Transformers in New Zealand's South Island electrical transmission network have been impacted by geomagnetically induced currents (GIC) during geomagnetic storms. We explore the impact of GIC on this network by developing a thin‐sheet conductance (TSC) model for the region, a geoelectric field model, and a GIC network model. (The TSC is composed of a thin‐sheet conductance map with underlying layered resistivity structure.) Using modeling approaches that have been successfully used in the United Kingdom and Ireland, we applied a thin‐sheet model to calculate the electric field as a function of magnetic field and ground conductance. We developed a TSC model based on magnetotelluric surveys, geology, and bathymetry, modified to account for offshore sediments. Using this representation, the thin sheet model gave good agreement with measured impedance vectors. Driven by a spatially uniform magnetic field variation, the thin‐sheet model results in electric fields dominated by the ocean‐land boundary with effects due to the deep ocean and steep terrain. There is a strong tendency for the electric field to align northwest‐southeast, irrespective of the direction of the magnetic field. Applying this electric field to a GIC network model, we show that modeled GIC are dominated by northwest‐southeast transmission lines rather than east‐west lines usually assumed to dominate.

During space weather events, geomagnetically induced currents (GICs) can be induced in high‐voltage transmission networks, damaging individual transformers within substations. A common approach to modeling a transmission network has been to assume that every substation can be represented by a single resistance to Earth. We have extended that model by building a transformer‐level network representation of New Zealand's South Island transmission network. We represent every transformer winding at each earthed substation in the network by its known direct current resistance. Using this network representation significantly changes the GIC hazard assessment, compared to assessments based on the earlier assumption. Further, we have calculated the GIC flowing through a single phase of every individual transformer winding in the network. These transformer‐level GIC calculations show variation in GICs between transformers within a substation due to transformer characteristics and connections. The transformer‐level GIC calculations alter the hazard assessment by up to an order of magnitude in some places. In most cases the calculated GIC variations match measured variations in GIC flowing through the same transformers. This comparison with an extensive set of observations demonstrates the importance of transformer‐level GIC calculations in models used for hazard assessment.

S U M M A R YMt Ruapehu is an active andesite cone volcano, which marks the southern termination of the Kermadec volcanic arc. Results from 40 broad-band magnetotelluric soundings have been analysed using the phase tensor. This approach provides a way of determining dimensionality, allowing for distortion removal, and visualizing data in a 3-D situation. The phase tensor analysis suggests that the shallow resistivity structure is largely 1-D in character, but that the deeper structure requires a 3-D interpretation. 1-D inversions show that at sites on Ruapehu a shallow conductive layer lies between a high resistivity layer, of a few hundred metres thickness, and higher resistivity layer corresponding to basement greywacke. The low resistivity layer is contiguous with the waters of the highly acidic Crater Lake, and thus is believed to be the hydraulically controlled upper limit of a zone of acid alteration overlain by dry volcanic rock and ash. To the southwest of the volcano the conductive layer merges with a surface conductor associated with Tertiary sediments. Following initial 2-D inversions, the deep resistivity structure has been derived through 3-D inversion of data from 38 sites. This indicates the existence of a dyke-like low resistivity zone that persists to at least 10 km depth and extends from beneath the summit of Ruapehu to the northeast where it appears to connect to a poorly constrained region of high conductivity, which lies outside the network of measurement sites. The low resistivity dyke-like feature may be identified with a volcanic feeder system, which also supplies the other volcanoes of the Tongariro Volcanic Centre and marks the conduit by which hot gases and (occasionally) magma reach the surface.

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Geomagnetically induced currents (GICs) during a space weather event have previously caused transformer damage in New Zealand. During the 2015 St. Patrick's Day Storm, Transpower NZ Ltd has reliable GIC measurements at 23 different transformers across New Zealand's South Island. These observed GICs show large variability, spatially and within a substation. We compare these GICs with those calculated from a modeled geolectric field using a network model of the transmission network with industry‐provided line, earthing, and transformer resistances. We calculate the modeled geoelectric field from the spectra of magnetic field variations interpolated from measurements during this storm and ground conductance using a thin‐sheet model. Modeled and observed GIC spectra are similar, and coherence exceeds the 95% confidence threshold, for most valid frequencies at 18 of the 23 transformers. Sensitivity analysis shows that modeled GICs are most sensitive to variation in magnetic field input, followed by the variation in land conductivity. The assumption that transmission lines follow straight lines or getting the network resistances exactly right is less significant. Comparing modeled and measured GIC time series highlights that this modeling approach is useful for reconstructing the timing, duration, and relative magnitude of GIC peaks during sudden commencement and substorms. However, the model significantly underestimates the magnitude of these peaks, even for a transformer with good spectral match. This is because of the limited range of frequencies for which the thin‐sheet model is valid and severely limits the usefulness of this modeling approach for accurate prediction of peak GICs.