A coastal zone oil spill model: Development and sensitivity studies

Reed, Mark; Gundlach, Erich R.; Kana, Timothy W.

doi:10.1016/s0269-8579(89)80019-x

Cited by 59 publications

(19 citation statements)

References 10 publications

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“…Some of the most sophisticated Lagrangian operational models are COZOIL (Reed et al, 1989), SINTEF OSCAR 2000 (Reed et al, 1995), OILMAP (Spaulding et al, 1994;(Lehr et al, 2002), MOTHY (Daniel et al, 2003), MOHID (Carracedo et al, 2006), the POSEIDON OSM (Pollani et al, 2001;Nittis et al, 2006), OD3D (Hackett et al, 2006), the Seatrack Web SMHI model (Ambjørn, 2007), MEDSLIK (Lardner et al, 1998Zodiatis et al, 2008a), GNOME (Zelenke et al, 2012) and OILTRANS (Berry et al, 2012). In all these papers equations and approximations are seldom given and the results are given as positions of the oil slick particles and time evolution of the total oil volume.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2013

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Abstract. The processes of transport, diffusion and transformation of surface oil in seawater can be simulated using a Lagrangian model formalism coupled with Eulerian circulation models. This paper describes the formalism and the conceptual assumptions of a Lagrangian marine surface oil slick numerical model and rewrites the constitutive equations in a modern mathematical framework. The Lagrangian numerical representation of the oil slick requires three different state variables: the slick, the particle and the structural state variables. Transformation processes (evaporation, spreading, dispersion and coastal adhesion) act on the slick state variables, while particle variables are used to model the transport and diffusion processes. The slick and particle variables are recombined together to compute the oil concentration in water, a structural state variable. The mathematical and numerical formulation of oil transport, diffusion and transformation processes described in this paper, together with the many simplifying hypothesis and parameterizations, form the basis of a new, open source Lagrangian surface oil spill model, the so-called MEDSLIK-II, based on its precursor MEDSLIK (Lardner et al., 1998Zodiatis et al., 2008a). Part 2 of this paper describes the applications of the model to oil spill simulations that allow the validation of the model results and the study of the sensitivity of the simulated oil slick to different model numerical parameterizations.

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Section: Introductionmentioning

confidence: 99%

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

et al. 2013

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“…Some hydrocarbons are not as prone to emulsification (e.g., kerosene, gasoline) which is not the case with oils containing high levels of asphaltenes (see [32]). Taken from [33], the (dimensionless) fractional water content evolves in time as…”

Section: Aging: a Consequence Of Grouping Chemicals And Unresolved Phmentioning

confidence: 99%

An Oil Fate Model for Shallow-Waters

Restrepo

Ramı́rez

Venkataramani

2015

JMSE

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We introduce a model for the dynamics of oil in suspension, appropriate for shallow waters, including the nearshore environment. This model is capable of oil mass conservation and does so by evolving the oil on the sea surface as well as the oil in the subsurface. The shallower portion of the continental shelf poses compounding unique modeling challenges. Many of these relate to the complex nature of advection and dispersion of oil in an environment in which wind, waves, as well as currents all play a role, as does the complex bathymetry and the nearshore geography. In this study we present an overview of the model as well as derive the most fundamental of processes, namely, the shallow water advectiion and dispersion processes. With regard to this basic transport, we superate several fundamental challenges associated with creating a transport model for oil and other buoyant pollutants, capable of capturing the dynamics at the large spatio-temporal scales demanded by environmental and hazard mitigation studies. Some of the strategies are related to dimension reduction and upscaling, and leave discussion of these to companion papers. Here we focus on wave-filtering, ensemble and depth-averaging. Integral to the model is the proposal of an ocean dynamics model that is consistent with the transport. This ocean J. Mar. Sci. Eng. 2015, 3 1505 dynamics model is detailed here. The ocean/oil transport model is applied to a couple of physically-inspired oil-spill problems in demonstrate its specialized capabilities.

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“…These models can vary from simple point source particle-tracking models, such as TESEO-PICHI (Castanedo et al 2006;Sotillo et al 2008), to complex models that attempt to comprehensively simulate the three-dimensional advectiondiffusion-transformations processes that oil undergoes (Wang et al 2008;Wang and Shen 2010). Some of the most sophisticated Lagrangian operational models are COZOIL (Reed et al 1989), SINTEF OSCAR 2000 (Reed et al 1995), OILMAP (Spaulding et al 1994;ASA 1997), GULFSPILL (Al-Rabeh et al 2000), ADIOS (Lehr et al 2002), MOTHY (Daniel et al 2003), MOHID (Carracedo et al 2006), the POSEIDON OSM (Pollani et al 2001Nittis et al 2006, OD3D (Hackett et al 2006), the Seatrack Web SMHI model (Ambjørn 2007), MEDSLIK (Lardner et al 1998, GNOME (Zelenke et al 2012), OILTRANS (Berry et al 2012), and MEDSLIK-II (De Dominicis et al 2013a). Which type of model to use depends on the model availability in the area of interest and final objectives: from short-term forecasting to longterm impact assessment.…”

Section: Introductionmentioning

confidence: 99%

A relocatable ocean model in support of environmental emergencies

et al. 2014

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During the Costa Concordia emergency case, regional, subregional, and relocatable ocean models have been used together with the oil spill model, MEDSLIK-II, to provide ocean currents forecasts, possible oil spill scenarios, and drifters trajectories simulations. The models results together with the evaluation of their performances are presented in this paper. In particular, we focused this work on the implementation of the Interactive RElo- Concordia emergency and on its validation using drifters released in the area of the accident. It is shown that thanks to the capability of improving easily and quickly its configuration, the IRENOM results are of greater accuracy than the results achieved using regional or subregional model products. The model topography, the initialization procedures, and the horizontal resolution are the key model settings to be configured. Furthermore, the IRENOM currents and the MEDSLIK-II simulated trajectories showed to be sensitive to the spatial resolution of the meteorological fields used, providing higher prediction skills with higher resolution wind forcing.

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A coastal zone oil spill model: Development and sensitivity studies

Cited by 59 publications

References 10 publications

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

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

An Oil Fate Model for Shallow-Waters

A relocatable ocean model in support of environmental emergencies

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