At 00:02 on 14th November 2016, a Mw 7.8 earthquake occurred in and offshore of the northeast of the South Island of New Zealand. Fault rupture, ground shaking, liquefaction, and co-seismic landslides caused severe damage to distributed infrastructure, and particularly transportation networks; large segments of the country’s main highway, State Highway 1 (SH1), and the Main North Line (MNL) railway line, were damaged between Picton and Christchurch. The damage caused direct local impacts, including isolation of communities, and wider regional impacts, including disruption of supply chains. Adaptive measures have ensured immediate continued regional transport of goods and people. Air and sea transport increased quickly, both for emergency response and to ensure routine transport of goods. Road diversions have also allowed critical connections to remain operable. This effective response to regional transport challenges allowed Civil Defence Emergency Management to quickly prioritise access to isolated settlements, all of which had road access 23 days after the earthquake. However, 100 days after the earthquake, critical segments of SH1 and the MNL remain closed and their ongoing repairs are a serious national strategic, as well as local, concern. This paper presents the impacts on South Island transport infrastructure, and subsequent management through the emergency response and early recovery phases, during the first 100 days following the initial earthquake, and highlights lessons for transportation system resilience.

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While it is well established that community members should participate in resilience planning, participation with genuine decision-making power remains rare. We detail an end-to-end disaster impact reduction modelling framework for infrastructure networks, embedded within a scenario-based participatory approach. Utilising the AF8+ earthquake scenario, we simulate hazard exposure, asset failure and recovery of interdependent critical infrastructure networks. Quantifying service levels temporally offers insights into possible interdependent network performance and community disconnection from national networks, not apparent when studying each infrastructure in isolation. Sequencing participation enables feedbacks between integrated modelling and participants’ impact assessments. Shared ownership of modelling outputs advances stakeholders’ understanding of resilience measures, allowing real-time implementation, increasing community resilience. Readily understood by central government, this format may increase support and resourcing, if nationally significant. Finally, this method tested integrated modelling and impacts assessments, identifying and enabling improvements for both.