Monitoring of sea-ice-atmosphere interface in the proximity of arctic tidewater glaciers: The contribution of marine robotics
- Creators
- Bruzzone G.
- Odetti A.
- Caccia M.
- Ferretti R.
- Others:
- Bruzzone, G.
- Odetti, A.
- Caccia, M.
- Ferretti, R.
Description
The Svalbard archipelago, with its partially closed waters influenced by both oceanic conditions and large tidal glaciers, represents a prime target for understanding the effects of ongoing climate change on glaciers, oceans, and ecosystems. An understanding of the role played by tidewater glaciers in marine primary production is still affected by a lack of data from close proximity to glacier fronts, to which, for safety reasons, manned surface vessels cannot get too close. In this context, autonomous marine vehicles can play a key role in collecting high quality data in dangerous interface areas. In particular, the contribution given by light, portable, and modular marine robots is discussed in this paper. The state-of-the-art of technology and of operating procedures is established on the basis of the experience gained in campaigns carried out by Italian National Research Council (CNR) robotic researchers in Ny-Alesund, Svalbard Islands, in 2015, 2017, and 2018 respectively. The aim was to demonstrate the capability of an Unmanned Semi-Submersible Vehicle (USSV): (i) To collect water samples in contact with the front of a tidewater glacier; (ii) to work in cooperation with Unmanned Aerial Vehicles (UAV) for sea surface and air column characterisation in the proximity of the fronts of the glaciers; and (iii) to perform, when equipped with suitable tools and instruments, repetitive sampling of water surface as well as profiling the parameters of the water and air column close to the fronts of the tidewater glaciers. The article also reports the issues encountered in navigating in the middle of bergy bits and growlers as well as the problems faced in using some sensors at high latitudes.
Additional details
- URL
- https://hdl.handle.net/11567/1098595
- URN
- urn:oai:iris.unige.it:11567/1098595
- Origin repository
- UNIGE