MATNOC (2019 – 2023)


MATNOC (2019 – 2023)


Full title: A management tool for coastal aquaculture based on knowledge on nearshore ocean circulation dynamics

Partners: Akvaplan-niva (project lead), the Meteorological Institute, the University of Oslo, Florida Golf Coast University

Funding: Research Council of Norway (MAROFF program) - https://prosjektbanken.forskni...

Project manager: Magnus Drivdal

Primary objectives:

1) To improve our understanding of the dynamical processes that impact near-surface drift of sea lice along a complex coastline and create the best possible inner-coastal model system for drift applications
2) To create a model-based decision support tool, where the user can interact with model data to make statistical estimates of sea lice dispersion and connectivity as well as drift forecasts for chosen locations. The tool should also give estimates of uncertainties on its predictions.

Summary: MATNOC’s main aim is to aid the aquaculture industry in reducing the spreading of sea lice, a main problem for the industry with large economic consequences. To achieve this, MATNOC has two distinct foci: 1) to improve the understanding and numerical model representation of near-coast upper-ocean transport processes, and 2) to build an improved digital risk assessment tool based on enhanced model capabilities to be used by the aquaculture industry. The project partners will assemble a numerical model system especially designed for near-shore drift prediction along the complex Norwegian coastline. For this purpose, the model will use unstructured computational grids that allow unprecedented resolution in narrow straits and fjords. It will also account for lice drift by both currents and waves, and major scientific component of the project will be to better understand interactions between currents and waves and their consequences for net drift. A dedicated field experiment will back up the development of the model system. Finally, the state-of-the art model system and the gained knowledge on current-wave interactions will be fed into a digital decision-making tool to be used by the aquaculture industry. The core novelty of this tool is that ensemble predictions made by the model system will be used to form uncertainty estimates that should improve the knowledge base for decision making.

Results: Salmon lice drift modelling app:

  • Outreach