# Characterization and Modelling of Potential Seaborne Disasters, in the ANA Region
## 4 Case Study: Exxon Valdez Oil Spill Disaster (1989, Alaska)
### Risk Modelling--Extension of the Models
#### 4.2.2 Bowtie Modelling of the Case Study  
When attempting to model and communicate the risk of oil spillage, one can categorise plan of actions into prevention of threat and the recovery against consequences [43], which is in line with both left and right sides of the bowtie as shown in Figure 3, where the knot (central part) of the bowtie is the hazard, or incident. Both prevention and response measures are the essence of resilience [4]. The prevention and response phases can also be visualised with respect to time flow from left to right in the same way resilience can be modelled with respect to time [45].  
The left side of the bowtie is usually based on a fault tree analysis (FTA), whereas on the right side, an event tree analysis (ETA) can be constructed where the focus is on consequences and negative outcomes that can lead to an escalation of the hazardous incident [4].  
So as demonstrated in Figure 3, the factors that led to the disaster can be categorised into those related to: failure of coastguards to monitor vessel, human errors where vessel position was not properly plotted, bad design and planning, and poor VTS. On the right side the consequences of the disaster can be categorised into: environmental in terms of crude oil spilled and clean-up costs, material loss in terms of the damage to the vessel as well as lost cargo, and impact on the community in terms of many factors such as for example, the perspectives of local communities and the consequences to their livelihoods and stablished social structures. Each of the categories on both sides of the model contains safety barriers in terms of means of both prevention (on the left-side) or correction (on the right side).  
Figure 3: A bowtie model of the oil spill of Exxon Valdez.  
Although Bowtie modelling is effective in communicating risk and is designed for visual representation, in its current form it does not provide quantitative analysis nor prioritisation in terms of order or weights to safety barriers.  
Hence, in order to enhance the bowtie methodology, it can be improved, for instance, by considering the following arrangements, by closeness to hazard, features, and category, as discussed below:  
1. Closeness to Hazard: Either before or after the hazard (the knot of the Bowtie), where more importance should be given to barriers nearer to the hazard. Such 'closeness' can be in terms of time or space.
2. Features: i.e., human, physical, software, procedures, etc. So we are not relying on one type. For example if a cyber attack occurs it will affect all safety barriers that rely on software.
3. Category: preventative versus reactive, where more preventative (upstream) is better than being reactive, and also a balanced portfolio scenario of both preventative and reactive is a good strategy.  
These proposed additional features are beyond the scope of this paper but will certainly be considered for future follow-up research. However, the main contributions of linking the Bowtie (in Figure 3) to both the FTA (in Figure 1) and RBD (in Figure 2), are summarized as follows. The three figures provide a concise visual representation for the causality, vulnerability, and safety barriers. So, with respect to causality, we can observe the failure through the relationship of causal factors in the form of basic events and logical gates (OR and AND gates) as shown in Figure 1. Such analysis subsequently leads to the understanding of the areas of vulnerability within the whole system through visualizing the relationship among these factors and hence provides a vulnerability analysis as shown in Figure 2. Finally, the bowtie modelling (Figure 3), which is informed by both the FTA (in Figure 1) and the RBD (in Figure 2), then classifies the safety barriers in the form of prevention (on the left side) and mitigation against consequence (on the right side) of the model.