Hindcast Modeling of Oil Slick Persistence from Natural Seeps

Remote Sensing of Environment 189 (2017) 96–107

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Remote Sensing of Environment

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Hindcast modeling of oil slick persistence from natural seeps

Samira Daneshgar Asl a,DmitryS.Dukhovskoyb, Mark Bourassa a,b, Ian R. MacDonald a,⁎ a Florida State University, Department of Earth, Ocean and Atmospheric Science, Tallahassee, FL 32306, USA b Center for Ocean-Atmospheric Prediction Studies, Florida State University, Tallahassee, FL 32306, USA article info abstract

Article history: Persistence of oil floating in the ocean is an important factor for evaluating hydrocarbon fluxes from natural seeps Received 9 June 2016 and anthropogenic releases into the environment. The objective of this work is to estimate the surface residence- Received in revised form 13 October 2016 time of the oil slick and to determine the importance of wind and surface currents on the trajectory and fate of the Accepted 3 November 2016 released oil. Oil slicks released from natural hydrocarbon seeps located in Green Canyon 600 lease block and its Available online xxxx surrounding region in the Gulf of Mexico were analyzed. A Texture Classifying Neural Network Algorithm was used to delineate georectified polygons for oil slicks from 41 synthetic aperture radar images. Trajectories of Keywords: Natural seep the oil slicks were investigated by employing a Lagrangian particle-tracking surface oil drift model. A set of nu- Oil slick merical simulations was performed by increasing hindcast interval in reverse time order from the image collec- Green Canyon 600 tion time in order to obtain the closest resemblance between the simulated oil pathways and the length and Synthetic aperture radar shape of the oil slicks observed in synthetic aperture radar images. The average surface residence-time predicted Surface oil drift model from the hindcast modeling was 6.4 h (±5.7 h). Analysis of a linear regression model, including observed oil slick lengths and variables of wind, surface current, and their relative direction, indicated a statistically significant negative effect of wind speed on the surface oil drift. Higher wind speed (N7ms−1) reduced length of the oil slicks. When wind and surface currents were driving forces of the surface oil drift model, a good agreement between simulated trajectories and subsequent satellite observations (R2 = 0.9) suggested that a wind scaling coefficient of 0.035 and a wind deflection angle of 20° to the right of the wind direction were acceptable approximations for modeling wind effects in this study. Results from the numerical experimentation were supported by in situ observations conducted by a wind-powered autonomous surface vehicle (SailDrone). Results indicated that the surface currents are, indeed, responsible for stretching oil slicks and that surface winds are largely responsible for the disappearance of the oil slicks from the sea surface. Under conditions of low wind and strong current, natural seeps can produce oil slicks that are longer than 20 km and persist for up to 48 h. © 2016 Published by Elsevier Inc.

1. Introduction currents (MacDonald et al., 2002; Garcia-Pineda et al., 2010). When the wind speed is under about 6 m s−1 the oil tends to remain on the sur- Natural hydrocarbon seeps are important for carbon cycle processes face, except for that which dissolves or evaporates (Reed et al., 1994). (Kvenvolden, 1993; MacDonald et al., 1996; Kvenvolden, 2002; For wind speeds N7ms−1, the frequency of breaking waves increases MacDonald et al., 2002; Judd, 2004), marine pollution (Allen et al., (Samuels et al., 1982) which disperses substantial portions of the oil 1970; Spies et al., 1996; Espedal and Wahl, 1999; Kvenvolden subsurface. The oil slick gradually dissipates through weathering pro- and Cooper, 2003; DiGiacomo et al., 2004), and marine ecosystems cesses such as spreading, flocculation, dissolution, and evaporation (MacDonald et al., 1989; MacDonald et al., 1990; Guinnasso and over ensuing time (MacDonald et al., 1993; Guinnasso and Murray, Kennicutt, 1994; MacDonald et al., 2004; Orcutt et al., 2010; D'souza 2003). Seeps are indicators for economically significant hydrocarbon et al., 2016). Based on the global estimates of crude-oil seepage rates, deposits and a dynamic component of the carbon cycle; however, the 47% of the crude oil entering the marine environment is from natural rates and magnitudes of this process are not well constrained in regard oil seeps (Kvenvolden and Cooper, 2003). At natural seeps, the oil that to the dissipation of oil that reaches the sea surface (Leifer et al., 2000; makes it to the sea surface spreads out in a thin layer (b1 μm) from MacDonald et al., 2002). the oil slick origin (OSO) and floats downstream with wind and surface In this paper, surface residence-time means the maximum time that oil in a slick can be detected by synthetic aperture radar (SAR). In a typical slick, this oil will be found at the point most distant from the OSO. A given natural oil slick includes oil near the OSO that has newly arrived at ⁎ Corresponding author. E-mail addresses: [email protected] (S. Daneshgar Asl), the surface and oil at the opposite end of the slick, which has been [email protected] (I.R. MacDonald). drifting on the surface for the residence-time determined by the specific

http://dx.doi.org/10.1016/j.rse.2016.11.003 0034-4257/© 2016 Published by Elsevier Inc. S. Daneshgar Asl et al. / Remote Sensing of Environment 189 (2017) 96–107 97 environmental conditions and the properties of the oil. Accurate esti- make it possible to refine wind scaling coefficient and deflection angle mate of residence-time for oil slicks released from a natural seep can and to consider the combination of wind and surface current compo- be used to calculate a flux based on the amount of oil present nents which vary over time. Strong surface current fields can either de- (National Research Council Committee on Oil in the Sea, 2003); howev- flect an oil slick in an opposing direction from the wind or elongate it in er, there have been few studies that quantify this aspect of the seepage the same direction as the wind (Espedal and Wahl, 1999). process. In this study, a two-dimensional surface oil drift model forced by Satellite remote sensing offers a means to analyze the characteristics 10-m wind fields and surface ocean currents was used to simulate of floating oil. Natural seeps produce persistent oil slicks at predictable the evolution of the oil slicks released from Green Canyon 600 locations. Surface films of oil dampen the short gravity-capillary ocean (GC600) natural seep that were observed by SAR satellite imagery. surface waves, reduce radar back-scatter, and generate dark targets in The simulation results were analyzed to estimate the surface resi- SAR images (Gade et al., 1998; Espedal and Wahl, 1999). Comprehen- dence-time of the oil slicks. To verify our findings and the impor- sive remote sensing surveys showed that there are about 914 seep tance of the wind and surface currents in deriving the trajectory of zones in the Gulf of Mexico (GoM) producing persistent oil slicks seen oil slicks on the sea surface, high resolution aerial photography, a by SAR (MacDonald et al., 2015). Some of the oil slicks are relatively Landsat 8 satellite image, and real-time surface winds from a wind- short and some of them are longer. This raises two important questions. powered autonomous surface vehicle (SailDrone) study were First, are very long oil slicks caused by an increased rate of discharge at analyzed. the natural hydrocarbon seep? Second, what is the relative contribution of wind and surface currents to the length and shape of the oil slicks? 2. Study area Addressing these questions can improve use of remote sensing during oil spills and for research on natural seeps. This study was conducted in the GoM at seep zones located in the The most important factors that determine the direction, rate of the Bureau of Ocean Energy Management (BOEM) lease block GC600 and movement, and persistence of an oil slick are the strength of surface three adjacent lease blocks Green Canyon 557, 601, and 644 (GC557, winds and currents (MacDonald et al., 2002; Guinnasso and Murray, GC601, and GC644). GC600 (Fig. 1) examined in this study is one of 2003; Korotenko et al., 2004; Garcia-Pineda et al., 2010). Previous stud- the most prolific natural hydrocarbon seeps that has been discovered ies performed in the North Sea proposed that oil released at a fixed point and a perennial source of surface oil slicks in the Northern GoM source moves with approximately 3% of the wind speed in a direction (Garcia-Pineda et al., 2010). The GC600 seeps are situated at a low-relief 15° to the right of the wind direction (Bern, 1993; Espedal and Wahl, ridge at a depth of about 1200 m and have been studied by analyzing 1999). This assumption was made under the conditions of no (strong) satellite images (Garcia-Pineda et al., 2010; D'souza et al., 2016) and surface current and no other factors influencing the spread pattern during oceanographic cruises with the use of submarines and Remotely (Espedal and Wahl, 1999). Based on drift card experiments, the results Operated Vehicles (Garcia-Pineda et al., 2010; Roberts et al., 2010; of computations using data from the Bravo blowout, wind, and tidal cur- D'souza et al., 2016). In addition, a wind-powered autonomous surface rents in the North Sea a wind scaling coefficient of 2.5–3% and a Coriolis vehicle (SailDrone) deployment and aerial photography provided in- deflection of 12–15° gave results in good agreement with observations situ observations at the lease block Green Canyon 574 (GC574) and its (Audunson, 1980). Repeated observations of natural oil slicks should surrounding region.

Fig. 1. Location of GC600 on the bathymetric map of the Northern GoM (marked with star). Grid shows lease block boundaries and designations. Location of all OSO points detected in 41 SAR images (green Triangles). The solid lines show the topography of the research area. Datum is WGS1984. (For interpretation of the references to color in this ﬁgure legend, the reader is referred to the web version of this article.) 98 S. Daneshgar Asl et al. / Remote Sensing of Environment 189 (2017) 96–107

3. Data and surface oil drift model thresholds and relative units are used for background threshold (http://www.ldi.ee/). The ROW sensor generates alarm when the level 3.1. SAR observations of detected pollution is higher than the present threshold (http:// www.ldi.ee/). Both wind speed and direction are averaged with a The study is conducted on the basis of the open archive for SAR im- 120 s “fixed boxcar” filter to remove momentary gusts from the signal; ages collected over the lease block GC600 and its surrounding region every 2 min, the wind speed and direction are set with the average value from January 2003 to November 2011. Image sources included Environ- of the previous two minutes' time. The wind sensor records for 2 min mental Satellite Advanced Synthetic Aperture Radar (ENVISAT ASAR) C every 10 min. This produces one wind speed and direction data point band imagery and European Remote Sensing (ERS-2) SAR images from every 10 min, by averaging the first two minutes' values. the European Space Agency, RADARSAT C band imagery and Advanced A coordinated series of observations with SailDrone autonomous Land Observing Satellite Phased Array-type L-band Synthetic Aperture marine data logger was conducted on 12 October 2015 in lease block Radar (ALOS PALSAR) imagery from the National Aeronautics and GC574 and its surrounding region. A Landsat 8 satellite image was also Space Administration and support from the Alaska Satellite Facility, collected on 12 October 2015 over the study area at 16:38:49 UTC. Infor- and COSMO-SkyMed X band images from the National Oceanic and mation about the oil slicks derived from the Landsat 8 satellite image in Atmospheric Administration. Table 1 lists the related information of the vicinity of the SailDrone locations was used to define the seeding ra- the available archive of SAR observations used in this study. dius for the numerical experiments. SailDrone is not equipped with an We matched the location of GC600 against the coverage of the 41 ocean current meter; however, at the time the satellite image was col- satellite images collected from January 2003 to November 2011 and de- lected, the SailDrone had difficulty maneuvering because there were termined whether the oil slicks were visible in archived SAR images. low wind and a strong surface current. To simulate the trajectory of After preliminary screening, a semi-automated image processing the visible oil slicks in the Landsat 8 satellite image, we used the input routine, the Texture Classifying Neural Network Algorithm (TCNNA) parameters in Table 2 and ran the surface oil drift model with HYCOM (Garcia-Pineda et al., 2009; Garcia-Pineda et al., 2015), was employed ocean surface currents and SailDrone 4.5-m winds adjusted to the 10- to delineate observed oil slicks within SAR images. Having oil slick poly- mwinds. gons extracted, the OSO points were selected as single points within de- The oil slicks were photographed for this study during overflights tected oil slicks. The OSO points were identified by the shape of oil slicks, with a single engine airplane equipped with a digital camera with a tele- because the origins tend to be wider than the ends, and by the tendency photo lens. An overflight during the SailDrone deployment on 14 of OSO points cluster over active seeps (Garcia-Pineda et al., 2010). October 2015 showed the vehicle operating near the oil slicks in ArcToolbox's Conversion and Spatial Statistics tools were used to mea- GC574 to confirm that features observed in the Landsat 8 satellite sure the width at the OSO, length, and area of the extracted oil slicks. image were oil slicks and not look-alikes (e.g. sargasum). The volume of oil slicks were also estimated based on the measured areas obtained from SAR images assuming 0.1 μm thickness. The 3.3. Surface oil drift model 0.1 μm value approximates the lower bound for oil layers that would create sheen on water (NOAA Hazmat, 2012). A two-dimensional surface oil drift model was developed for several applications in this project. This model is based on Lagrangian particle- 3.2. Field validation: SailDrone and aerial observations of oil slicks tracking trajectory algorithm commonly used in oil spill models (Garcı́a-Martı́nez and Flores-Tovar, 1999; Abascal et al., 2009), with In addition to SAR images of the oil slick, in situ observations of oil several user-modifiable options facilitating parameter sensitivity and trajectories collected by a SailDrone were employed in this study. other studies. The model simulates the evolution of an oil spill on the SailDrone is a wind and solar-powered, unmanned autonomous surface surface of the ocean by computing the two-dimensional trajectories of vehicle equipped with high resolution meteorological and oceano- large numbers of simulated discrete “particles”, or Lagrangian elements graphic sensors to collect real-time observations (Meinig et al., 2015). SailDrone incorporates an anemometer at the height of 4.5 m to read Table 2 the surface values of wind speed and direction (Meinig et al., 2015)and Input parameters of the surface oil drift model. an autonomous Remote Optical Watcher (ROW) to detect oil pollution on the surface of the water in real time. The ROW sensor uses oil fluores- Parameters Values cence to detect surface hydrocarbons. The device measures two param- Start point Geographic location of OSO point estimated eters, signal and background. Arbitrary units are used for signal from SAR Npa 100 b Th 0 Table 1 c Tmax 0 Information of SAR images used in this study. Their OSO points are presented in Fig. 1. d Rs 1/3 width of the oil slick at the OSO estimated from SAR No. Instrument Satellite Polarization Band Resolution (m) dte 0.25 f,h 4 ASAR ENVISAT VV C 68 Cw 0.035 7 ASAR ENVISAT VV C 70 θg −20 2 ASAR ENVISAT VV C 73 Start time of running model Estimated from surface oil drift model 2 ASAR ENVISAT VV C 76 End time of running model Time at which SAR image was collected 1 SAR ERS-2 VV C 11 Start date Estimated from surface oil drift model 9 SAR RADARSAT-1 HH C 12 End date Date at which SAR image was collected 1 SAR RADARSAT-1 HH C 23 Surface currentsi Upper-most layer from HYCOM GOMl0.04 1 SAR RADARSAT-1 HH C 24 experiment 20.1, 31.0, and 32.5 3 SAR RADARSAT-1 HH C 48 a Number of particles seeded per time step. 1 SAR RADARSAT-1 HH C 49 b Half-life (h). 1 SAR RADARSAT-2 VH C 23 c Maximum life (h). 1 PALSAR ALOS HH + HV L 12 d Seeding radius (the radius within which the particles are seeded around the OSO, m). 2 PALSAR ALOS Unknown L 68 e Model time step (h). 1 ScanSAR Cosmo-SkyMed 1 VV X 47 f Wind scaling coefficient. 1 ScanSAR Cosmo-SkyMed 2 VV X 47 g Wind deflection angle (degrees). 1 ScanSAR Cosmo-SkyMed 3 VV X 46 h For experiment with surface currents alone this parameter becomes zero. 3 ScanSAR Cosmo-SkyMed 3 VV X 47 i For experiment with wind alone this parameter becomes zero. 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(LEs), each representing a volume of oil or related constituents. The ini- where ν is the diffusion coefficient (Table 2)andΔt is the time scale of tial positions of the LEs are prescribed such that the model may either be the process. This diffusion can be spatially variable, for example it may initialized from an observed map of surface oil distribution, or added to be enhanced within a specified region about the source to simulate ad- the surface of the ocean randomly distributed near the source within ditional spreading by gravitational effects. the seeding radius (Rs). The trajectories of the LEs are computed within a time-varying velocity field that is some combination of ocean surface 3.4. Model forcing fields currents and 10-m winds from analysis, reanalysis, or forecast model products. LEs are removed from the system randomly based on a The surface oil drift model is forced by the surface ocean currents prescribed half-life (Th), or total life (Tmax), to simulate removal of oil and surface winds. Ocean currents are derived from the HYbrid Coordi- from the surface through processes such as evaporation, dissipation nate Ocean Model (HYCOM) (Bleck, 2002; Chassignet et al., 2003)GoM into the water column, biological activity or other oil removal 1/25° (GOMl0.04) analysis experiment 20.1, 31.0, and 32.5 (http:// mechanisms. hycom.org). The GoM model has 1/25° equatorial resolution and latitu- The oil particle trajectories are computed as a superposition of dinal resolution of 1/25° cos (lat) or ~3.5 km for each variable at mid- advective processes and turbulent diffusion latitudes. It has 20 coordinate surfaces in the vertical (Zamudio and Hogan, 2008; Dukhovskoy et al., 2015). The data assimilation is per- dx formed using the Navy Coupled Ocean Data Assimilation (NCODA) ¼ uaðÞþx; t udðÞx; t ð1Þ dt (Cummings, 2005) system with a model forecast as the first guess. NCODA assimilates available satellite altimeter observations (along where u is the advective (oil drift) velocity and u is the diffusive a d tracks obtained via the Naval Oceanographic Office (NAVOCEANO) Al- velocity. The advective velocity is calculated as linear combination of timeter Data Fusion Center), satellite and in situ sea surface temperature the surface ocean currents, 10-m wind vector and waves as well as available in situ vertical temperature and salinity profiles from fl ua ¼ uc þ Cwkku10 Θ þ Csus ð2Þ XBTs, ARGO oats and moored buoys using a 3D-VAR technique. The system is run in real time at the NAVOCEANO Major Shared Resource Center. The HYCOM NCODA GoM predictions were the major source where uc is the ocean surface current velocity of seawater estimated of oceanographic information for the oil drift forecasts performed by from the topmost grid layer of an ocean model, Cw is the wind scaling several research groups during the Deepwater Horizon spill event. coefficient, u10 is the 10-m wind velocity over the sea surface and Θ is a unit vector being directed at angle θ from the wind (wind deflection Surface winds were derived from Cross-Calibrated Multi-Platform Ocean Surface Wind Vectors (CCMP) (Atlas et al., 2011). CCMP is a global angle), Cs is the wave coefficient and us is the wave-induced Stokes drift. Typically, wind scaling coefficient varies from 0.025 to 0.044 gridded ocean vector wind product obtained from satellite scatterometer (ASCE, 1996). The role of waves in oil transport can be important in and radiometer wind observations assimilated into the atmospheric the nearshore areas and in the presence of swell waves. The inclusion model. The winds represent 10-m winds above the sea surface, have 6- of wave-induced transport provides a mechanism for beaching of sur- hour temporal resolution and are on a 0.25-degree horizontal grid. Both ocean surface currents and 10-m winds have been temporally interpolat- face oil (Sobey and Barker, 1997). Values of Cs have been estimated within the range around 0.05–1.5% of the wave-induced Stokes drift ed into 15-minute intervals. (Castanedo et al., 2006). The Stokes drift can be estimated from the winds (under the assumption of fully developed wave field) or from 3.5. Model experiments the wave spectral field. In many applications, wind and wave effects are combined being represented by the wind scaling coefficient. The surface oil drift model was implemented in the GC600 study Under light wind conditions without breaking waves Reed et al. area to simulate the movement of the oil slicks visible in SAR images. In this model, a complete ensemble of particles shows the simulated (1994) found that Cw = 0.035 provides realistic simulation of oil slick drift in offshore areas. Based on previous research the wind deflection representation of an oil slick at a given time. The input parameters of angle varies from about 15° to 20° as the wind speed ranges from 3 to the surface oil drift model are listed in Table 2. For this study, the turbu- 15 m s−1 (Stolzenbach et al., 1977). The wind deflection angle is typical- lent diffusion, wave-induced Stokes drift, and wind-dependent wind ly 20° clockwise from the wind direction (in the Northern Hemisphere). deflection angle were not considered in the surface oil drift model. The model has an option to use wind-dependent wind deflection angle The best set of parameters for the model (Table 2) was determined with negative values corresponding to the clockwise rotation from the iteratively in a way that simulated distribution of the oil particles most wind direction (Samuels et al., 1982) closely resembling the length and shape of the oil slick observed in SAR image. A surface residence-time estimate for an observed oil slick ∘ −8 was the hindcast interval that best reproduced the length and shape θ ¼ −25 exp −10 kku10 =νkg ð3Þ of that oil slick in the simulated representation. −6 2 −1 To study the effect of wind and surface currents on the trajectory and where νk is the kinematic viscosity of the seawater (1.05 × 10 m s ) and g is the gravitational acceleration. fate of the natural oil slicks, the following simulations with the surface The last term in Eq. (1) approximates oil spreading under the influ- oil drift model were performed: 1) wind and surface currents com- fi ence of turbulent diffusion and other unresolved lateral mixing process- bined; 2) surface currents alone (wind scaling coef cient was set to 0), and 3) wind alone (ocean currents were set to 0). es. The turbulent diffusive velocities ud are approximated as random fluctuations defined for each time step based as the “random walk” Several hindcasted oil slicks failed to follow a trajectory consistent (Garcıa-Mart́ ıneź and Flores-Tovar, 1999; Lonin, 1999; Abascal et al., with the observed oil slicks when the model was run with wind alone 2009) (three cases) and surface currents alone (two cases). These results were designated as unresolved and were not included in the summary ¼ ξ ðÞπξ ðÞ ud kkud exp i2 4 totals. where ξ is a random variable from the standard Gaussian distribution 3.6. Model validation and rffiffiffiffiffiffi Final distribution of oil particles from the hindcast simulations was 6ν compared against the observed oil slicks in SAR images. Additionally, kk¼u ð5Þ d Δt the model performance was assessed by quantifying the difference 100 S. Daneshgar Asl et al. / Remote Sensing of Environment 189 (2017) 96–107 between the simulated and observed positions of the oil slicks. To do (b18 km), 7.3% were within two standard deviations (b27 km), and this, the directional vectors connecting the OSO location and the farthest the rest of the data were significantly different from the mean (p b 0.05). point of each oil slick were tracked both in the model and observations. Fig. 4 shows the simulation results of the surface oil drift model ap- The differences in the angle from the North of the vectors for the model plied to three separate slicks under three different driving conditions and observations were used as a measure of disagreement between the using SAR images collected on 8 July 2003, 19 September 2005, and 22 simulations and the SAR data. Based on these analysis, the best set of July 2010. model parameters was determined that provided the closest resem- The surface residence-time of the oil slicks was estimated by using blance between the simulated and observed oil slicks. It was found the hindcast interval that best reproduced the length and shape of the that when the combined effects of wind and surface currents were con- corresponding oil slick. When wind and surface currents were driving sidered, the wind scaling coefficient of 0.035 and the wind deflection forces of the surface oil drift model with the average speed of angle of 20° to the right of the wind direction yielded the most accurate 5.9 m s−1 and 0.4 m s−1 respectively, an average surface residence- trajectory of the oil particles compared to SAR data. time of an oil slick was estimated to be 6.4 h. A linear regression analysis was performed to explain the relationship between the oil slick lengths observed in SAR images and variables 4. Results of wind, surface current, and their relative direction since none of the parameters alone can explain the observed and simulated length of SAR images detected the fraction of oil that is released into the water the oil slicks. column from the seafloor and rises to the surface as oil slicks. Oil slicks Eq. (6) shows the linear regression analysis with coefficient of deter- were consistently imaged over GC600 and its surrounding region. Oil mination (R2) of 0.37. L is the length of the oil slicks extracted from the −1 slicks were clearly detected as elongated dark patches in 41 SAR images 41 SAR images (km), Vw is wind speeds (m s ), Vo is the surface cur- collected from January 2003 to November 2011. Fig. 1 shows the loca- rent speeds (m s−1), and θ is the angles between the surface current tion of all OSO points in the study area detected in SAR images. and wind vectors, assuming that the wind deflection angle is 20° to Fig. 2 delineates the distribution of the oil slicks extracted by TCNNA the right of the wind direction. The calculated values of the coefficients from the 41 SAR images. The complex variability of slick shape and in Eq. (6) are shown in Table 3. The F-test demonstrates that the linear direction is a result of changing wind, surface currents, and waves regression model fits the data set (the p-value 0.0006). (MacDonald et al., 2002). Each oil slick represents a snapshot of the oil that reached the surface OSO and drifted with wind and surface currents L ¼ α þ β Vw þ γ Vo þ υ Vw V o cosθ þ ε ð6Þ for an unknown time prior to the SAR image collection. Fig. 3 shows the histogram of the oil slick lengths (average, 9.2 km) Individual t-tests of the model coefficients show that wind speeds measured in ArcGIS. Some of the oil slicks were relatively short (5– and surface current speeds are significant (the p-value b 0.05). The 10 km long) and some of them were longer (20–50 km long). In all, results demonstrated the importance of the wind and surface currents 87.8% of the oil slick lengths were within one standard deviation on oil slick movement.

Fig. 2. Locations of oil slicks extracted by TCNNA from the 41 SAR images in BOEM lease block GC600 and its surrounding region, January 2003 to November 2011. Each color represents a unique oil slick. The minimum interval between satellite collection dates was 12 h; therefore, these oil slicks are temporally independent representation of surface oil movement at this site. Datum is WGS1984. S. Daneshgar Asl et al. / Remote Sensing of Environment 189 (2017) 96–107 101

18 slicks (km) and the surface current speeds (m s−1). The relative angles 16 between wind and current-driven advection was not signiﬁcant in our data set. 14 − Fig. 5 shows a scatter plot of the wind velocity (m s 1) vs the length 12 of the oil slicks observed in the 41 SAR images. Under the strong wind N −1 b 10 conditions ( 7ms ), the oil slicks were shorter ( 10 km) (squares). Therefore, wind speed of N7ms−1 was the dominant factor causing dis- 8 appearance of the oil slicks from the sea surface and producing shorter 6 oil slicks because the oil gets dispersed and mixed (both horizontally N Number of oil slicks and vertically) by wind and ocean. Longer oil slicks ( 10 km) were 4 observed when the wind speed was b7ms−1 (Fig. 5). 2 Assuming the Vo =0inEq. (6) gave us a better understanding of the 0 relative contribution of the wind speed to the length of the oil slick. For −1 1-5 5-10 10-15 15-20 20-30 30-50 wind speeds N7ms , the effect of the wind was to decrease the length – Length of oil slicks visible in SAR (km) of the oil slicks (Fig. 6) presumably by increasing the frequency of breaking waves (Samuels et al., 1982; Reed et al., 1994)andmixing

Fig. 3. Histogram of oil slick lengths in the 41 SAR images. the oil into the water. The angle from the North of the observed and simulated oil slicks was estimated when wind and surface currents were driving forces of There is a statistically signiﬁcant negative relationship between the the surface oil drift model. When the combined effects of wind and sur- − length of the oil slicks (km) and the wind speeds (m s 1)(Table 3). face currents were considered, there is a good agreement between sim- However, there is a positive relationship between the length of the oil ulated trajectories and synchronous observations of the oil slicks from

Fig. 4. Simulated trajectory of the oil slicks extracted using TCNNA analysis under three different driving conditions (solid points): surface currents alone (orange), wind alone (red), and wind and surface currents combined (blue). The circle shows a buffer around the OSO point that has a radius equal to the length of the oil slick (pink). Each hindcast simulation was iterated until the simulated slick exceeded its corresponding buffer. Datum is WGS1984. (For interpretation of the references to color in this ﬁgure legend, the reader is referred to the web version of this article.) 102 S. Daneshgar Asl et al. / Remote Sensing of Environment 189 (2017) 96–107

Table 3 October 2015 over the lease block GC574 and its surrounding region. Summary statistics for the linear model. α is the intercept, β is the wind speeds coefficient, The low-wind conditions (the average wind speed of 2.1 m s−1) were γ is the surface current speeds coefficient, and υ is the coefficient of the relative angles be- favorable for observing natural oil slicks. The ripples generated on the tween the surface current and rotated wind vectors. ocean surface were suppressed by oil layers making the oil slicks easily Coefficients Standard tp-Value detectable in the Landsat 8 satellite image. Projecting the SailDrone error track onto the Landsat 8 satellite image indicated that the vehicle had α 21.21 5.1 4.17 0.0002 crossed narrow oil slicks (Fig. 8B). The ROW sensor would therefore β − − 2.93 0.7 4.01 0.0003 alternate between clean surface water and brief exposure to patches γ 16.14 7.3 2.22 0.0329 fl υ −0.01 0.7 −0.01 0.9887 of oating oil. In Fig. 8C, the orange signal line exceeding the green lower threshold line indicates fluorescence response from oil films. Source of variation df Sum of squares Mean square F Significance F High-resolution SailDrone observations detected the presence of sur- Regression 3 1159.6 386.5 7.3 0.0006 face oil with high precision. They also revealed that features observed Residual error 37 1971.7 53.3 in the Landsat 8 satellite image were oil slicks and not look-alikes. Total 40 3131.3 78.3 Analysis of the satellite observation confirmed extensive oil slicks with an average length of 19 km on 12 October 2015. Low-wind conditions (b2.5 m s−1) were dominant during this time. The hindcast simu- SAR images (Fig. 7) demonstrating that the wind scaling coefficient of lation of these oil slicks was conducted with the surface oil drift model. 0.035 and the wind deflection angle of 20° to the right of the wind direc- Simulation results indicated that these oil slicks had a surface residence- tion were reasonable values to simulate the trajectory of the oil slicks. time of about 41.6 and 50.6 h. There is an overlap between the simulated trajectory and the SailDrone locations at surface hydrocarbon peaks 4.1. Daily flux estimation (Fig. 9). Results confirmed that the surface oil drift model did a good job of reproducing oil slicks formed when surface currents were strong The volume of observed oil slicks from the analyzed SAR images was but wind was weak. Moreover, surface currents were indeed responsi- estimated on the basis of the measured areas and estimated oil slick ble for generating long oil slicks. thickness of 0.1 μm (NOAA Hazmat, 2012). Daily flux of each oil slick Additionally, high-resolution, aerial imaging of the oil slicks on 14 (m3 d−1) was calculated by the following equation. October 2015 validated the presence of oil slicks at GC574 and its surrounding region (Fig. 10). 24 Daily flux ¼ Area 0:1μm ð7Þ surf ace residence−time 5. Discussion Our results indicated an average volume of 0.8 m3 and an average − flux of 2.6 m3 d 1 (stdev 1.7). Trajectories of oil slicks from the natural oil seeps in the Northern To compare these results to other flux estimations in GC600 GoM during 2003–2015 were analyzed based on SAR images and (Johansen et al., 2016), we excluded 4 of the oil slicks that had OSO points hindcast simulations with a surface oil drift model. In general, the simula- outside of the lease block GC600. This modified our results to an average tions agreed with satellite observations. Numerical experiments demon- − volume of 0.7 m3 and an average flux of 2.7 m3 d 1 (stdev 1.8). strated that a wind scaling coefficient of 0.035 and a wind deflection angle of 20° to the right of the wind direction provided the most accurate 4.2. SailDrone results hindcasts of natural oil slick trajectories in the GoM. The wind scaling co- efficientconsiderstheensembleeffects of turbulent diffusion and wave- We analyzed data collected with a SailDrone to further verify the fact induced Stokes drift instead of treating these processes as separate com- that surface currents play a major role in extending oil slicks on the sea ponents of the model. Further observations are needed to model subtle surface. Fig. 8A shows the path of the SailDrone with a location recorded processes in oil transport. Additionally, 3D oil drift modeling techniques every 10 min on 12 October 2015 between 00:00:00 UTC to 23:59:59 can be developed in future research to simulate vertical mixing of the oil. UTC. A Landsat 8 satellite image was collected at 16:38:49 UTC on 12

Fig. 5. Scatter plot of wind velocity (m s−1) vs length of the oil slick (km) observed in the 41 SAR images. Squares show oil slicks b10 km under wind speed N7ms−1 and circles Fig. 6. Scatter plot of wind velocity (m s−1) vs the estimated length of the oil slick (km) −1 −1 show oil slicks under wind speed b7ms . The current speeds during these conditions assuming the Vo = 0 in Eq. (6). Squares show oil slicks under wind speed N7ms and varied between 0.1 and 0.5 m s−1. circles show oil slicks under wind speed b7ms−1. S. Daneshgar Asl et al. / Remote Sensing of Environment 189 (2017) 96–107 103

Fig. 7. Scatter plot of the angle from the North of the observed oil slicks vs the simulated trajectories (positive in the clockwise direction and negative in the counterclockwise direction) when wind and surface currents were driving forces of the surface oil drift model. The linear regression coefﬁcients and the coefﬁcient of determination are listed.

Numerical experiments provided an estimate of the oil slick surface sedimentation (Huang, 1983; Wang et al., 2005), which are largely un- residence-time. When wind and surface currents are driving forces of determined for open ocean conditions. In the discussed numerical ex- the surface oil drift model, the estimate of the surface residence-time periments, the surface oil drift model did not account for oil discharge is 6.4 h. data such as type of oil (density and viscosity), its temporal variations A simple linear regression model including winds, surface currents, due to weathering processes, rate and volume of the release at the sur- and their relative angle of transport as variables showed that there is a face, and time-varying and spatial distribution of oil thickness. Including negative significant relationship between the length of the oil slicks these parameters in the surface oil drift model with real-time wind and (km) with the wind speeds (m s−1) and a positive relationship between current data and with more satellite observations of the oil slick, one the length of the oil slicks (km) with the surface current speeds (m s−1). could generate a nonlinear regression model between the variables The regression model explained 37% of the variance of the data. Wind and observations that could explain the unexplained variance of the speeds N7ms−1 significantly reduced the length of the oil slicks to simple linear regression model. b10 km even under strong surface currents of up to 0.5 m s−1. This is An archive of SAR images made it possible to map the oil slicks from consistent with previous modeling results showing that oil will tend natural hydrocarbon seeps in the lease block GC600 and its surrounding to remain on the surface when the wind speed is about 6 m s−1. region. When the observed OSOs were overlain in a single plot (Fig. 1), When the wind speed exceeds 7 m s−1,thefrequencyofbreaking we confirmed a high density of surface oil slicks at GC600. waves increases and surface streaks begin to appear (Samuels et al., Results indicated an average oil surface residence-time of 6.4 h when 1982; Reed et al., 1994). These surface streaks disperse the oil into the the combined effects of wind and surface currents were considered. This water column and remove it from the surface of the ocean to the finding would nearly double the surface flux estimate of natural seeps point where it is no longer visible in SAR images. Oil slicks N10 km gen- which was previously estimated assuming the lifespan of an oil slick erally occurred when the wind speed was b7ms−1. Therefore, under to be 8–24 h (National Research Council Committee on Oil in the Sea, low wind conditions surface currents were the dominant factor in the 2003; Zatyagalova et al., 2007; MacDonald et al., 2015). Based on our re- elongation of the oil slicks from the natural seeps. These conditions sults, periods of low wind generated long oil slicks (N10 km) on the sur- might not apply in the event of a much larger release of oil because face of the ocean which indicates high surface flux of natural seeps. emulsification occurs during such releases and emulsified oil will These long oil slicks had an average surface residence-time of 14.4 h. behave differently from very thin natural oil slicks. This would introduce a bias in calculations of basin-wide flux that in- The unexplained variance in the simple linear regression model clude images of very large oil slicks. Therefore, an accurate estimate of might be due to the following: 1) HYCOM surface currents of our the surface flux from natural seeps would ideally utilize SAR images of study area and period did not match well with real ocean currents; 2) oil slicks under similar wind conditions. although, SailDrone data confirmed changes in wind direction particu- Based on our observations on 12 October 2015 the SailDrone proved larly for low winds on the order of a 10 min time scale, CCMP wind to be an effective tool not only for measuring surface winds but also for used in this study did not include small variations of the wind speed detecting thin, narrow oil slicks under open ocean conditions. We and sudden changes of the wind direction within its spatial and tempo- analyzed SailDrone data over the GC574 site and incorporated HYCOM ral resolution. Several chemical, physical, and biological processes can surface currents and SailDrone 10-m wind data in the surface oil drift affect the movement of oil once it reaches the sea surface; these include model to simulate the trajectory of the oil slicks from natural seeps in advection, spreading, evaporation, dissolution, emulsification, turbulent the region. Under average low wind speed of 2.1 m s−1 and strong diffusion, dispersion, auto-oxidation, biodegradation, and sinking/ surface currents, the oil slicks (average length, 19 km) had an average 104 S. Daneshgar Asl et al. / Remote Sensing of Environment 189 (2017) 96–107

Fig. 8. (A) Path of the SailDrone collected on 12 October 2015 (blue arrows). Background is a 30 m spatial resolution color composite of bands 4, 3, and 2 of the Landsat 8 satellite image collected at 16:38:49 UTC on 12 October 2015. The SailDrone location at the time the satellite image was collected is highlighted with a star, (B) locations of the SailDrone (orange dots) from the time the satellite image was captured until 16:42:06 UTC where it was approximately 170 m away situated in the visible oil slicks in Landsat 8 satellite image. Datum is WGS1984, and (C) fluorometer data showing surface hydrocarbon peaks. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) surface residence-time of 46 h. The idea that very large oil slicks always from the GoM does not indicate large variability in oil discharge rates indicate an increase in rate of oil discharge from natural hydrocarbon over multi-day intervals (Johansen et al., 2016). seeps (MacDonald et al., 2000) was not supported with our results be- Byestimatingthelifespanofoilattheseasurfacewecanestimatea cause we found that long periods of low wind and strong surface cur- flux based on the amount of oil present. Previous studies of the Gulf- rents will yield a great proliferation of persistent oil slicks that can wide seepage rates multiplied the volume of oil slicks by the presumed stay on the surface of water for several days. However, direct long- survival time (8–24 h) for natural oil slicks to derive an approximate esti- term observations of oil and gas venting at the seafloor have only re- mate of daily flux (MacDonald et al., 2002). The survival time for natural cently become possible (Römer et al., 2014). Preliminary evidence oil slicks can be quite variable since they continuously change their shape S. Daneshgar Asl et al. / Remote Sensing of Environment 189 (2017) 96–107 105

Fig. 9. The overlap between the simulated trajectory and the SailDrone path at surface hydrocarbon peaks. Datum is WGS1984. and direction of drift over time-scales b24 h (MacDonald et al., 2013). thickness) of the output to the oil slicks and their corresponding surface For the GC600 seep zone, we estimated an average daily ﬂux of residence-time. This shows a good agreement with the Johansen et al. 2.7 m3 d−1 (stdev 1.8) based on the volume (assuming 0.1 μm layer (2016) study that calculated the ﬂux of oil and gas bubbles released

Fig. 10. Aerial photograph of oil slicks at GC574 on 14 October 2015. 106 S. Daneshgar Asl et al. / Remote Sensing of Environment 189 (2017) 96–107 from individual vents on the seafloor using autonomous video time lapse Espedal, H.A., Wahl, T., 1999. Satellite SAR oil spill detection using wind history information.Int.J.RemoteSens.20,49–65. cameras. Based on their results, the GC600 seep zone consisted of a clus- Gade, M., Alpers, W., Hühnerfuss, H., Wismann, V.R., Lange, P.A., 1998. On the reduction of ter of 10 individual vents within a 1-km radius and they measured a daily the radar backscatter by oceanic surface films: scatterometer measurements and release rate of 0.3–3.6 m3 d−1 (Johansen et al., 2016). Their measured their theoretical interpretation. Remote Sens. Environ. 66, 52–70. fl Garcıa-Mart́ ınez,́ R., Flores-Tovar, H., 1999. Computer modeling of oil spill trajectories ux range is based on oily and gaseous bubbles; however, in this study with a high accuracy method. Spill Sci. Technol. Bull. 5, 323–330. the oil slicks were only comprised of the oil bubbles that make it to the Garcia-Pineda, O., Zimmer, B., Howard, M., Pichel, W., Li, X., MacDonald, I.R., 2009. Using sea surface. SAR images to delineate ocean oil slicks with a texture-classifying neural network al- – A relatively simple oil advection model, when combined with obser- gorithm (TCNNA). Can. J. Remote. Sens. 35, 411 421. Garcia-Pineda, O., MacDonald, I., Zimmer, B., Shedd, B., Roberts, H., 2010. Remote-sensing vational validation obtained from a natural oil seep, confirmed wind evaluation of geophysical anomaly sites in the outer continental slope, northern Gulf effect parametrizations known from previous work. The results show of Mexico. Deep-Sea Res. II Top. Stud. Oceanogr. 57, 1859–1869. that dispersion of thin oil slicks is largely wind-driven. Without this Garcia-Pineda, O., MacDonald, I., Silva, M., Shedd, W., Daneshgar Asl, S., Schumaker, B., 2015. Transience and persistence of natural hydrocarbon seepage in Mississippi wind effect, surface residence-times for thin oil layers can extend for Canyon, Gulf of Mexico. Deep-Sea Res. II Top. Stud. Oceanogr. 129, 119–129. days not hours. Output from natural seeps appears to be relatively con- Huang, J.C., 1983. A review of the state-of-the-art of oil spill fate/behavior models. Int. Oil stant; there is no evidence for secular variations in the oil slicks observed Spill Conf. Proc. 1983, 313–322. Johansen, C., Marty, E., Natter, M., Silva, M., Hill, J., Viso, R., Lobodin, V.V., Diercks, A.R., over GC600. Improved understanding of chemical and environmental Woolsey, M., Woolsey, A., Macelloni, L., Maskimova, E.V., Shedd, W., Joye, S., factors that affect surface oil residence-time could be investigated by Abrams, M., MacDonald, I.R., 2016. Constraining the fate of hydrocarbons (poster observations at natural seeps in future research. presentation). Ocean Science Meeting New Orleans, LA. 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