A significant portion of economic loss from the Canterbury Earthquake sequence in 2010-2011 was attributed to losses to residential buildings. These accounted for approximately $12B of a total $40B economic losses (Horspool, 2016). While a significant amount of research effort has since been aimed at research in the commercial sector, little has been done to reduce the vulnerability of the residential building stock.
Geospatial liquefaction models aim to predict liquefaction using data that is free and readily-available. This data includes (i) common ground-motion intensity measures; and (ii) geospatial parameters (e.g., among many, distance to rivers, distance to coast, and Vs30 estimated from topography) which are used to infer characteristics of the subsurface without in-situ testing. Since their recent inception, such models have been used to predict geohazard impacts throughout New Zealand (e.g., in conjunction with regional ground-motion simulations). While past studies have demonstrated that geospatial liquefaction-models show great promise, the resolution and accuracy of the geospatial data underlying these models is notably poor. As an example, mapped rivers and coastlines often plot hundreds of meters from their actual locations. This stems from the fact that geospatial models aim to rapidly predict liquefaction anywhere in the world and thus utilize the lowest common denominator of available geospatial data, even though higher quality data is often available (e.g., in New Zealand). Accordingly, this study investigates whether the performance of geospatial models can be improved using higher-quality input data. This analysis is performed using (i) 15,101 liquefaction case studies compiled from the 2010-2016 Canterbury Earthquakes; and (ii) geospatial data readily available in New Zealand. In particular, we utilize alternative, higher-quality data to estimate: locations of rivers and streams; location of coastline; depth to ground water; Vs30; and PGV. Most notably, a region-specific Vs30 model improves performance (Figs. 3-4), while other data variants generally have little-to-no effect, even when the “standard” and “high-quality” values differ significantly (Fig. 2). This finding is consistent with the greater sensitivity of geospatial models to Vs30, relative to any other input (Fig. 5), and has implications for modeling in locales worldwide where high quality geospatial data is available.
During the 2011 M7.8 Kaikōura earthquake, ground motions recorded near the epicentre showed a significant spatial variation. The Te Mara farm (WTMC) station, the nearest to the epicentre, recorded 1g and 2.7g of horizontal and vertical peak ground accelerations (PGA), respectively. The nearby Waiu Gorge (WIGC) station recorded a horizontal PGA of 0.8g. Interestingly, however, the Culverden Airlie Farm (CULC) station that was very closely located to WIGC recorded a horizontal PGA of only 0.25g. This poster demonstrates how the local geological condition could have contributed to the spatially variable ground motions observed in the North Canterbury, based on the results of recently conducted geophysical investigations. The surficial geology of this area is dominated by alluvial gravel deposits with traces of silt. A borehole log showed that the thickness of the sediments at WTMC is over 76 metres. Interestingly, the shear wave velocity (Vs) profiles obtained from the three strong motion sites suggest unusually high shear wave velocity of the gravelly sediments. The velocity of sediments and the lack of clear peaks in the horizontal-to-vertical (H/V) spectral ratio at WTMC suggest that the large ground motion observed at this station was likely caused by the proximity of the station to the causative fault itself; the site effect was likely insignificant. Comparisons of H/V spectral ratios and Vs profiles suggest that the sediment thickness is much smaller at WIGC compared with CULC; the high PGA at WIGC was likely influenced by the high-frequency amplification caused by the response of shallow sediments.
Background and methodology The Mw 7.8, 14th November 2016 earthquake centred (item b, figure 1) in the Hurunui District of the South Island, New Zealand, damaged critical infrastructure across North Canterbury and Marlborough. We investigate the impacts to infrastructure and adaptations to the resulting service disruption in four small rural towns (figure 1): Culverden (a), Waiau (c), Ward (d) and Seddon (e). This is accomplished though literary research, interviews and geospatial analysis. Illustrating our methods, we have displayed here a Hurunui District hazard map (figure 2b) and select infrastructure inventories (figures 2a, 3).
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.
Our poster will present on-going QuakeCoRE-founded work on strong motion seismology for Dunedin-Mosgiel area, focusing on ground motion simulations for Dunedin Central Business District (CBD). Source modelling and ground motion simulations are being carried out using the SCEC (Southern California Earthquakes Center) Broad Band simulation Platform (BBP). The platform computes broadband (0-10 Hz) seismograms for earthquakes and was first implemented at the University of Otago in 2016. As large earthquakes has not been experienced in Dunedin in the time of period of instrumental recording, user-specified scenario simulations are of great value. The Akatore Fault, the most active fault in Otago and closest major fault to Dunedin, is the source focused on in the present study. Simulations for various Akatore Fault source scenarios are run and presented. Path and site effects are key components considered in the simulation process. A 1D shear wave velocity profile is required by SCEC BBP, and this is being generated to represent the Akatore-to-CBD path and site within the BBP. A 3D shear velocity model, with high resolution within Dunedin CBD, is being developed in parallel with this study (see Sangster et al. poster). This model will be the basis for developing a 3D shear wave velocity model for greater Dunedin-Mosgiel area for future ground motion simulations, using Canterbury software (currently under development).
Nowadays the telecommunication systems’ performance has a substantial impact on our lifestyle. Their operationality becomes even more substantial in a post-disaster scenario when these services are used in civil protection and emergency plans, as well as for the restoration of all the other critical infrastructure. Despite the relevance of loss of functionality of telecommunication networks on seismic resilience, studies on their performance assessment are few in the literature. The telecommunication system is a distributed network made up of several components (i.e. ducts, utility holes, cabinets, major and local exchanges). Given that these networks cover a large geographical area, they can be easily subjected to the effects of a seismic event, either the ground shaking itself, or co-seismic events such as liquefaction and landslides. In this paper, an analysis of the data collected after the 2010-2011 Canterbury Earthquake Sequence (CES) and the 2016 Kaikoura Earthquake in New Zealand is conducted. Analysing these data, information gaps are critically identified regarding physical and functional failures of the telecommunication components, the timeline of repair/reconstruction activities and service recovery, geotechnical tests and land planning maps. Indeed, if these missing data were presented, they could aid the assessment of the seismic resilience. Thus, practical improvements in the post-disaster collection from both a network and organisational viewpoints are proposed through consultation of national and international researchers and highly experienced asset managers from Chorus. Finally, an outline of future studies which could guide towards a more resilient seismic performance of the telecommunication network is presented.
On 14 November 2016, a magnitude (Mw) 7.8 earthquake struck the small coastal settlement of Kaikōura, Aotearoa-New Zealand. With an economy based on tourism, agriculture, and fishing, Kaikōura was immediately faced with significant logistical, economic, and social challenges caused by damage to critical infrastructure and lifelines, essential to its main industries. Massive landslips cut offroad and rail access, stranding hundreds of tourists, and halting the collection, processing and distribution of agricultural products. At the coast, the seabed rose two metres, limiting harbour-access to high tide, with implications for whale watching tours and commercial fisheries. Throughout the region there was significant damage to homes, businesses, and farmland, leaving owners and residents facing an uncertain future. This paper uses qualitative case study analysis to explore post-quake transformations in a rural context. The aim is to gain insight into the distinctive dynamics of disaster response mechanisms, focusing on two initiatives that have emerged in direct response to the disaster. The first examines the ways in which agriculture, food harvesting, production and distribution are being reimagined with the potential to enhance regional food security. The second examines the rescaling of power in decision-making processes following the disaster, specifically examining the ways in which rural actors are leveraging networks to meet their needs and the consequences of that repositioning on rural (and national) governance arrangements. In these and other ways, the local economy is being revitalised, and regional resilience enhanced through diversification, capitalising not on the disaster but the region's natural, social, and cultural capital. Drawing on insights and experience of local stakeholders, policy- and decision-makers, and community representatives we highlight the diverse ways in which these endeavours are an attempt to create something new, revealing also the barriers which needed to be overcome to reshape local livelihoods. Results reveal that the process of transformation as part of rural recovery must be grounded in the lived reality of local residents and their understanding of place, incorporating and building on regional social, environmental, and economic characteristics. In this, the need to respond rapidly to realise opportunities must be balanced with the community-centric approach, with greater recognition given to the contested nature of the decisions to be made. Insights from the case examples can inform preparedness and recovery planning elsewhere, and provide a rich, real-time example of the ways in which disasters can create opportunities for reimagining resilient futures.
