Marine oil spill incidents cause severe damage to marine ecosystems and economic losses to coastal areas. Traditional two-dimensional oil spill simulation models struggle to accurately characterize the three-dimensional transport of oil spills in complex seas and their interactions with topography. Therefore, this study aims to develop a high-precision three-dimensional marine oil spill dynamics model to improve the accuracy of oil spill prediction and early warning.This study integrates refined marine environmental field reconstruction techniques and couples multi-source high-resolution dynamic data including wind, current and wave. Based on the Lagrangian particle tracking algorithm, an oil spill simulation framework is established, which combines oil weathering calculation and three-dimensional motion calculation. The weathering module quantitatively simulates physical and chemical changes of oil, such as evaporation, emulsification, dispersion and biodegradation. The motion module accurately describes the horizontal and vertical dynamic transport of oil particles. Meanwhile, three-dimensional seabed terrain spatial constraints, enhanced coastline boundary processing and environmental field accuracy optimization mechanisms are designed, breaking through the limitations of traditional two-dimensional simulation. Taking the East China Sea as the study area, this research selects the Sanchi oil spill incident as a case study. It adopts ERA5 reanalysis wind data and HYCOM ocean circulation model data as driving forces, and validates the model with multi-source remote sensing oil spill monitoring data. Results show that the simulated center position of the oil slick has a small deviation from remote sensing observations, with consistent drift direction and high area agreement. The model can accurately reproduce the three-dimensional transport trajectory of the oil spill.
Shang et al. (Fri,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: