Hybrid broadband simulation methods typically compute high-frequency portion of ground-motions using a simplified-physics approach (commonly known as “stochastic method”) using the same 1D velocity profile, anelastic attenuation profile and site-attenuation (κ0) value for all sites. However, these parameters relating to Earth structure are known to vary spatially. In this study we modify this conventional approach for high-frequency ground-shaking by using site-specific input parameters (referred to as “site-specific”) and analyze improvements over using same parameters for all sites (referred to as “generic”). First, we theoretically understand how different 1D velocity profiles, anelastic attenuation profiles and site-attenuation (κ0) values affects the Fourier Acceleration Spectrum (FAS). Then, we apply site-specific method to simulate 10 events from the 2010-2011 Canterbury earthquake sequence to assess performance against the generic approach in predicting recorded ground-motions. Our initial results suggest that the site-specific method yields a lower simulation standard deviation than generic case.
Background This study examines the performance of site response analysis via nonlinear total-stress 1D wave-propagation for modelling site effects in physics-based ground motion simulations of the 2010-2011 Canterbury, New Zealand earthquake sequence. This approach allows for explicit modeling of 3D ground motion phenomena at the regional scale, as well as detailed nonlinear site effects at the local scale. The approach is compared to a more commonly used empirical VS30 (30 m time-averaged shear wave velocity)-based method for computing site amplification as proposed by Graves and Pitarka (2010, 2015), and to empirical ground motion prediction via a ground motion model (GMM).
Developing a holistic understanding of social, cultural, and economic impacts of disasters can help in building disaster risk knowledge for policy making and planning. Many methods can help in developing an understanding of the impacts of a disaster, including interviews and surveys with people who have experienced disaster, which may be invasive at times and create stress for the participants to relive their experiences. In the past decade, social media, blog posts, video blogs (i.e. “vlogs”), and crowdsourcing mechanisms such as Humanitarian OpenStreetMap and Ushahidi, have become prominent platforms for people to share their experiences and impacts of an event from the ground. These platforms allow for the discovery of a range of impact information, from physical impacts, to social, cultural, and psychological impacts. It can also reveal interesting behavioural information such as their decision to heed a warning or not, as people tend to share their experiences and their reactions online. This information can help researchers and authorities understand both the impacts as well as behavioural responses to hazards, which can then shape how early warning systems are designed and delivered. It can also help to identify gaps in desired behavioural responses. This poster presents a selection of cases identified from the literature and grey literature, such as the Haiti earthquake, the Christchurch earthquake, Hurricane Sandy, and Hurricane Harvey, where online platforms were widely used during and after a disaster to document impacts, experiences, and behavioural responses. A summary of key learnings and areas for future research is provided.
Abstract This study provides a simplified methodology for pre-event data collection to support a faster and more accurate seismic loss estimation. Existing pre-event data collection frameworks are reviewed. Data gathered after the Canterbury earthquake sequences are analysed to evaluate the relative importance of different sources of building damage. Conclusions drawns are used to explore new approaches to conduct pre-event building assessment.
This poster discusses several possible approaches by which the nonlinear response of surficial soils can be explicitly modelled in physics-based ground motion simulations, focusing on the relative advantages and limitations of the various methodologies. These methods include fully-coupled 3D simulation models that directly allow soil nonlinearity in surficial soils, the domain reduction method for decomposing the physical domain into multiple subdomains for separate simulation, conventional site response analysis uncoupled from the simulations, and finally, the use of simple empirically based site amplification factors We provide the methodology for an ongoing study to explicitly incorporate soil nonlinearity into hybrid broadband simulations of the 2010-2011 Canterbury, New Zealand earthquakes.
Tsunami events including the 2004 Indian Ocean Tsunami and the 2011 Tohoku Earthquake and Tsunami confirmed the need for Pacific-wide comprehensive risk mitigation and effective tsunami evacuation planning. New Zealand is highly exposed to tsunamis and continues to invest in tsunami risk awareness, readiness and response across the emergency management and science sectors. Evacuation is a vital risk reduction strategy for preventing tsunami casualties. Understanding how people respond to warnings and natural cues is an important element to improving evacuation modelling techniques. The relative rarity of tsunami events locally in Canterbury and also globally, means there is limited knowledge on tsunami evacuation behaviour, and tsunami evacuation planning has been largely informed by hurricane evacuations. This research aims to address this gap by analysing evacuation behaviour and movements of Kaikōura and Southshore/New Brighton (coastal suburb of Christchurch) residents following the 2016 Kaikōura earthquake. Stage 1 of the research is engaging with both these communities and relevant hazard management agencies, using a survey and community workshops to understand real-event evacuation behaviour during the 2016 Kaikōura earthquake and subsequent tsunami evacuations. The second stage is using the findings from stage 1 to inform an agent-based tsunami evacuation model, which is an approach that simulates of the movement of people during an evacuation response. This method improves on other evacuation modelling approaches to estimate evacuation times due to better representation of local population characteristics. The information provided by the communities will inform rules and interactions such as traffic congestion, evacuation delay times and routes taken to develop realistic tsunami evacuation models. This will allow emergency managers to more effectively prepare communities for future tsunami events, and will highlight recommended actions to increase the safety and efficiency of future tsunami evacuations.
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.
Knowing how to rapidly rebuild disaster-damaged infrastructure, while deciding appropriate recovery strategies and catering for future investment is a matter of core interest to government decision makers, utility providers, and business sectors. The purpose of this research is to explore the effects of decisions and outcomes for physical reconstruction on the overall recovery process of horizontal infrastructure in New Zealand using the Canterbury and Kaikoura earthquakes as cases. A mixed approach including a systematic review, questionnaire survey and semi-structured interviews is used to capture perspectives of those involved in reconstruction process and gain insights into the effect of critical elements on infrastructure downtime. Findings from this research will contribute towards advancements of a systems dynamics model considering critical decision-making variables across phases of the reconstruction process to assess how these variables affect the rebuild process and the corresponding downtime. This project will improve the ability to explore alternative resilience improvement pathways and test the efficacy of alternative means for facilitating a faster and better reconstruction process.
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.
The lived reality of the 2010-2011 Canterbury earthquakes and its implications for the Waimakariri District, a small but rapidly growing district (third tier of government in New Zealand) north of Christchurch, can illustrate how community well-being, community resilience, and community capitals interrelate in practice generating paradoxical results out of what can otherwise be conceived as a textbook ‘best practice’ case of earthquake recovery. The Waimakariri District Council’s integrated community based recovery framework designed and implemented post-earthquakes in the District was built upon strong political, social, and moral capital elements such as: inter-institutional integration and communication, participation, local knowledge, and social justice. This approach enabled very positive community outputs such as artistic community interventions of the urban environment and communal food forests amongst others. Yet, interests responding to broader economic and political processes (continuous central government interventions, insurance and reinsurance processes, changing socio-cultural patterns) produced a significant loss of community capitals (E.g.: social fragmentation, participation exhaustion, economic leakage, etc.) which simultaneously, despite local Council and community efforts, hindered community well-being in the long term. The story of the Waimakariri District helps understand how resilience governance operates in practice where multi-scalar, non-linear, paradoxical, dynamic, and uncertain outcomes appear to be the norm that underpins the construction of equitable, transformative, and sustainable pathways towards the future.
