bend, New York. The study reach began approximately 1,000 meters upstream from the State Route 26 bridge in Great Bend, New York. Depth data were collected primarily with a 1,200 kilohertz Teledyne RD Instruments RioPro acoustic Doppler current profiler (ADCP) with position data from differential global navigation satellite system (GNSS) Hemisphere V102 DGPS antenna (any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government). Water surface elevations were established using real-time kinematic (RTK) GNSS surveys; additional bathymetry points were also measured using RTK-GNSS. Quality assurance of the RTK-GNSS included observations of two documented benchmarks. Files included in this Data Release include shapefiles with detailed attribute tables and accompanying comma-separated values files of ADCP bathymetry points (BlackR_ADCPBeamDepths.shp and .csv), GNSS bathymetry points (BlackR_GNSS_BathyPoints.shp and .csv), and GNSS water surface elevation points (BlackR_GNSS_WSEPoints.shp and.csv), and a comma-separated values file of the GNSS benchmark occupation data (BlackR_GNSS_Benchmarks.csv).

This repository of global hydrogeologic datasets contains aquifer properties on 0.5° scale, including depth to groundwater (Fan et al., 2013), aquifer thickness (de Graaf et al., 2015), WHYMap aquifer classes (Richts et al., 2011), porosity and permeability (Gleeson et al., 2014), digitized and geo-processed from their respective sources. Globally gridded aquifer properties could be used independently to estimate global groundwater availability or used as critical inputs to the superwell model to simulate groundwater extraction and provide estimates of pumped volumes and unit costs under user-specific scenarios. Key resources related to this data are: Niazi, H., Ferencz, S., Graham, N., Yoon, J., Wild, T., Hejazi, M., Watson, D., & Vernon, C. (2024; In-prep). Long-term Hydro-economic Assessment Tool for Evaluating Global Groundwater Cost and Supply: Superwell v1. Geoscientific Model Development. superwell model repository which uses this data to simulate groundwater extraction and provides estimates of the global extractable volumes and unit-costs ($/km3) of accessible groundwater production under user-specified extraction scenarios. Repository Overview Main output: aquifer_properties.csv contains all processed outputs, including aquifer properties like porosity, permeability, aquifer thickness, and depth to groundwater. shapefiles.zip: contains all digitized GIS databases and shapefile for all aquifer properties prep_inputs.R: R script that processes the shapefiles to produce the aquifer_properties file plot_inputs.R: R script for plotting the maps and conducting preliminary analysis on the available groundwater volume basin_to_country_mapping.csv, basin_country_region_mapping.csv and continent_county_mapping.csv provide the mapping between continents, 32 energy-economic macro regions, countries, and water basins for post-processing aquifer_properties.csv Maps: Each map visualizes the spatial distribution of one of the aquifer properties across the globe map_in_Porosity.png map_in_Permeability.png map_in_Aquifer_thickness.png map_in_Depth_to_water.png map_in_Grid_area_km.png map_in_WHYClass.png Sample inputs sample_inputs.py: this script samples inputs from the aquifer_properties dataset, ensuring the sampled and original inputs maintain the same distributions sampled_data_100.csv contains 100 sampled data points and sampled_data_100.png compares their distributions Dataset Overview The main outputs are consolidated in a comprehensive aquifer_properties.csv file and include the following fields: GridCellID: Unique identifier for each (roughly 0.5°) grid cell Continent: Continent name Country: Country name GCAM_basin_ID: Identifier for GCAM hydrologic basin Basin_long_name: Full name of the basin WHYClass: Hydrogeologic classification based on WHYMap aquifer classes (Richts et al., 2011) Porosity: Soil porosity (%) (Gleeson et al., 2014) Permeability: Soil permeability (in square meters; Gleeson et al., 2014) Aquifer_thickness: Thickness of the aquifer (in meters; de Graaf et al., 2015) Depth_to_water: Depth to groundwater (in meters; Fan et al., 2013) Grid_area: Area of the grid cell (in square meters) Key References The datasets are digitized versions of global hydrogeologic properties from the following key literature sources: Depth to Groundwater: Fan, Y., Li, H., & Miguez-Macho, G. (2013). Global Patterns of Groundwater Table Depth. Science, 339(6122), 940-943. https://doi.org/10.1126/science.1229881 Aquifer Thickness: de Graaf, I. E. M., Sutanudjaja, E. H., van Beek, L. P. H., & Bierkens, M. F. P. (2015). A high-resolution global-scale groundwater model. Hydrol. Earth Syst. Sci., 19(2), 823-837. https://doi.org/10.5194/hess-19-823-2015 Porosity and Permeability: Gleeson, T., Moosdorf, N., Hartmann, J., & van Beek, L. P. H. (2014). A glimpse beneath earth's surface: GLobal HYdrogeology MaPS (GLHYMPS) of permeability and porosity. Geophysical Research Letters, 41(11), 3891-3898. https://doi.org/10.1002/2014GL059856 Aquifer classes: Richts, A., Struckmeier, W. F., & Zaepke, M. (2011). WHYMAP and the Groundwater Resources Map of the World 1:25,000,000. In J. A. A. Jones (Ed.), Sustaining Groundwater Resources: A Critical Element in the Global Water Crisis (pp. 159-173). Springer Netherlands. https://doi.org/10.1007/978-90-481-3426-7_10 Cite as Niazi, H., Watson, D., Hejazi, M., Yonkofski, C., Ferencz, S., Vernon, C., Graham, N., Wild, T., & Yoon, J. (2024). Global Geo-processed Data of Aquifer Properties by 0.5° Grid, Country and Water Basins. MSD-LIVE Data Repository. https://doi.org/10.57931/2307831 Contact Reach out to Hassan Niazi or open an issue in superwell repository for questions or suggestions.

These data are bathymetry (river bottom elevation) in XYZ format, generated from the April 4-5, 2017, bathymetric survey of the Rolling Fork and Beech Fork near Boston, Kentucky. The bathymetry was collected from approximately 1.9 miles upstream from Kentucky State Highway 62 on the Beech Fork and approximately 1.5 miles upstream from Kentucky State Highway 62 on the Rolling Fork, to 2.6 miles downstream from Kentucky State Highway 62 on the Rolling Fork. Hydrographic data were collected using an acoustic Doppler current profiler (ADCP) with integrated Differential Global Positioning System (DGPS). Data were collected as the surveying vessel traversed the river, approximately perpendicular to the velocity vectors at 70 cross sections which were spaced approximately 500 feet apart along the river. Data collection software integrated and stored the depth and DGPS data from the ADCP. Real-time kinematic GPS was collected recording the water-surface elevation. Data processing required computer software to extract bathymetry data from the raw data files and to compile the information. The final csv file contains columns of Easting and Northing in meters WGS84 - UTM Zone 16N, and Elevation in feet NAVD88.

This dataset contains multi-beam bathymetry data in CSV format (lon,lat,-depth) taken by KONGSBERG EM 122 + EM 2040 onboard the research vessel Legend in the Philippine Sea on transects adjacent to the US EEZ. This dataset is U.S. State Department transit only cruise related to the MSR Program and is a part of the World Data Service for Geophysics. Data are fully processed and stored in CSV format.

This dataset contains nearshore bathymetry measurements collected at Point Barrow, Alaska. The data were collected in July 2022 using a single beam echosounder and global navigation satellite system receiver and subsequently processed by the U.S. Geological Survey, Pacific Coastal and Marine Science Center. Results are provided in a comma-separated value format (csv).

These data are the survey results from a five-mile section of the Platte River at, and upstream of the Nebraska Army National Guard Camp Ashland Training Site including the side channel chutes on the east bank. All survey data were collected along planned transect lines that were spaced 492.125 US survey feet apart beginning near the mouth of the Elkhorn River and ending near the U.S. Highway 6 bridge. An effort was made to get complete elevation data for each transect from top of bank to top of bank. Survey grade Global Navigation and Satellite Systems (GNSS) receiving antennas connected to a real time network (RTK high precision network https://hprtk.net) were used to measure elevation along the transects, at the top of banks, along the slope of the banks, at control structures, on islands and sandbars and on the streambed in areas of the wetted channel that were wadable. GNSS data collection methods followed level 3, RTN procedures as described by (Rydlund and Densmore, 2012). An acoustic Doppler current profiler (ADCP) was used to measure streambed elevation in areas of the wetted channel that were not wadable. ADCP data were processed using Velocity Mapping Toolbox (Parsons and others, 2013) to convert measured depths to elevation. This data release contains two comma separated value files. The CSV file named PlatteRiver_GNSS_SurveyData_20200924-20210402.csv contains the GNSS survey data. The CSV file named Bathy_ADCP_final_data_SPCS.csv contains bathymetric survey data.

The U.S. Army Corps of Engineers Fish Passage Facility, located on the White River, Washington State, collects upstream-migrating fish and transfers them to trucks, allowing the fish to access the watershed upstream of Mud Mountain Dam. The structure, constructed in 2019, includes an impoundment held by gates that can be raised or lowered remotely. Those gates are typically lowered during higher flows to allow sediment trapped in the impoundment to flush downstream. Starting in 2020, the USGS collected repeat bathymetric surveys of the White River in the immediate vicinity of the facility to help document how the local channel bed responded to various gate operation strategies. Surveys were generally conducted as soon as possible after high flows that exceeded 4,000 ft3/s. Surveys were conducted using acoustic doppler current profilers (ADCPs) mounted on a remote-control boat, providing XY-depth data, combined with global navigation satellite system (GNSS) surveys used to measure water surface elevations and the location of waters edge. The data were used to construct continuous one-meter digital elevation models (DEMs) of the channel bed. The extent covered in a given survey varied between survey dates, primarily as a function of where water depths were sufficient to operate the ADCP, though all surveys covered the forebay just upstream of the impoundment. A total of ten surveys were conducted between December 15, 2020 and September 30, 2022. Each survey is packaged into a zip file containing: the final one-meter DEM; a csv of all GNSS data; a csv of all xy-depth data from ADCPs; a geopackage, containing the full set of final XYZ points used to construct the DEM, a polygon defining the extents of the DEM, lines defining the waters edge, where depth was enforced to be zero, and lines defining the linear referencing used to link ADCP and GNSS data; a folder containing the original ADCP output in proprietary and ASCII formats; an R script containing all processing steps used to construct the final DEM; and metadata specific to that survey date. An additional folder ('white_fpf_supporting_scripts_data.zip') contains two R scripts, each containing custom functions used in processing the data, and a CSV of coordinates for the four control points used to validate survey datum in the latter surveys.