MEDSLIK-II, a Lagrangian marine surface oil spill model for short-term forecasting – Part 2: Numerical simulations and validations

Abstract

Abstract. In this paper we use MEDSLIK-II, a Lagrangian marine surface oil spill model described in Part 1 , to simulate oil slick transport and transformation processes for realistic oceanic cases, where satellite or drifting buoys data are available for verification. The model is coupled with operational oceanographic currents, atmospheric analyses winds and remote sensing data for initialization. The sensitivity of the oil spill simulations to several model parameterizations is analyzed and the results are validated using surface drifters, SAR (synthetic aperture radar) and optical satellite images in different regions of the Mediterranean Sea. It is found that the forecast skill of Lagrangian trajectories largely depends on the accuracy of the Eulerian ocean currents: the operational models give useful estimates of currents, but high-frequency (hourly) and high-spatial resolution is required, and the Stokes drift velocity has to be added, especially in coastal areas. From a numerical point of view, it is found that a realistic oil concentration reconstruction is obtained using an oil tracer grid resolution of about 100 m, with at least 100 000 Lagrangian particles. Moreover, sensitivity experiments to uncertain model parameters show that the knowledge of oil type and slick thickness are, among all the others, key model parameters affecting the simulation results. Considering acceptable for the simulated trajectories a maximum spatial error of the order of three times the horizontal resolution of the Eulerian ocean currents, the predictability skill for particle trajectories is from 1 to 2.5 days depending on the specific current regime. This suggests that re-initialization of the simulations is required every day.

MEDSLIK-II, a Lagrangian marine surface oil spill model for short-term forecasting – Part 1: Theory

“…Equation (70) can be used to provide an estimate of the number of particles for a given spill scenario and oil tracer grid discretization, knowing the lower concentration level of interest. In Part 2 of this paper (De Dominicis et al, 2013), several sensitivity experiments will be carried out to show the impact of different choices regarding number of particles and tracer grid spatial resolution.…”

The MEDESS-GIB database: tracking the Atlantic water inflow

“…Likewise, drifter observation provides very useful information for oil spill and search and rescue operations. Indeed, trajectories from Lagrangian drifter buoy data have been widely used as proxies of oil spills or floating objects on the ocean surface (Reed et al, 1994;Al-Rabeh, 1994;Price et al, 2006;García-Ladona et al, 2005;Barron et al, 2007;Sotillo et al, 2008;DeDominicis et al, 2013;Sayol et al, 2014).…”

A Dynamic Remote Sensing Data-Driven Approach for Oil Spill Simulation in the Sea

“…Therefore, many scholars have combined remote sensing detection with numerical models, so as to provide continuous tracking of the oil range and spill diffusion direction. For example, for the first time, the combination of satellite images of surface oil slicks with Lagrangian trajectory models has been implemented in the operational oil spill trajectory hindcast/forecast for the Deepwater Horizon oil spill [2,14,15]; Zodiatis et al (2012) used the MEDSLIKoil spill model to forecast the drift and spreading of oil slicks detected from satellite images using MyOcean (www.myocean.eu.org) forecasting data, through the implementation of MEDESS-4MS(www.medess4ms.eu) project services [16]; and Dominicis et al (2013) used the MEDSLIK-II Lagrangian marine model to simulate oil slick transport and transformation processes for realistic oceanic cases and the model initialized using the slick position and slick shape provided by satellite systems, both SAR (synthetic aperture radar) and optical images [17].…”

“…As for the combined model, the location and size of the surface oil slick are inferred from satellite images, and they should be frequently re-initialized to reduce the forecast errors, which could be accumulated from the initial locations (conditions), as learned from the rapid response to the Deepwater Horizon oil spill in the Gulf of Mexico [2,14,15]. However, other related model parameters, such as data on wind forcing, sea surface temperature and sea currents, are from empirical values, model calculations or some meteorological data centers [17], and they may not be precise. Then, the inaccurate data could affect the model's predictive accuracy.…”