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.
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.
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.
The Canterbury earthquake sequence in New Zealand’s South Island induced widespread liquefaction phenomena across the Christchurch urban area on four occasions (4 Sept 2010; 22 Feb; 13 June; 23 Dec 2011), that resulted in widespread ejection of silt and fine sand. This impacted transport networks as well as infiltrated and contaminated the damaged storm water system, making rapid clean-up an immediate post-earthquake priority. In some places the ejecta was contaminated by raw sewage and was readily remobilised in dry windy conditions, creating a long-term health risk to the population. Thousands of residential properties were inundated with liquefaction ejecta, however residents typically lacked the capacity (time or resources) to clean-up without external assistance. The liquefaction silt clean-up response was co-ordinated by the Christchurch City Council and executed by a network of contractors and volunteer groups, including the ‘Farmy-Army’ and the ‘Student-Army’. The duration of clean-up time of residential properties and the road network was approximately 2 months for each of the 3 main liquefaction inducing earthquakes; despite each event producing different volumes of ejecta. Preliminary cost estimates indicate total clean-up costs will be over NZ$25 million. Over 500,000 tonnes of ejecta has been stockpiled at Burwood landfill since the beginning of the Canterbury earthquakes sequence. The liquefaction clean-up experience in Christchurch following the 2010-2011 earthquake sequence has emerged as a valuable case study to support further analysis and research on the coordination, management and costs of large volume deposition of fine grained sediment in urban areas.
The research is funded by Callaghan Innovation (grant number MAIN1901/PROP-69059-FELLOW-MAIN) and the Ministry of Transport New Zealand in partnership with Mainfreight Limited. Need – The freight industry is facing challenges related to climate change, including natural hazards and carbon emissions. These challenges impact the efficiency of freight networks, increase costs, and negatively affect delivery times. To address these challenges, freight logistics modelling should consider multiple variables, such as natural hazards, sustainability, and emission reduction strategies. Freight operations are complex, involving various factors that contribute to randomness, such as the volume of freight being transported, the location of customers, and truck routes. Conventional methods have limitations in simulating a large number of variables. Hence, there is a need to develop a method that can incorporate multiple variables and support freight sustainable development. Method - A minimal viable model (MVM) method was proposed to elicit tacit information from industrial clients for building a minimally sufficient simulation model at the early modelling stages. The discrete-event simulation (DES) method was applied using Arena® software to create simulation models for the Auckland and Christchurch corridor, including regional pick-up and delivery (PUD) models, Christchurch city delivery models, and linehaul models. Stochastic variables in freight operations such as consignment attributes, customer locations, and truck routes were incorporated in the simulation. The geographic information system (GIS) software ArcGIS Pro® was used to identify and analyse industrial data. The results obtained from the GIS software were applied to create DES models. Life cycle assessment (LCA) models were developed for both diesel and battery electric (BE) trucks to compare their life cycle greenhouse gas (GHG) emissions and total cost of ownership (TCO) and support GHG emissions reduction. The line-haul model also included natural hazards in several scenarios, and the simulation was used to forecast the stock level of Auckland and Christchurch depots in response to each corresponding scenario. Results – DES is a powerful technique that can be employed to simulate and evaluate freight operations that exhibit high levels of variability, such as regional pickup and delivery (PUD) and linehaul. Through DES, it becomes possible to analyse multiple factors within freight operations, including transportation modes, routes, scheduling, and processing times, thereby offering valuable insights into the performance, efficiency, and reliability of the system. In addition, GIS is a useful tool for analysing and visualizing spatial data in freight operations. This is exemplified by their ability to simulate the travelling salesman problem (TSP) and conduct cluster analysis. Consequently, the integration of GIS into DES modelling is essential for improving the accuracy and reliability of freight operations analysis. The outcomes of the simulation were utilised to evaluate the ecological impact of freight transport by performing emission calculations and generating low-carbon scenarios to identify approaches for reducing the carbon footprint. LCA models were developed based on simulation results. Results showed that battery-electric trucks (BE) produced more greenhouse gas (GHG) emissions in the cradle phase due to battery manufacturing but substantially less GHG emissions in the use phase because of New Zealand's mostly renewable energy sources. While the transition to BE could significantly reduce emissions, the financial aspect is not compelling, as the total cost of ownership (TCO) for the BE truck was about the same for ten years, despite a higher capital investment for the BE. Moreover, external incentives are necessary to justify a shift to BE trucks. By using simulation methods, the effectiveness of response plans for natural hazards can be evaluated, and the system's vulnerabilities can be identified and mitigated to minimize the risk of disruption. Simulation models can also be utilized to simulate adaptation plans to enhance the system's resilience to natural disasters. Novel contributions – The study employed a combination of DES and GIS methods to incorporate a large number of stochastic variables and driver’s decisions into freight logistics modelling. Various realistic operational scenarios were simulated, including customer clustering and PUD truck allocation. This showed that complex pickup and delivery routes with high daily variability can be represented using a model of roads and intersections. Geographic regions of high customer density, along with high daily variability could be represented by a two-tier architecture. The method could also identify delivery runs for a whole city, which has potential usefulness in market expansion to new territories. In addition, a model was developed to address carbon emissions and total cost of ownership of battery electric trucks. This showed that the transition was not straightforward because the economics were not compelling, and that policy interventions – a variety were suggested - could be necessary to encourage the transition to decarbonised freight transport. A model was developed to represent the effect of natural disasters – such as earthquake and climate change – on road travel and detour times in the line haul freight context for New Zealand. From this it was possible to predict the effects on stock levels for a variety of disruption scenarios (ferry interruption, road detours). Results indicated that some centres rather than others may face higher pressure and longer-term disturbance after the disaster subsided. Remedies including coastal shipping were modelled and shown to have the potential to limit the adverse effects. A philosophical contribution was the development of a methodology to adapt the agile method into the modelling process. This has the potential to improve the clarification of client objectives and the validity of the resulting model.
