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Landslides are widespread phenomenon that occur in any terrestrial area with slopes, causing massive property damage and, in the worst-case scenario, human losses. This propension to suffer losses is particularly high for developing countries due to their urban development, population growth and drastic land use changes. Social and economic consequences of landslides can be reduced through detailed planning and management strategies, which can be aided by risk analysis. In this study, we performed a detailed quantitative risk analysis for landslides in the whole Central-Asia (4,000,000 km2). Landslide-induced risk was computed in terms of exposed population and expected economic losses to buildings and linear infrastructures (roads and railways) adopting a 200 m spatial resolution. The purpose of our study is to produce the first regional-scale landslide risk assessment for Central-Asia in order to inform regional-scale risk mitigation strategies and it represents an advance step in the landslide risk analysis for extremely broad areas.

Central Asia is an area characterized by complex tectonics and active deformation; the related seismic activity controls the earthquake hazard level that, due to the occurrence of secondary and tertiary effects, also has direct implications for the hazard related to mass movements such as landslides, which are responsible for an extensive number of casualties every year. Climatically, this region is characterized by strong rainfall gradient contrasts due to the diversity of climate and vegetation zones. The region is drained by large, partly snow- and glacier-fed rivers that cross or terminate in arid forelands; therefore, it is also affected by a significant river flood hazard, mainly in spring and summer seasons. The challenge posed by the combination of different hazards can only be tackled by considering a multi-hazard approach harmonized among the different countries, in agreement with the requirements of the Sendai Framework for Disaster Risk Reduction. This work was carried out within the framework of the Strengthening Financial Resilience and Accelerating Risk Reduction in Central Asia (SFRARR) project as part of a multi-hazard approach and is focused on the first landslide susceptibility analysis at a regional scale for Central Asia. To this aim the most detailed landslide inventories, covering both national and transboundary territories, were implemented in a random forest model, together with several independent variables. The proposed approach represents an innovation in terms of resolution (from 30 to 70 m) and extension of the analyzed area with respect to previous regional landslide susceptibility and hazard zonation models applied in Central Asia. The final aim was to provide a useful tool for land use planning and risk reduction strategies for landslide scientists, practitioners, and administrators.

Landslide is a common natural disaster occurring in Indonesia during the rainy season from November to February. Attempts have been made to develop an early warning system based on the rainfall derived from satellite observation. It is essential to verify the accuracy level of the rainfall threshold in predicting the occurrence of rainfall, causing landslides and non-landslides to model the lower limit that can be used as an early warning device of the landslides. In this analysis, modelling was carried out with an empirical (intensity—duration/ID) approach using 220 data of rainfall that triggered landslide with satellite-based TRMM in Indonesia territory. The intensity and duration of antecedent rainfall were utilized in rainfall threshold modelling. The rainfall threshold was validated with ROC analysis. This method used seven statistics indices and ROC curve to determine the accuracy rate of the rainfall threshold. The results showed empirical equation I = 7.83D−0.328 within the interval time 2–18 days. The results of the analysis of the ROC on the rainfall threshold indicate that the model has a good accuracy rate and can be used in an early warning system of landslide even though it still has a fairly high error rate.

In this study, the main focus is the application and improvement of four empirical models, which account for the pyroclastic cover deposit thickness (PCDT) spatial distribution with respect to the bedrock surrounding the Somma-Vesuvius volcano(Campania, southern Italy). Three models, which are already known in the literature, link the depth to bedrock to the morphologicalfeatures of a slope. An original model called SEPT (slope exponential pyroclastic thickness) is presented in this manuscript and combinesthe initial total thickness of ash-fall pyroclastic cover with the slope gradient. All models were applied and validated using field measurements derived from this and preceding studies in the study area. The main finding is that the spatial distribution of the depth to bedrock in mountainous peri-volcanic areas mainly depends on the initial thickness of air-fallen material at a given position and slope angle. These findings allowed for the recognition of an ash-fall pyroclastic depositional environment that is characterized by different processes from those existing in other geomorphological frameworks and in which the soil thickness along the slopes is controlled by the weathering of bedrock and the formation of soilin situ. Finally, in this research, a reliable approach is proposed that is also applicable to other peri-volcanic areas of the world to assess the spatial distribution of the depth to bedrock, which is a fundamentally important parameter in distributed geomorphologic and hydrologic modeling.