INTRODUCTION This project falls under the Flagship 3: Wellington Coordinated Project. It supports other projects within FP3 to create a holistic understanding of risks posed by collapsed buildings due to future earthquake/s and the secondary consequences of cordoning in the short, mid and long term. Cordoning of the Christchurch CBD for more than two years and its subsequent implications on people and businesses had a significant impact on the recovery of Christchurch. Learning from this and experiences from the Kaikōura earthquake (where cordons were also established around selected buildings, Figure 3) have highlighted the need to understand the effects of cordons and plan for it before an earthquake occurs
Detailed studies on the sediment budget may reveal valuable insights into the successive build-up of the Canterbury Plains and their modification by Holocene fluvialaction connected to major braided rivers. Additionally, they bear implications beyond these fluvial aspects. Palaeoseismological studies claim to have detected signals of major Alpine Fault earthquakes in coastal environments along the eastern seaboard of the South Island (McFadgen and Goff, 2005). This requires high connectivity between the lower reaches of major braided rivers and their mountain catchments to generate immediate significant sediment pulses. It would be contradictory to the above mentioned hypothesis though. Obtaining better control on sediment budgets of braided rivers like the Waimakariri River will finally add significant value to multiple scientific and applied topics like regional resource management. An essential first step of sediment budget studies Is to systematically map the geomorphology, conventionally in the field and/or using remote-sensing applications, to localise, genetically identify, and classify landforms or entire toposequences of the area being investigated. In formerly glaciated mountain environments it is also indispensable to obtain all available chronological information supporting subsequent investigations.
The 14 November 2016 Kaikōura earthquake had major impacts on New Zealand's transport system. Road, rail and port infrastructure was damaged, creating substantial disruption for transport operators, residents, tourists, and business owners in the Canterbury, Marlborough and Wellington regions, with knock-on consequences elsewhere. During both the response and recovery phases, a large amount of information and data relating to the transport system was generated, managed, analysed, and exchanged within and between organisations to assist decision making. To improve information and data exchanges and related decision making in the transport sector during future events and guide new resilience strategies, we present key findings from a recent post-earthquake assessment. The research involved 35 different stakeholder groups and was conducted for the Ministry of Transport. We consider what transport information was available, its usefulness, where it was sourced from, mechanisms for data transfer between organisations, and suggested approaches for continued monitoring.
Natural catastrophes are increasing worldwide. They are becoming more frequent but also more severe and impactful on our built environment leading to extensive damage and losses. Earthquake events account for the smallest part of natural events; nevertheless seismic damage led to the most fatalities and significant losses over the period 1981-2016 (Munich Re). Damage prediction is helpful for emergency management and the development of earthquake risk mitigation projects. Recent design efforts focused on the application of performance-based design engineering where damage estimation methodologies use fragility and vulnerability functions. However, the approach does not explicitly specify the essential criteria leading to economic losses. There is thus a need for an improved methodology that finds the critical building elements related to significant losses. The here presented methodology uses data science techniques to identify key building features that contribute to the bulk of losses. It uses empirical data collected on site during earthquake reconnaissance mission to train a machine learning model that can further be used for the estimation of building damage post-earthquake. The first model is developed for Christchurch. Empirical building damage data from the 2010-2011 earthquake events is analysed to find the building features that contributed the most to damage. Once processed, the data is used to train a machine-learning model that can be applied to estimate losses in future earthquake events.
This research aims to explore how business models of SMEs revolve in the face of a crisis to be resilient. The business model canvas was used as a tool to analyse business models of SMEs in Greater Christchurch. The purpose was to evaluate the changes SMEs brought in their business models after hit by a series of earthquake in 2010 and 2011. The idea was to conduct interviews of business owners and analyse using grounded theory methods. Because this method is iterative, a tentative theoretical framework was proposed, half way through the data collection. It was realised that owner specific characteristics were more prominent in the data than the elements business model. Although, SMEs in this study experienced several operational changes in their business models such as change of location and modification of payment terms. However, the suggested framework highlights how owner specific attributes influence the survival of a small business. Small businesses and their owners are extremely interrelated that the business models personify the owner specific characteristics. In other words, the adaptation of the business model reflects the extent to which the owner possess these attributes. These attributes are (a) Mindsets – the attitude and optimism of business owner; (b) Adaptive coping – the ability of business owner to take corrective actions; and (c) Social capital – the network of a business owner, including family, friends, neighbours and business partners.
