Future directions in oil spill modeling - Tor Nordam; Ana J. Abascal; Germán Aragón; Hossein Babaei; Christopher H. Barker; Kuo-Hsien Chang; Knut-Frode Dagestad; Rodrigo Duran; Deborah French-McCay; Melissa Gloekler; Aurelien Hospital; Panagiota Keramea; et al. (Ocean Modelling, 2026)

High-resolution modeling of local-scale currents using machine learning and artificial intelligence: application to Santander Bay - Mirko Rupani; Ana J. Abascal; Rodrigo Manzanas; Javier García-Alba; Andrés García (Frontiers in Marine Science, 2026)
High-resolution hydrodynamic data are essential for coastal and estuarine management. However, traditional downscaling methods based on numerical modeling remain computationally expensive, limiting their applicability for long-term hindcasts and operational forecasting systems. This study evaluates the use of machine learning for the reconstruction of sea surface height and surface currents in a semi-enclosed estuary, using Santander Bay as a case study. Three techniques spanning increasing model complexity are analyzed: K-nearest neighbors, Adaptive Boosting, and long short-term memory networks. The models are trained to emulate high-resolution hydrodynamic-model outputs using a comprehensive set of tidal, meteorological, and fluvial forcings. Performance is assessed through spatial validation, cluster-based analysis, representative-point time series, and independent comparison against in-situ observations. Results show that all techniques successfully reproduce the main hydrodynamic patterns, with accuracy increasing with model complexity. Long short-term memory networks achieve the highest skill in tidally energetic regions, while Adaptive Boosting provides more stable performance in low-energy and shoreline areas. Computational cost analysis demonstrates that all machine-learning approaches achieve speedups of several orders of magnitude relative to numerical modelling, with inference costs that are negligible at both point and domain scales. These findings demonstrate the potential of machine learning as a computationally efficient approach for high-resolution modelling of coastal hydrodynamics, with important implications for operational forecasting and coastal management applications.

Body size and isotopic profiles enable discrimination between long-term resident and highly migrant contingents of Atlantic bluefin tuna - Antonio Medina; Ana Magro; Francisco J. Abascal; José L. Varela (Marine Environmental Research, 2024)

Environmental risk assessment of coastal dredging based on clustering of meteocean forcing - Chang He; Francesco De Leo; Alessandro Stocchino; Zhen-Yu Yin; Ana J. Abascal; Yin-Fu Jin (Coastal Engineering, 2024)

Lagrangian modelling of oil concentrations at sea: A sensitivity analysis to the grid resolution and number of Lagrangian elements - Andrés Martínez; Ana J. Abascal; Andrés García; Germán Aragón; Raúl Medina (Marine Pollution Bulletin, 2024)

Modelling floating riverine litter in the south-eastern Bay of Biscay: a regional distribution from a seasonal perspective - Irene Ruiz; Anna Rubio; Ana J. Abascal; Oihane C. Basurko (Ocean Science, 2022)
Although rivers contribute to the flux of litter to the marine environment, estimates of riverine litter amounts and detailed studies on floating riverine litter behaviour once it has reached the sea are still scarce. This paper provides an analysis of the seasonal behaviour of floating marine litter released by rivers within the south-eastern Bay of Biscay based on riverine litter characterizations, drifters, and high-frequency radar observations and Lagrangian simulations. Virtual particles were released in the coastal area as a proxy of the floating fraction of riverine litter entering from rivers and reaching the open waters. Particles were parameterized with a wind drag coefficient (Cd) to represent their trajectories and fate according to the buoyancy of the litter items. They were forced with numerical winds and measured currents provided by high-frequency radars covering selected seasonal week-long periods between 2009 and 2021. To gain a better insight into the type and buoyancy of the items, samples collected from a barrier placed at the Deba River (Spain) were characterized at the laboratory. Items were grouped into two categories: low-buoyancy items (objects not exposed to wind forcing, e.g. plastic bags) and highly buoyant items (objects highly exposed to wind forcing, e.g. bottles). Overall, low-buoyancy items encompassed almost 90 % by number and 68 % by weight. Weakly buoyant items were parameterized with Cd = 0 % and highly buoyant items with Cd = 4 %; this latter value is the result of the joint analysis of modelled and observed trajectories of four satellite drifting buoys released at the Adour (France), Deba (Spain), and Oria (Spain) river mouths. Particles parameterized with Cd = 4 % drifted faster towards the coast through the wind, notably during the first 24 h. In summer, over 97 % of particles beached after 1 week of simulation. In autumn this value fell to 54 %. In contrast, low-buoyancy items took longer to arrive at the shoreline, particularly during spring with fewer than 25 % of particles beached by the end of the simulations. The highest concentrations (>200 particles km−1) were recorded during summer for Cd = 4 % in the French region of Pyrénées-Atlantiques. Results showed that the regions in the study area were highly affected by rivers within or nearby the region itself. These results couple observations and a river-by-river modelling approach and can assist decision-makers on setting emergency responses to high fluxes of floating riverine litter and on defining future monitoring strategies for heavily polluted regions within the south-eastern Bay of Biscay.

Long-term changes in the frequency, intensity and duration of extreme storm surge events in southern Europe - Alba Cid; Melisa Menéndez; Sonia Castanedo; Ana J. Abascal; Fernando J. Méndez; Raúl Medina (Climate Dynamics, 2016)

A high resolution hindcast of the meteorological sea level component for Southern Europe: the GOS dataset - Alba Cid; Sonia Castanedo; Ana J. Abascal; Melisa Menéndez; Raúl Medina (Climate Dynamics, 2014)

Movements and environmental preferences of the shortfin mako, Isurus oxyrinchus, in the southeastern Pacific Ocean - Francisco J. Abascal; Manuel Quintans; Ana Ramos-Cartelle; Jaime Mejuto (Marine Biology, 2011)