Modelling the impact of lifeline infrastructure failure during natural hazard events

In the case of natural hazard-caused disasters, direct impacts including building damage and loss of life are relatively well studied. Indirect disruption, on the other hand, including supply chain disruption and business interruption is harder to predict, quantify, visualise and insure. The need to better prepare for indirect disruption comes from the increasing cost and interruption it causes. A component of indirect disruption is the failure of lifelines such as power, communication, transportation and water; critical infrastructure and essential services that modern society relies on for everyday living. The disruption of lifeline services during natural hazard events has the potential to impact populations by exacerbating the hazard itself and/or hindering the ability to respond to or recover from the event. Lifeline failure can also propagate outside the reach of the hazard footprint, causing disruption in regions not directly impacted by the event. In preparation for the true impacts of natural hazard events on society there is a need to better understand the exposure of lifeline infrastructure, the interconnectedness and behaviour of lifeline networks and to identify vulnerable populations that rely on their operation. Current research on lifeline networks focus efforts on the evaluation of network characteristics, their optimisation and robustness to random failure, or the consequences of targeted attacks. Limited research has been undertaken on the impacts of natural hazard events on these systems and the flow-on effects of failure for disaster response and recovery. This thesis utilises mathematical graph theory tools alongside natural hazard modelling to analyse and quantify the extent of lifeline disruption during natural hazard events and the flow on effects of service failure. A future eruption of Mount Fuji in Japan is used as the major case study scenario to assess the usefulness of graph theory techniques in aiding disaster mitigation, emergency response and community recovery. In particular graph theory was used to assess the impacts of ash fall on the evacuation plans for Yamanashi Prefecture with regards to a future 1707 Hoei type eruption. It was found that: Ash induced road closures have the potential to affect current evacuation plans for Yamanashi Prefecture, particularly for those residents who are set to evacuate at or after the onset of a future eruption. Ash fall accumulation on roads, even after a few hours from the onset of an eruption, can inhibit road use, resulting in long detours or the inability for residents to be able to evacuate unassisted. After the cessation of an eruption, ash fall can impact the return of evacuees to their homes by either blocking roads or damaging buildings, affecting safety. Evacuees will have to wait for roads to be cleared of ash, and buildings to be assessed for damage, before they are able to return. In an eruption scenario where wind conditions are predominantly westerly the current plan for residents to evacuate to the north east of Yamanashi prefecture is not advisable. Assigned host locations in the northeast would be impacted by ash fall themselves; adding additional pressure on these communities and potentially resulting in further evacuations. This scenario provided the opportunity to test graph theory techniques in natural hazard risk assessment and to demonstrate how graph theory can assist post event recovery in a real world context. Methods developed in this study can be used to further explore impacts of ash fall, or other volcanic phenomena, in other prefectures around Mount Fuji or other volcanoes throughout Japan. Moreover, these methods can be used to address the exposure and risk to lifelines from other natural hazard events or even to compare between them. The results of this thesis show that graph theory techniques, alongside Geographic Information Systems tools and hazard modelling, with an understanding of the use and vulnerability of particular lifelines, can help to envisage potential problems that could result from lifeline failure and aid in the process of recovery. Not only is it important to make lifeline infrastructure more resilient to disruption from future natural hazard shocks, there is also a need to increase resilience by preparing communities to cope with service outages. For true shared responsibility to occur, local governments and communities need to be better informed and prepared so they can cope with the absence of lifelines during a disaster. Collaboration between all stakeholders is required to bridge information gaps and to create holistic disaster scenarios in order to provide more realistic and accurate assessments of future natural hazard impacts -- abstract.