Landslides are significant hazards, especially in seismically-active mountainous regions, where shaking amplified by steep topography can result in widespread landsliding. These landslides present not only an acute hazard, but a chronic hazard that can last years-to-decades after the initial earthquake, causing recurring impacts. The Mw 7.8 Kaikōura earthquake caused more than 20,000 landslides throughout North Canterbury and resulted in significant damage to nationally significant infrastructure in the coastal transport corridor (CTC), isolating Kaikōura from the rest of New Zealand. In the years following, ongoing landsliding triggered by intense rainfall exacerbated the impacts and slowed the recovery process. However, while there is significant research on co-seismic landslides and their initial impacts in New Zealand, little research has explored the evolution of co-seismic landslides and how this hazard changes over time. This research maps landslides annually between 2013 and 2021 to evaluate the changes in pre-earthquake, co-seismic and post-earthquake rates of landsliding to determine how landslide hazard has changed over this time. In particular, the research explores how the number, area, and spatial distribution of landslides has changed since the earthquake, and whether post-earthquake mitigation works have in any way affected the long-term landslide hazard. Mapping of landslides was undertaken using open-source, medium resolution Landsat-8 and Sentinel-2 satellite imagery, with landslides identified visually and mapped as single polygons that capture both the source zone and deposit. Three study areas with differing levels of post-earthquake mitigation are compared: (i) the northern CTC, where the majority of mitigation was in the form of active debris removal; (ii) the southern CTC, where mitigation was primarily via passive protection measures; and (iii) Mount Fyffe, which has had no mitigation works since the earthquake. The results show that despite similar initial impacts during the earthquake, the rate of recovery in terms of landslide rates varies substantially across the three study areas. In Mount Fyffe, the number and area of landslides could take 45 and 22 years from 2021 respectively to return to pre-earthquake levels at the current rate. Comparatively, in the CTC, it could take just 5 years and 3-4 years from 2021 respectively. Notably, the fastest recovery in terms of landslide rates in the CTC was primarily located directly along the transport network, whereas what little recovery did occur in Mount Fyffe appeared to follow no particular pattern. Importantly, recovery rates in the northern CTC were notably higher than in the southern CTC, despite greater co-seismic impacts in the former. Combined, these results suggest the active, debris removal mitigation undertaken in the northern CTC may have had the effect of dramatically reducing the time for landslide rates to return to pre-earthquake levels. The role of slope angle and slope aspect were explored to evaluate if these observations could be driven by local differences in topography. The Mount Fyffe study area has higher slope angles than the CTC as a whole and landslides predominantly occurred on slightly steeper slopes than in the CTC. This may have contributed to the longer recovery times for landsliding in Mount Fyffe due to greater gravitational instability, however the observed variations are minor compared to the differences in recovery rates. In terms of slope aspect, landslides in Mount Fyffe preferentially occurred on north- and south-facing slopes whereas landslides in the CTC preferred the east- and south-facing slopes. The potential role of these differences in landslide recovery remains unclear but may be related to the propagation direction of the earthquake and the tracking direction of post-earthquake ex-tropical cyclones. Finally, landslides in the CTC are observed to be moving further away from the transport network and the number of landslides impacting the CTC decreased significantly since the earthquake. Nevertheless, the potential for further landslide reactivation remains. Therefore, despite the recovery in the CTC, it is clear that there is still risk of the transport network being impacted by further landsliding, at least for the next 3-5 yrs.
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.
