Planimetric ice-flow convergence and flow-orthonormal strain rate across the Antarctic Ice Sheet, with links to model result files

Fast-flowing ice streams discharge most of the ice from the interior of the Antarctic Ice Sheet coastward. Understanding how their tributary organisation is governed and evolves is essential for developing reliable models of the ice sheet's response to climate change. Despite much research on ice-stream mechanics, this problem is unsolved, because the complexity of flow within and across the tributary networks has hardly been interrogated. Here I present the first map of planimetric flow convergence across the ice sheet, calculated from satellite measurements of ice surface velocity, and use it to explore this complexity. The convergence map of Antarctica elucidates how ice-stream tributaries draw ice from the interior. It also reveals curvilinear zones of convergence along lateral shear margins of streaming, and abundant convergence ripples associated with nonlinear ice rheology and changes in bed topography and friction. Flow convergence on ice-stream tributaries and their feeding zones is markedly uneven, and interspersed with divergence at distances of the order of kilometres. For individual drainage basins as well as the ice sheet as a whole, the range of convergence and divergence decreases systematically with flow speed, implying that fast flow cannot converge or diverge as much as slow flow. I therefore deduce that flow in ice-stream networks is subject to mechanical regulation that limits flow-orthonormal strain rates. These properties and the gridded data of convergence and flow-orthonormal strain rate in this archive provide targets for ice- sheet simulations and motivate more research into the origin and dynamics of tributarization.

Single beam survey data and links to sea-bottom video files off Helgoland

Kelp forests represent a major habitat type in coastal waters worldwide and their structure and distribution is predicted to change due to global warming. Despite their ecological and economical importance, there is still a lack of reliable spatial information on their abundance and distribution. In recent years, various hydroacoustic mapping techniques for sublittoral environments evolved. However, in turbid coastal waters, such as off the island of Helgoland (Germany, North Sea), the kelp vegetation is present in shallow water depths normally excluded from hydroacoustic surveys. In this study, single beam survey data consisting of the two seafloor parameters roughness and hardness were obtained with RoxAnn from water depth between 2 and 18 m. Our primary aim was to reliably detect the kelp forest habitat with different densities and distinguish it from other vegetated zones. Five habitat classes were identified using underwater-video and were applied for classification of acoustic signatures. Subsequently, spatial prediction maps were produced via two classification approaches: Linear discriminant analysis (LDA) and manual classification routine (MC). LDA was able to distinguish dense kelp forest from other habitats (i.e. mixed seaweed vegetation, sand, and barren bedrock), but no variances in kelp density. In contrast, MC also provided information on medium dense kelp distribution which is characterized by intermediate roughness and hardness values evoked by reduced kelp abundances. The prediction maps reach accordance levels of 62% (LDA) and 68% (MC). The presence of vegetation (kelp and mixed seaweed vegetation) was determined with higher prediction abilities of 75% (LDA) and 76% (MC). Since the different habitat classes reveal acoustic signatures that strongly overlap, the manual classification method was more appropriate for separating different kelp forest densities and low-lying vegetation. It became evident that the occurrence of kelp in this area is not simply linked to water depth. Moreover, this study shows that the two seafloor parameters collected with RoxAnn are suitable indicators for the discrimination of different densely vegetated seafloor habitats in shallow environments.