Over the past 10 years, the record of oil spills per year has decreased. However, the size and quantity of oil tanker ships have been growing, which will increase the hazard of a spill. Thus, spill predictions are a crucial component of monitoring because they enable a more effective response to the accident by assessing the impact beforehand and better developing a strategy to mitigate the environmental impact. These predictions are provided by Lagrangian models. The model used in this work will be able to forecast the geographical and temporal propagation of the oil spill slick with the support of the hydrodynamic and atmospheric forcing from external numerical models, which are required to compute these processes. The objective of this work is to construct a Python-based Early Warning System that will serve as the link between the user and the oil spill model. The MOHID Lagrangian module will be adapted to the region of Cartagena Bay, Colombia, as well as for the Algarve Coast, to assess oil spills events and test the adaptability of the tool. Furthermore, the operational models BASIC and SOMA will supply the hydrodynamic and atmospheric forcing for the simulations. The results in this paper showed the high level of adaptability of the tool, as well as its simplicity being adapted to users without deep knowledge in ocean modelling.
