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An updated global bathymetry and topography grid is presented using a spatial sampling interval of 15 arc sec. The bathymetry is produced using a combination of shipboard soundings and depths predicted using satellite altimetry. New data consists of >33.6 million multibeam and singlebeam measurements collated by several institutions, namely, the National Geospatial‐Intelligence Agency, Japan Agency for Marine‐Earth Science and Technology, Geoscience Australia, Center for Coastal and Ocean Mapping, and Scripps Institution of Oceanography. New altimetry data consists of 48, 14, and 12 months of retracked range measurements from Cryosat‐2, SARAL/AltiKa, and Jason‐2, respectively. With respect to SRTM15_PLUS (Olson et al.,), the inclusion of these new data results in a ∼1.4‐km improvement in the minimum wavelength recovered for sea surface free‐air gravity anomalies, a small increase in the accuracy of altimetrically derived predicted depths, and a 1.24% increase, from 9.60% to 10.84%, in the total area of ocean floor that is constrained by shipboard soundings at 15‐arc sec resolution. Bathymetric grid cells constrained by satellite altimetry have estimated uncertainties of ±150 m in the deep oceans and ±180 m between coastlines and the continental rise. Onshore, topography data are sourced from previously published digital elevation models, predominately SRTM‐CGIAR V4.1 between 60°N and 60°S. ArcticDEM is used above 60°N, while Reference Elevation Model of Antarctica is used below 62°S. Auxiliary grids illustrating shipboard data coverage, marine free‐air gravity anomalies, and vertical gradient gradients are also provided in common data formats. Key Points An updated global elevation grid is presented using a spatial sampling interval of 15 arc sec New bathymetry data include more than 33.6 million ship soundings and more than 6 years of non‐repeat altimetry measurements The percentage of seafloor mapped by echo soundings remains low; our current compilation covers only 10.84% at 15‐arc sec resolution

Despite many of years of mapping effort, only a small fraction of the world ocean’s seafloor has been sampled for depth, greatly limiting our ability to explore and understand critical ocean and seafloor processes. Recognizing this poor state of our knowledge of ocean depths and the critical role such knowledge plays in understanding and maintaining our planet, GEBCO and the Nippon Foundation have joined forces to establish the Nippon Foundation GEBCO Seabed 2030 Project, an international effort with the objective of facilitating the complete mapping of the world ocean by 2030. The Seabed 2030 Project will establish globally distributed regional data assembly and coordination centers (RDACCs) that will identify existing data from their assigned regions that are not currently in publicly available databases and seek to make these data available. They will develop protocols for data collection (including resolution goals) and common software and other tools to assemble and attribute appropriate metadata as they assimilate regional grids using standardized techniques. A Global Data Assembly and Coordination Center (GDACC) will integrate the regional grids into a global grid and distribute to users world-wide. The GDACC will also act as the central focal point for the coordination of common data standards and processing tools as well as the outreach coordinator for Seabed 2030 efforts. The GDACC and RDACCs will collaborate with existing data centers and bathymetric compilation efforts. Finally, the Nippon Foundation GEBCO Seabed 2030 Project will encourage and help coordinate and track new survey efforts and facilitate the development of new and innovative technologies that can increase the efficiency of seafloor mapping and thus make the ambitious goals of Seabed 2030 more likely to be achieved.

To date, ∼20% of the ocean floor has been surveyed by ships at a spatial resolution of 400 m or better. The remaining 80% has depth predicted from satellite altimeter‐derived gravity measurements at a relatively low resolution. There are many remote ocean areas in the southern hemisphere that will not be completely mapped at 400 m resolution during this decade. This study is focused on the development of synthetic bathymetry to fill the gaps. There are two types of seafloor features that are not typically well resolved by satellite gravity; abyssal hills and small seamounts (<2.5 km tall). We generate synthetic realizations of abyssal hills by combining the measured statistical properties of mapped abyssal hills with regional geology including fossil spreading rate/orientation, rms height from satellite gravity, and sediment thickness. With recent improvements in accuracy and resolution, it is now possible to detect all seamounts taller than about 800 m in satellite‐derived gravity and their location can be determined to an accuracy of better than 1 km. However, the width of the gravity anomaly is much greater than the actual width of the seamount so the seamount predicted from gravity will underestimate the true seamount height and overestimate its base dimension. In this study, we use the amplitude of the vertical gravity gradient (VGG) to estimate the mass of the seamount and then use their characteristic shape, based on well‐surveyed seamounts, to replace the smooth‐predicted seamount with a seamount having a more realistic shape. Plain Language Summary The floor of the deep ocean remains as the last uncharted frontier in the inner solar system. The deep seawater (>1,000 m) prevent any type of exploration from conventional satellite remote sensing. Echosounders aboard large vessels have mapped about 20% of the seafloor, however, vast areas in the southern hemisphere will not be mapped in our lifetimes. The deep ocean floor has more than 90% of the active volcanoes; hydrothermal circulation of seawater through the crust of the seafloor spreading ridges replenishes the nutrients needed for life on Earth. This study is an effort to fill the large gaps in seafloor coverage by creating a synthetic abyssal hill fabric using geological information such as the age of the seafloor, the spreading rate and direction when it formed, and the thickness of the sediments covering the original topography. In addition, we use the latest satellite‐derived gravity to estimate the locations and shapes of about 20,000 uncharted seamounts. The combination of mapped (20%) and synthetic (80%) topography is useful for modeling ocean circulation and ocean tides although it may give a false impression that 100% of the seafloor has been mapped. Key Points ∼20% of the ocean floor topography has been surveyed by ships, the remaining 80% is predicted by satellite altimetry We increased the resolution of the predicted depth using spectral properties of abyssal hills and the characteristic shapes of seamounts We estimate the height and radius of 19,000 uncharted seamounts

Seamounts are isolated elevations in the seafloor with circular or elliptical plans, comparatively steep slopes, and relatively small summit area (Menard, 1964). The vertical gravity gradient (VGG), which is the curvature of the ocean surface topography derived from satellite altimeter measurements, has been used to map the global distribution of seamounts (Kim & Wessel, 2011, https://doi.org/10.1111/j.1365-246x.2011.05076.x). We used the latest grid of VGG to update and refine the global seamount catalog; we identified 19,325 new seamounts, expanding a previously published catalog having 24,643 seamounts. Seven hundred thirty‐nine well‐surveyed seamounts, having heights ranging from 421 to 2,500 m, were used to estimate the typical radially symmetric seamount morphology. First, an Empirical Orthogonal Function (EOF) analysis was used to demonstrate that these small seamounts have a basal radius that is linearly related to their height—their shapes are scale invariant. Two methods were then used to compute this characteristic base to height ratio: an average Gaussian fit to the stack of all profiles and an individual Gaussian fit for each seamount in the sample. The first method combined the radial normalized height data from all 739 seamounts to form median and median‐absolute deviation. These data were fit by a 2‐parameter Gaussian model that explained 99.82% of the variance. The second method used the Gaussian function to individually model each seamount in the sample and further establish the Gaussian model. Using this characteristic Gaussian shape we show that VGG can be used to estimate the height of small seamounts to an accuracy of ∼270 m. Key Points We used the latest vertical gravity gradient maps to update and refine a global seamount catalog, finding 19,325 new seamounts Smaller seamounts (<2,500 m tall) having good bathymetry coverage (739) were modeled with a radially symmetric Gaussian function Two modeling approaches show that smaller seamounts have a sigma to height ratio of 2.4 which agrees with an earlier study by Smith (1988)

The response of tidal and residual currents to small-scale morphological differences over abrupt deep-sea topography (Seine Seamount) was estimated for bathymetry grids of different spatial resolution. Local barotropic tidal model solutions were obtained for three popular and publicly available bathymetry grids (Smith and Sandwell TOPO8.2, ETOPO1, and GEBCO08) to calculate residual currents from vessel-mounted acoustic Doppler current profiler (VM-ADCP) measurements. Currents from each tidal solution were interpolated to match the VM-ADCP ensemble times and locations. Root mean square (RMS) differences of tidal and residual current speeds largely follow topographic deviations and were largest for TOPO8.2-based solutions (up to 2.8 cm s −1 ) in seamount areas shallower than 1,000 m. Maximum RMS differences of currents obtained from higher resolution bathymetry did not exceed 1.7 cm s −1 . Single depth-dependent maximum residual flow speed differences were up to 8 cm s −1 in all cases. Seine Seamount is located within a strong mean flow environment, and RMS residual current speed differences varied between 5 % and 20 % of observed peak velocities of the ambient flow. Residual flow estimates from shipboard ADCP data might be even more sensitive to the choice of bathymetry grids if barotropic tidal models are used to remove tides over deep oceanic topographic features where the mean flow is weak compared to the magnitude of barotropic tidal, or baroclinic currents. Realistic topography and associated flow complexity are also important factors for understanding sedimentary and ecological processes driven and maintained by flow–topography interaction.

We propose a novel technique to measure the small-scale three-dimensional features of a shallow-water coral reef using a small drone equipped with a consumer-grade camera, a handheld GPS and structure from motion (SfM) algorithms. We used a GoPro HERO4 with a modified lens mounted on a DJI Phantom 2 drone (maximum total take-off weight <2 kg) to perform a 10 min flight and collect 306 aerial images with an overlap equal or greater than 90%. We mapped an area of 8380 m 2 , obtaining as output an ortho-rectified aerial photomosaic and a bathymetric digital elevation model (DEM) with a resolution of 0.78 and 1.56 cm pixel −1 , respectively. Through comparison with airborne LiDAR data for the same area, we verified that the location of the ortho-rectified aerial photomosaic is accurate within ~1.4 m. The bathymetric difference between our DEM and the LiDAR dataset is −0.016 ± 0.45 m (1σ). Our results show that it is possible, in conditions of calm waters, low winds and minimal sun glint, to deploy consumer-grade drones as a relatively low-cost and rapid survey technique to produce multispectral and bathymetric data on shallow-water coral reefs. We discuss the utility of such data to monitor temporal changes in topographic complexity of reefs and associated biological processes.

Reservoirs play a critical role in the global water cycle by regulating the flow of water from the environment into human systems. Accurate estimation of the area‐storage‐depth relationships for global reservoirs is essential for effective hydrological modeling and reservoir storage monitoring. Bathymetry reconstruction presents a promising approach to derive this information. Current bathymetry methods either rely on simple approximations or are constrained by dependence on altimetry data or field survey data. To overcome these limitations, this study presents a pioneering approach involving training a deep learning model to reconstruct bathymetry and establish precise area‐storage‐depth relationships. We trained the deep learning model with approximately 160,000 simulated reservoirs derived from Shuttle Radar Topography Mission (SRTM) and fine‐tuned the model based on hundreds of reservoirs with bathymetry data. By employing the trained model and SRTM, the bathymetry of 7,250 reservoirs in the Global Reservoir and Dam Database were subsequently reconstructed. The method is validated against comprehensive reference data sets, including 54 test reservoirs with bathymetry data, 118 satellite altimetry‐based reservoirs, and 68 LiDAR‐based reservoirs. The reconstructed bathymetry achieves a mean absolute error of 7.87 m and a mean error of +2.05 m for the test reservoirs. The validation against satellite altimetry and LiDAR‐based references significantly outperforms previous geometric approximation techniques, with median normalized root mean square error (NRMSE) values of 20.6% for area‐storage and 22.1% for area‐level curves. Additionally, the reservoir storage variations are estimated with precision, outperforming previous methods. The proposed deep learning‐based approach presents a robust solution for accurate reservoir bathymetry estimation and establishes more reliable area‐storage‐depth relationships for reservoirs worldwide. Plain Language Summary Reservoirs are large bodies of water used to store and manage our water supply. Knowing how much water they can hold and at what depth is crucial for our understanding of water management and the environment. Our study explored the application of machine learning to better estimate how much water is in a reservoir at any given depth. Traditional methods have shown limitations, prompting us to develop a machine learning model for more precise estimations. Using our new approach, we were able to more accurately measure the depth and storage of over 7,000 reservoirs worldwide. Our results were more precise than older methods when we tested them against well‐known data sets. This new machine learning‐based method can provide better information about water storage in reservoirs, which is important for managing water resources and understanding the environment. Key Points We employed deep learning to reconstruct bathymetry for 7,250 global reservoirs, refining area‐storage‐depth relationships Validation against various data sources achieved a mean absolute error of 7.87 m The method realized a 20.6% normalized root mean square error (NRMSE) for area‐storage, surpassing prior methods

ROMS, a high-resolution regional ocean model, was used to study how climate change may affect the northwestern Atlantic Ocean. A control (CTRL) simulation was conducted for the recent past (1976–2005), and simulations with additional forcing at the surface and lateral boundaries, obtained from three different global climate models (GCMs) using the RCP8.5 scenario, were conducted to represent the future (2070–99). The climate change response was obtained from the difference between the CTRL and each of the three future simulations. All three ROMS simulations indicated large increases in sea surface temperatures (SSTs) over most of the domain except off the eastern U.S. seaboard resulting from weakening of the Gulf Stream. There are also substantial intermodel differences in the response, including a southward shift of the Gulf Stream in one simulation and a slight northward shift in the other two, with corresponding changes in eddy activity. The depth of maximum warming varied among the three simulations, resulting in differences in the bottom temperature response in coastal regions, including the Gulf of Maine and the West Florida Shelf. The surface salinity decreased in the northern part of the domain and increased in the south in all three experiments, although the freshening extended much farther south in one ROMS simulation relative to the other two, and also relative to the GCM that provided the large-scale forcing. Thus, while high resolution allows for a better representation of currents and bathymetry, the response to climate change can vary considerably depending on the large-scale forcing.

The development of bathymetric LiDAR technology has contributed significantly to both the quality and quantity of river bathymetry data. Although several bathymetric LiDAR sensors are available today, studies that evaluate the performance of the different bathymetric LiDAR sensors comparatively are still lacking. This study evaluates the performance of three bathymetric LiDAR sensors, CZMIL Supernova, Riegl VQ880-G, and Riegl VQ840-G, used with different acquisition approaches, in mapping Lærdal River bathymetry in Norway. The performance was evaluated based on comparing the sensors against a multibeam echosounder (MBES), a terrestrial laser scanner (TLS), and by an intercomparison between the individual sensors. The comparison was completed by comparing point clouds from the instruments and through the comparison of DEMs created from the point clouds. For the comparison against the MBES, the results show that the median residuals range between 3 to 13 cm, while against the TLS the median residuals range between 0 to 5 cm. The comparison of the CZMIL sensor against the two Riegl sensors shows median residuals of around 12 cm where the CZMIL map is shallower against the VQ880-G and deeper against the VQ840-G sensor. For the two Riegl sensors, the results show a median difference of 2 cm with the VQ880-G map deeper. We do observe that areas with high residuals are linked to river features such as large substrate variability, steep banks, and whitewater/turbulent flow. The study shows that all the LiDAR instruments provide high-quality representations of the river geometry and create a solid foundation for planning, modelling, or other work in rivers where detailed bathymetry is needed.

Ocean melting has thinned Antarctica’s ice shelves at an increasing rate over the past two decades, leading to loss of grounded ice. The Ross Ice Shelf is currently close to steady state but geological records indicate that it can disintegrate rapidly, which would accelerate grounded ice loss from catchments equivalent to 11.6 m of global sea level rise. Here, we use data from the ROSETTA-Ice airborne survey and ocean simulations to identify the principal threats to Ross Ice Shelf stability. We locate the tectonic boundary between East and West Antarctica from magnetic anomalies and use gravity data to generate a new high-resolution map of sub-ice-shelf bathymetry. The tectonic imprint on the bathymetry constrains sub-ice-shelf ocean circulation, protecting the ice shelf grounding line from moderate changes in global ocean heat content. In contrast, local, seasonal production of warm upper-ocean water near the ice front drives rapid ice shelf melting east of Ross Island, where thinning would lead to faster grounded ice loss from both the East and West Antarctic ice sheets. We confirm high modelled melt rates in this region using ROSETTA-Ice radar data. Our findings highlight the significance of both the tectonic framework and local ocean–atmosphere exchange processes near the ice front in determining the future of the Antarctic Ice Sheet.The boundary between West and East Antarctica is a tectonic feature that bisects the Ross Ice Shelf. This boundary constrains ocean circulation under the ice, which affects ice stability, according to airborne survey data and ocean simulations.