Mechanistic and scientific approaches to resilience assume that there is a “tipping point” at which a system can no longer absorb adversity; after this point, it is liable to collapse. Some of these perspectives, particularly those stemming from ecology and psychology, recognise that individuals and communities cannot be perpetually resilient without limits. While the resilience paradigm has been imported into the social sciences, the limits to resilience have often been disregarded. This leads to an overestimation of “human resourcefulness” within the resilience paradigm. In policy discourse, practice, and research, resilience seems to be treated as a “limitless” and human quality in which individuals and communities can effectively cope with any hazard at any time, for as long as they want and with any people. We critique these assumptions with reference to the recovery case in Ōtautahi Christchurch, Aotearoa New Zealand following the 2010-11 Canterbury earthquake sequence. We discuss the limits to resilience and reconceptualise resilience thinking for disaster risk reduction and sustainable recovery and development.
This thesis presents the application of data science techniques, especially machine learning, for the development of seismic damage and loss prediction models for residential buildings. Current post-earthquake building damage evaluation forms are developed for a particular country in mind. The lack of consistency hinders the comparison of building damage between different regions. A new paper form has been developed to address the need for a global universal methodology for post-earthquake building damage assessment. The form was successfully trialled in the street ‘La Morena’ in Mexico City following the 2017 Puebla earthquake. Aside from developing a framework for better input data for performance based earthquake engineering, this project also extended current techniques to derive insights from post-earthquake observations. Machine learning (ML) was applied to seismic damage data of residential buildings in Mexico City following the 2017 Puebla earthquake and in Christchurch following the 2010-2011 Canterbury earthquake sequence (CES). The experience showcased that it is readily possible to develop empirical data only driven models that can successfully identify key damage drivers and hidden underlying correlations without prior engineering knowledge. With adequate maintenance, such models have the potential to be rapidly and easily updated to allow improved damage and loss prediction accuracy and greater ability for models to be generalised. For ML models developed for the key events of the CES, the model trained using data from the 22 February 2011 event generalised the best for loss prediction. This is thought to be because of the large number of instances available for this event and the relatively limited class imbalance between the categories of the target attribute. For the CES, ML highlighted the importance of peak ground acceleration (PGA), building age, building size, liquefaction occurrence, and soil conditions as main factors which affected the losses in residential buildings in Christchurch. ML also highlighted the influence of liquefaction on the buildings losses related to the 22 February 2011 event. Further to the ML model development, the application of post-hoc methodologies was shown to be an effective way to derive insights for ML algorithms that are not intrinsically interpretable. Overall, these provide a basis for the development of ‘greybox’ ML models.
Background: Up to 6 years after the 2011 Christchurch earthquakes, approximately one-third of parents in the Christchurch region reported difficulties managing the continuously high levels of distress their children were experiencing. In response, an app named Kākano was co-designed with parents to help them better support their children’s mental health. Objective: The objective of this study was to evaluate the acceptability, feasibility, and effectiveness of Kākano, a mobile parenting app to increase parental confidence in supporting children struggling with their mental health. Methods: A cluster-randomized delayed access controlled trial was carried out in the Christchurch region between July 2019 and January 2020. Parents were recruited through schools and block randomized to receive immediate or delayed access to Kākano. Participants were given access to the Kākano app for 4 weeks and encouraged to use it weekly. Web-based pre- and postintervention measurements were undertaken. Results: A total of 231 participants enrolled in the Kākano trial, with 205 (88.7%) participants completing baseline measures and being randomized (101 in the intervention group and 104 in the delayed access control group). Of these, 41 (20%) provided full outcome data, of which 19 (18.2%) were for delayed access and 21 (20.8%) were for the immediate Kākano intervention. Among those retained in the trial, there was a significant difference in the mean change between groups favoring Kākano in the brief parenting assessment (F1,39=7, P=.012) but not in the Short Warwick-Edinburgh Mental Well-being Scale (F1,39=2.9, P=.099), parenting self-efficacy (F1,39=0.1, P=.805), family cohesion (F1,39=0.4, P=.538), or parenting sense of confidence (F1,40=0.6, P=.457). Waitlisted participants who completed the app after the waitlist period showed similar trends for the outcome measures with significant changes in the brief assessment of parenting and the Short Warwick-Edinburgh Mental Well-being Scale. No relationship between the level of app usage and outcome was found. Although the app was designed with parents, the low rate of completion of the trial was disappointing. Conclusions: Kākano is an app co-designed with parents to help manage their children’s mental health. There was a high rate of attrition, as is often seen in digital health interventions. However, for those who did complete the intervention, there was some indication of improved parental well-being and self-assessed parenting. Preliminary indications from this trial show that Kākano has promising acceptability, feasibility, and effectiveness, but further investigation is warranted. Trial Registration: Australia New Zealand Clinical Trials Registry ACTRN12619001040156; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=377824&isReview=true
Welcome to the Recover newsletter Issue 6 from the Marine Ecology Research Group (MERG) of the University of Canterbury. Recover is designed to keep you updated on our MBIE-funded earthquake recovery project called RECOVER (Reef Ecology, Coastal Values & Earthquake Recovery). This 6th instalment features the ‘new land’ created by the earthquake uplift of the coastline, recreational uses of beaches in Marlborough, and pāua survey work and hatchery projects with our partners in Kaikōura.
This paper presents on-going challenges in the present paradigm shift of earthquakeinduced ground motion prediction from empirical to physics-based simulation methods. The 2010-2011 Canterbury and 2016 Kaikoura earthquakes are used to illustrate the predictive potential of the different methods. On-going efforts on simulation validation and theoretical developments are then presented, as well as the demands associated with the need for explicit consideration of modelling uncertainties. Finally, discussion is also given to the tools and databases needed for the efficient utilization of simulated ground motions both in specific engineering projects as well as for near-real-time impact assessment.
This is a joint Resilience Framework undertaken by the Electrical, Computer and Software Engineering Department of the University of Auckland in association with West Power and Orion networks and partially funded by the New Zealand National Science Challenge and QuakeCoRE. The Energy- Communication research group nearly accomplished two different researches focusing on both asset resilience and system resilience. Asset resilience research which covers underground cables system in Christchurch region is entitled “2010-2011 Canterbury Earthquake Sequence Impact on 11KV Underground Cables” and system resilience research which covers electricity distribution and communication system in West Coast region is entitled “NZ Electricity Distribution Network Resilience Assessment and Restoration Models following Major Natural Disturbance“. As the fourth milestone of the aforementioned research project, the latest outcome of both projects has been socialised with the stakeholders during the Cigre NZ 2019 Forum.
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.
On 15 August 1868, a great earthquake struck off the coast of the Chile-Peru border generating a tsunami that travelled across the Pacific. Wharekauri-Rekohu-Chatham Islands, located 800 km east of Christchurch, Aotearoa-New Zealand (A-NZ) was one of the worst affected locations in A-NZ. Tsunami waves, including three over 6 metres high, injured and killed people, destroyed buildings and infrastructure, and impacted the environment, economy and communities. While experience of disasters, and advancements in disaster risk reduction systems and technology have all significantly advanced A-NZ’s capacity to be ready for and respond to future earthquakes and tsunami, social memory of this event and other tsunamis during our history has diminished. In 2018, a team of scientists, emergency managers and communication specialists collaborated to organise a memorial event on the Chatham Islands and co-ordinate a multi-agency media campaign to commemorate the 150th anniversary of the 1868 Arica tsunami. The purpose was to raise awareness of the disaster and to encourage preparedness for future tsunami. Press releases and science stories were distributed widely by different media outlets and many attended the memorial event indicating public interest for commemorating historical disasters. We highlight the importance of commemorating disaster anniversaries through memorial events, to raise awareness of historical disasters and increase community preparedness for future events – “lest we forget and let us learn.”
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.
