Landslides are widespread geomorphological features on the North Island of New Zealand, where they represent one of the primary landscape-forming processes. This study focuses on the steepland terrain surrounding Gisborne, a city located on the east coast of the North Island, at the Hikurangi subduction margin. This terrain consists of young, weak, sedimentary rocks and soils; the most common modes of slope failures are soil creep, slides and flows in shallow, clay-rich soil and regolith, triggered by heavy rainfall. Based on observational data from Sentinel-1 imagery, this study leverages results from interferometric synthetic aperture radar (InSAR) processing to reveal the distribution of deformation across Gisborne’s steepland periphery from January 2016 to December 2021. Velocities in the line of sight were obtained from the stack of interferograms and projected along the direction of maximum slope, to extract the true displacement on the slopes. The ascending and descending data sets were combined to reveal the vertical and horizontal components of the deformation. The results were combined with a regional LiDAR data set, aerial imagery and field observations to delineate areas of slope deformation. Finally, slope deformation time series data was compared with rainfall records to identify seasonal changes, due to shrinking and swelling of expansive soils. Although the InSAR displacement data contains some noise, results could be used to identify 132 unstable slopes within the study area, caused by soil creep and earthflows. Also, the shrink-swell of expansive soils causes a seasonal pattern of displacements, which varied by 10–20 mm/year between Austral winter and summer, strongly correlated to rainfall.

Mountain glaciers are useful climatic indicators in alpine regions where instrumental climate observations are sparse. Here we review the state of understanding of past and present glaciation in North Island, New Zealand. During the last glacial, mountain glaciers descended to ∼1200 m on both Ruapehu and Tongariro. Glacier modelling indicates that air temperatures during the Last Glacial Maximum were 5–7°C lower than present. Under such conditions, favourable topoclimatic mass balance processes (e.g. snowblow, shading) may have promoted marginal glaciation in lower‐elevation North Island mountain ranges. The timing and magnitude of North Island glacier changes are broadly consistent with moraine chronologies and climatic reconstructions from glaciers in the Southern Alps. As of 2016, several glaciers and multi‐year snow patches persist in North Island (totalling 3.0 km ² – a 25% reduction in area since 1988), all situated on the summit region of Ruapehu volcano. Mass balance of these glaciers is modulated by topoclimatic factors and debris cover, which may reduce rates of mass loss in the near term. However, given the proximity of the present equilibrium line altitude to the summit elevation of Ruapehu, complete loss of the present glaciers is probable under even moderate warming scenarios predicted for the coming decades.

Physically-based models play a critical role in assessing rainfall-induced landslide susceptibility, offering valuable insights into landslide hazard prediction and risk mitigation. This study conducts a scientometric and systematic review of deterministic, physically-based, rainfall-induced landslide susceptibility mapping, synthesising research trends and advancements in this domain. A comprehensive literature search, conducted through the Scopus database following PRISMA guidelines, identifying 70 key studies for in-depth analysis. The findings reveal significant progress, including the integration of climate change projections, enhanced real-time monitoring systems, and advancements in high-resolution data processing. Despite these developments, challenges persist in achieving a balance between model complexity and practical applicability. This review highlights the need for standardised validation protocols, robust uncertainty analysis, and interdisciplinary approaches that merge physical modelling with machine learning techniques. By evaluating the evolution and current state of deterministic physically-based landslide modelling, this study provides a valuable reference for researchers and practitioners, contributing to the advancement of more reliable and accessible landslide susceptibility assessments.