Plastic pollution has been observed in many marine environments surrounding the Iberian Peninsula, from the surface water to deeper waters, yet studies on their pathways and accumulation areas are still limited. In this study, a global ocean reanalysis model was combined with a particle-tracking Lagrangian model to provide insights into the pathways and accumulation patterns of microplastics originating in southern Portuguese coastal waters (SW Iberian). The study investigates microplastics floating on the surface as well as submerged at different water depths. Model results suggest that the North Atlantic Gyre is the main pathway for microplastics in surface and subsurface waters, transporting the microplastics southwards and eastwards towards the Mediterranean Sea and the Canary Islands. Currents flowing out of the Mediterranean Sea act as the main pathway for microplastics in deep waters, transporting the microplastics along western Iberia. An average residence time of twenty days in the coastal waters suggests that microplastics do not accumulate close to their sources due to their relatively fast transport to adjacent ocean areas. Notably, a significant proportion of microplastics leave the model domain at all depths, implying that SW Iberia may act as a source of microplastics for the adjacent areas, including the Mediterranean Sea, Morocco, the Canary Islands, Western Iberia, and the Bay of Biscay.

This paper presents the Simulation Management System for Operational Coastal Hydrodynamic Models, or SMS-Coastal, and its novel methodology designed to automate forecast simulations of coastal models. Its working principle features a generic framework that can be easily configured for other applications, and it was implemented with the Python programming language. The system consists of three main components: the Forcing Processor, Simulation Manager, and Data Converter, which perform operations such as the management of forecast runs and the download and conversion of external forcing data. The SMS-Coastal was tested on two model realisations using the MOHID System: SOMA, a model of the Algarve coast in Portugal, and BASIC, a model of the Cartagena Bay in Colombia. The tool proved to be generic enough to handle the different aspects of the models, being able to manage both forecast cycles.

Observing System Simulation Experiments (OSSEs) provide a framework in which to evaluate the impact of prospective ocean-observation networks on model forecasting performance prior to their actual deployment. This study presents the design and validation of an OSSE tailored for the operational coastal model of southern Portugal, SOMA. The system adopts the fraternal twins approach and a univariate data-assimilation scheme based on Ensemble Optimal Interpolation to update the model’s 3D temperature structure with SST. The methodology provides a flexible framework that preserves the statistical structure of real observation errors while remaining independent of SOMA. This allows straightforward transfer to other applications, thereby broadening its applicability and making it useful as a starting point in the design of observation networks beyond that presented in this case study. The OSSE experiments were compared against corresponding Observing System Experiments (OSEs) using real satellite SST products. Results show that the designed OSSE is internally consistent, sensitive to observation density, and capable of reproducing realistic correction patterns that closely match those obtained in the OSEs. These findings provide strong evidence that the SOMA OSSE system is a reliable tool for assessing the potential impact of future surface-observation strategies.