ALBERT

Description: Understanding the dynamics and fate of methane (CH 4 ) release from oceanic seepages on margins and shelves into the water column, and quantifying the budget of its total discharge at different spatial and temporal scales, currently represents a major scientific undertaking. Previous works on the fate of methane escaping from the seafloor underlined the challenge in both, estimating its concentration distribution and identifying gradients. In April 2019, the Envri Methane Cruise has been conducted onboard the R/V Mare Nigrum in the Western Black Sea to investigate two shallow methane seep sites at ∼120 m and ∼55 m water depth. Dissolved CH 4 measurements were conducted with two continuous in-situ sensors: a membrane inlet laser spectrometer (MILS) and a commercial methane sensor (METS) from Franatech GmbH. Additionally, discrete water samples were collected from CTD-Rosette deployment and standard laboratory methane analysis was performed by gas chromatography coupled with either purge-and-trap or headspace techniques. The resulting vertical profiles (from both in situ and discrete water sample measurements) of dissolved methane concentration follow an expected exponential dissolution function at both sites. At the deeper site, high dissolved methane concentrations are detected up to ∼45 m from the seabed, while at the sea surface dissolved methane was in equilibrium with the atmospheric concentration. At the shallower site, sea surface CH 4 concentrations were four times higher than the expected equilibrium value. Our results seem to support that methane may be transferred from the sea to the atmosphere, depending on local water depths. In accordance with previous studies, the shallower the water, the more likely is a sea-to-atmosphere transport of methane. High spatial resolution surface data also support this hypothesis. Well localized methane enriched waters were found near the surface at both sites, but their locations appear to be decoupled with the ones of the seafloor seepages. This highlights the need of better understanding the processes responsible for the transport and transformation of the dissolved methane in the water column, especially in stratified water masses like in the Black Sea.

Description: Landscape maps based on multivariate cluster analyses provide an objective and comprehensive view on the (marine) environment. They can hence support decision making regarding sustainable ocean resource handling and protection schemes. Across a large number of scales, input parameters and classification methods, numerous studies categorize the ocean into seascapes, hydro-morphological provinces or clusters. Many of them are regional, however, while only a few are on a basin scale. This study presents an automated cluster analysis of the entire Atlantic seafloor environment, based on eight global datasets and their derivatives: Bathymetry, slope, terrain ruggedness index, topographic position index, sediment thickness, POC flux, salinity, dissolved oxygen, temperature, current velocity, and phytoplankton abundance in surface waters along with seasonal variabilities. As a result, we obtained nine seabed areas (SBAs) that portray the Atlantic seafloor. Some SBAs have a clear geological and geomorphological nature, while others are defined by a mixture of terrain and water body characteristics. The majority of the SBAs, especially those covering the deep ocean areas, are coherent and show little seasonal and hydrographic variation, whereas other, nearshore SBAs, are smaller sized and dominated by high seasonal changes. To demonstrate the potential use of the marine landscape map for marine spatial planning purposes, we mapped out local SBA diversity using the patch richness index developed in landscape ecology. It identifies areas of high landscape diversity, and is a practical way of defining potential areas of interest, e.g. for designation as protected areas, or for further research. Clustering probabilities are highest (100%) in the center of SBA patches and decrease towards the edges (〈 98%). On the SBA point cloud which was reduced for probabilities 〈98%, we ran a diversity analysis to identify and highlight regions that have a high number of different SBAs per area, indicating the use of such analyses to automatically find potentially delicate areas. We found that some of the highlights are already within existing EBSAs, but the majority is yet unexplored.