The Lake Coleridge Rock Avalanche Deposits (LCRADs) are located on Ryton Station in the middle Rakaia Valley, approximately 80 km west of Christchurch. Torlesse Supergroup greywacke is the basement material and has been significantly influenced by both active tectonics and glaciation. Both glacial and post-glacial processes have produced large volumes of material which blanket the bedrock on slopes and in the valley floors. The LCRADs were part of a regional study of rock avalanches by WHITEHOUSE (1981, 1983) and WHITEHOUSE and GRIFFITHS (1983), and a single rock avalanche event was recognised with a weathering rind age of 120 years B.P. that was later modified to 150 ± 40 years B.P. The present study has refined details of both the age and the sequence of events at the site, by identifying three separate rock avalanche deposits (termed the LCRA1, LCRA2 and LCRA3 deposits), which are all sourced from near the summit of Carriage Drive. The LCRA1 deposit is lobate in shape and had an estimated original deposit volume of 12.5 x 10⁶ m³, although erosion by the Ryton River has reduced the present day debris volume to 5.1 x 10⁶ m³. An optically stimulated luminescence date taken from sandy loess immediately beneath the LCRA1 deposit provided a maximum age for the rock avalanche event of 9,720 ± 750 years B.P., which is believed to be realistic given that this is shortly after the retreat of Acheron 3 ice from this part of the valley. Emplacement of rock avalanche material into an ancestral Ryton riverbed created a natural dam with a ~17 M m³ lake upstream. The river is thought to have created a natural spillway over the dam structure at ~557 m (a.s.l), and to have existed for a number of years before any significant downcutting occurred. Although a triggering mechanism for the LCRA1 deposit was poorly constrained, it is thought that stress rebound after glacial ice removal may have initiated failure. Due to the event occurring c.10,000 years ago, there was a lack of definition for a possible earthquake trigger, though the possibility is obvious. The LCRA₂ event had an original deposit volume of 0.66 x 10⁶ m³, and was constrained to the low-lying area adjacent to the Ryton River that had been created by river erosion of the LCRA1 deposit. Further erosion by the Ryton River has reduced the deposit volume to 0.4 x 10⁶ m³. A radiocarbon date from a piece of mānuka found within the LCRA2 deposit provided an age of 668 ± 36 years B.P., and this is thought to reliably date the event. The LCRA2 event also dammed the Ryton River, and the preservation of dam-break outwash terraces downstream from the deposit provides clear evidence of rapid dam erosion and flooding after overtopping, and breaching by the Ryton River. Based on the mean annual flow of the Ryton River, the LCRA2 lake would have taken approximately two weeks to fill assuming that there were no preferred breach paths and the material was relatively impermeable. The LCRA2 event is thought to have been coseismic with a fault rupture along the western segment of the PPAFZ, which has been dated at 600 ± 100 years B.P. by SMITH (2003). The small LCRA3 event was not able to be dated, but it is believed to have failed shortly after the LCRA2 event and it may in fact be a lag deposit of the second rock avalanche event possibly triggered by an aftershock. The deposit is only visible at one locality within the cliffs that line the Ryton River, and its lack of geomorphic expression is attributed to it occurring closely after the LCRA2 event, while the Ryton River was still dammed from the second rock avalanche event. A wedge-block of some 35,000 m³ of source material for a future rock avalanche was identified at the summit of Carriage Drive. The dilation of the rock mass, combined with unfavourably oriented sub-vertical bedding in the Torlesse Supergroup bedrock, has allowed toppling-style failure on both of the main ridge lines around the source area for the LCRADs. In the event of a future rock avalanche occurring within the Ryton riverbed an emergency response plan has been developed to provide a staged response, especially in relation to the camping ground located at the mouth of the Ryton River. A long-term management plan has also been developed for mitigation measures for the Ryton riverbed and adjacent floodplain areas downstream of a future rock avalanche at the LCRAD site.
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.
