The technological evolution in terms of computational capacity, data acquisition systems, numerical modelling and operational oceanography is supplying opportunities for designing and building holistic approaches and complex tools for newer and more efficient management (planning, prevention and response) of coastal water pollution risk events.
A combined methodology to dynamically estimate time and space variable individual vessel accident risk levels and shoreline contamination risk from ships has been developed, integrating numerical metocean forecasts and oil spill simulations with vessel tracking automatic identification systems (AIS). The risk rating combines the likelihood of an oil spill occurring from a vessel navigating in a study area – the Portuguese continental shelf – with the assessed consequences to the shoreline. The spill likelihood is based on dynamic marine weather conditions and statistical information from previous accidents. The shoreline consequences reflect the virtual spilled oil amount reaching shoreline and its environmental and socio-economic vulnerabilities. The oil reaching shoreline is quantified with an oil spill fate and behaviour model running multiple virtual spills from vessels along time, or as an alternative, a correction factor based on vessel distance from coast. Shoreline risks can be computed in real time or from previously obtained data.
Results show the ability of the proposed methodology to estimate the risk
properly sensitive to dynamic metocean conditions and to oil transport
behaviour. The integration of meteo-oceanic
Maritime surveillance systems are becoming more effective and have been developed for coastal regions (e.g. terrestrial and satellite-based Automatic Identification Systems – AISs, UAVs), and the maritime security rules are becoming more restrictive, following the MARPOL convention (e.g. shift to ships with double hull). However, the increasing global ship traffic (four times as many ships now as in 1992; Tournadre, 2014) and maritime transport of oil products (ITOPF, 2015) make it more difficult to significantly reduce the environmental, economic and social risks posed by potential spills. Additionally, the use of increasingly larger vessels (up to 100 000–150 000 tonnes) means that if a major accident takes place, the amount of oil released could be vast.
In fact, the environmental and socio-economic issues associated with spills is and will always be a main topic: spill events are continuously happening, most of them unknown to the general public because of their small-scale impact – for instance, half of the total oil spills in the marine environment come from operative discharges by shipping and in most of these cases the discharges are illegal (GESAMP, 2007). Nevertheless, some oil spills become authentic media phenomena in this information era, due to their large dimensions and environmental and social-economic impacts on ecosystems and local communities, and also due to some spectacular or shocking pictures generated (Leschine, 2002).
Consequently, the planning and prevention in the management of spill incidents at sea is extremely important in the reduction and minimization of potential impacts. Latest scientific and technological developments on coastal monitoring and operational oceanography have provided the opportunity to build more complex and integrated decision support systems for coastal risk management. The increasing operational predictive capacity of marine weather conditions (Hurlburt et al., 2009; Schiller, 2011) and better knowledge of the fate and behaviour processes of pollutants spilt at sea or coastal zones (Fingas, 2015; Johansen et al., 2015; Zhao et al., 2014a, b; Gong et al., 2014), together with the presence of advanced surveillance monitoring tools (Fischer and Bauer, 2010), can be integrated in order to provide a safer support for decision making in emergency or planning issues associated with pollution risks.
The development of risk assessment studies have been used for multiple purposes, including contingency planning for response and preparedness, developing spill prevention measures, or evaluating oil exploration sites (Etkin, 2014).
Over the years, innovative oil spill hazard or risk assessment studies in coastal and marine environments have been published, considering historical data, reference situations, and typical or extreme scenarios (Castanedo et al., 2009; den Boer et al., 2014; Otero et al., 2014; WSP Canada Inc., 2014; Liubartseva et al., 2015), supporting contingency planning and strategic decision making. Silveira et al. (2013) developed also a new method to calculate the ship risk collision, applied in the Portuguese continental shelf with AIS data, but without connection to oil spill hazard assessment or taking into consideration metocean conditions. But none of the previous studies were developed and applied in real-time risk assessment.
Other studies and methodologies developed dynamic approaches, with the possibility of being used in real-time support (Grifoll et al., 2010; Eide et al., 2007a, b; Bi and Si, 2012; Olita et al., 2012; Goldman et al., 2015; Canu et al., 2015). However, the method proposed by Grifoll et al. (2010) does not include a fate and behaviour oil spill model for a better determination of areas affected by oil. The work developed by Eide et al. (2007a, b) included an oil spill model – however, the simulations were previously obtained, based on typical scenarios, and without considering the dynamic changing of environmental conditions. Bi and Si (2012) also presented a novel method for dynamic risk assessment of oil spill accidents based on numerical simulation, but in this case the method was only applied to an on-demand spill event or scenario, instead of providing continuous risk mapping based on ship traffic. Olita et al. (2012), Canu et al. (2015) and Goldman et al. (2015) do not integrate directly metocean modelling data in the risk (accident probability) model, and the latter two papers do not use vessel data.
In this work, we present an innovative and holistic methodology for dynamic
shoreline risk quantification, with full integration of numerical metocean
forecasts and oil spill simulations with the existing monitoring tools
(AIS), and with the possibility of being used to study past periods,
projected scenarios and also to support continuous monitoring, contributing
to real-time maritime situational awareness. The main purpose is to build a
decision support system capable of quantifying time and space variable
shoreline pollution risk levels, coming from ships along the coast, and
combining multiple information layers:
instant vessel information (AIS); regional statistics information on vessels accidents history, coastal
vulnerabilities; instant metocean forecasting data; continuously simulated oil spill fate and behaviour from ships along the
coast.
The development of a risk assessment approach integrating economic, environmental and social aspects combined with operational oceanography and available surveillance monitoring systems is in line with the blue growth paradigm, resulting in an innovative, holistic and sustainable approach for the maritime sector.
The relevance of integrating the oil spill model and metocean data from forecasting systems in the risk algorithm is evaluated on a study area described in the next section.
The whole system has been implemented and tested in the Portuguese
continental shelf. This peripheral area is a high shipping density zone (more
than 55 000 commercial vessels per year crossing this area, and an average
number of 600 ships of all types present in the studied area, according to
MarineTraffic,
In this geographical zone, the activities in the near-shore area assume a very relevant role in the social, environmental and economic context (vast potential in natural resources, fishing, aquaculture, maritime commerce and port activity, leisure, sports and tourism activities).
Ship density map around the pilot area in 2014. The white
rectangle represents the area considered in this work to study the shoreline
contamination risk in the Portuguese continental coast (source:
In Portugal, the direct contribution of the maritime economy amounted to about 2.5 % of national gross value added in 2010 and 2.3 % of national employment (DGPM, 2012). Tourism, on the other hand, is gaining an important weight in the economy and is currently representing 48 % of the total employment related to maritime activities (DGPM, 2012), as the country is widely known as a sun and beach destination within Europe counting with a wide accommodation and restoration infrastructure.
The high frequency of ships navigating in the Portuguese coast, together with the Portuguese dependency on the economy of the sea and natural resources, raise the awareness for the risk of water pollution events in this area.
The method proposed for quantification of risk combines the likelihood of an
oil spill occurring from a vessel navigating in the study area with the
assessed consequences to the shoreline, where risk is the product of the
probability (or frequency) of oil spill accidents from maritime traffic,
times the severity (or consequences) of the events:
Governed by the previous expression, different types or risk levels are
determined:
the individual risk of oil spill accident for each vessel, depending on the
vessel itself and on the metocean conditions, which is not dependent on the
coastal consequences; the risk of shoreline contamination taking in account coastal vulnerability
indices with the integration of the above risks of oil spill accidents of
all the vessels present in the vicinity of a given coastal stretch. To
account for the potential consequences and amount of oil reaching shoreline,
two strategies are implemented and evaluated:
a modelled one using an oil spill transport and behaviour spill modelling
for each vessel a non-modelled one based on a correction factor function of the distance
between the vessel and the coast stretch.
The methodology and some of the statistic data is based on the risk assessment produced for Portugal and Galicia in the scope of the EROCIPS project (Filipe and Pratas, 2007). A previous description of the risk model is available in ARCOPOL plus report (Fernandes, 2014).
The probability is based on dynamic marine weather conditions and statistical information (frequency constants for each accident type) from previous accidents. The severity of the consequences are the result of the combination of hypothetical spilled oil amount reaching shoreline and the coastal vulnerability on those affected areas.
In order to simplify the development of the scale of risk and its values,
logarithmic values are used, defined by indices, following IMO
recommendations (IMO, 2002):
Full details of the risk assessment model implemented are given in Sect. 2.7.
Variable vessel information is used in the computation of risk. The properties used are the geographical position, cargo type, speed, vessel type, weight (DWT), name and ID (MMSI and IMO number). Vessels with less than 100 DWT, passenger vessels and fishing vessels navigating outside restricted waters are not considered in this study, based on the assumptions from Filipe and Pratas (2007), and also for computational reasons (the risk model takes in consideration approximately 150 vessels every instant, after applying the mentioned filtering). It is assumed that a vessel is navigating in restricted waters if distance to shoreline is not greater than 3 nautical miles, or if water depth is not deeper than 20 m.
The vessel information is obtained from AIS data. Presently the system is
configured to seamlessly collect real-time data from
The coastal vulnerability is used to quantify the consequences of shoreline contamination, with a risk algorithm. This coastal vulnerability can be obtained from different vulnerability indices: the coastal sensitivity index (CSI), the socio-economic index (SESI) and the ecological index (ECSI). The ecological index has not yet been implemented for the pilot area, but the risk modelling system is prepared to include it, once data are available.
The characterization of the coastal sensitivity and socio-economic indices in the pilot area (Portuguese continental coast) was made in the scope of the EROCIPS project. Along with desk work, based on Aerial photos and on Google Earth, field surveys were conducted of the whole Portuguese continental shoreline. This information is available on the web through Google Earth (MARETEC, 2007), and this KML format is directly imported to the developed risk assessment tool.
The vulnerability indices obtained for the pilot area were defined with a very high spatial discretization, dividing the shoreline into multiple segments or stretches in extensions that can be as small as 200 m, realistically representing the variability of the shoreline.
The CSI index represents the quantification, on a logarithmic scale, of the valuation of the environmental sensitivity (ecological, landscape) of the areas of the maritime coast and/or the surrounding waters that can be reached by sea pollution from hydrocarbons and/or other dangerous substance spills.
For the general group of areas of the maritime coast, NOAA's ESI
(Environmental Sensitivity Index) was adapted for the Portuguese Continental
Coast (modifications were related to the specificities of the Portuguese
shoreline). The ranking of this index, which varies in the range 1–10, coincides
with the scale of the NOAA's ESI (NOAA, 2002), defined to characterize zones
of the shoreline as a function of the following parameters:
exposure to wave and tidal energy; slope of the coast (intertidal zone); type of substrate (size, permeability and mobility); biological productivity and sensitivity; ease of clean-up.
The colours used to visualize the CSI ranking are the same as used in NOAA's ESI (a list description of CSI is included in Appendix A, Table A1).
In regions like coastal shoreline (restricted) waters, commercial ports, all-purpose terminals, fishing ports, marinas or yacht harbours and unrestricted waters, CSI is invariable and considered to be 6. However, as this tool is only estimating risks of shoreline contamination, coastal vulnerability indices of restricted or unrestricted waters/open sea are not considered by the risk model.
The CSI values obtained in the pilot area vary from a range of 1–10, with an average value of 4.1 and a median value of 3.
This index (SESI) intends to reflect the social-economic importance to the populations of the exploitation of the coastal zone under analysis (e.g. a beach not often used, or used but without significant infrastructures, and/or a beach with important economic value – restaurants, etc.). While the CSI already considers the normal habitats for that shoreline, it does not consider other improvements that can exist in the zone and that are not specific to the characterization of the CSI index, such as fisheries or aquaculture, that therefore have to be considered through the social-economic index SESI. This index varies from 1 to 5 (a complete list description of SESI is included in Appendix A – Table A2).
The SESI values obtained in the pilot area vary in the range of 1–5, with an average value of 1.8 and a median value of 1.
The ECSI is used to consider special protected areas that are not included in the CSI. This index varies from 1 to 5. Although the risk model has been prepared to include this ecological index, this has not been set up yet for the area of study – therefore, a constant value of 3 is now temporarily used as ECSI in all shoreline stretches. Currently a methodological definition of this index is being pursued in the scope of the ARCOPOL platform project.
Wind, current, waves and visibility are taken into account for the
probability of an accident, which is modified with correction factors
adjusted by those meteo-oceanic conditions. These parameters can be imported
to the system's database in real time from online internal or third-party
forecasting systems (as long as model output files are provided in native
MOHID format – HDF5 – or in standard CF-compliant netCDF formats, available
online in web servers – preferably FTP or THREDDS catalogue). The system
implemented in this work imports MARETEC-IST's forecast
regional solutions available online in
Current and water properties (temperature and salinity) are obtained from the PCOMS-MOHID model (Mateus et al., 2012; Pinto et al., 2012). PCOMS is a 3-D hydro-biogeochemical model of the Iberian Western Atlantic region. Ocean boundary conditions are provided by the Mercator-Ocean PSY2V4 North Atlantic and by tidal levels computed by a 2-D version of MOHID (Neves, 2013; Ascione Kenov et al., 2014), forced by FES2004, and running on a wider region. PCOMS has a horizontal resolution of 6.6 km and a vertical discretization of 50 layers with increasing resolution from the sea bottom upward, reaching 1 m at the surface (Ascione Kenov et al., 2014).
Atmospheric conditions (wind velocity, surface air temperature, atmospheric pressure and visibility) are obtained from the meteorological forecasting system IST-MM5, using MM5 model (Grell et al., 1994) with a 9 km spatial resolution. This operational model was initially implemented by Sousa (2002), and updated in 2005 (Trancoso, 2012). This model is also used as atmospheric forcing of PCOMS-MOHID.
The wave parameters (wave period, wave height, wave direction and wave
length) are obtained from the Portuguese wave forecasting system implemented
at MARETEC-IST, using the WaveWatchIII model (version 3.14 – Tolman, 2009) with
a 5km spatial resolution, and wind forcing provided by Global Forecasting
System (GFS) from the National Oceanic and Atmospheric Administration
(NOAA), with a spatial resolution of 0.5
These meteo-oceanic properties are also used to feed the oil spill fate and behaviour model integrated in the system, which is used to estimate the hypothetical vessel-based spilled oil amount reaching shoreline.
The integrated oil spill model used in this work is the MOHID oil spill fate and behaviour component, integrated in MOHID Lagrangian transport module, where simulated pollutants are represented by a cloud of discrete particles (or super-particles) advected by wind, current and waves, and spread due to random turbulent diffusion or mechanical spreading. The MOHID oil spill modelling component was initially developed in MOHID in 2001 (Fernandes, 2001), and over the years the model has been operationally applied in different incidents (Carracedo et al., 2006; Janeiro et al., 2014), field exercises and studies worldwide, allowing the simulation of all major oil transport and weathering processes at sea. The source code of the oil spill modelling system was recently updated to include full 3-D movement of oil particles, wave-induced currents and oil–shoreline interaction (Fernandes et al., 2013), as well as blowout emissions (Leitão, 2013).
This oil spill model has the ability to run integrated with the hydrodynamic solution, or independently (coupled offline to metocean models), this latter being the option adopted for integration in the developed dynamic risk tool, taking advantage of metocean models previously run, and thus optimizing the computational efficiency.
The oil spill model is freely available for public access, since it is integrated in the MOHID numerical modelling system which follows a FOSS (free/open source software) strategy.
The dynamic risk tool continuously runs the MOHID oil spill model to simulate hypothetical spills from multiple vessels across the coast, then taking into account the fraction of oil that would approach the coastline.
Two different integrated risk types (they are integrated because they take into consideration different types of incidents) are computed: (a) risk of oil spill incident; (b) risk of shoreline contamination.
Both integrated risk types are variable in space and time due to variable vessel information and metocean conditions (that influence probability of an accident, as well as fate and behaviour of oil spills simulated). The simultaneous calculation of the risk posed by each vessel crossing a pilot area is integrated, allowing the generation of a dynamic shoreline risk map for that zone.
The risk of oil spill incident quantifies the severity based on vessel dead weight tonnage and vessel position, with higher or lower risk if the vessel is navigating in restricted or unrestricted waters, respectively. This risk type does not take into consideration the effects on shoreline, and is represented in each vessel.
Different types of incidents are considered in the risk model: grounding,
foundering and structural failures, collision (with a ship or with port
facilities), fire and explosion, illegal and operational discharges. In
order to obtain the integrated ship risk of spill incident, the partial
probability and severity indices are integrated. Probability indices from
the different types of incidents are summed, and a weighted average
severity index from the different types of incidents is determined. The sum
of the probability indices (
The detailed determination of
The risk of shoreline contamination takes into account the interaction with the coastline, therefore the severity/shoreline consequences additionally include the virtual spilled oil fraction reaching shoreline and its environmental and socio-economic vulnerabilities, instead of simply considering the vessel deadweight tonnage and position. The oil reaching shoreline is quantified with an oil spill fate and behaviour model that continuously simulates virtual oil spills from the vessels included in the domain. Alternatively, a “non-modelled” shoreline contamination risk rating is computed, without using the oil spill model for the determination of shoreline impact – in this case, a vessel shoreline proximity correction factor is used and subtracted to the risk value (with this correction factor decreasing as the vessel approaches the coastline). This risk type is represented in shoreline stretches, taking into consideration the effects from multiple vessels affecting that zone. The division of shoreline stretches for characterization of shoreline contamination risk is based on the same division used in the coastal vulnerability characterization.
The shoreline contamination risks provided are in fact a percentile (by default, percentile 98, but can be customized) of the shoreline contamination risks determined from the different vessels. Shoreline contamination risks below a user-defined value are not considered.
The probability/frequency of occurrence of a specific type of incident in a ship leading to an oil spill is obtained from statistical constants (frequency of incidents per distance navigated, or annual incident frequency) corrected with a combination of a different factors identified as relevant in the generation of those incidents (e.g. visibility, currents, proximity to coast, etc.).
The choice of using the probability of incidents for each vessel per distance
unit navigated was made because otherwise the annual frequency of accidents is too
static, i.e. if hypothetically there is a ship anchored for an entire year,
it will still provide a risk similar to a ship in circulation, which is not
entirely true. A dynamic probability will be inevitably achieved using
frequency of accidents per km navigated
Generically, the probability of incident in a specific time period is
computed as
The distance navigated by the ship is obtained directly by ship velocity (from AIS data) and time step for risk analysis (defined by the end-user).
Since illegal/operational discharges occur based on human decisions, their
probability is not influenced by environmental conditions. Thus, no
correction factors are applied to the calculation of this probability. Also
in this type of incident, the probability is not based on incident frequency
per distance navigated, but on annual frequency – it is assumed that
deliberate discharges occur independently of vessel speed. The probability
of operational discharges (
A logarithmic scale from 1 to 8 was adopted for the index of probability.
The correspondence between annual probability and index of probability can
be represented by the following equation (derived from the Table C1 in
Appendix C), based on Filipe and Pratas (2007), and inspired by IMO
recommendation (IMO, 2002):
The annual probability (
Different frequency/probability constants of incidents are included in the risk model as a way to include some differentiation based on type of incidents and some probabilistic data obtained from statistical information on past incidents. These values can be changed by the end-user at any time.
In this study, frequency constants of incidents per distance unit navigated are obtained from IAEA (2001), and missing constants are obtained from the combination of previous reports of the Lloyd's Register accidents database (the relationship between annual frequency constants was used to extrapolate frequency constants per distance navigated). The numerical values of the frequency constants used can be found in Appendix C (Table C2).
Summary of multiple correction factors used by each type of
accident (
According to IAEA (2001), the frequency of incidents due to fire and explosion does not vary significantly with the region. Therefore, the frequency for this type of accident per distance navigated is kept constant.
Also in the same report, there is no reference to illegal/operational discharges. For this kind of incident, annual incident frequency is assumed, since these discharges are independent of vessel speed. It is also assumed that such discharges do not occur in restricted waters.
Multiplying correction factors are used to modify the probabilities of spill incidents based on metocean conditions (wind velocity, current velocity, wave height and visibility), proximity to coast and ship type. The correction factors are not applied to the probability of having operational/illegal discharges because these incidents are considered deliberate or independent of (and not controlled by) external effects. The values used can also be changed or calibrated by the end-user.
The correction factors included by default in this study were obtained from the Risk Assessment Report for the Portuguese and Galician Coast – EROCIPS (Filipe and Pratas, 2007), and the values used are listed in Appendix C (Tables C3 and C4). Table 1 summarizes the multiple correction factors used by each type of accident.
A minimum or residual probability of an accident per unit time must be assumed, to avoid the determination of null or (nearly null) probabilities when vessels are anchored or moving very slowly (because the risk model computes the incident probability based on ship velocity). Even at slow motion or stopped, a ship has always a risk of a spill accident. For instance, there is still a chance of collision with another ship, or to anchor in a danger zone and eventually generate a grounding accident (depending on the weather and oceanographic conditions).
This probability is obtained as a function of a minimum velocity. Below this
velocity value, the vessel is assumed to have a constant accident
probability. The minimum velocity is user defined, and by default the value
of 0.36 m s
The severity index list of hydrocarbon and other hazardous substances spills, whether in open sea or in restricted waters due to the various types of accidents, follows IMO recommendations (IMO, 2002) and is described in Filipe and Pratas (2007). A logarithmic scale from 1 to 8 was adopted, following the same scale as the probability index (Table D1 in Appendix D gives details of the severity index).
The severity in the risk of spill incident varies with the ship position
(restricted/unrestricted waters), and with the hypothetical amount of
spilt product. Typical values of amount of oil spilt are estimated based on
the ship type, weight and the type of incident, in order to estimate the
severity index of spill incident (
As mentioned before, the risk of shoreline contamination from each vessel considers the risk of spill incidents plus the interaction with the coast, taking into consideration the coastal vulnerability, and the potential contamination of the near-shore. This potential contamination is computed by two different approaches: by estimating the oil fraction reaching the coastline – a method herein called the “modelled” risk of shoreline contamination; or alternatively by a correction factor based on ship distance to coastline – a method herein called the “non-modelled” risk of shoreline contamination.
In both approaches (modelled and non-modelled), the computed severity index
of shoreline contamination (
The fraction
For non-modelled risk, a vessel shoreline proximity correction factor is
subtracted to the severity of spill incident index (with this correction
factor decreasing as the vessel approaches the coastline)
The determination of this factor depends on distance between spill site and shoreline, and on type of oil product/ship type (further details are in Table D4 in Appendix D).
For modelled risk, a modified severity of spill incident is adopted, in a
more complex and realistic approach to determine the impact risk of oil
spills on the shoreline, since fate and behaviour of oil spilled is taken
into account, using the MOHID oil spill model, as described in Sect. 2.6. The
modified severity of spill incident is obtained by using the regular
equation for severity of spill incident in restricted waters (Appendix D,
Table D3), but with a modified amount of oil spill (
Risk matrix based on probability and severity indices, with
corresponding representation with colour. 2 <
The quantification of modelled maximum oil contaminating a specific shoreline stretch is based on the maximum amount of oil present inside an area near the shoreline stretch. The definition of this “near-shore” area for each shoreline stretch is based on the distance to the shoreline stretch; thus, if the modelled oil reaches this near-shore area, it is assumed as relevant to the quantification of shoreline contamination risk. The near-shore distance is user defined, and by default it has a value of 2000 m from the coast. The time period used in the quantification of maximum oil spilled reaching near the shoreline stretch has a default value of 24 h (configurable). Updates and new oil spill simulations from updated vessel positions are made every hour (this value is also configurable). The oil spill model simulations are made assuming always the same oil product released. The oil product included in the risk model (Carpinteria, medium oil from Group III) was chosen based on the profile of being a “worst-case scenario” for shoreline contamination, being a crude product from oil group III with low weathering effects along time.
The risk matrix is the result of crossing both probability and severity indices, in order to obtain a risk rating – see Table 2. The sum of both indices generates a risk index classification scale between 2 and 16. These values are categorized with different risk levels and corresponding colours.
Independently of the integrated risk types applied (e.g. risk of spill incident; modelled risk of shoreline contamination; non-modelled risk of shoreline contamination), the same risk matrix should be applied.
In the case of shoreline contamination risk, at the present stage of the work, the visualization of risk values in the implemented software tool follows a continuous risk scale (bounded by the same limits as defined in the risk matrix categorization scheme), instead of a categorized scale, and using a different colour pattern from that proposed in Table 2. This option facilitates the visualization of variability in shoreline risk levels during the development period. In the future, the visualization of this risk level will be updated to the categorized view and using the same colour pattern as defined and presented in Table 2.
No risk acceptance/tolerability criteria were defined in the present work.
General information workflow in the risk modelling system.
This risk assessment methodology has been implemented as a plugin from MOHID Studio, which is a GIS desktop interface that can also be used to run the MOHID water modelling system. MOHID Studio is a commercial platform property of Action Modulers, and has been entirely developed in c#.NET language, using SQL Server components and the MOHID model.
The main philosophy of the software architecture was to create separate
layers, allowing distributed tasks in different processes or computers, and
a lighter graphic user interface (GUI). The general information workflow in
the software framework is presented in Fig. 2. According to this, the main
software framework is composed of four main components, exchanging
information between them:
an SQL Server or SQL Lite database, where all the data and meta-data are
stored (metocean model outputs are not stored; only indexed); a desktop service (Action Server), which is continuously loading/
downloading updated data from different data sources (AIS data, metocean
model outputs, etc.), managing the MOHID oil spill model, processing all
information (and computing risk levels) and storing data on the database; MOHID oil spill project/executable file, which is continuously generating
and running virtual oil spill simulations based on ship positions, and on
instructions managed by the Action Server desktop service. A GUI (MOHID Studio), directly connected to the database,
and showing requested data to the end-user. MOHID Studio can also be used to
configure Action Server, and to run the on-demand risk assessment tool for
specific periods.
MOHID Studio and Action Server do not need to be running on the same computer. The software architecture has also been developed to enable the publication of real-time risk mapping data in external platforms, including WMS layers, to facilitate the interoperability of the system.
In this section, the response of the proposed risk model to different metocean conditions is evaluated in the pilot area, and the GUI developed in this work is also presented. Since the dynamic risk tool is capable of running in real time or on demand (for historical periods or virtual scenarios), it is assumed that a dynamic behaviour and proper response to the different variables means that the developed tool is also ready and able to provide results in an operational way.
The risk modelling tool is able to run in continuous mode, allowing the user to follow in real time the ship traffic and specific vessel details, the evolution of risks crossed with background dynamic web maps (e.g. Google maps, Bing Maps, Open Street Maps) and many other geographic layers and features (Fig. 3) – e.g. visualizing metocean fields, topography, running oil spills on-demand, etc. When zooming the view, it is possible to check the very high level of resolution of the vulnerability indices and the associated risk levels being computed (Fig. 4).
GUI layout, with simultaneous visualization of ship incident risks, shoreline contamination risks, surface water velocity and Google map layer. Ship incident risk colours are presented in categorized view (green, yellow, orange and red).
Close-up image of the GUI for the Lisbon area – simultaneous visualization of coastal sensitivity index and Bing Hybrid map layer.
Metocean conditions have a direct effect on risk of ship incident, because they can influence the probability of an accident occurring, according to the methodology proposed. These effects are included in the risk model through categorized correcting factors based on the range of metocean conditions.
Ship incident risk levels (green ships mean lower risk, yellow means
medium risk and orange ships mean higher risk) in the pilot area with
background metocean conditions used. (
Ship incident risk levels (green ships mean lower risk, yellow means
medium risk and orange ships mean higher risk) in the pilot area with
background metocean conditions used. Computation is made with same vessel
positions as used in Fig. 5. (
One of the exercises performed in this study was to analyse the evolution of ship incident risks according to some of these metocean conditions used, organized in the same classes as the ones used in the correcting factors. In Fig. 5, ship incident risk levels are shown in different colour classes for different instants, together with wave model data (Fig. 5a and b) and wind speed (Fig. 5c and d) used in the risk model. Generally, the lower ship incident risk levels (in green) are present in ships crossing geographical areas where wind or wave conditions belong to lower classes. The same behaviour can be seen for vessels with higher incident risk levels – they tend to be determined in vessels crossing areas where wind speed or significant wave height are greater. It is also clear in Fig. 5 that the presence of a ship in different wave classes can contribute more significantly to different risk levels than wind speed – this is due to the fact that the wind multiplying correcting factor varies from 0.8 to 2, while the wave correcting factor used varies from 0.1 to 1 or 0.22 to 1.78 (detailed values of correcting factors used can be found in Appendix C).
A better evaluation of the importance of metocean conditions in the risk model can be tested using different metocean conditions for the same ship positions. Figure 6 illustrates the ship incident risk levels using different metocean conditions (6 months later), and exactly the same ship information as used in Fig. 5. Figure 6 clearly shows the dynamic change of risk levels directly affected by the wind and waves, for the same vessel traffic. Comparing Figs. 5 and 6, different ship risk levels can be observed. The effects of the other environmental conditions (visibility and surface water velocity) are similar to the properties illustrated here.
When compared with ship incident spills, the evaluation of shoreline contamination risk from spills is more complex, as this parameter depends additionally on the coastal vulnerability indices, and is a result of an integration of risks from the different ships affecting each shoreline stretch. While it is easy to find different shoreline risk levels along the coast (e.g. Fig. 3), it can be difficult to evaluate, isolate and study the dependence of risk model on the multiple factors – for instance, metocean conditions, vessel traffic conditions, coastal vulnerability, oil transport and weathering module. To facilitate the analysis, we start by evaluating the risk model behaviour without integrating the oil transport and weathering module (this matter is studied in Sect. 3.4).
Location points for the shoreline contamination risk detailed study.
Evolution of shoreline contamination risk in P
Metocean conditions used in the risk model, at points P
In order to achieve this objective, an initial study was performed, with the
selection of two different locations with exactly the same coastal
vulnerability (Fig. 7), and subject to the same metocean conditions along
the simulation. Therefore, the shoreline contamination risk levels are only
influenced by the different vessels in the proximity. The results were
generated, based on the registered vessel positions from two 1-week
periods (between 18 and 25 January 2013 and between 18 and 25 June 2013),
generating model risk outputs every 6 h. The metocean conditions were
defined as constant in the whole model domain along the two simulation
periods. Typical winter (rough) and summer (calm) conditions were defined
for January and June periods, respectively. Winter conditions: surface
current velocity, 0.55 m s
As can be seen in Fig. 8, differences could be found when comparing both
points for the same metocean conditions. The differences in risk values for
both points subject to the same metocean conditions can only be explained by
the different vessels in the proximity (with P
Metocean conditions used in the risk model, at points P
Evolution of shoreline contamination risk at P
Additionally, Fig. 8 also illustrates the differences in risk values when comparing winter vs. summer conditions for the same point, justified by the response of the risk model to the metocean conditions. This aspect is further explained in what follows.
Since the developed system is able to digest variable metocean conditions
from forecasting systems, a second exercise with two simulations (winter:
18–25 January and summer: 18–25 June 2013)
was performed for the same points as considered in the previous study (P
Integrated shoreline contamination risk for the whole pilot area,
with AIS vessel information between 18 and 25 January 2013, and
using different space and time constant metocean conditions. Winter/rough
conditions: surface current velocity, 0.55 m s
The temporal variations and differences between shoreline contamination risk
levels in winter and summer conditions identified can only be explained by
the variation in metocean model conditions, since all the other conditions
were kept constant. In general, risks are higher in January for both points,
as expected (due to winter metocean conditions). The obtained results show
risk variations of 0.5–1 risk units in a 6 h interval, meaning that the
computed shoreline contamination risk is dynamically responding to the
variations of combined effects of metocean conditions and vessel traffic.
The risks obtained at P
Additionally to the evaluation of risk model behaviour in isolated shore locations, we performed a more complete and integrative set of analyses, considering all shore locations in the pilot area. In these analyses, the different shoreline contamination risks along the coast were integrated in the form of instant mean averages, and 1045 different shore locations along the Portuguese coast were considered. The risk model was run every 6 hours between 18 and 25 January 2013 (winter conditions), and between 18 and 25 June 2013 (summer conditions). The main purpose of performing these integrated analyses in the whole studied area is to obtain a more representative evaluation of the model risk behaviour.
The first integrated analysis for the whole area of study consisted in
running the risk model using the same conditions as used on Sect. 3.3.1:
constant metocean conditions in each period, in both space and time along
the runs – thus with temporal evolution of the risk levels totally
dependent on vessel traffic conditions. The summer scenario was run using
vessel AIS information from the period between 18 and 25 June 2013:
surface current velocity, 0.25 m s
Integrated shoreline contamination risks (instant mean, maximum and
standard deviation) for the whole pilot area, obtained with three values for
metocean model parameters (direct model output; model
Additionally to the previously specified constant metocean conditions used, a set of three simulations was run for each of the selected periods (18–25 January; 18–25 June 2013), using the exact modelled metocean solutions provided by the operational forecasting systems, and additionally increasing and decreasing those solutions in 50 % for the properties that directly affect the risk level. The main purpose of this set of simulations is to obtain a more sensitive analysis of the risk methodology under different realistic conditions, assuming that the chosen modelled scenarios will cover a representative part of the marine weather situations found in the pilot area. This set of analyses can also provide a clearer idea about the thresholds of the presented tool. The results from this set of simulations are shown in Fig. 13. These images also provide information about the maximum values and standard deviation for each instant, showing the dynamic variation along the coast.
Recorded AIS vessel positions per 6 h time intervals in the pilot area, obtained in two different weeks: 18–25 January and 18–25 June 2013.
The rougher metocean conditions previously identified for 19 January
are responsible for a peak in shoreline contamination risk on that day, in
winter conditions (Fig. 13a). In each of Fig. 13a and b, the
increase of metocean parameters generates an increase on computed risk
levels, both mean and maximum values in the whole domain. Increasing or
decreasing by 1.5 times (50 %) the metocean properties can result in a
modification of risk levels up to 0.5 risk units. Mean risk values are
generally around 8 risk units, with maximum risk values of 10, which is
below the critical risk threshold (12) defined in the risk matrix (Table 2).
Once again, instant standard deviations are generally around 0.5 or even
higher, and the obtained mean and maximum values for the whole domain can
vary more than 0.5 risk units at each time step (6 h), in both periods
(although stronger variations are detected in January, which can also be
explained by the stronger irregularity in metocean conditions for that
period). These results confirm the previous analyses conducted for P
Comparing Fig. 13a and b, it can also be seen that the modelled risk is not necessarily lower in the summer period, despite the calmer metocean conditions. Indeed, in the first days of the simulations, the opposite situation is verified – shoreline risk level is greater in summer. The main reason is the fact that vessel traffic was denser in the summer period, compared to the winter period (Fig. 14). The records indicate an average of 677 vessel positions recorded every 6 h, during the selected January period. In the selected June period, an average of 725 vessel positions was recorded. Therefore, after comparing the integrated risk levels between Fig. 13a and b, it can be said that in the modelled periods, the vessel traffic assumed more importance than the metocean conditions in the determination of the risk. Actually, the simple presence of a small number of mega-tankers in the nearshore is enough to increase the risk values. This also demonstrates the complexity of the system.
The different metocean conditions directly affect accident probabilities (through correction factors), but can also influence oil weathering processes – for instance, higher evaporation rates are expected in the summer, reducing oil amount reaching shoreline, and consequently reducing shoreline risk in summer – as identified in Olita et al. (2012). On the other hand, stronger wind conditions in winter can also cause a more intense oil dispersion in the water column, contributing to a lower shoreline contamination risk in winter, as expressed in Liubartseva et al. (2015). The direct influence of oil weathering processes are studied in this chapter.
Different tests were performed to evaluate the relevance of having an oil fate and behaviour spill model integrated in this risk modelling tool. Calibration tests were also performed.
Integrated shoreline contamination risk levels at different time instants from 21 and 22 January 2013. Results presented as mean values for the shoreline in the whole pilot area studied. Shoreline risk levels computed with four different approaches: non-modelled approach; modelled approach using onshore wind; modelled approach without oil weathering processes, using onshore wind; modelled approach using offshore wind; modelled approach without oil weathering processes, using offshore wind.
First, it is important to evaluate the risk model response to different
environmental conditions, favourable or unfavourable to shoreline spill
contamination. In that sense, two opposite environmental modelling scenarios
were defined in this scope, as the basis of the exercise here proposed: the
same ship position and metocean conditions were used in both scenarios,
except for wind direction (wind magnitude was not modified). The onshore wind
scenario was set with a wind direction of 240
Since the developed risk model includes two different methods to compute the shoreline contamination risk (estimation of oil reaching the shoreline based on oil spill model – “modelled” approach; or based on ship proximity to shoreline – “non-modelled” approach), the two previous runs are also interesting to evaluate the relative dynamic response of the “modelled” shoreline contamination risk against the “non-modelled” approach, which therefore is independent of wind or current directions (thus there is no onshore and offshore differentiation for the shoreline contamination risk computed using a “non-modelled” approach).
Additionally to the three previous runs, two more runs were included, turning off the oil spill weathering processes in both onshore and offshore wind scenarios. These two runs consisted in understanding how significant it is to integrate the oil-spill-specific weathering processes (mainly the oil spreading, evaporation, dispersion and emulsification) in the risk model, instead of simply using a generic Lagrangian model.
Those five different types of shoreline contamination results (non-modelled approach; on-shore wind scenario; offshore wind scenario; on-shore wind scenario with no oil weathering processes; offshore wind scenario with no oil weathering processes) were organized as mean values, see Fig. 15.
The results allow us to, firstly, understand the relevance of including an oil transport model in the risk approach, mainly because it reduces the predicted risk according to favourable metocean conditions (in this case, the wind direction) – the difference between on-shore wind scenario and the others is very significant. Secondly, it can be seen that the developed risk model benefits from the modelling of the oil weathering processes, as there is a difference between onshore wind scenario with and without oil weathering processes. The default oil product used (a medium crude oil named Carpinteria) has a relatively low evaporation rate (more significant in the first hours) and almost null dispersion. However, Carpinteria has significant emulsification potential, able to generate a polluted emulsion (with a high water content) with more mass than the initial oil spilt. In other words, this oil product, once spilt in water and subject to weathering processes, can increase its mass (through the incorporation of water in oil), therefore increasing the amount of pollutant reaching the shoreline, and increasing the risk of contamination when compared to shoreline contamination risk computed without oil weathering processes. This is in fact what is observed in some instants from Fig. 15.
Evolution in time of approximated oil mass lost and water content in
oil (as percentage of mass) as a result of the main weathering processes, in four
oil types, under regular metocean conditions in the pilot area (wind:
10 m s
Integrated shoreline contamination risk levels at different time instants from 21 and 22 January 2013, obtained using four different oil products (Bunker C, Fuel Oil no. 2, Diesel Fuel Oil, Carpinteria), under onshore wind.
The adoption of Carpinteria as the default oil product for risk modelling is based on a “worst-case scenario” approach, related to the environmental problems that it can pose to shoreline areas due to low evaporation and dispersion, and significant emulsification. Using other oil products in this risk model would result in different risk values, due to differences in oil mass lost from the surface (to atmosphere, water column, etc.) related to oil weathering. To test the influence of different oil products in the risk model, a new set of tests with four different oil products was performed, using the previously defined environmental scenarios – onshore and offshore wind conditions.
The oil products selected have different weathering behaviour (Table 3).
Results for the onshore wind scenario are presented in Fig. 16 (results from the offshore wind scenario are not presented, since they show the same behaviour pattern as onshore conditions, although with lower risk values). Carpinteria keeps generating higher risk values, due to mass increase (emulsification). Bunker C fuel oil, which is a heavy fuel oil with low weathering effect, tends to generate risk values similar to Carpinteria. Diesel fuel oil and Fuel oil no. 2 tend to generate lower risk values, because they usually have more significant weathering processes – particularly diesel fuel oil.
Integrated shoreline contamination risk levels at different time
instants from 21 and 22 January 2013, under onshore and offshore wind,
and using different values in parameter
A side test that was implemented during the development and implementation
phase was the calibration of the risk model, specifically in the adoption of
the parameter shoreline stretch extension unit –
The work developed in this study aimed at the conceptualization, development and implementation of a novel holistic methodology for dynamic spill risk assessment from ship traffic, fully integrated with metocean and oil spill forecasting systems, and to evaluate the dynamic behaviour and response of the risk levels under different parameters. These objectives were accomplished, since the risk methodology was fully implemented in a software tool, the dynamic behaviour of the risk was demonstrated in the pilot area, and the system is being tested operationally by the authors of the project as well as the Portuguese Maritime Authority – DGAM-SCPM, allowing it to be used both in real-time (providing support to monitoring activities) and on-demand situations (supporting contingency planning).
The software system here described has been designed to be easily transferable to other areas, adopting generic approaches to download specific data layers (e.g. metocean forecasting system, AIS data, etc.), and being easily user-customized in terms of risk model parametrization. The possibility of running the risk model in a central server and providing outputs to external platforms following OGC standards increases the interoperability of the system.
The role of different variables in the risk model was presented with specific examples, with special emphasis on the relative significance of metocean, vessel traffic conditions and oil spill modelling systems integrated for the pilot area. The results from the risk modelling software tool are in agreement with what was expected from the proposed methodology for risk. Using an oil transport model (together with metocean modelling systems) in the estimation of the risk of oil reaching the coastline can provide a more robust and dynamic risk assessment. The results presented here have shown that the mere fact of having intensive ship traffic in the proximity of some coastal areas does not necessarily mean that the risk of shoreline contamination is high, depending on the instantaneous metocean conditions. If they are favourable to transport an eventual oil spill to offshore, the risk of shoreline contamination will be low. Also, it was shown that even if the metocean and the sea state conditions are stable and not extremely rough (reducing the probability of having ship accidents) – the risk of having ship incidents may not be necessarily reduced, depending on a combination of multiple dynamic factors, including the ship traffic intensity.
The results obtained from the sensitive analysis to different metocean conditions suggested that the correction factors in terms of probability could eventually by intensified in the future, in order to increase the relative weight of metocean conditions in the risk model, and therefore the dynamic risk change based on marine weather conditions.
The inclusion of oil weathering processes in the determination of shoreline contamination risk generates differences in risk values, depending on the oil product considered in the risk model. By default, the risk model uses an oil product that represents a worst-case scenario for the shoreline (low evaporation and dispersion; high incorporation of water through emulsion). Calibration tests in the risk model were also pursued, in terms of consequences (e.g. increasing the relative weight of oil spill model results in the risk model), in order to improve and fine tune the performance.
In all the results presented in this work (including conditions very favourable to shoreline oil pollution, calm and rough metocean conditions), the mean and maximum risk values tend to be below the critical risk threshold defined in the risk matrix presented in Table 2 (critical risk values are between 12 and 16). Therefore, the predefined risk matrix may be adapted in the future to better reflect the minimum and maximum values detected in the pilot area. In parallel, less relative weight to the coastal vulnerability indices might be used, in order to increase the amplitude and dynamic component of the risk, associated with vessel traffic, metocean conditions and the oil spill weathering model.
Additionally, it should be noted that this study was mainly focused on the testing and evaluation of the risk model dynamic behaviour and response to the different variables, and comparing the amplitude of risk values in the pilot area. The evolution of risk values over longer time periods was beyond the scope of the study at this stage. This type of study is expected to be pursued in the future, for the same pilot area included in this work.
Independently of the methodology developed and the results achieved with
this study, a number of assumptions, limitations and lack of data were
identified as relevant for improving the risk model:
Using frequency constants to estimate the probability of having incidents may
need continuous and periodic update, because the frequent changes in the
ship industry (e.g. obligation of double hull ships, mega-tankers, maritime
surveillance, etc.) can change the probability of having incidents. The coastal vulnerability indices included should also be regularly
updated and reviewed to reflect the present situation in terms of
environment and socio-economic aspects of the coast. Several schemes have been developed for estimating the probability of
ship-to-ship collisions using more complex approaches (e.g. Silveira et al.,
2013); however, these algorithms have not been included yet in this risk model. Heterogeneous spatial resolutions were considered for the different
variables used in the risk model. We have assumed that the computed risk
index resolution is equal to the coastal vulnerability (which has a high
resolution – 200 m or less – allowing responders to properly visualize,
manage and prioritize different shoreline areas), but we have in mind that a
better spatial resolution of the metocean models would potentially improve
representation of the coastal processes and consequently, the risk model.
The software tool is ready to accommodate more metocean models with higher
spatial resolution, which can be particularly interesting when studying or
monitoring the risk levels at a local scale (e.g. in a Port and its
neighbourhood area). Nevertheless, as a first dynamic implementation and for
the regional purpose of this work (focused on the Portuguese/Western
Iberian shelf), we consider that the proposed approach is capable of
demonstrating and providing satisfactory results. Moreover, the included
metocean models have been previously reported as valid for studying coastal
processes and coastal management support (Mateus et al., 2012; Trancoso,
2012; Franz et al., 2014) In the risk model adopted, there is no differentiation between identical
ships from different countries, inspected at different ports, constructed or
managed by different companies, or with different number of deficiencies
detected in the recent past. This information is presently available online
through EMSA's THETIS system, and in the future can be seen as a relevant
added value for integration in the risk model, if possible. The actual volume of contaminants, and product type transported by each ship
is not included in the risk model, since the information is not publicly
available (an approximation based on ship type and dead weight tonnage is
adopted). This information would be rather important to improve the
realistic quantification of estimated risk. No risk acceptance or tolerability criteria were defined in the present risk
model. The future definition of these tolerability criteria will facilitate
the adoption of mitigation measures in the case of unacceptable/intolerable
risks detected.
Aside from these identified considerations, the work presented here opens interesting opportunities for the future both in terms of risk planning and monitoring activities. A tool like this can improve the decision support model, allowing the prioritization of individual ships or geographical areas, and facilitating strategic and dynamic tug positioning. The possibility of being used for past or hypothetical scenarios may provide an interesting tool not only for identifying “hot-spots” in terms of shoreline contamination risk, but also to estimate future situations like the increasing of ship traffic or the size and cargo transported by the ships. Furthermore, the same risk model approach can be considered in the future to estimate other types of environmental threats, including impacts from spills in offshore platforms, impacts from onshore activities and industries involving discharges to the water environment, or even the environmental impact of maritime transport emissions on coastal air quality.
This section provides additional detail about the classification adopted for the coastal vulnerability indices adopted in the pilot area, namely the coastal sensitivity index (CSI, Table A1) and the socio-economic index (SESI, Table A2).
Classes used for coastal sensitivity index (CSI).
Classes used for socio-economical index (SESI).
Different types of risk of spill incidents, and corresponding spill incident frequency constants.
Table B1 describes the types of incidents considered in the risk model, as well as the nomenclature used and the corresponding spill incident frequency constants (per distance unit navigated or annual frequency for illegal/operational discharges).
In the determination of these risk indices for each type of incident, the
generic risk formula (sum of probability and severity indices) applies. For
example, for ships navigating in restricted waters,
the risk of spill incident from a ship-to-ship collision is
An integrated risk index is also determined (
Thus, if a ship is navigating in restricted waters:
Alternatively, if a ship is navigating in unrestricted waters, a similar
approach is followed:
To estimate the index of probability (Table C1), frequency constants obtained from reported spill incidents are used (per distance unit navigated or annual frequency for illegal/operational discharges) for the various types of accidents. Frequency constant values are shown in Table B1.
The probability of spill incidents is influenced by certain conditions that can reduce or increase the probability. The developed risk model includes correction factors to take into consideration these conditions. Table C2 expresses the correction factors adopted.
Classification of probability of ship incidents and correspondence between annual probability and index of probability (obtained from Filipe and Pratas, 2007, and inspired by IMO recommendation – IMO, 2002).
Correction factors related to current (
Table D1 shows the correspondence between severity/consequences and index of severity, obtained from Filipe and Pratas (2007) and inspired by IMO recommendation (IMO, 2002).
Table D2 illustrates how to determine the amount of oil spilled (
Classification of severity of ship incidents and correspondence between severity and index of severity.
The computation of severity of non-modelled risk of shoreline contamination
includes the subtraction of a correction factor (
Average amount of spilled oil per incident type and ship type.
Quantification of severity index of spill incident, based on oil amount ship type.
Subtracting correction factor (
This work has been sponsored by projects ARCOPOL PLUS (2011-1/150) and ARCOPOL PLATFORM (2013-1/252) (EU Atlantic Area).
The authors thank Francisco Campuzano for the development and maintenance of the hydrodynamic and wave modelling systems, as well as Rosa Trancoso for the development of atmospheric model, and Jorge Palma for its operational maintenance. The authors want also to thank MarineTraffic for cooperation and the provision of AIS data support in the scope of the ARCOPOL PLUS project. Last, but not least, a special thanks to the Portuguese Maritime Authority (DGAM-SCPM) for cooperation and support in beta testing. Edited by: A. Crise