An assessment was carried out to judge the performance of the modeled ocean currents in oil spill trajectory prediction. Ocean circulation is the key factor in determining the drift pattern of the spilled marine oil pollutant. General National Oceanic and Atmospheric Administration Operational Modeling Environment (GNOME), an oil spill trajectory model, in diagnostic mode was set for simulating drift pattern of Heavy Fuel Oil (HFO). On 28 January 2017, 0345 hrs, Indian Standard Time (IST), approximately 196.4 MT of HFO was spilled due to vessel collision. The oil spill model was set and run during 28-Jan-2017 to 05-Feb-2017 with 196 tons of HFO. Wind velocity fields were obtained from European Centre for Medium-Range Weather Forecasts (ECMWF). The modeled ocean currents were obtained from High resolution Operational Ocean Forecasting and reanalysis System (HOOFS) with two model set ups such as Indian ocean (IO) and Bay Of Bengal (BOB). Ocean current pattern were also obtained from Hybrid Co-ordinate Ocean Model (HYCOM) and Global Ocean Data Assimilation System (GODAS) based Modular Ocean Model (GM4P1). The oil drift patterns were simulated individually for the spillage due to MT Dawn vessel, by forcing GNOME with the above said wind and ocean currents. Radar data obtained for 29-Jan-2017, from Sentinel -1A was processed for detecting oil slicks. The respective drift patterns obtained were compared individually with the oil slick signatures of Sentinel -1A on 29-Jan-2017. It was found that the drift pattern obtained while using the ocean currents of HOOFS_BOB was in better agreement with that of the observed slicks. Unlike other oil drift patterns, offshore spread of the slicks are well captured while using the ocean currents of HOOFS_BOB. This paper illustrates the method of oil spill trajectory prediction using various ocean currents and validating the simulated drift with the ground truth. It also emphasize the need of using various modeled ocean currents in assessing the performance of oil spill trajectory model.
Ocean currents play a dominant role in drifting the oil slicks (Chang et al. 2011). Simulated ocean currents from numerical model set ups, determine the length and time scale of oil drift. They can drift the oil for a longer distance in terms of miles within shorter period of time. Ocean currents have to be appropriate, otherwise, the oil particles may drift in an unexpected direction. The resultant oil movement is estimated as the vector sum of the wind drift, ocean currents and diffusion. In case of Gulf of Mexico oil spill, GNOME was forced separately with wind and ocean currents. It was found that contribution of ocean currents towards the oil drift was more than that of winds (Chang et al. 2011). Hence it is essential to assess the performance of the oil spill trajectory model before issuing the advisory. The spill response operations are planned as per the issued advisory. In this study, an assessment was carried out to evaluate the performance of the oil spill model, while using simulated ocean currents of various Ocean General Circulation Models (OGCM).
1.1 Study area and details of HFO spillage
On 28 January 2017, 0345 hrs, Indian Standard Time (IST), BW Maple, an outbound Liquefied Petroleum Gas (LPG) tanker, and an inbound chemical tanker, MT Dawn Kanchipuram, collided about two nautical miles (13.228º N, 80.363º E) off Kamarajar Port, Ennore. According to port authorities, the hull of the vessel MT Dawn was ripped, damaging the ships accommodation block and the cargo piping on deck. This accident resulted in the spillage of 196.4 MT of Heavy Fuel Oil (HFO) (Prasad et al. 2018). The location map of the spill zone is shown in Figure 1
In this paper, the method of generating oil drift patterns, using simulated ocean currents and validating them are illustrated. The oil spill model was set and run during the period 28-Jan-2017 to 05-Feb-2017 with 196.4 tons of HFO. The wind velocity fields are obtained from European Centre for Medium-Range Weather Forecasts (ECMWF). The modeled ocean currents were obtained from High resolution Operational Ocean Forecasting and reanalysis System (HOOFS) with two model set ups such as Indian ocean (IO) and Bay Of Bengal (BOB). Ocean current pattern were also obtained from Hybrid Co-ordinate Ocean Model (HYCOM) and Global Ocean Data Assimilation System (GODAS) based Modular Ocean Model (GM4P1).
1.2 Objective of this study
It is to assess the performance of the oil spill model in simulating the drift pattern of spilled Heavy Fuel Oil during Ennore oil spill, using ocean currents of ocean general circulation models. The following sections elaborate the method of simulating and validating the oil drift patterns.
2. MATERIALS REQUIRED FOR PERFORMING THE OIL SPILL TRAJECTORY PREDICTION
In-order to generate oil drift pattern, an oil spill trajectory model, atmospheric and ocean forcings are required. They are discussed in detail as follows.
2.1 Details of the oil spill trajectory prediction model
Δx - Zonal displacement by movers
Δy - Meridional displacement by movers
Δt - time elapsed between time steps
u,v are the velocity of the forcing parameters.
y- is the latitude in radians
Xprob - Probability of the lagrangian element, oil particle to get evaporated
ti- time elapsed
P1, P2, P3 are the percentage of constituents such as Paraffin, Naphtha and Aromatics respectively.
H1, H2 and H3 are the half life of the constituents, respectively.
2.2 Details of the Atmospheric and ocean forcings
2.2.1 Atmospheric fields
Windage is defined as the movement of oil parcels by wind. Based on the observation and analytical derivation, it was estimated that approximately 3% of the winds speed is utilized to spread out the oil in its direction. However, Lehr Simecek-Beatty (2000) suggested the range of 1–4% may be also used based on over flight reports. GNOME uses this range as a default with uniform distribution. GNOME is also used to simulate the drift of the objects that have greater wind effects such as floating debris, drifting vessels etc., Wind drift of 3% was estimated from the velocity of European Centre for Medium Weather range Forecast (ECMWF) winds. The model derived winds were compared and validated with in situ data obtained from the Automatic Weather Stations installed on board ships plying in the region. The RMS error was found to be less than 2.6 m s−1 (Harikumar et al 2012). Hence this ECMWF winds were considered to force GNOME.
2.2.2 Ocean circulation pattern
Ocean current patterns are obtained from in-house operational Ocean General Circulation Models (OGCM) of INCOIS such as Regional Ocean Modeling System(ROMS), Hybrid Co-ordinate Ocean Model (HYCOM) and Global Ocean Data Assimilation System based Modular Ocean Model (GM4p1).
184.108.40.206 Regional Ocean Modeling System
The Ocean General Circulation Model, Regional Ocean Modeling System (ROMS) version 3.6 was developed in Rutgers University, United States of America. This ROMS was utilized to set up operational ocean forecasting system at Indian National Centre for Ocean Information Services (INCOIS). ROMS is one among the worldwide used ocean models. To get the numerical solution, the primitive equations that governs the ocean dynamics and thermodynamics are represented on Arakawa C-grid (Haidvogel et al. 2000; Shchepetkin and McWilliams, 2005). In the horizontal layers, orthogonal curvilinear coordinates are used and sigma co-ordinates, which follow the terrain, are used in the vertical layers (Song et al.,1994; Haidvogel et al. 2000). As far as the Indian Ocean model is concerned, HOOFS_IO (so called ROMS) is set for domain 30°E to 120°E, 30°S to 30°N.
220.127.116.11.1 HOOFS_IO set up
The domain of the model set up is shown in Figure 2a. The horizontal spatial grid resolution is ~9 km. In the top 200m water column, the grids are arranged into 26 levels. The east and south boundaries are treated as open and the tracer, momentum fields are relaxed to INCOIS-Global ocean data assimilation system (GODAS). K-Profile Parameterization (KPP) mixing scheme (Large et al., 1994) is used to parameterize the vertical mixing. For horizontal mixing, harmonic and bi-harmonic viscosities are combined with diffusion schemes. Air-sea heat fluxes are computed using bulk parameterisation scheme. The salinity on the sea surface is derived from the monthly climatology of World Ocean Analysis (WOA) 2009 (Levitus et al. 2010). The model spin up period is from January 2010 to August 2013. The analysed atmospheric forcing is obtained from National Centre for Medium Range Weather Forecast (NCMRWF) atmospheric fields on six-hourly basis. (http://www.incois.gov.in/portal/HOOFS). The ocean currents of HOOFS_IO will be hereafter called as HIO currents.
18.104.22.168.2 HOOFS_Bay of Bengal_East Coast set up
A high resolution ocean circulation model was set up that covers the domain 77º E to 99º E and 4º N to 23º N. It also includes the Andaman Sea within it. The grid resolution is ~2.23 km in the horizontal and also has 40 sigma levels in the vertical. The southern boundary is open and the western boundaries are nudged to the basin scale of Indian Ocean HOOFS. KPP mixing scheme is used in the model (Large et al. 1994) to parameterize the vertical mixing. Atmospheric fields are obtained from NCMRWF. Tidal forcing from TPX07.2 model in the southern and western open boundaries is used. Ten tidal constituents M2, S2, N2, K2, K1, O1, P1, Q1, Mf and Mm are included in computing the tidal potential (Egbert and Erofeeva, 2002). Ocean currents from this set up are mentioned as HBO currents hereafter. Figure 2b shows the model domain of HBO currents.
22.214.171.124 NOAA's Hybrid coordinate ocean model (HYCOM)
NOAA's Global HYCOM has a resolution of 0.0833° in the horizontal and uses hybrid coordinates such as isopycnal, sigma, z-level in the vertical. The output is interpolated onto a regular 1/12° grid horizontally and 40 standard depth levels. The output NetCDF files contain ocean temperature, salinity, eastward and northward currents, and elevation. The model set up also assimilates the satellite altimeter observations, in situ sea surface temperature, vertical temperature and salinity profiles from buoys, using Navy Coupled Ocean Data Assimilation (NCODA) system. Navy Global HYCOM provides boundary conditions to Navy regional models. This modeling system is also run by NOAA, with different atmospheric forcing, as the Global Real-Time Ocean Forecast System (Global RTOFS.https://www.ncdc.noaa.gov/data-access/model-data/model-datasets/navoceanohycom-glb). The bathymetry data of the study region is shown in Figure 2c. The ocean currents of NOAA HYCOM is hereafter called as HYC currents.
126.96.36.199 INCOIS-GODAS based MOM4P1
Global Ocean Data Assimilation System (GODAS) based on MOM4p0d adopted from NOAA/NCEP was operationalised at Indian National Centre for Ocean Information Services (INCOIS) in 2013. This system uses state of the art ocean general circulation model called Modular Ocean Model (MOM) version-4p0d and 3D VAR assimilation techniques. Temperature and salinity profiles from all the in-situ observations over global ocean are assimilated to produce best analysis products (Ravichandran et al. 2013; Sivareddy et al. 2015). More recent improved version MOM4P1 was released in late 2009. Many operational and research centres around the world are using this version. The domain of the model set up is shown below in Figure 2d. Recently, Rahaman et al. (2016) upgrade the GODAS with MOM4P1 (GM4P1) and have shown the improved ocean analysis with this upgradation. Rahaman et al. (2016) have validated the zonal, meridional and surface current speeds of GM4P1 with that of the buoys deployed at 90° E, 1.5° S and 80.5° E, 0° N. The above said parameters during the year 2003–2007 were taken for the comparison. The mean, standard deviation and the Root - Mean Square Deviation of the surface currents at location 90° E, 1.5° S are estimated to be 34.96 cm/s, 20.78 cm/s and 22.88 cm/s respectively. The mean, standard deviation and the Root - mean square deviation of the surface currents at location 80.5° E, 0° N are estimated to be 54.75 cm/s, 32.55 cm/s and 35.79 cm/s respectively. However, the validations were not available in the shelf and slope regions of Indian Ocean. Presently the global model (0.5°) is operational and the ocean currents of GM4P1 were utilized for the oil spill trajectory predictions. Source: http://www.incois.gov.in/portal/MOM. The bathymetry of this model set up is shown in Figure 2d.
3.1 Simulation of oil drift pattern using operational ocean currents
The method to generate oil drift pattern using met-ocean forcings is illustrated in Figure 3. GNOME is an oil spill trajectory model that computes the drift of the spilled marine oil pollutant from the resultant of the forcings such as wind, current and diffusion. The trajectory model GNOME was set with the met-ocean forcings such as winds, ocean currents and diffusion. The necessary details such as location, date, time of the spill, pollutant and its quantity spilled are fed into the trajectory model.
3.2 Oil spill trajectory model set up
The wind fields were obtained from European Centre for Medium Range Weather Forecast (ECMWF) and the ocean current pattern is obtained from OGCMs such as ROMS, HYCOM and GM4p1. The domain (65° E - 90 E, 5° N - 25° N) of the oil spill trajectory model for this case was set as shown in Figure 4.
GNOME was set individually with the ocean currents of HOOFS_IO, HOOFS_BOB, HYCOM and GM4p1 along with ECMWF winds and diffusion. The HFO drift patterns are obtained as the vector resultant of the ECMWF winds, diffusion ocean currents. The oil drift patterns are generated individually by triggering the oil spill model with the respective forcings. The mean vector plot of winds and ocean currents of various OGCM's during the period (27 to 30 January 2017) are shown in the Figure 5. The star denotes the spill location in all the panels. Winds were towards coast during the spill period (Figure 5a). Near the spill location, the simulated HIO currents are towards south (Figure 5b). The HYC currents are towards southwest (Figure 5c). But the GMP currents are moving towards south-south west (Figure 5d). The HBO currents were towards southwest (Figure 4e). The mean wind speed was in the range of 3–5 m/s. The mean magnitudes of all ocean currents were in between 0.01–0.21 m/s.
4.0 RESULTS AND DISCUSSION
The trajectory model was run with a quantity of 196.4 Metric Tons from 0400 hrs of 28 January 2017, to 2300 hrs of 05 February 2017 to get the advisory on the drift and spread of HFO. In-order to intercompare the trajectories obtained while forcing GNOME with ocean currents of various OGCM's, the trajectory model was run individually. Trajectories obtained were compared with the observations obtained on 0600hrs of 29 January 2017.
4.1 Extraction of oil slick signatures from SAR data
Prasad et al. 2018 explained the method of extracting the oil slick signatures from Sentinel -1A dataset. The radar dataset was acquired in Vertical - vertical polarisation mode on 29 Jan 2017. It is subjected to calibration, so that backscatter values are directly related to the pixels. The dark spots obtained were further investigated for oil slicks. Finally the identified zone of oil slicks were considered for the comparison.
4.2 Comparison of the alongshore spread between the simulated and observed HFO drift
Figure 6(a–d), denotes the comparison between the simulated and observed drift of HFO on 0600hrs of 29 January 2017. In Figure 6(a–d), the area covered by black polyline track denotes the drifted zone of HFO on 0600hrs of 29 January 2017 obtained from SAR data (Prasad et al. 2018). The alongshore spread of the simulated and the observed HFO drift were compared separately. The yellow HBO trajectory (Figure 6a) was towards West-South -West and hence beaching of HFO was noticed as that of the actual observation. The resultant HIO trajectory (Figure 6b) was towards southwest, but the beaching started approximately 4.4 km from the observed location. The black trajectory is obtained while using NOAA HYCOM ocean currents (Figure 6c). In this case the beaching occured approximately 4.0 km away from the actual observation. The grey GMP trajectory was obtained while using GM4P1 currents (Figure 6d). It was noticed that, the resultant grey trajectory is well in agreement with that of the actual observation.
The alongshore spread was well captured, while using the forecasted ocean currents of HOOFS_BOB and GM4P1 (Figure 6a and 6d). In the figure, the dot (x) symbol denotes the floating (beached) status of the oil particles. The offshore HFO spread was underestimated while using GMP currents. From the Figure 6d, it is seen that the spread of grey coloured spots were more towards south. The drift pattern obtained while using HBO currents were towards southeast, which are represented by the yellow coloured splots of Figure 6a. Form this experiment is clear that, the oil drift pattern obtained while using HBO currents is well in agreement with the observed oil slicks of SAR data.
HFO drift patterns were simulated with ocean currents of general circulation models using the oil spill trajectory model GNOME. The modeled ocean currents had various resolutions and co-ordinates. While forcing GNOME with these ocean currents for generating the spill trajectories of HFO spillage, the drift patterns obtained with HBO and GMP currents agreed well with observed oil remnants. The offshore spread of HFO was captured well while using HOOFS_BOB currents. This assessment helps the deciding authority to issue the oil spill advisory, during the real response operations. Based on the performance of the oil spill model set up along with the respective forcings, the oil spill advisories can be issued to the regulatory authority and oil spill responders on nowcast and forecast modes. This oil spill trajectory prediction assessment with multi-model ocean currents improves the confidence of modellers and researchers in issuing oil spill advisories during real oil spill incidents.
The authors express their sincere gratitude to Director, INCOIS for extending the support in carrying out this study. Authors acknowledge that, the executable of oil spill model GNOME is adopted from NOAA and set up in diagnostic mode for simulating oil spill trajectories of Indian ocean scenario. Thanks are due to the developers of NOAA GNOME. Dr.Francis and Dr.Hasibur Rahaman are thanked for providing the modeled ocean currents of ROMS and GM4p1 respectively. The authors also thank NOAA HYCOM consortium from which HYCOM currents were utilized. The authors thank the officials of Indian Coast Guard, Chennai for their information on oil spill and support in validating the trajectory predictions of spilled HFO. ArcMap tool was used to plot and generate the trajectory output in native EPS format.