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On November 14, 2016, the northeastern South Island of New Zealand was hit by the magnitude Mw 7.8 Kaikōura earthquake, which is characterized by the most complex rupturing mechanism ever recorded. The widespread landslides triggered by the earthquake make this event a great case study to revisit our current knowledge of earthquake-triggered landslides in terms of factors controlling the spatial distribution of landslides and the rapid assessment of geographic areas affected by widespread landsliding. Although the spatial and size distributions of landslides have already been investigated in the literature, a polygon-based co-seismic landslide inventory with landslide size information is still not available as of June 2021. To address this issue and leverage this large landslide event, we mapped 14,233 landslides over a total area of approximately 14,000 km2. We also identified 101 landslide dams and shared them all via an open-access repository. We examined the spatial distribution of co-seismic landslides in relation to lithologic units and seismic and morphometric characteristics. We analyzed the size statistics of these landslides in a comparative manner, by using the five largest co-seismic landslide inventories ever mapped (i.e., Chi-Chi, Denali, Wenchuan, Haiti, and Gorkha). We compared our inventory with respect to these five ones to answer the question of whether the landslides triggered by the 2016 Kaikōura earthquake are less numerous and/or share size characteristics similar to those of other strong co-seismic landslide events. Our findings show that the spatial distribution of the Kaikōura landslide event is not significantly different from those belonging to other extreme landslide events, but the average landslide size generated by the Kaikōura earthquake is relatively larger compared to some other large earthquakes (i.e., Wenchuan and Gorkha).

Successive major landslides during October and November 2018 in Baige village, eastern Tibet, dammed the Jinsha River on two occasions, and the subsequent dam breaches instigated a multi-hazard chain that flooded many towns downstream. Analysis of high-resolution aerial images and field investigations unveiled three potentially unstable rock mass clusters in the source area of the landslides, suggesting possible future failures with potential for river-damming and flooding. In order to evaluate and understand the disaster chain effect linked to the potentially unstable rock mass, we systematically studied the multi-hazard scenarios through an integrated numerical modelling approach. Our model begins with an evaluation of the probability of landslide failure, including runout and river damming, and then addresses the dam breach and resultant flood—hence simulating and visualising an entire disaster chain. The model parameters were calibrated using empirical data from the two Baige landslides. Then, we predict the future cascading hazards via seven scenarios according to all possible combinations of potential rock mass failure. For each scenario, the landslide runouts, dam-breaching, and flooding are numerically simulated with full consideration of uncertainties among the model input parameters. The maximum dam breach flood extent, depth, velocity, and peak arrival time are predicted at sequential sites downstream. As a first attempt to simulate the full spectrum of a landslide-induced multi-hazard chain, our study provides insights and substantiates the value provided by multi-hazard modelling. The integrated approach described here can be applied to similar landslide-induced chains of hazards in other regions.

In this paper, a study aimed to assess the rockfall hazard along a portion of the SS18 coastal road, located in the coastal area of Maratea (Basilicata Region, Southern Italy), is presented. The relevance of this study derives from the location of the study area, because the SS18 is a strategic roads in a touristic area, and, since the hazard assessment was performed in 2004 within a project financed by the Viability Regional Department of Autonomous National Company of Roads (ANAS), from the possibility to validate the results by using real rockfall events occurred after 2004. The procedure for assessing the rockfall hazard was composed of four sequential analyses: (i) geomechanical and kinematic characterization of rock mass, (ii) implementation of Romana’s (1985) Slope Mass Rating (SMR) method for identifying the potential boulder release areas (rockfall initiation areas), (iii) determination of rockfall trajectories by using a 3D numerical model (ROTOMAP), (iv) calculation and mapping of the hazard index by combining three factors, i.e., (a) lithological features of outcropping materials on rock faces, (b) kinematic compatibility defined by simulating the rockfall trajectories, and (c) spatial distribution of occurred rockfall events. Finally, the proposed methodology was validated by combining the distribution of the hazard levels along the road with the location on the SS18 of the rockfall events occurred from 2004 to 2014.

Landslide risk assessment is often a difficult task due to the lack of temporal data on landslides and triggering events (frequency), run-out distance, landslide magnitude and vulnerability. The probability of occurrence of landslides is often very difficult to predict, as well as the expected magnitude of events, due to the limited data availability on past landslide activity. In this paper, a qualitative procedure for assessing the exposure of elements at risk is presented for an area of the Apulia region (Italy) where no temporal information on landslide occurrence is available. Given these limitations in data availability, it was not possible to produce a reliable landslide hazard map and, consequently, a risk map. The qualitative analysis was carried out using the spatial multi-criteria evaluation method in a global information system. A landslide susceptibility composite index map and four asset index maps (physical, social, economic and environmental) were generated separately through a hierarchical procedure of standardising and weighting. The four asset index maps were combined in order to obtain a qualitative weighted assets map, which, combined with the landslide susceptibility composite index map, has provided the final qualitative landslide exposure map. The resulting map represents the spatial distribution of the exposure level in the study area; this information could be used in a preliminary stage of regional planning. In order to demonstrate how such an exposure map could be used in a basic risk assessment, a quantification of the economic losses at municipal level was carried out, and the temporal probability of landslides was estimated, on the basis of the expert knowledge. Although the proposed methodology for the exposure assessment did not consider the landslide run-out and vulnerability quantification, the results obtained allow to rank the municipalities in terms of increasing exposure and risk level and, consequently, to identify the priorities for designing appropriate landslide risk mitigation plans.

Landslide risk assessment is based on spatially integrating landslide hazard with exposed elements-at-risk to determine their vulnerability and to express the expected direct and indirect losses. There are three components that are relevant for expressing landslide hazard: spatial, temporal, and magnitude probabilities. At a medium-scale analysis, this is often done by first deriving a landslide susceptibility map, and to determine the three types of probabilities on the basis of landslide inventories linked to particular triggering events. The determination of spatial, temporal, and magnitude probabilities depend mainly on the availability of sufficiently complete historical records of past landslides, which in general are rare in most countries (e.g., India, etc.). In this paper, we presented an approach to use available historical information on landslide inventories for landslide hazard and risk analysis on a medium scale (1:25,000) in a perennially typical data-scarce environment in Darjeeling Himalayas (India). We demonstrate how the incompleteness in the resulting landslide database influences the various components in the calculation of specific risk of elements-at-risk (e.g., buildings, population, roads, etc.). We incorporate the uncertainties involved in the risk estimation and illustrate the range of expected losses in the form of maximum and minimum loss curves. The study demonstrates that even in data-scarce environments, quantitative landslide risk assessment is a viable option, as long as the uncertainties involved are expressed.

The increased socio-economic significance of landslides has resulted in the application of statistical methods to assess their hazard, particularly at medium scales. These models evaluate where, when and what size landslides are expected. The method presented in this study evaluates the landslide hazard on the basis of homogenous susceptible units (HSU). HSU are derived from a landslide susceptibility map that is a combination of landslide occurrences and geo-environmental factors, using an automated segmentation procedure. To divide the landslide susceptibility map into HSU, we apply a region-growing segmentation algorithm that results in segments with statistically independent spatial probability values. Independence is tested using Moran’s I and a weighted variance method. For each HSU, we obtain the landslide frequency from the multi-temporal data. Temporal and size probabilities are calculated using a Poisson model and an inverse-gamma model, respectively. The methodology is tested in a landslide-prone national highway corridor in the northern Himalayas, India. Our study demonstrates that HSU can replace the commonly used terrain mapping units for combining three probabilities for landslide hazard assessment. A quantitative estimate of landslide hazard is obtained as a joint probability of landslide size, of landslide temporal occurrence for each HSU for different time periods and for different sizes.