Vertical walls in marine environments can harbour high biodiversity and provide natural protection from bottom-trawling activities. However, traditional mapping techniques are usually restricted to down-looking approaches which cannot adequately replicate their 3D structure. We combined sideways-looking multibeam echosounder (MBES) data from an AUV, forward-looking MBES data from ROVs and ROV-acquired videos to examine walls from Rockall Bank and Whittard Canyon, Northeast Atlantic. High-resolution 3D point clouds were extracted from each sonar dataset and structure from motion photogrammetry (SfM) was applied to recreate 3D representations of video transects along the walls. With these reconstructions, it was possible to interact with extensive sections of video footage and precisely position individuals. Terrain variables were derived on scales comparable to those experienced by megabenthic individuals. These were used to show differences in environmental conditions between observed and background locations as well as explain spatial patterns in ecological characteristics. In addition, since the SfM 3D reconstructions retained colours, they were employed to separate and quantify live coral colonies versus dead framework. The combination of these new technologies allows us, for the first time, to map the physical 3D structure of previously inaccessible habitats and demonstrates the complexity and importance of vertical structures.

Statistical methods constitute a useful approach to understand and quantify the uncertainty that governs complex tsunami mechanisms. Numerical experiments may often have a high computational cost. This forms a limiting factor for performing uncertainty and sensitivity analyses, where numerous simulations are required. Statistical emulators, as surrogates of these simulators, can provide predictions of the physical process in a much faster and computationally inexpensive way. They can form a prominent solution to explore thousands of scenarios that would be otherwise numerically expensive and difficult to achieve. In this work, we build a statistical emulator of the deterministic codes used to simulate submarine sliding and tsunami generation at the Rockall Bank, NE Atlantic Ocean, in two stages. First we calibrate, against observations of the landslide deposits, the parameters used in the landslide simulations. This calibration is performed under a Bayesian framework using Gaussian Process (GP) emulators to approximate the landslide model, and the discrepancy function between model and observations. Distributions of the calibrated input parameters are obtained as a result of the calibration. In a second step, a GP emulator is built to mimic the coupled landslide-tsunami numerical process. The emulator propagates the uncertainties in the distributions of the calibrated input parameters inferred from the first step to the outputs. As a result, a quantification of the uncertainty of the maximum free surface elevation at specified locations is obtained.