The oil spill model code MEDSLIK-II is a freely available community model

http://medslikii.bo.ingv.it

MEDSLIK-II calculates the advection–diffusion processes using a Lagrangian approach. The oil slick is discretized into constituent particles. Each particle moves due to currents, wind, and waves, data on which are provided by external basin-scale or regional oceanographic and atmospheric models.

The oil transformation processes at the surface are calculated by means of bulk formulas that describe the changes in the surface oil volume due to three main processes, known collectively as weathering (evaporation, dispersion, and spreading). Formation of water-in-oil emulsion is also taken into consideration. If an oil particle arrives on the coast, the model is able to simulate the adsorption of particles into the coastal environment, taking into account the probability that oil may be washed back into the water.

As outputs, MEDSLIK-II provides the oil concentrations at the surface, in the dispersed water-column fraction, and on the coast. Mass balance components of the oil are calculated as a function of time, which allows temporal tracking of oil weathering.

As detailed above, MEDSLIK-II requires the input of data about sea currents, sea surface temperatures, and atmospheric wind.

The ocean current data and sea surface temperature are provided by hourly forecast and daily analysis fields produced by the Mediterranean Forecasting System (MFS) from the Copernicus Marine Environment Monitoring Service (CMEMS

http://marine.copernicus.eu

Covering the entire Mediterranean Sea with some extension into the Atlantic, MFS provides oceanographic data at 1/16∘ × 1/16∘ horizontal resolution and 71 unevenly spaced vertical levels . The analyses are produced by a data assimilation system that uses satellite and in situ data .

The Adriatic Forecasting System encompasses the whole Adriatic basin and extends southward of the Strait of Otranto into the northern Ionian Sea (39.0–45.8∘ N, 12.2–20.8∘ E). The system is implemented at the horizontal resolution of 1/45∘ × 1/45∘ and 31 sigma layers in the vertical. The lateral boundary conditions for the sea current velocity, temperature, and salinity are imposed from MFS on a daily basis.

The atmospheric wind data are provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) forecasts and analyses, at 0.125∘ horizontal and 6 h temporal resolution.

The oil spill forecasting module provides a resilient 24/7 operational service, supporting both basic and advanced use. In the basic mode, the system runs the oil spill scenarios according to the initial information about the starting spill position and date, type of the oil, the oil volume or spill rate and duration, simulation length (the time period of tracking the oil spill), and time step (output interval). Necessary oceanographic and atmospheric data can be chosen from a set of available model outputs.

To calculate the Stokes drift, the latest version of WITOIL uses the empirical, so-called JONSWAP wave spectrum as a function of wind speed and fetch . Recently, have modified MEDSLIK-II for the direct usage of wave model outputs, which is more accurate and computationally efficient than JONSWAP parameterization. This important option will be adapted in the next version of WITOIL.

Advanced use of WITOIL requires users who are experienced in oil spill modeling to set up advanced parameters and interpret results. A set of advanced parameters was selected from a full list of MEDSLIK-II parameters published in . Parameters that can be varied by advanced users are listed in Table . The Stokes drift and a wind drift factor were rated as being of much importance, and were highly discussed in literature (; ). Horizontal diffusivity is responsible for spatial extension of the oil spill in time. Three parameters that mainly control the weathering processes (evaporation, dispersion, and spreading) can be also modified according to the user's needs. The number of oil particles representing the oil slick can be also changed to control the Lagrangian discretization of the oil slick. Finer discretization results in smoother oil spill patterns, but it requires more computational time.

In addition to manually changing the advanced parameters and tracking the differences, WITOIL provides automatic calculation of uncertainties with respect to some initial conditions of the oil spill. Currently, there is not a common approach to the calculation of uncertainties in oil spill modeling. Our methodology is based on a parametric analysis, typical of the atmospheric Lagrangian models . Due to a large number of parameters that control the oil movement and transformation in MEDSLIK-II, the number of possible uncertainty scenarios is enormous. WITOIL allows users to evaluate the uncertainty that has arisen from unknown initial conditions online: (1) the starting position, (2) the starting date, and (3) the starting and final dates, which can be varied within user-specified ranges (Table ). The spread obtained in oil concentrations and represented in probability terms can be interpreted as a measure of uncertainty.

In Scenario I, uncertainty caused by an unknown initial oil spill position is calculated in the case of so-called “cross” and “hexagon” configurations (Fig. ). For any location of interest (x0,y0) within the uncertainty radius R, the set of spills with the spill indices of i=1, 2, ..., N is generated. In the case of cross configuration N=5. In the case of hexagon configuration N=7. All the spills begin at the same time, and they finish synchronously. The final oil concentrations Ci(x,y) are aggregated to produce a dimensionless concentration function Hi(x,y) defined as Hi(x,y)=1,Ci(x,y)>Cthr0,Ci(x,y)≤Cthr, where Cthr is the threshold oil concentration, specified by users. For the examples described below, we assume that Cthr=0. A function P(x,y) expressed in probability terms (%) can be used as a measure of uncertainty:

P(x,y)=100%1N∑i=1NHi(x,y). Values of P(x,y) range between 0 and 100 %, indicating the area where exceeding the threshold concentration is the most probable.

Although the hexagon configuration produces slightly smoother probability patterns than the cross ones, the latter is quicker to calculate.

In Scenario II, uncertainty caused by an unknown starting date of the oil spill is calculated. Hence, for the point of (x0,y0), a set of oil spills with the spill indices of i=1, 2, ..., N are generated inside the uncertainty interval T0 with respect to the starting date of t0. As Fig.  indicates, initial dates of these spills are uniformly shifted relative to each other. At the time of interest t∗, the final oil concentrations Ci(x,y) are aggregated to produce a dimensionless concentration function Hi(x,y) due to Eq. (). In Fig. , the gray boxes depict the final oil concentrations. As a result, the probability function P(x,y) calculated by means of Eq. () can be interpreted as a measure of the uncertainty regarding the initial date of the oil spill.

In Scenario III, a set of spills is also initiated for the location of (x0,y0). Apart from variation of the starting date t0 within the range of T0, the final date of the spillage t∗ varies within the uncertainty interval T∗ (Fig. ). As Fig.  shows, both the initial and final dates are shifted relative to each other (in contrast to Scenario II, where the simulation length is frozen for all the spills). To calculate P(x,y), Eqs. () and () are applied.

The hazard module is tailored to plan a response strategy and increase the level of preparedness prior to real oil spill accidents. Hazard maps represent the MEDSLIK-II outputs statistically based on historical variability of the meteo-oceanographic data over a long-term period . This information can also be used for optimization in deployment of spill response equipment and training the response personnel.

Nowadays, accidents on offshore oil and gas facilities are mentioned among risks that might result in severe marine pollution in the Mediterranean Sea. In the present work, we calculated hazard maps in the case of potential accidents on six Adriatic oil platforms: Carmen, Dora, Elisa, Elsa, Gilga, and Ombrina (Fig. ). To perform the calculations, the oil spill model MEDSLIK-II was coupled with the daily AFS simulations and 6 h ECMWF analyses over the 7-year time period (2007–2013). On a daily basis, we initiated a typical oil platform leak scenario representing a failure in the oil transportation system :

continuous oil spill with duration of 120 h;

An example of using the oil spill forecasting module is shown in Fig. . Information about a hypothetical oil spill located in the southern Adriatic Sea (41.36∘ N, 17.50∘ E), near the Ancona (Italy) to Igoumenitsa (Greece) shipping lane, was put into the WITOIL DSS. The accident started on 16 February 2015 at midnight. It was a continuous spill with a rate of 80 t h-1 and expected duration of 72 h. The oil type of Aboozar with an American Petroleum Institute (API) gravity of 26.96 and density of 893 kg m-3 was spilled. An oil spill forecast was carried out by means of the ECMWF and MFS meteo-oceanographic datasets. The corresponding field of sea surface currents, underlying the area of the accident, indicated that initially, the spill was located in the region of relatively weak currents at the periphery of the South Adriatic Gyre . The sea surface current field presented in Fig.  is a product of MFS visualized by means of SeaConditions

http://www.sea-conditions.com

After receiving the forecast of the oil spill movement, uncertainty of this forecast can be obtained online. First of all, uncertainty with respect to the starting oil spill position was calculated in the framework of Scenario I (Fig. ) described above. An uncertainty radius of R=2 nautical miles or about 2.3 km was chosen as an example. As Fig.  indicates, south and southeast directions at a distance of about 9 km were found to be the most probable (> 75 %) for the oil location in 72 h after the accident. Besides, there was a small but nonzero probability (about 20 %) that oil went west of the starting position. Both cross and hexagon configurations gave very similar probability distributions.

An example of the uncertainty evaluation with respect to the starting date of oil spill is presented in Fig. . In Scenario II (Fig. ), seven spills were run inside the uncertainty interval of T0=36 h. The spills were shifted by 6 h relative to each other. Probability distribution indicated a narrow comet-shaped band with the highest values at the distance of 4–9 km southwest and southeast of the starting position. In Fig. , the probability spread was insignificant, which allowed the conclusion to be established that, for variations of the starting spill time in the interval of 36 h, uncertainty in the oil spill forecast was not high.

Seven spills were also initiated inside the uncertainty intervals of T0=T∗=36 h in Scenario III (Fig. ). The spills were shifted by 6 h. In contrast to Scenario II, the obtained probability distribution demonstrated more significant spatial spread. As Fig.  shows, the most probable (> 71 %) direction was southwest, while the southeast one was less probable (about 14 %). It can be concluded that, in the aforementioned cases, the main forecast uncertainty arose from the degree of the South Adriatic Gyre influence.

To calculate hazards from potential accidents on six Adriatic oil platforms (Gilda, Carmen, Elisa, Dora, Elsa, and Ombrina), over 15 000 MEDSLIK-II runs were carried out for the 2007–2013 time period. In general, more than 1500 monthly hazard maps were produced, including hazard maps of the sea surface and the coastline and maps of the dispersed oil fraction. Additionally, more than 400 averaged hazard maps were calculated: yearly, seasonally averaged maps and monthly climatology maps. Currently, the hazard module has not yet been incorporated in the WITOIL DSS. However, a further version of WITOIL will be able to sort and visualize all the precalculated hazard maps.

Two oil platforms, Carmen and Ombrina, were found to be the most hazardous to the Italian coastal zone. Monthly averaged maps (Fig. ) demonstrated the most significant spatial variability of hazards.

As Fig. a, d indicate, the shape of the highest surface hazard area followed the climatological shape of the Western Adriatic Coastal Current . The same feature was found on the hazard maps for the dispersed fraction of the oil, but for a lower level of hazards (Fig. c, f). For both platforms, the maximum magnitude of hazard on the coastline tended to exceed the values at the sea surface and in the water column. It means that in the case of accidents on the oil platforms, the most hazardous impact is expected on the Italian coastline.

In the case of Carmen, hazards reached the maximum values of more than 0.8 on the coastline from Porto Sant'Elpidio to Porto San Giorgio (Fig. b). Sea surface hazard distributions revealed high hazards (> 0.5) near Porto Sant'Elpidio, while a narrow area of relatively moderate hazards (> 0.3) extended up to Pescara (Fig. a). Hazards from the dispersed oil fraction were rather low (0.05–0.25) but due to a long-term cumulative effect, some impact on fishery stocks near Pescara and Ortona (Fig. c) might be expected.

Possible accidents on Ombrina led to high coastline hazards (> 0.8) in Zona Industriale Porto di Vasto and Termoli, covering the northern coast of the Gargano Peninsula (Fig. e), where Gargano National Park is situated. Additionally, the coastline of the Tremiti Islands (16 km north of the Gargano Peninsula, 42.12∘ N, 15.51∘ E) also indicated significant hazards (0.5–0.7). Sea surface hazards showed high values (> 0.5) in the area between the platform and Termoli (Fig. d). Hazards from the dispersed oil were rather low (0.05–0.25) but they was be able to impact the diving sites near the Tremiti archipelago (Fig. f).

A simplified version of the WITOIL DSS

https://play.google.com/store/apps/details?id=it.cmcc.witoil