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MAROFF-2-Maritim virksomhet og offsh-2

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

Alternative title: Et forvaltningsverktøy for lakseoppdrett basert på simuleringer av kystnær havsirkulasjon

Awarded: NOK 12.0 mill.

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 aims: 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. Ocean circulation and upper-ocean-transport determines the spreading of sea lice between aquaculture sites. Accurate knowledge of these processes can help the aquaculture industry to avoid fish farm locations that spreads/receives infection to/from many other locations. From this, it is possible to optimize the location structure to minimize the pressure from sea lice. Also, delousing operations may result in sea lice falling off the host fish and spreads to other locations. A transport forecast, created by an ocean circulation model, can help avoid spreading to neighboring locations. At this stage of the project, we have assembled a numerical model system especially designed for near-shore drift prediction along the complex Norwegian coastline. The model uses unstructured computational grids that allow unprecedented resolution in narrow straits and fjords. A major scientific component of the project is 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. At this stage all parts of the project are going well. We have been working to couple wave and ocean models. As a result of this work the Meteorological Institute has upgraded it operational wave model to write out fields for ocean models. The operational ocean model with wave interactions included is in a testing phase. In our studies of nearshore transport processes, we have focused on net transport through narrow tidal straits. We have shown that the net transport through such straits can be significant. To represent the transport through tidal straits in models, high resolution (~50 m) is necessary. We have developed a new sealice model based on the particle model OpenDrift. This includes the development of a driver for unstructured grids in OpenDrift. In the sealice model, the particles have characteristics based on accepted knowledge of sealice biology. An integral part of the project is delivering a digial riskassessment tool for the aquaculture industry. The tool lets the user visually define scenarios under sets of defined preconditions. The first version of this tool is now developed. This version lets the user study the physical variables salinity, temperature and current velocities in addition to the spreading of sealice. It is also possible to do simple spreading scenarios directly in the web browser.

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.

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MAROFF-2-Maritim virksomhet og offsh-2