Macroseismic intensities play a key role in the engineering, seismological, and loss modeling communities. However, at present, there is an increasing demand for instrumental data-based loss estimations that require statistical relationships between intensities and strong-motion data. In New Zealand, there was an urgent need to update the ground motion to intensity conversion equation (GMICE) from 2007, developed prior to a large number of recent earthquakes including the 2010–2011 Canterbury and 2016 Kaikōura earthquake sequences. Two main factors now provide us with the opportunity to update New Zealand’s GMICE: (1) recent publication of New Zealand’s Strong-Motion Database, corresponding to 276 New Zealand earthquakes with magnitudes 3.5–7.8 and 4–185 km depths; and (2) recent generation of a community intensity database from GeoNet’s “Felt Classic” (2004–2016) and “Felt Detailed” (2016–2019) questionnaires, corresponding to around 930,000 individual reports. Ground-motion data types analyzed are peak ground velocity (PGV) and peak ground acceleration (PGA). The intensity database contains 67,572 felt reports from 917 earthquakes, with magnitudes 3.5–8.1, and 1797 recordings from 247 strong-motion stations (SMSs), with hypocentral distances of 5–345 km. Different regression analyses were tested, and the bilinear regression of binned mean strong-motion recordings for 0.5 modified Mercalli intensity bins was selected as the most appropriate. Total least squares regression was chosen for reversibility in the conversions. PGV provided the best-fitting results, with lower standard deviations. The influence of hypocentral distance, earthquake magnitude, and the site effects of local geology, represented by the mean shear-wave velocity in the first 30 m depth, on the residuals was also explored. A regional correction factor for New Zealand, suitable for adjustment of global relationships, has also been estimated.

Water networks are vulnerable to earthquakes and failures of network components can result in a lack of availability of services, sometimes leading to relocation of the community. In New Zealand, there are statutory requirements for the water network providers to address the resilience of infrastructure assets. This is done by identifying and managing risks related to natural hazards and planning for appropriate financial provision to manage those risks. In addition to this, the impact from the Canterbury region earthquakes has accelerated the need for understanding the potential risk to critical infrastructure networks to minimise socio-economic impact. As such, there is a need for developing pragmatic approaches to deliver appropriate hazard and risk information to the stakeholders. Within the context of improving resilience for water networks, this study presents a transparent and staged approach to risk assessment by adopting three significant steps: (i) to define an earthquake hazard scenario for which the impact needs to be assessed and managed; (ii) to identify vulnerable parts of the network components; and (iii) to estimate likely outage time of services in the areas of interest. The above process is illustrated through a case study with water supply and wastewater networks of Rotorua Lakes Council by estimating ground motion intensities, damage identification and outage modelling affected by number of crews and preferred repair strategies. This case study sets an example by which other councils and/or water network managers could undertake risk assessment studies underpinned by science models and develop resilience management plans.

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Fire following earthquake (FFE) is a significant hazard in urban areas subject to high seismicity. Wellington City has many characteristics that make it susceptible to ignitions and fire spread. These include proximity to major active faults, closely spaced timber-clad buildings, vulnerable water and gas infrastructure, frequent high winds and challenging access for emergency services. We modelled the ignitions, fire spread and suppression for five earthquake sources. Uncertainty in ground motions, the number and location of ignitions, weather conditions and firefighting capacity were accounted for. The mean loss per burn zone (area burnt due to ignition and fire spread) is $46m without fire suppression, indicating the potential property damage avoided by controlling the fire spread. The mean total loss for earthquake scenarios ranges from $0.28b for the Wairau Fault through to $3.17b for a Hikurangi Subduction Zone scenario, including the influence of fire suppression. Wind speed has a strong influence on the potential losses for each simulation and is a more significant factor than the number of ignitions for evaluating losses. Areas in Wellington City of relatively high risk are identified, which may inform risk mitigation strategies. The models may be applied to other urban areas.