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Tens of thousands of landslides were generated over 10, 000 km2 of North Canterbury and Marlborough as a consequence of the 14 November 2016, MW7.8 Kaikōura Earthquake. The most intense landslide damage was concentrated in 3500 km2 around the areas of fault rupture. Given the sparsely populated area affected by landslides, only a few homes were impacted and there were no recorded deaths due to landslides. Landslides caused major disruption with all road and rail links with Kaikōura being severed. The landslides affecting State Highway 1 (the main road link in the South Island of New Zealand) and the South Island main trunk railway extended from Ward in Marlborough all the way to the south of Oaro in North Canterbury. The majority of landslides occurred in two geological and geotechnically distinct materials reflective of the dominant rock types in the affected area. In the Neogene sedimentary rocks (sandstones, limestones and siltstones) of the Hurunui District, North Canterbury and around Cape Campbell in Marlborough, first-time and reactivated rock-slides and rock-block slides were the dominant landslide type. These rocks also tend to have rock material strength values in the range of 5-20 MPa. In the Torlesse 'basement' rocks (greywacke sandstones and argillite) of the Kaikōura Ranges, first-time rock and debris avalanches were the dominant landslide type. These rocks tend to have material strength values in the range of 20-50 MPa. A feature of this earthquake is the large number (more than 200) of valley blocking landslides it generated. This was partly due to the steep and confined slopes in the area and the widely distributed strong ground shaking. The largest landslide dam has an approximate volume of 12(±2) M m3 and the debris from this travelled about 2.7 km2 downslope where it formed a dam blocking the Hapuku River. The long-term stability of cracked slopes and landslide dams from future strong earthquakes and large rainstorms are an ongoing concern to central and local government agencies responsible for rebuilding homes and infrastructure. A particular concern is the potential for debris floods to affect downstream assets and infrastructure should some of the landslide dams breach catastrophically. At least twenty-one faults ruptured to the ground surface or sea floor, with these surface ruptures extending from the Emu Plain in North Canterbury to offshore of Cape Campbell in Marlborough. The mapped landslide distribution reflects the complexity of the earthquake rupture. Landslides are distributed across a broad area of intense ground shaking reflective of the elongate area affected by fault rupture, and are not clustered around the earthquake epicentre. The largest landslides triggered by the earthquake are located either on or adjacent to faults that ruptured to the ground surface. Surface faults may provide a plane of weakness or hydrological discontinuity and adversely oriented surface faults may be indicative of the location of future large landslides. Their location appears to have a strong structural geological control. Initial results from our landslide investigations suggest predictive models relying only on ground-shaking estimates underestimate the number and size of the largest landslides that occurred.

Tectonic deformation within fault damage zones can influence slope stability and landslide failure mechanisms due to rock mass strength effects and the presence of tectonic structures. Here, we used detailed site investigations to evaluate controls on deformation within the Half Moon Bay landslide complex, located ~1 km from the surface trace of the Hope fault in the South Island of New Zealand. During the 2016 Mw 7.8 Kaikōura earthquake, the slope experienced up to ~13 m of displacement and partially transitioned into a rock avalanche (with a volume of ~350,000 m³). Deep-seated deformation of the entire slope predated the 2016 earthquake. Results of geomorphological analysis, field mapping, geophysical surveys, slope displacement, and a 60-mdeep borehole in the incipient portion of the landslide indicated the presence of a subvertical tectonic fabric and intense fracturing and weathering of the rock mass, which gradually decrease with depth. Based on these results, we established a conceptual model wherein the landslide failure mechanism is a combination of flexural toppling along the subvertical structures coupled with joint-step-path sliding along preexisting, closely spaced discontinuities within the graywacke rock mass. Coseismic slope displacements revealed a large area of incipient failure behind the headscarp of the 2016 rock avalanche, which will likely result in further avalanching at the site. This case study demonstrates that inherited tectonic structures (combined with seismicity and weathering in an oversteepened coastal slope) play an important role in the evolution of hillslopes near active faults. [ABSTRACT FROM AUTHOR]

Coastal hillslopes often host higher concentrations of earthquake-induced landslides than those further inland, but few studies have investigated the reasons for this occurrence. As a result, it is unclear if regional earthquake-induced landslide susceptibility models trained primarily on inland hillslopes are effective predictors of coastal susceptibility. The 2016 Mw 7.8 Kaikōura earthquake on the northeastern South Island of New Zealand resulted in ca. 1600 landslides > 50 m 2 on slopes > 15 ∘ within 1 km of the coast, contributing to an order of magnitude greater landslide source area density than inland hillslopes within 1 to 3 km of the coast. In this study, logistic regression modelling is used to investigate how landslide susceptibility differs between coastal and inland hillslopes and to determine the factors that drive the distribution of coastal landslides initiated by the 2016 Kaikōura earthquake. Strong model performance (area under the receiver operator characteristic curve or AUC of ca. 0.80 to 0.92) was observed across eight models, which adopt four simplified geology types. The same landslide susceptibility factors, primarily geology, steep slopes, and ground motion, are strong model predictors for both inland and coastal landslide susceptibility in the Kaikōura region. In three geology types (which account for more than 90 % of landslide source areas), a 0.03 or less drop in model AUC is observed when predicting coastal landslides using inland-trained models. This suggests little difference between the features driving inland and coastal landslide susceptibility in the Kaikōura region. Geology is similarly distributed between inland and coastal hillslopes, and peak ground acceleration (PGA) is generally lower in coastal hillslopes. Slope angle, however, is significantly higher in coastal hillslopes and provides the best explanation for the high density of coastal landslides during the 2016 Kaikōura earthquake. Existing regional earthquake-induced landslide susceptibility models trained on inland hillslopes using common predictive features are likely to capture this signal without additional predictive variables. Interestingly, in the Kaikōura region, most coastal hillslopes are isolated from the ocean by uplifted shore platforms. Enhanced coastal landslide susceptibility from this event appears to be a legacy effect of past erosion from wave action, which preferentially steepened these coastal hillslopes. [ABSTRACT FROM AUTHOR]

Important infrastructure such as highways or railways traverse unstable terrain in many mountainous and scenic parts of the world. Rockfalls and landslides result in frequent maintenance needs, system unreliability due to frequent closures and restrictions, and safety hazards. Seismic activity significantly amplifies these negative economic and community impacts by generating large rockfalls and landslides as well as weakening the terrain. This paper interrogates a rich database of repeat terrestrial lidar scans collected during the Canterbury New Zealand Earthquake Sequence to document geomorphic processes as well as quantify rockfall activity rates through time. Changes in the activity rate (spatial distribution) and failure depths (size) were observed based on the Rockfall Activity Index (RAI) morphological classification. Forecasting models can be developed from these relationships that can be utilized by transportation agencies to estimate increased maintenance needs for debris removal to minimize road closures from rockfalls after seismic events. [ABSTRACT FROM AUTHOR]