OS4.7

EDI
Marine pollution – detection, characterization and monitoring using oceanographic modelling and remote sensing

Marine pollution, such as natural and anthropogenic oil slicks, are of great concern. Surface oil slicks become a great hazard if they reach coastal or sea ice infested areas. Lower concentration levels of submerged substances, e.g., from produced water releases, can have a harmful effect on marine organisms in the long term. Operational ocean surveillance relies heavily on remote sensing data for detection, and ocean circulation models are commonly used to forecast and evaluate drift patterns and concentration changes. Localization, monitoring and slick redistribution information from remote sensing techniques is essential for a fast response and effective clean-up operation. Mapping of submerged substances relies more on ocean circulation modeling and in-situ measurements. Surveillance is still manual-labor intensive, though increased availability of free remote sensing images through, e.g. the Sentinel satellites, has opened up possibilities for developments of automated and semi-automated techniques for oil slick detection, characterization and tracking.
Within this session we welcome contributions covering all aspects of circulation and drift modelling for marine pollution as well as further developments on ocean surveillance using a range of satellites, including but not limited to synthetic aperture radar and optical sensors. Submissions with a focus on observation-model synthesis and interdisciplinary studies are particularly encouraged.

Convener: Malin JohanssonECSECS | Co-conveners: Göran Broström, Oscar Garcia, Cathleen Jones, Johannes Röhrs
vPICO presentations
| Thu, 29 Apr, 15:30–17:00 (CEST)

vPICO presentations: Thu, 29 Apr

15:30–15:35
15:35–15:45
|
EGU21-3737
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ECS
|
solicited
Jona Raphael, Ben Eggleston, Ryan Covington, Tatianna Evanisko, Sasha Bylsma, and John Amos

Operational oil discharges from ships, also known as “bilge dumping,” have been identified as a major source of petroleum products entering our oceans, cumulatively exceeding the largest oil spills, such as the Exxon Valdez and Deepwater Horizon spills, even when considered over short time spans. However, we still don’t have a good estimate of

  • How much oil is being discharged;
  • Where the discharge is happening;
  • Who the responsible vessels are.

This makes it difficult to prevent and effectively respond to oil pollution that can damage our marine and coastal environments and economies that depend on them.

 

In this presentation we will share SkyTruth’s recent work to address these gaps using machine learning tools to detect oil pollution events and identify the responsible vessels when possible. We use a convolutional neural network (CNN) in a ResNet-34 architecture to perform pixel segmentation on all incoming Sentinel-1 synthetic aperture radar (SAR) imagery to classify slicks. Despite the satellites’ incomplete oceanic coverage, we have been detecting an average of 135 vessel slicks per month, and have identified several geographic hotspots where oily discharges are occurring regularly. For the images that capture a vessel in the act of discharging oil, we rely on an Automatic Identification System (AIS) database to extract details about the ships, including vessel type and flag state. We will share our experience

  • Making sufficient training data from inherently sparse satellite image datasets;
  • Building a computer vision model using PyTorch and fastai;
  • Fully automating the process in the Amazon Web Services (AWS) cloud.

The application has been running continuously since August 2020, has processed over 380,000 Sentinel-1 images, and has populated a database with more than 1100 high-confidence slicks from vessels. We will be discussing preliminary results from this dataset and remaining challenges to be overcome.

 

Our objective in making this information and the underlying code, models, and training data freely available to the public and governments around the world is to enable public pressure campaigns to improve the prevention of and response to pollution events. Learn more at https://skytruth.org/bilge-dumping/

How to cite: Raphael, J., Eggleston, B., Covington, R., Evanisko, T., Bylsma, S., and Amos, J.: Applying machine learning to satellite imagery and vessel-tracking data to detect chronic oil pollution from ships at sea and identify the polluters, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3737, https://doi.org/10.5194/egusphere-egu21-3737, 2021.

15:45–15:47
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EGU21-5462
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ECS
Cristina Vrinceanu, Stephen Grebby, and Stuart Marsh

Marine pollution has been traditionally addressed in Earth Observation studies through the use of Synthetic Aperture Radar (SAR) imagery. In operational processes, the contrast between the dark oil surfaces, characterized by a low backscatter return, and the rough, bright sea surface with higher backscatter has been exploited for decades.

Many of the processing techniques involve the use of semi-automatic workflows. Dark spot segmentation and feature classification are, undisputedly, common computational tasks. However, effective discrimination between oil slicks and other ocean phenomena (e.g. biogenic slicks, wind streaks, greasy ice) remains a challenge. To complete this task, a trained human operator is often employed in the final validation step. Thus, the process is time and resource consuming over large expanses, while the results are highly subjective. Automating this process and reducing computation and analysis time is the ultimate goal.

New algorithms based on the use of artificial intelligence for oil slick detection have recently emerged. While there are studies proving their effectiveness in successfully segmenting and classifying oil slicks, questions regarding their operational feasibility remain. Do they improve the quality of the detection? Are they more capable of discriminating between the various dark formations? What are the computational and data resources required for training, validation, and deployment of such an algorithm?

This project focusses on the development of a new customized algorithm for natural oil slicks detection. As part of this development, we analyzed the state-of-the-art methods and performed a comparison of the latest deep learning methods and classic semi-automatic techniques. Here, we present an in-depth analysis of selected segmentation and convolutional neural networks algorithms and various frameworks. The primary objective is to evaluate their effectiveness and expose their deficiencies for the detection and classification of natural oil slicks against anthropogenic pollution and other dark formation caused by ‘look-alikes’.

This presentation centers on the results that have been obtained by utilizing high-resolution open SAR data acquired by the Copernicus Sentinel-1 satellites. The evaluation is based on study sites located in the Black Sea, where two known oil seepage areas are actively generating consistent productive slicks as well as underdeveloped oil traces. 

How to cite: Vrinceanu, C., Grebby, S., and Marsh, S.: Is Deep Learning more effective in detecting natural oil slicks? A comparison of semi-automated and AI techniques, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5462, https://doi.org/10.5194/egusphere-egu21-5462, 2021.

15:47–15:49
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EGU21-978
Merv Fingas

Abstract: The thickness of oil spills on the sea is an important but poorly studied topic. Means to measure slick thickness are reviewed. More than 30 concepts are summarized. Many of these are judged not to be viable for a variety of scientific reasons. Two means are currently available to remotely measure oil thickness, namely, passive microwave radiometry and time of acoustic travel. Microwave radiometry is commercially developed at this time. Visual means to ascertain oil thickness are restricted by physics to thicknesses smaller than those of rainbow sheens (~3 µm), which rarely occur on large spills, and thin sheen. One can observe that some slicks are not sheen and are probably thicker. These three thickness regimes are not useful to oil spill countermeasures, as most of the oil is contained in the thick portion of a slick, the thickness of which is unknown and ranges over several orders of magnitude. There is a continuing need to measure the thickness of oil spills. This need continues to increase with time, and further research effort is needed. Several viable concepts have been developed but require further work and verification. One of the difficulties is that ground truthing and verification methods are generally not available for most thickness measurement methods.

How to cite: Fingas, M.: Remotely Measuring Oil Slick Thickness: An Epic Challenge, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-978, https://doi.org/10.5194/egusphere-egu21-978, 2021.

15:49–15:51
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EGU21-9209
Sermsak Jaruwatanadilok, Xueyang Duan, Benjamin Holt, and Cathleen Jones

A layer of oil on the sea surface reduces the components of ocean wave spectra corresponding to the capillary and gravity-capillary waves leading to significantly reduced radar backscatter and making slicks appear dark in synthetic aperture radar (SAR) images.  The ratio of the backscatter between clean and slicked ocean surfaces is known as the ‘damping ratio,’ and has been shown to be sensitive to variations within slicks that correlate with the oil layer’s thickness and fractional water volume.  Although the relative thickness relationship is well established and can be used to identify areas of ‘thicker’ oil within a slick, quantifying the thickness from SAR alone remains to be shown.  There is considerable uncertainty regarding the potential capability of SAR to quantitatively determine the oil layer thickness for slicks in open water given the dependence on bulk and interfacial oil layer properties and the variation of the properties typical in this setting and for different types of oil.  Here, we report the results of a study modeling radar backscatter from slicked and unslicked ocean surfaces based on electromagnetic scattering theory and accounting for oil properties and meteorological conditions.  The electromagnetic scattering model is used to evaluate whether the oil thickness can be quantified with reasonable accuracy based upon SAR backscatter intensities alone, and how information about metocean conditions, oil properties and ocean temperature and salinity can be used to calibrate the model to obtain more accurate thickness estimates.

The research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration.

How to cite: Jaruwatanadilok, S., Duan, X., Holt, B., and Jones, C.: Realistic Electromagnetic Modeling of SAR’s Capability for Oil Spill Thickness Measurement, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9209, https://doi.org/10.5194/egusphere-egu21-9209, 2021.

15:51–15:53
|
EGU21-1858
Benjamin Holt, Frank Monaldo, Cathleen Jones, and Oscar Garcia

We describe an effort to develop a quantifiable approach for determining the thicker components of oil spills using microwave synthetic aperture radar (SAR) imagery that can be utilized in an operational context to guide clean-up efforts. The presence of mineral oil on the surface can suppress the SAR returns in two ways. First, surface oil dampens the capillary waves making those areas darker in SAR imagery, an effect that been used to determine oil extent. The second is by modifying the dielectric properties of the surface from those of clean seawater to either pure oil or a mixture of oil and water as the oil weathers and thickens to form an emulsion. The emulsion provides an intermediate conductive surface layer between the highly conductive ocean itself and the very low, ‘radar transparent’ sheen layers, resulting in a further reduction in the radar returns for areas with thicker oil within an inhomogeneous oil slick. The challenges are to quantify the thickness and conditions for which this thicker layer becomes separable from the thinner oil, determine whether multiple thicker components can be identified, identify which airborne and spaceborne SAR systems can be used for this purpose, and determine under what range of environmental conditions, particularly wind speed, it is possible.

 

We are planning to hold field campaigns with in situ measurements and SAR and multispectral remote sensor data collections from drones, aircraft, and satellites. The field measurements include surface collections of oil, underwater spectrophotometry, and drone-based infrared, ultraviolet, and optical collections.  Coincident with the field measurements, the airborne L-band NASA-UAVSAR SAR system will image the seep fields to track temporal changes and overpassing satellite acquisitions will be acquired. UAVSAR provides fine resolution, low noise radar imagery under all weather and solar conditions and is fully polarimetric, which enables evaluation of multiple methods to characterize the oil slick. The system noise floor of this instrument, considerably less than all satellite SAR instruments, enables a detailed examination of the zones of reduced backscatter caused by varying oil thickness levels. The primary satellite SAR will be C-band Sentinel-1, accompanied potentially by C-band Radarsat-2 and L-band ALOS-2. Both the UAVSAR and satellite SAR analysis will utilize the contrast ratio, defined as the normalized radar cross section (NRCS) in open water divided by the NRCS in oil-covered water. The larger the ratio, the thicker the oil. The operational algorithm for oil thickness is under development using satellite SAR data and will be staged in NOAA’s SAR Ocean Product System (SAROPS) that currently produces SAR-derived wind speed and oil spill extent operationally, with the latter using the Texture-Classifying Neural Network (TCNNA) to automatically delineate oil versus non-oil covered areas. We are planning field campaigns at the natural oil seep area offshore of Santa Barbara, California, in March 2021 and during the 2022 Norwegian Clean Sea Association for Operating Companies’ (NOFO’s) coordinated releases of oil in the North Sea. Recent field collections and analysis will be shown, as available.

How to cite: Holt, B., Monaldo, F., Jones, C., and Garcia, O.: Transitioning SAR-derived Oil Spill Thickness Measurements into an Operational Context, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1858, https://doi.org/10.5194/egusphere-egu21-1858, 2021.

15:53–15:55
|
EGU21-2761
Oscar Garcia-Pineda, Frank Monaldo, George Graettinger, Ellen Ramirez, Lisa DiPinto, Cathleen Jones, Benjamin Holt, and Gordon Staples

The offshore natural oil seeps along the California coast near Santa Barbara are a natural testing site for the calibration of remote sensing systems aimed at the detection of oil spills. The main difference between these seeps and other permanent sources of floating oil (natural and unnatural seeps in the Gulf of Mexico) is the petroleum composition. Moreover, while it has been documented that most natural seeps worldwide change their rate of oil discharge over time, the Santa Barbara seeps have maintained a high rate, frequently forming thick layers of floating oil in recent years. This allowed us to perform multiple experiments developing floating oil layer thickness measurement techniques from sea-level instruments. These measurements were then used in validation of airborne and satellite remote sensors.

At the Santa Barbara seeps, we have tested our previously developed method of measuring oil thickness with a crystal tube sampling mechanism that extracts an undisturbed floating oil profile at the sea surface. Samples are then post-processed to quantify the volume of oil captured. Our newer system consists of a submerged spectrophotometer that measures the ultraviolet (UV) and infrared (IR) light attenuation of the floating oil from a fixed UV-IR light source above the water. Both methods have been used for cross validation. The sampling tube is more accurate and precise for thicknesses below 50 um (from silver-rainbow sheens to metallic). Both systems work consistently on thicknesses ranging from >50 um to 350um (the latter was the thickest sample of oil measured at the seep sites). However, the advantage of the submerged spectrophotometer is the real time interpretation of the data. The maximum thickness measured in the laboratory for the submerged spectrophotometer was 2.5mm, while the maximum thickness measured from the sampling tube was 7cm of oil.

These thickness measuring instruments have been used to validate thermal and multispectral sensors mounted on an Unmanned Aerial System (UAS). By overlaying the thickness measurements collected in the field with synchronous data collected from the UAS sensors we can relate the thermal reflective radiation and multispectral signatures from different oil thicknesses. Maps with oil thickness classifications generated from the UAS data are then used to correlate with quasi-synchronous high resolution satellite images obtained by WorldView2-3, Planet, ALOS-2, and RADARSAT-2, all of which are hosted and viewable on the NOAA-Environmental Response Management Application (ERMA).  Further field expeditions scheduled for 2021 will include the UAVSAR sensor, an L-band airborne synthetic aperture radar operated by the NASA Airborne Science Program. This NASA microwave sensor operates at the same frequency as one of the sensors on the upcoming NASA-ISRO SAR (NISAR) mission scheduled to launch in 2022 and data acquired will be used to both improve thickness algorithm development and simulate the expected performance of the NISAR instrument for oil slick detection and characterization. We will prepare these methods to move to operational use as this new resource comes online adding a significant response asset to oil spill characterization and response.

How to cite: Garcia-Pineda, O., Monaldo, F., Graettinger, G., Ramirez, E., DiPinto, L., Jones, C., Holt, B., and Staples, G.: Measurement of floating oil layer thicknesses at the Santa Barbara seeps in California to support interpretation of satellite imagery, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2761, https://doi.org/10.5194/egusphere-egu21-2761, 2021.

15:55–15:57
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EGU21-14530
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ECS
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Highlight
Noelia Abascal Zorrilla, Harry Cook, James Delaney, Matthew Faith, Anne Vallette, and François-Régis Martin-Lauzer

Plastic pollution is widely recognised to be an emerging ecological disaster (Eriksen et al., 2014). While a steady increase in the amount of marine litter is being observed, plastics constitute some 60 to 80% of the total waste (Miladinova et al., 2020), which drift and settle through sinking and beaching. The Black Sea, a semi-enclosed basin with numerous litter inflows by huge watershed rivers, and with only one spillway at the Bosporus, is an ideal test area for the development of litter detection and tracking technologies. Although the occurrence of marine litter in the Black Sea is poorly known, with lack of data in the abundance of floating debris (Miladinova et al., 2020), remote sensing from space (RSS) is considered a promising tool for the observation of floating marine plastics because of its wide observation cover. However, success was only obtained i.in areas with huge accumulations of litter (canals, harbours and estuaries, e.g. rows of litter in the sea after flooding), and ii.with applying “Ocean Colour” RSS methods designed for the assessment of concentration of phytoplankton or other particulates, which are far-off fitting the needs of detecting and tracking scattered macro-litter patches or rows, though they could apply to micro-plastics.

Within the conventional framework of DCRIT (detection-classification-recognition-identification-tracking and targeting) and based on the classic methodologies derived from Multidimensional Signal Detection Theory (MSDT), we are currently developing a scheme to address the issue of recognising faint signatures of marine litter in Earth Observation (EO) data sets. Most of the RSS studies are focused on the detection of plastic using (a) its spectral signature over water through applying indices such as Normalized Difference Vegetation Index (NDVI) or Floating Debris Index (FDI) owing to the issue of EO pixel size greater than litter accumulation width, with (b) universal thresholds. In our case, we adjust the detection thresholds to the ‘a-priori’ information on litter presence, provided by a model, to the environmental andthe RSS observation conditions, balancing the probability of detection and false alarms using a Bayesian approach.The ‘detector’ is the heir of the binary classification algorithm developed by ARGANS Ltd on a grant by European Space Agency (ESA), which is abinary detector followed by a multi-label classification using a deterministic decision tree to distinguish natural from anthropogenic debris. The ‘a priori’ information is provided by a marine litter model deployed in the Black Sea, locating the main litter accumulation areas. Then, the posterior probability of the uncertain classification of pixels as plastic is the conditional probability that it is assigned considering the observation conditions and the plastics’ presence information coming from the model. To assess the confidence of detection, the Bayes theorem is combined with Receiver Operating Characteristic (ROC) curves. The latter ones can be used to assign higher probabilities to observations with a positive classification and lower probabilities to observations that do not. A further analysis combining both tools allows to improve the thresholds selection to classify pixels as plastic as a function of the background information.

How to cite: Abascal Zorrilla, N., Cook, H., Delaney, J., Faith, M., Vallette, A., and Martin-Lauzer, F.-R.: Plastic waste’s fate in the Black Sea: monitoring litter input and dispersal in the marine environment, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14530, https://doi.org/10.5194/egusphere-egu21-14530, 2021.

15:57–15:59
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EGU21-13528
Ana M. Mancho, Guillermo García-Sánchez, Antonio G. Ramos, Josep Coca, Begoña Pérez-Gómez, Enrique Álvarez-Fanjul, Marcos G. Sotillo, Manuel García-León, Víctor J. García-Garrido, and Stephen Wiggins

This presentation discusses a downstream application from Copernicus Services, developed in the framework of the IMPRESSIVE project, for the monitoring of  the oil spill produced after the crash of the ferry “Volcan de Tamasite” in waters of the Canary Islands on the 21st of April 2017. The presentation summarizes the findings of [1] that describe a complete monitoring of the diesel fuel spill, well-documented by port authorities. Complementary information supplied by different sources enhances the description of the event. We discuss the performance of very high resolution hydrodynamic models in the area of the Port of Gran Canaria and their ability for describing the evolution of this event. Dynamical systems ideas support the comparison of different models performance. Very high resolution remote sensing products and in situ observation validate the description.

Authors acknowledge support from IMPRESSIVE a project funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 821922. SW acknowledges the support of ONR Grant No. N00014-01-1-0769

References

[1] G.García-Sánchez, A. M. Mancho, A. G. Ramos, J. Coca, B. Pérez-Gómez, E. Álvarez-Fanjul, M. G. Sotillo, M. García-León, V. J. García-Garrido, S. Wiggins. Very High Resolution Tools for the Monitoring and Assessment of Environmental Hazards in Coastal Areas.  Front. Mar. Sci. (2021) doi: 10.3389/fmars.2020.605804.

How to cite: Mancho, A. M., García-Sánchez, G., Ramos, A. G., Coca, J., Pérez-Gómez, B., Álvarez-Fanjul, E., Sotillo, M. G., García-León, M., García-Garrido, V. J., and Wiggins, S.: Monitoring and assessment of an oil spill event with very high resolution tools. , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13528, https://doi.org/10.5194/egusphere-egu21-13528, 2021.

15:59–16:01
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EGU21-6543
Camilla Brekke, Martine Espeseth, Knut-Frode Dagestad, Johannes Röhrs, Lars Hole, and Andreas Reigber

Integrated analysis of remote sensing and numerical oil drift simulations for improved oil spill preparedness capabilities

Camilla Brekke1, Martine M. Espeseth1, Knut-Frode Dagestad2, Johannes Röhrs2, Lars Robert Hole2, and Andreas Reigber3

 

1UiT The Arctic University of Norway, Tromsø, Norway

2The Norwegian Meteorological Institute, Oslo, Norway

3DLR, Microwaves and Radar Institute, Oberpfaffenhofen-Weßling, Germany

 

We present results from a successfully conducted free-floating oil spill field experiment followed by an integrated analysis of remotely sensed data and drift simulations. The experiment took place in the North Sea in the summer of 2019 during Norwegian Clean Seas Association for Operating Companies’ annual oil-on-water exercise. Two types of oils were applied: a mineral oil emulsion and a soybean oil emulsion. The dataset collected contains a collection of close-in-time radar (aircraft and space-borne) and optical data (aircraft, aerostat, and drone) acquisitions of the slicks. We compare oil drift simulations, applying various configurations of wind, wave, and current information, with observed slick positions and shape. We describe trajectories and dynamics of the spills, slick extent, and their evolution, and the differences in detection capabilities in optical instruments versus multifrequency quad-polarimetric synthetic aperture radar (SAR) imagery acquired by DLRs large-scale airborne SAR facility (F-SAR). When using the best available forcing from in situ data and forecast models, good agreement with the observed position and extent are found in this study. The appearance in the optical images and the SAR time series from F-SAR were found to be different between the soybean and mineral oil types. Differences in mineral oil detection capabilities are found between SAR and optical imagery of thinner sheen regions. From a drifting perspective, the biological oil emulsions could replace the viscous similar mineral oil emulsion in future oil spill preparedness campaigns. However, from a remote sensing and wildlife perspective, the two oils have different properties. Depending on the practical application, further investigation on how the soybean oil impact the seabirds must be conducted in order to recommend the soybean oil as a viable substitute for mineral oil.

 

This study is published as open access in Journalof Geophysical Research: Oceans[1], and we encourage the audience to read this article for detailed acquaintance with the work.

 

Reference:

[1]Brekke, C., Espeseth, M. M., Dagestad, K.-F., Röhrs, J., Hole, L. R., & Reigber,A. (2021). Integrated analysis of multisensor datasets and oil driftsimulations—a free-floating oil experiment in the open ocean. Journalof Geophysical Research: Oceans, 126, e2020JC016499. https://doi.org/10.1029/2020JC016499

How to cite: Brekke, C., Espeseth, M., Dagestad, K.-F., Röhrs, J., Hole, L., and Reigber, A.: Integrated analysis of remote sensing and numerical oil drift simulations for improved oil spill preparedness capabilities, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6543, https://doi.org/10.5194/egusphere-egu21-6543, 2021.

16:01–16:03
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EGU21-853
Laura Gomez-Navarro, Erik van Sebille, Aurelie Albert, Jean-Marc Molines, Laurent Brodeau, Julien Le Sommer, and Clement Ubelmann

Understanding the pathways of floating material (e.g. larvae, plastics, oil) at the surface ocean is important to improve our knowledge on the surface circulation and for its ecological and environmental impacts.  For example, knowing where floating plastic and oil spills accumulate in the surface ocean can help ocean clean-up strategies.  One of the main methods of research is virtual particle simulations, which simulate the dispersion of floating material in the Ocean.  

 

Previous studies have tried to understand the surface dispersion and accumulation via these numerical simulations. To define the circulation, the velocity outputs of ocean general circulation models are needed. Oceanic models have improved in the past years, but many still do not fully represent the ocean dynamics at the fine-scales (below 100 km).  The spatial resolution of ocean models and whether they include a tidal-forcing are two important model parameterizations that can determine how well the ocean dynamics are represented at the fine-scales. In this study we try to answer: How do these model characteristics affect the dispersion and accumulation of virtual particles at the ocean surface?

 

To answer this, we use the ocean surface velocity outputs of different NEMO simulations to simulate the trajectories of virtual particles, and we evaluate the impact of different NEMO simulations’ spatial resolution and the presence or not of a tidal-forcing. As tidal-forcing has a big impact on the ocean model’s representation of internal tides and waves, we focus on a region where there is a high internal-tide signal: the Azores Islands.  We evaluate these impacts by looking at whether there is a difference in particles’ accumulation and dispersion in the different model scenarios.

How to cite: Gomez-Navarro, L., van Sebille, E., Albert, A., Molines, J.-M., Brodeau, L., Le Sommer, J., and Ubelmann, C.: The effect of ocean model tidal-forcing and spatial resolution on virtual particle dispersion and accumulation at the ocean surface, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-853, https://doi.org/10.5194/egusphere-egu21-853, 2021.

16:03–16:05
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EGU21-4662
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Highlight
Svitlana Liubartseva, Ivan Federico, Giovanni Coppini, and Rita Lecci

Being situated in a semi-enclosed Mediterranean lagoon, the Port of Taranto represents a transport, industrial and commercial hub, where the port infrastructure, a notorious steel plant, oil refinery and naval shipyards coexist with highly-dense urban zone, recreation facilities, mussel farms, and vulnerable environmental sites. A Single Buoy Mooring in the center of the Mar Grande used by tankers and subsea pipeline that takes oil directly from tanker to refinery are assumed to stay at risk of accidental oil spills, despite significant progress in technology and prevention.

The oil spill model MEDSLIK-II (http://medslik-ii.org) coupled to the high resolution Southern Adriatic Northern Ionian coastal Forecasting System (SANIFS http://sanifs.cmcc.it Federico et al., 2017) is used to model hypothetical oil spill scenarios in stochastic mode. 15,000+ hypothetical individual spills are generated from randomly selected start locations: 50% from a buoy and 50% along the subsea pipeline 2018–2020. Individual spill scenario is based on a real crude oil spill caused by a catastrophic pipeline failure happened in Genoa in April 2016 (Vairo et al., 2017). The model outputs are processed statistically to represent quantitively: (1) timing of the oil drift; (2) hazard maps in probability terms at the sea surface and on the coastline; (3) oil mass balance; (4) local-zone contamination assessment.

The simulations reveal that around 48% of the spilled oil will evaporate during the first 8 hours after the accident. Being transported by highly variable currents and waves, the rest is additionally exposed to multiply reflections from sea walls and concrete wharfs that dominate in the study area. As a result, the oil will be dispersed almost isotropically in the Mar Grande, indicating a rather moderate or small level of concentrations over the minimum threshold values (French McCay, 2016).

We have concluded that at a probability of 50%, the first oil beaching event will happen within 14 hours after the accident. The most contaminated areas are predicted on and around the nearest Port berths, on the coastlines of the urban area and on the tips of the breakwaters that frame the Mar Grande openings. The remote areas of the West Port and Mar Piccolo are expected to be the least contaminated ones.

Results are applicable to contingency planning, ecological risk assessment, cost-benefit analysis, and education.

This work is conducted in the framework of the IMPRESSIVE project (#821922) co-funded by the European Commission under the H2020 Programme.

References

Federico, I., Pinardi, N., Coppini, G., Oddo, P., Lecci, R., Mossa, M., 2017. Coastal ocean forecasting with an unstructured grid model in the southern Adriatic and northern Ionian seas. Nat. Hazards Earth Syst. Sci., 17, 45–59, doi: 10.5194/nhess-17-45-2017.

French McCay, D., 2016. Potential effects thresholds for oil spill risk assessments. Proc. of the 39 AMOP Tech. Sem., Environment and Climate Change Canada, Ottawa, ON, 285–303.

Vairo, T., Magrì, S., Qualgliati, M., Reverberi, A.P., Fabiano, B., 2017. An oil pipeline catastrophic failure: accident scenario modelling and emergency response development. Chem. Eng. Trans., 57, 373–378, doi: 10.3303/CET1757063.

How to cite: Liubartseva, S., Federico, I., Coppini, G., and Lecci, R.: Oil spill risk assessment for a Single Buoy Mooring terminal in the Port of Taranto (Southern Italy), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4662, https://doi.org/10.5194/egusphere-egu21-4662, 2021.

16:05–16:07
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EGU21-9618
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ECS
Aigars Valainis and Uldis Bethers

Our goal was to investigate the performance of the in-house mathematical drift model using the oil spills according to the Marine Search and Rescue Service (MRCC) Riga data for the Eastern part of Baltic Sea 

There have been 15 cases in the Latvian territorial waters in 2016 when satellite imagery has identified potential marine pollution on the sea surface. Two additional reports of potential marine pollution have been received from ships. Satellite imagery from CleanSeaNet has identified 16 possible cases in the Latvian territorial waters in 2017, and further 19 possible cases in 2018.

We consider the following three cases of possible oil (and other pollutants) spills:

1) Possible oil spill in 2016.01.26 north of the harbor of Ventspils in Irbe strait.

2) Possible oil spill from ballast waters in 2017.05.14.

3) Possible pollutant (vegetable oil) spill in 2018.07.25.

Investigation of MRCC Riga sea pollution cases has revealed the following constraints and requirements for the FiMar oil drift model. First, the detected pollutant slicks are of size above 5 km2 and already of complex structure. This follows from the CleanSeaNet service detection capabilities and spill occurrence pattern; for example, dumping of ballast waters happens during the night, with spill being detected with the sunrise. Second, the pollutant slicks have a short lifespan and unknown chemical composition. Therefore, future development should focus on backtracking from a large target to a single most probable location in time-space.

The condition of nearly divergence-free flow is usually met in large-scale flows, but nonlinear changes in the properties of the oil are impossible to handle simply by reversing the direction of the wind field and the current field.

Method inspired by (Breivik, Bekkvik, Wettre and Ommundsen, 2011, BAKTRAK: Backtracking drifting objects using an iterative algorithm with a forward trajectory model.) was introduced into FiMar software and verified. This amends traditional reverse-time backtracking where a trajectory model is initialized and run in the forward direction, whereupon the individual ensemble particles that come within an acceptable time-space distance of the observation are used to initialize a new forward run. Unsuccessful particle trajectories thereafter are discarded. This procedure is then iterated until an acceptable number of trajectories end up within the target area (defined as a time-space radius around the location of the observation) is reached and time-space distribution of possible initial locations for the drifting object/ pollutant slick has been established.

The study was funded by Latvian Academy of Sciences, project lzp-2018/1-0162 DRIMO – Drift Modelling for pollution reduction and safety in the Baltic Sea, 2018-2021. 

How to cite: Valainis, A. and Bethers, U.: Backtracking oil slicks by using an iterative algorithm with a forward trajectory model. Implementation and verification., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9618, https://doi.org/10.5194/egusphere-egu21-9618, 2021.

16:07–16:09
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EGU21-16039
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ECS
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Andrés Martínez

A METHODOLOGY FOR OPTIMIZING MODELING CONFIGURATION IN THE NUMERICAL MODELING OF OIL CONCENTRATIONS IN UNDERWATER BLOWOUTS: A NORTH SEA CASE STUDY

Andrés Martíneza,*, Ana J. Abascala, Andrés Garcíaa, Beatriz Pérez-Díaza, Germán Aragóna, Raúl Medinaa

aIHCantabria - Instituto de Hidráulica Ambiental de la Universidad de Cantabria, Avda. Isabel Torres, 15, 39011 Santander, Spain

* Corresponding author: martinezga@unican.es

Underwater oil and gas blowouts are not easy to repair. It may take months before the well is finally capped, releasing large amounts of oil into the marine environment. In addition, persistent oils (crude oil, fuel oil, etc.) break up and dissipate slowly, so they often reach the shore before the cleanup is completed, affecting vasts extension of seas-oceans, just as posing a major threat to marine organisms.

On account of the above, numerical modeling of underwater blowouts demands great computing power. High-resolution, long-term data bases of wind-ocean currents are needed to be able to properly model the trajectory of the spill at both regional (open sea) and local level (coastline), just as to account for temporal variability. Moreover, a large number of particles, just as a high-resolution grid, are unavoidable in order to ensure accurate modeling of oil concentrations, of utmost importance in risk assessment, so that threshold concentrations can be established (threshold concentrations tell you what level of exposure to a compound could harm marine organisms).

In this study, an innovative methodology has been accomplished for the purpose of optimizing modeling configuration: number of particles and grid resolution, in the modeling of an underwater blowout, with a view to accurately represent oil concentrations, especially when threshold concentrations are considered. In doing so, statistical analyses (dimensionality reduction and clustering techniques), just as numerical modeling, have been applied.

It is composed of the following partial steps: (i) classification of i representative clusters of forcing patterns (based on PCA and K-means algorithms) from long-term wind-ocean current hindcast data bases, so that forcing variability in the study area is accounted for; (ii) definition of j modeling scenarios, based on key blowout parameters (oil type, flow rate, etc.) and modeling configuration (number of particles and grid resolution); (iii) Lagrangian trajectory modeling of the combination of the i clusters of forcing patterns and the j modeling scenarios; (iv) sensitivity analysis of the Lagrangian trajectory model output: oil concentrations,  to modeling configuration; (v) finally, as a result, the optimal modeling configuration, given a certain underwater blowout (its key parameters), is provided.

It has been applied to a hypothetical underwater blowout in the North Sea, one of the world’s most active seas in terms of offshore oil and gas exploration and production. A 5,000 cubic meter per day-flow rate oil spill, flowing from the well over a 15-day period, has been modeled (assuming a 31-day period of subsequent drift for a 46-day modeling). Moreover, threshold concentrations of 0.1, 0.25, 1 and 10 grams per square meter have been applied in the sensitivity analysis. The findings of this study stress the importance of modeling configuration in accurate modeling of oil concentrations, in particular if lower threshold concentrations are considered.

How to cite: Martínez, A.: A methodology for optimizing modeling configuration in the numerical modeling of oil concentrations in underwater blowouts: a North Sea case study, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16039, https://doi.org/10.5194/egusphere-egu21-16039, 2021.

16:09–16:11
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EGU21-11011
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ECS
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Highlight
Amanda T. Nylund, Rickard Bensow, Mattias Liefvendahl, Arash Eslamdoost, Anders Tengberg, Ulf Mallast, Ida-Maja Hassellöv, Göran Broström, and Lars Arneborg

This interdisciplinary study with implications for fate and transport of pollutants from shipping, investigates the previously overlooked phenomenon of ship induced mixing. When a ship moves through water, the hull and propeller induce a long-lasting turbulent wake. Natural waters are usually stratified, and the stratification influences both the vertical and horizontal extent of the wake. The altered turbulent regime in shipping lanes governs the distribution of discharged pollutants, e.g. PAHs, metals, nutrients and non-indigenous species. The ship related pollutant load follows the trend in volumes of maritime trade, which has almost tripled since the 1980s. In heavily trafficked areas there may be one ship passage every ten minutes; today shipping constitutes a significant source of pollution.

To understand the environmental impact of shipping related pollutants, it is essential to know their fate following regional scale transport. However, previous modelling efforts assuming discharge at the surface will not adequately reflect the input values in the regional models. Therefore, it is urgent to bridge the gaps between the spatiotemporal scales from high-resolution numerical modeling of the flow hydrodynamics around the ship, mixing processes and interaction of the ship and wake with stratification, and parameterization in regional oceanographic modeling. Here this knowledge gap is addressed by combining an array of methods; in situ measurements, remote sensing and numerical flow modeling.

A bottom-mounted Acoustic Doppler Current Profiler was placed under a ship lane, for in-situ measurements of the vertical and temporal expansion of turbulent wakes. In addition, ex-situ measurements with Landsat 8 Thermal Infrared Sensor were used to estimate the longevity and spatial extent of the thermal signal from ship wakes. The computational modelling was conducted using well resolved 3D RANS modelling for the hull and the near wake (up to five ship lengths aft), a method typically used for the near wake behaviour in analysing the propulsion system. As this is not feasible to use for a far wake analysis, the predicted wake is then used as input for a 2D+time modelling for the sustained wake up to 30min after the ship passage. These results, both from measurements and numerical models, are then combined to analyse how ship-induced turbulence influence at what depth discharged pollutants will be found.

This first step to cover the mesoscales of the turbulent ship wake is necessary to assess the impact of ship related pollution. In-situ measurements show median wake depth 13.5m (max 31.5m) and median longevity 10min (max 29min). Satellite data show median thermal wake signal 13.7km (max 62.5km). A detailed simulation model will only be possible to use for the first few 100m of the ship wake, but the coupling to a simplified 2D+time modelling shows a promising potential to bridge our understanding of the impact of the ship wake on the larger scales. Our model results indicate that the natural stratification affects the distribution and retention of pollutants in the wake region. The depth of discharge and the wake turbulence characteristics will in turn affect the fate and transport of pollutants on larger spatiotemporal scales.

How to cite: Nylund, A. T., Bensow, R., Liefvendahl, M., Eslamdoost, A., Tengberg, A., Mallast, U., Hassellöv, I.-M., Broström, G., and Arneborg, L.: The importance of the turbulent ship wake regime for pollutant fate and transport, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11011, https://doi.org/10.5194/egusphere-egu21-11011, 2021.

16:11–16:13
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EGU21-9520
Carsten Hansen

Near-surface Stokes drift in operational wave forecast

We apply an operational model for the mean drift of small particles near and at the surface. The drift is a sum of the Stokes drift and the current. The Stokes drift is derived from a wave model and the current is calculated by means of a 3D hydrodynamic model.

In an operational wave model setup (based on WAVEWATCH-III version 7) a parametric tail is supplied as an extension to the prognostic wave spectrum. The parametrisation is based on the modelled spectra level and the first circular moment (the spectral spread) near the highest prognostic frequency, which is typically around 0.5 Hz in operational wave modelling.

New source terms formulations has been introcuced in wave modelling (e.g. WAVEWATCH-III with effect from 2019) that reproduces the spreading and spectral level well compared to many independent observations.

The Stokes drift is calculated at a discrete number of depths down to Z = 2/Ks, where Ks is a wave number scale estimated from the prognostic spectrum. The calculation is integrated in the wave model output.

The application is evaluated in complex weather situations between January 1 and January 6 2020 in the European North West Shelf region. A set of wave model setups are compared with a variation of values of the prognostic spectral cut-off.

It is demonstrated that a the near-surface drift profile is resolved well with an order of ten discrete depths, and the parametric tail extension requires a low computational time.

How to cite: Hansen, C.: Near-surface Stokes drift in operational wave forecast, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9520, https://doi.org/10.5194/egusphere-egu21-9520, 2021.

16:13–17:00