Numerous rockfalls released during the 2010–2011 Canterbury earthquake sequence affected vital road sections for local commuters. We quantified rockfall fatality risk on two main routes by adapting a risk approach for roads originally developed for snow avalanche risk. We present results of the collective and individual fatality risks for traffic flow and waiting traffic. Waiting traffic scenarios particularly address the critical spatial-temporal dynamics of risk, which should be acknowledged in operational risk management. Comparing our results with other risks commonly experienced in New Zealand indicates that local rockfall risk is close to tolerability thresholds and likely exceeds acceptable risk.
Canterbury mayors say their ratepayers are already paying for earthquake recovery, roading, water and storm water infrastructure, so a shiny new Christchurch stadium is way down the priority list.
Lyttelton is a fun and exciting place to do archaeology. I’ve been lucky enough to get to do a bit of archaeology in Lyttelton in the last few months, mainly out in the road, because of the digging that’s been … Continue reading →
The only Christchurch street still closed following the 2011 earthquake will reopen later in 2018 only to close again in 2019 so the council can extend the route for the tram. The proposal has raised the ire of fledgling businesses along High st worried about the disruption the road works will cause them and wanting the work done now, before they open their doors to the public.
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.
Land cover change information in urban areas supports decision makers in dealing with public policy planning and resource management. Remote sensing has been demonstrated as an efficient and accurate way to monitor land cover change over large extents. The Canterbury Earthquake Sequence (CES) caused massive damage in Christchurch, New Zealand and resulted in significant land cover change over a short time period. This study combined two types of remote sensing data, aerial imagery (RGB) and LiDAR, as the basis for quantifying land cover change in Christchurch between 2011 – 2015, a period corresponding to the five years immediately following the 22 February 2011 earthquake, which was part of the CES. An object based image analysis (OBIA) approach was adopted to classify the aerial imagery and LiDAR data into seven land cover types (bare land, building, grass, shadow, tree and water). The OBIA approach consisted of two steps, image segmentation and object classification. For the first step, this study used multi-level segmentation to better segment objects. For the second step, the random forest (RF) classifier was used to assign a land cover type to each object defined by the segmentation. Overall classification accuracies for 2011 and 2015 were 94.0% and 94.32%, respectively. Based on the classification result, land cover changes between 2011 and 2015 were then analysed. Significant increases were found in road and tree cover, while the land cover types that decreased were bare land, grass, roof, water. To better understand the reasons for those changes, land cover transitions were calculated. Canopy growth, seasonal differences and forest plantation establishment were the main reasons for tree cover increase. Redevelopment after the earthquake was the main reason for road area growth. By comparing the spatial distribution of these transitions, this study also identified Halswell and Wigram as the fastest developing suburbs in Christchurch. These results provided quantitative information for the effects of CES, with respect to land cover change. They allow for a better understanding for the current land cover status of Christchurch. Among those land cover changes, the significant increase in tree cover aroused particularly interest as urban forests benefit citizens via ecosystem services, including health, social, economic, and environmental benefits. Therefore, this study firstly calculated the percentages of tree cover in Christchurch’s fifteen wards in order to provide a general idea of tree cover change in the city extent. Following this, an automatic individual tree detection and crown delineation (ITCD) was undertaken to determine the feasibility of automated tree counting. The accuracies of the proposed approach ranged between 56.47% and 92.11% in thirty different sample plots, with an overall accuracy of 75.60%. Such varied accuracies were later found to be caused by the fixed tree detection window size and misclassifications from the land cover classification that affected the boundary of the CHM. Due to the large variability in accuracy, tree counting was not undertaken city-wide for both time periods. However, directions for further study for ITCD in Christchurch could be exploring ITCD approaches with variable window size or optimizing the classification approach to focus more on producing highly accurate CHMs.
On November 14 2016 a magnitude 7.8 earthquake struck the south island of New Zealand. The earthquake lasted for just two minutes with severe seismic shaking and damage in the Hurunui and Kaikōura districts. Although these are predominantly rural areas, with scattered small towns and mountainous topography, they also contain road and rail routes that are essential parts of the national transport infrastructure. This earthquake and the subsequent recovery are of particular significance as they represent a disaster following in close proximity to another similar disaster, with the Canterbury earthquakes occurring in a neighboring district five years earlier. The research used an inductive qualitative case study to explore the nature of the Kaikōura recovery. That recovery process involved a complex interplay between the three parties; (a) the existing local government in the district, (b) central government agencies funding the recovery of the local residents and the national transport infrastructure, and (c) recovery leaders arriving with recent expertise from the earlier Canterbury disaster. It was evident that three groups: locals, government, and experts represented a multi-party governance debate in which the control of the Kaikōura earthquake recovery was shared amongst them. Each party had their own expertise, adgenda and networks that they brought to the Kaikōura recovery, but this created tensions between external expertise and local, community leadership. Recent earthquake research suggests that New Zealand is currently in the midst of an earthquake cluster, with further seismic disasters likely to occur in relatively close succession. This is likely to be compounded by the increasing frequency of other natural disasters with the effects of climate change. The present study investigates a phenomenon that may become increasingly common, with the transfer of disaster expertise from one event to another, and the interface between those experts with local and national government in directing recoveries. The findings of this study have implications for practitioners and policy makers in NZ and other countries where disasters are experienced in close spatial and temporal proximity.
