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Central Asia regions are characterized by active tectonics, high mountain chains with extreme topography with glaciers, and strong seasonal rainfall events. These key predisposing factors make large landslides a serious natural threat in the area, causing several casualties every year. The mountain crests are divided by wide lenticular or narrow, linear intermountain tectonic depressions, which are incised by many of the most important Central Asia rivers and are also subject to major seasonal river flood hazard. This multi-hazard combination is a source of potential damming scenarios, which can bring cascading effects with devastating consequences for the surrounding settlements and population. Different hazards can only be managed with a multi-hazard approach coherent within the different countries, as suggested by 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 with the aim of providing a damming susceptibility analysis at a regional scale for Central Asia. To achieve this, a semi-automated GIS-based mapping method, centered on a bivariate correlation of morphometric parameters defined by a morphological index, originally designed to assess the damming susceptibility at basin/regional scale, was modified to be adopted nationwide and applied to spatially assess the obstruction of the river network in Central Asia for mapped and newly formed landslides. The proposed methodology represents an improvement to the previously designed methodology, requiring a smaller amount of data, bringing new preliminary information on damming hazard management and risk reduction, and identifying the most critical area within the Central Asia regions. [ABSTRACT FROM AUTHOR]

Landslides are widespread phenomena that occur in any terrestrial area with slopes, causing massive property damage and, in the worst-case scenario, loss of human life. This propensity 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 of Central Asia (4 000 000 km2). Landslide-induced risk was computed, using a 200 m spatial resolution, in terms of exposed population and expected economic losses to buildings and linear infrastructures (roads and railways). 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 advanced step in the landslide risk analysis for extremely broad areas. [ABSTRACT FROM AUTHOR]

Central Asia regions are characterized by active tectonics, high mountain chains with extreme topography with glaciers and strong seasonal rainfall events. These key predisposing factors make large landslides a serious natural threat in the area, causing several casualties every year. The mountain crests are divided by wide lenticular or narrow, linear intermountain tectonic depressions, which are incised by many of the most important Central Asia rivers and are also subject to major seasonal river flood hazard. This multi-hazard combination is a source of potential damming scenarios which can bring cascading effects with devastating consequences for the surrounding settlements and population. Different hazards can only be managed with a multi-hazard approach coherent within the different countries, as suggested by the requirements of the Sendai Framework for Disaster Risk Reduction. This work was carried out within the framework of the SFRARR Project (“Strengthening Financial Resilience and Accelerating Risk Reduction in Central Asia”) as a part of a multi-hazard approach with the aim of providing a damming susceptibility analysis at a regional scale for Central Asia. To achieve this, a semi-automated GIS- based mapping method, centred on a bivariate correlation of morphometric parameters defined by a morphological index, originally designed to assess the damming susceptibility at basin/regional scale, was modified to be adopted nationwide and applied to spatially assess the obstruction of the river network in Central Asia for mapped and newly formed landslides. The proposed methodology represents an improvement of the previously designed, requiring a smaller amount of data, bringing new information on the damming hazard management and risk reduction for the Central Asia regions. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In this work, we apply a physically based model, namely the HIRESSS (HIgh REsolution Slope Stability Simulator) model, to forecast the occurrence of shallow landslides at the regional scale. HIRESSS is a physically based distributed slope stability simulator for analyzing shallow landslide triggering conditions during a rainfall event. The modeling software is made up of two parts: hydrological and geotechnical. The hydrological model is based on an analytical solution from an approximated form of the Richards equation, while the geotechnical stability model is based on an infinite slope model that takes the unsaturated soil condition into account. The test area is a portion of the Aosta Valley region, located in the northwest of the Alpine mountain chain. The geomorphology of the region is characterized by steep slopes with elevations ranging from 400ma.s.l. on the Dora Baltea River's floodplain to 4810ma.s.l. at Mont Blanc. In the study area, the mean annual precipitation is about 800-900 mm. These features make the territory very prone to landslides, mainly shallow rapid landslides and rockfalls. In order to apply the model and to increase its reliability, an in-depth study of the geotechnical and hydrological properties of hillslopes controlling shallow landslide formation was conducted. In particular, two campaigns of on site measurements and laboratory experiments were performed using 12 survey points. The data collected contributed to the generation of an input map of parameters for the HIRESSS model. In order to consider the effect of vegetation on slope stability, the soil reinforcement due to the presence of roots was also taken into account; this was done based on vegetation maps and literature values of root cohesion. The model was applied using back analysis for two past events that affected the Aosta Valley region between 2008 and 2009, triggering several fast shallow landslides. The validation of the results, carried out using a database of past landslides, provided good results and a good prediction accuracy for the HIRESSS model from both a temporal and spatial point of view. [ABSTRACT FROM AUTHOR]

In this work, we apply a physically-based model, namely the HIRESSS (High REsolution Stability Simulator) model, to forecast the occurrence of shallow landslides at regional scale. The final aim is the set-up of an early warning system at regional scale for shallow landslides. HIRESSS is a physically based distributed slope stability simulator for analysing shallow landslide triggering conditions in real time and in large areas using parallel computational techniques. The software can run in real-time by assimilating weather data and uses Monte Carlo simulation techniques to manage the geotechnical and hydrological input parameters. The test area is a portion of the Valle d'Aosta region, located in North-West Alpine mountain chain. The geomorphology of the region is characterized by steep slopes with elevations ranging from 400ma.s.l. of Dora Baltea's river floodplain to 4810ma.s.l. of Mont Blanc. In the study area, the mean annual precipitation is about 800-900mm. These features lead to a high hydrogeological hazard in the whole territory, as mass movements interest the 70% of the municipality areas (mainly shallow rapid landslides and rock falls). In order to apply the model and to increase its reliability, an in-depth study of the geotechnical and hydrological properties of hillslopes controlling shallow landslides formation was conducted. In particular, two campaigns of on site measurements and laboratory experiments were performed with 12 survey points. The data collected contributes to generate input map of parameters for HIRESSS model. In order to take into account the effect of vegetation on slope stability, the contribution of the root cohesion has been also taken into account based on the vegetation map and literature values. The model was applied in back analysis on two past events that have affected Valle d'Aosta region between 2008 and 2009, triggering several fast shallow landslides. The validation of the results, carried out using a database of past landslides, has provided good results and a good prediction accuracy of the HIRESSS model both from temporal and spatial point of view. A statistical analysis of the HIRESSS outputs in terms of failure probability has been carried out in order to define reliable alert levels for regional landslide early warning systems. [ABSTRACT FROM AUTHOR]

Regional scale landslide early warning systems are usually based on rainfall thresholds that can be used to issue timely warnings with a poor spatial resolution, as the warning is usually generalized for a wide alert zone. On the contrary, susceptibility maps can have a very fine spatial resolution, but they do not carry any temporal information. However, recent approaches have been proposed to integrate rainfall thresholds and susceptibility maps, bringing together the pros of the two methodologies and overcoming their cons. In this work, we exemplify a recently proposed methodology (Segoni et al., 2018) to combine a susceptibility map and a set of rainfall thresholds into a dynamic hazard matrix to refine the outputs of an existing regional scale landslide early warning system. Northern Tuscany (Italy) was the selected test site, because a recent landslide susceptibility map and a set of recently updated rainfall thresholds were available. These products were processed before being integrated into the matrix: the susceptibility map was reclassified, and the threshold set was expanded defining additional thresholds. Subsequently, the susceptibility classes (S1, low susceptibility; S2 medium susceptibility; S3 high susceptibility) and the rainfall rate classes (R1, R2, R3) were combined in the hazard matrix, defining five hazard classes, from H0 (null hazard) to H4 (high hazard). The most important and most innovative part of the proposed methodology is the calibration of the dynamic matrix. The calibration has two objectives: (i) setting a strict correspondence between hazard classes, physical interpretation and expected ground effects (thus providing a solid basis to plan adequate hazard management procedures); (ii) optimizing the forecasting capability of the system to maximize correct predictions and to minimize false and missed alarms.The proposed approach was validated against an independent dataset. The validation demonstrated that the use of the proposed procedure in a regional warning system brings two main advantages: (i) it is possible to better hypothesize when and where landslide are expected and with which hazard degree (a good correspondence was observed between forecasted hazard levels and landslides occurred), thus fostering a more effective hazard and risk management (e.g., setting priorities of intervention); (ii) the spatial resolution of the regional scale warning system is markedly refined because from time to time the areas where a given landslide hazard level is expected represent only a fraction (ranging from 2% to 61% in our test site) of the alert zone.ReferencesSegoni S, Tofani V, Rosi A, Catani F and Casagli N (2018) Combination of Rainfall Thresholds and Susceptibility Maps for Dynamic Landslide Hazard Assessment at Regional Scale. Front. Earth Sci. 6:85. doi: 10.3389/feart.2018.00085 [ABSTRACT FROM AUTHOR]