Numerical modeling used to create a digital twin for mollusk farming: exploratory studies with MOHID BIVALVES in the bays of Santa Catarina Island, Brazil

The prediction of the dynamics of mollusk farming in Santa Catarina, Southern Brazil, in response to environmental changes is essential for ecosystem management and the implementation of public policies. Climate changes or the carrying capacity of the environment can affect commercial exploitation and quantify the environmental impacts of farming. Computational mathematical models, such as MOHID Bivalves, have been used to analyze these issues, combining descriptions of ecological and physical processes to solve complex problems. MOHID, based on a computational grid to solve mass transport equations, was coupled with a biogeochemical module based on DEB theory, which describes the physiological response of an organism to environmental changes. The objective of this research was to evaluate the hydrodynamic numerical tool MOHID Bivalves applied to aquaculture, focusing on predicting the growth of Crassostrea gigas in the bays of Santa Catarina Island, Southern Brazil. Parameterizations and numerous simulations were performed to adjust the model to field data between 2013, 2015, and 2019 in the southern and northern locations of the bays. Preliminary results showed that the model satisfactorily reproduced the growth dynamics in shell length of C. gigas at both study sites, even without site-specific calibration. Mathematical models with hydrodynamic numerical simulation are the best option to accurately predict the dynamics of mollusk farming in response to natural or anthropogenic changes, whether for optimizing commercial exploitation or quantifying environmental impacts. It was concluded that the model is suitable for the cultivation configurations of C. gigas in the bays of Santa Catarina Island, Southern Brazil, providing a powerful tool to sustainably optimize aquaculture practices. The next stages of the study include applying this parameterization associated with the water quality model to predict the carrying capacity of the bays and predictive studies of better farming scenarios.