Abstract. In areas of intensive ship traffic, ships pass every 10 min. Considering the amount of ship traffic and the predicted increase in global maritime trade, there is a need to consider all types of impacts shipping has on the marine environment. While the awareness about, and efforts to reduce, chemical pollution from ships is increasing, less is known about physical disturbances, and ship-induced turbulence has so far been completely neglected. To address the potential importance of ship-induced turbulence on, e.g., gas exchange, dispersion of pollutants, and biogeochemical processes, a characterisation of the temporal and spatial scales of the turbulent wake is needed. Currently, field measurements of turbulent wakes of real-size ships are lacking. This study addresses that gap by using two different methodological approaches: in situ and ex situ observations. For the in situ observations, a bottom-mounted acoustic Doppler current profiler (ADCP) was placed at 32 m depth below the shipping lane outside Gothenburg harbour. Both the acoustic backscatter from the air bubbles in the wake and the dissipation rate of turbulent kinetic energy were used to quantify the turbulent wake depth, intensity, and temporal longevity for 38 ship passages of differently sized ships. The results from the ADCP measurements show median wake depths of 13 m and several occasions of wakes reaching depths > 18 m, which is in the same depth range as the seasonal thermocline in the Baltic Sea. The temporal longevity of the observable part of the wakes had a median of around 10 min and several passages of > 20 min. In the ex situ approach, sea surface temperature was used as a proxy for the water mass affected by the turbulent wake (thermal wake), as lowered temperature in the ship wake indicates vertical mixing in a thermally stratified water column. Satellite images of the thermal infrared sensor (TIRS) onboard Landsat-8 were used to measure thermal wake width and length, in the highly frequented and thus major shipping lane north of Bornholm, Baltic Sea. Automatic information system (AIS) records from both the investigated areas were used to identify the ships inducing the wakes. The satellite analysis showed a median thermal wake length of 13.7 km (n=144), and the longest wake extended over 60 km, which would correspond to a temporal longevity of 1 h 42 min (for a ship speed of 20 kn). The median thermal wake width was 157.5 m. The measurements of the spatial and temporal scales are in line with previous studies, but the maximum turbulent wake depth (30.5 m) is deeper than previously reported. The results from this study, combined with the knowledge of regional high traffic densities, show that ship-induced turbulence occurs at temporal and spatial scales large enough to imply that this process should be considered when estimating environmental impacts from shipping in areas with intense ship traffic.
Driven by the unprecedented availability of data, machine learning has become a pervasive and transformative technology across industry and science. Its importance to marine science has been codified as one goal of the UN Ocean Decade. While increasing amounts of, for example, acoustic marine data are collected for research and monitoring purposes, and machine learning methods can achieve automatic processing and analysis of acoustic data, they require large training datasets annotated or labelled by experts. Consequently, addressing the relative scarcity of labelled data is, besides increasing data analysis and processing capacities, one of the main thrust areas. One approach to address label scarcity is the expert-in-the-loop approach which allows analysis of limited and unbalanced data efficiently. Its advantages are demonstrated with our novel deep learning-based expert-in-the-loop framework for automatic detection of turbulent wake signatures in echo sounder data. Using machine learning algorithms, such as the one presented in this study, greatly increases the capacity to analyse large amounts of acoustic data. It would be a first step in realising the full potential of the increasing amount of acoustic data in marine sciences.
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