This report contributes to a collaborative project between the Marlborough District Council (MDC) and University of Canterbury (UC) which aims to help protect and promote the recovery of native dune systems on the Marlborough coast. It is centred around the mapping of dune vegetation and identification of dune protection zones for old-growth seed sources of the native sand-binders spinifex (Spinifex sericeus) and pīngao (Ficinia spiralis). Both are key habitat-formers associated with nationally threatened dune ecosystems, and pīngao is an important weaving resource and Ngāi Tahu taonga species. The primary goal is to protect existing seed sources that are vital for natural regeneration following major disturbances such as the earthquake event. Several additional protection zones are also identified for areas where new dunes are successfully regenerating, including areas being actively restored in the Beach Aid project that is assisting new native dunes to become established where there is available space.
Welcome to the Recover newsletter Issue 5 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 5th instalment covers the question of how much of the coast uplifted how much, recent lab work on seaweed responses to stressors, and more on our drone survey work to quantify earthquake impacts and recovery along 130 km of coastline in the intertidal zone!
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.
Following the Christchurch earthquake of 22 February 2011 a number of researchers were sent to Christchurch, New Zealand to document the damage to masonry buildings as part of “Project Masonry”. Coordinated by the Universities of Auckland and Adelaide, researchers came from Australia, New Zealand, Canada, Italy, Portugal and the US. The types of masonry investigated were unreinforced clay brick masonry, unreinforced stone masonry, reinforced concrete masonry, residential masonry veneer and churches; masonry infill was not part of this study. This paper focuses on the progress of the unreinforced masonry (URM) component of Project Masonry. To date the research team has completed raw data collection on over 600 URM buildings in the Christchurch area. The results from this study will be extremely relevant to Australian cities since URM buildings in New Zealand are similar to those in Australia.
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.”
A number of field testing techniques, such as standard penetration test (SPT), cone penetration test (CPT), and Swedish weight sounding (SWS), are popularly used for in-situ characterisation. The screw driving sounding (SDS) method, which has been recently developed in Japan, is an improved version of the SWS technique and measures more parameters, including the required torque, load, speed of penetration and rod friction; these provide more robust way of characterising soil stratigraphy. It is a cost-efficient technique which uses a machine-driven and portable device, making it ideal for testing in small-scale and confined areas. Moreover, with a testing depth of up to 10-15m, it is suitable for liquefaction assessment. Thus, the SDS method has great potential as an in-situ testing method for geotechnical site characterisation, especially for residential house construction. In this paper, the results of SDS tests performed at a variety of sites in New Zealand are presented. The soil database was employed to develop a soil classification chart based on SDS-derived parameters. Moreover, using the data obtained following the 2010-2011 Christchurch Earthquake Se-quence, a methodology was established for liquefaction potential evaluation using SDS data. http://www.isc5.com.au/wp-content/uploads/2016/09/1345-2-ORENSE.pdf
Soil Liquefaction during Recent Large-Scale Earthquakes contains selected papers presented at the New Zealand – Japan Workshop on Soil Liquefaction during Recent Large-Scale Earthquakes (Auckland, New Zealand, 2-3 December 2013). The 2010-2011 Canterbury earthquakes in New Zealand and the 2011 off the Pacific Coast of Tohoku Earthquake in Japan have caused significant damage to many residential houses due to varying degrees of soil liquefaction over a very wide extent of urban areas unseen in past destructive earthquakes. While soil liquefaction occurred in naturally-sedimented soil formations in Christchurch, most of the areas which liquefied in Tokyo Bay area were reclaimed soil and artificial fill deposits, thus providing researchers with a wide range of soil deposits to characterize soil and site response to large-scale earthquake shaking. Although these earthquakes in New Zealand and Japan caused extensive damage to life and property, they also serve as an opportunity to understand better the response of soil and building foundations to such large-scale earthquake shaking. With the wealth of information obtained in the aftermath of both earthquakes, information-sharing and knowledge-exchange are vital in arriving at liquefaction-proof urban areas in both countries. Data regarding the observed damage to residential houses as well as the lessons learnt are essential for the rebuilding efforts in the coming years and in mitigating buildings located in regions with high liquefaction potential. As part of the MBIE-JSPS collaborative research programme, the Geomechanics Group of the University of Auckland and the Geotechnical Engineering Laboratory of the University of Tokyo co-hosted the workshop to bring together researchers to review the findings and observations from recent large-scale earthquakes related to soil liquefaction and discuss possible measures to mitigate future damage. http://librarysearch.auckland.ac.nz/UOA2_A:Combined_Local:uoa_alma21151785130002091
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.
