ALKOR AL547

Area:
Baltic Sea, Eckernförde Bay
Time:
20.10.2020 - 31.10.2020
Institution:
GEOMAR
Chief scientist:
Stefan Sommer

This cruise is planned for the implementation of a robotic sensing underwater network within the Helmholtz funded project ARCHES. Major aims are to demonstrate that i. the sensing platforms of the network can interact and communicate between each other and ii. the network responds autonomously to changes in the environment and adopt its measurement strategy. The network consists of three stationary lander (BIGO, MANSIO, Flux lander) and four mobile platforms comprising the two crawlers VIATOR and NOMAD, one autonomous underwater vehicle (AUV Paul) and the towed camera and sensing platform OFOS. The network will be installed in the Eckernförde Bay close to the time series station Boknis Eck in the western Belt Sea (Baltic Sea). This allows to online include data of this time series station into the environmental analysis of the network.
Although ARCHES focus on underwater robotics, scientifically it addresses ventilation pathways in coastal waters. Deoxygenation represents a pressing problem particularly in semi-enclosed basins where extensive agricultural land-use and nutrient loading results in severe eutrophication.
The working program of the cruise consists of three major phases: I. implementation and initial tests of the network (3 days), II. testing various operational modes of the network (6.5 days), and III. demobilization of the network (2 days). During phase II, the network shall autonomously respond to environmental changes with major focus on oxygen and changes its measurement strategy according to a predefined protocol. 6.5 working days for phase II are urgently required since fluctuations of oxygen in the Eckernförde Bay strongly depends on weather, which acts primarily on diurnal timescales to a few days. To achieve these aims cruise will be conducted by different teams enclosing, oceanographers, technicians, software engineers, and mathematicians in the area of time series analysis and machine learning.