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This study aimed to develop a physical-based approach for predicting the spatial likelihood of shallow landslides at the regional scale in a transition zone with extreme topography. Shallow landslide susceptibility study in an area with diverse vegetation types as well as distinctive geographic factors (such as steep terrain, fractured rocks, and joints) that dominate the occurrence of shallow landslides is challenging. This article presents a novel methodology for comprehensively assessing shallow landslide susceptibility, taking into account both the positive and negative impacts of plants. This includes considering the positive effects of vegetation canopy interception and plant root reinforcement, as well as the negative effects of plant gravity loading and preferential flow of root systems. This approach was applied to simulate the regional-scale shallow landslide susceptibility in the Dadu River Basin, a transition zone with rapidly changing terrain, uplifting from the Sichuan Plain to the Qinghai–Tibet Plateau. The research findings suggest that: (1) The proposed methodology is effective and capable of assessing shallow landslide susceptibility in the study area; (2) the proposed model performs better than the traditional pseudo-static analysis method (TPSA) model, with 9.93% higher accuracy and 5.59% higher area under the curve; and (3) when the ratio of vegetation weight loads to unstable soil mass weight is high, an increase in vegetation biomass tends to be advantageous for slope stability. The study also mapped the spatial distribution of shallow landslide susceptibility in the study area, which can be used in disaster prevention, mitigation, and risk management. [ABSTRACT FROM AUTHOR]

Landslide susceptibility assessment is an important means of helping to reduce and manage landslide risk. The existing studies, however, fail to examine the spatially varying relationships between landslide susceptibility and its explanatory factors. This paper investigates the spatial variation in such relationships in Liangshan, China, leveraging a spatially explicit model, namely, geographical random forest (GRF). By comparing with random forest (RF), we found that GRF achieves a higher performance with an AUC of 0.86 due to its consideration of the spatial heterogeneity among variables. GRF also provides a higher-quality landslide susceptibility map than RF by correctly placing 92.35% of the landslide points in high-susceptibility areas. The local feature importance derived from GRF allows us to understand that the impact of conditioning factors varies across space, which can provide implications for policy development by local governments to place different levels of attention on different conditioning factors in specific counties to prevent and mitigate landslides. To account for the spatial dependence among the data in the model performance assessment, we use spatial cross-validation (CV) to split the data into subsets spatially rather than randomly for model training and testing. The results show that spatial CV can effectively address the over-optimistic bias in model error evaluation. [ABSTRACT FROM AUTHOR]

Physically-based landslide susceptibility analysis has been extensively employed to predict the likelihood of landslides owing to its remarkable predictive capability. However, due to an insufficiently comprehensive analysis of the vegetation-disaster connection, this approach may face limitations in its application to regions where there is significant variation in vegetation diversity and topography. An integrated model was thus proposed for predicting landslide susceptibility with the consideration of the positive and negative effects of vegetation on slope stability. However, to accurately implement the slope failure process under the influence of vegetation, it is important to have a high temporal resolution rainfall scenario for simulating the interception process of vegetation crowns on rainfall. Analysis of vegetation hydrological processes is associated with difficulties in a regional area because of limited data availability. The stochastic simulation method for extreme rainfall scenarios, adopted in this study, is known to be effective in dealing with uncertainty caused by insufficient data. The method, which comprises the Gumbel model and Bounded Random Cascade Model (BRCM), was used to overcome the difficulties in integrating the comprehensive effects of vegetation, the results of which provide refined rainfall scenarios for landslide susceptibility prediction from the perspective of the failure mechanism of vegetated slopes. The application of the proposed method for landslide susceptibility zoning was validated using a confusion matrix and ROC curve analysis in a practical case. Results showed that the method performed effectively and reliably. Furthermore, this study investigated the influence of different extreme rainfall scenarios on slope stability. Results revealed that as the return period of extreme rainfall increased from 50 to 150 years, the overall proportion of susceptible areas for different rainfall durations exhibited an upward trend. Additionally, the study found that increasing vegetation biomass had a positive impact on slope stability in the study area. The findings of this study are significant for disaster prevention and mitigation in mountainous regions, providing scientific support for risk management strategies. • Consideration of the positive and negative effects of vegetation • Construction of random rainfall scenarios as a trigger • Statistical analysis of the impact of vegetation on landslide susceptibility [ABSTRACT FROM AUTHOR]

The Belt and Road Initiative is a collaboration project launched by the Chinese Government to connect more than 65 countries all over the word by developing infrastructures, facilities, and support collaborations among involved Countries. The Silk Road Disaster Risk Reduction is a sub-project of the Belt and Road Initiative focused on mitigation and prevention of natural risks in the involved countries. In this context, this work presents a method to approach landslide susceptibility zoning on a continental scale that takes into account the limitations due to the completeness of landslide inventories and the scale and data quality of causal factors. A first attempt to produce a pixel-based statistical susceptibility map is described. All the data and software used in this work are open and open source. The landslide susceptibility zoning has been carried out in south-Asia using the NASA-COOLR landslide dataset through the Weight of Evidence method and it has been evaluated and validated by means of the ROC analysis. The results reveal a good prediction capacity and highlights that slope, relative relief and annual precipitation are the causative factors that play a major role in predisposing slope instability in the study area. Based on them, the method will be applied to the rest of the Belt and Road Countries. [ABSTRACT FROM AUTHOR]

Kathmandu Kyirong Highway (KKH) is one of the most strategic Sino-Nepal highways. Low-cost mitigation measures are common in Nepalese highways, however, they are not even applied sufficiently to control slope instability since the major part of this highway falls still under the category of feeder road, and thus less resources are made available for its maintenance. It is subjected to frequent landslide events in an annual basis, especially during monsoon season. The Gorkha earthquake, 2015 further mobilized substantial hillslope materials and damaged the road in several locations. The aim of this research is to access the dynamic landslide susceptibility considering pre, co and post seismic mass failures. We mapped 5,349 multi-temporal landslides of 15 years (2004–2018), using high resolution satellite images and field data, and grouped them in aforementioned three time periods. Landslide susceptibility was assessed with the application of 'certainty factor' (CF). Seventy percent landslides were used for susceptibility modelling and 30% for validation. The obtained results were evaluated by plotting 'receiver operative characteristic' (ROC) curves. The CF performed well with the 'area under curve' (AUC) 0.820, 0.875 and 0.817 for the success rates, and 0.809, 0.890 and 0.760 for the prediction rates for respective pre, co and post seismic landslide susceptibility. The accuracy for seismic landslide susceptibility was better than pre and post-quake ones. It might be because of the differences on completeness of the landslide inventory, which might have been possibly done better for the single event based co-seismic landslide mapping in comparison with multitemporal inventories in pre and post-quake situations. The results obtained in this study provide insights on dynamic spatial probability of landslide occurrences in the changing condition of triggering agents. This work can be a good contribution to the methodologies for the evaluation of the dynamic landslide hazard and risk, which will further help to design the efficient mitigation measures along the mountain highways. [ABSTRACT FROM AUTHOR]

Geohazard chain processes in mountainous areas generally entail a landslide, followed by a dammed lake, a dam breach, and then outburst flooding. These chains have greater destructive power and a larger area of coverage than a single process, of which a representative event is the April 2000 Yigong landslide in Tibet, China. In this study, a two-part, numerical back-analysis of the entire chain process is carried out. Enhanced one-layer Savage-Hutter models, which incorporate a multiscale, empirical friction model (velocity-weakening) and appropriate erosion mechanics, are solved using a non-staggered central differencing scheme. A reasonable reproduction of the geohazard event chain was obtained. Results show that the use of the multiscale friction law is able to reproduce the dynamic process of the landslide with acceptable accuracy. In addition, the variation of soil shear resistance along the dam depth (against the water flow above) during the dam breach is considered in the study, in which the outburst flooding process is better modeled. The numerical results, validated by field measurements, provide reliable assessment and interpretation of the actual event. [ABSTRACT FROM AUTHOR]

Mountain highways in China are located in various natural geographical areas with intense tectonic activity, steep topography and a high frequency of extreme precipitation events. These conditions make the highways vulnerable to the occurrence of multiple large debris flows simultaneously during heavy rainfall. To manage this hazard risk, a broader understanding of the hazard effects of debris flows and the vulnerability of highways is needed to reduce the losses resulting from these hazardous events. Accordingly, we analysed the effects of debris flow hazards on mountain highways and established an updated systematic indicator system to describe the vulnerability of highway infrastructure and movable hazard-affected objects. Next, we proposed a new integrated model of highway vulnerability based on the environmental sensibility, structural properties and functional effects of the highway infrastructure and on the exposure probability and quantity of movable hazard-affected objects. By analysing the characteristics of elements affected by debris flows, we developed a systematic and quantitative method of vulnerability assessment for mountain highways. Finally, this implemented method was applied to a case study in the Xiqu section of the Sichuan-Tibet Highway, an area seriously affected by debris flows during each rainy season. The hazard characteristics of disasters were analysed, and the affected highway sections were divided into four vulnerability levels. The analysis of the results indicated that the calculated vulnerability coincides with the actual effects of the disaster, which strongly suggests that the vulnerability assessment generated by the proposed method can serve as a pertinent guide for route selection, road rehabilitation and hazard mitigation of highways affected by debris flows in mountainous regions. [ABSTRACT FROM AUTHOR]

• Slope geomorphic units sufficiently describe landslide morphologic characteristics. • Quantitative analysis of hazard is achieved by considering effects of roots on soil. • A more systematic step is proposed for evaluating regional landslide susceptibility. Susceptibility assessments are crucial in identifying hazards in densely populated mountain areas featuring active internal and external dynamic geological processes. A comprehensive analysis of the interactions among ecological, hydrological, and geotechnical factors was performed to develop a quantitative method for assessing regional landslide susceptibility based on slope failure and landslide formation mechanisms. Using slope geomorphic units (SGUs) that can sufficiently describe the comprehensive features of landforms and hazard-forming factors related to landslides, an integrated model was built to analyze potential slip planes and slope stability by considering the different reinforcement effects of roots on soil, the root anchoring capacities of various vegetation types, and the corresponding vegetation weight loads. Using this SGU-based method, the occurrence possibility of regional-scale landslides was determined by analyzing the propagation directions and maximum moving distances of landslide materials in unstable areas. Taking the landslides in the Dadu River Basin, China, as a case study, a landslide susceptibility map was obtained and validated by conducting a field study and remote sensing interpretation of actual landslides; the assessment results were in accordance with the actual disaster situation. The distributions of zones with high or very high susceptibility (i.e., high-altitude valleys with steep slopes, abundant rainfall, and severe soil erosion) are closely correlated with the slope topography and stability. These findings suggest that the proposed assessment methodology can provide scientific support for preventing or mitigating landslide hazards and may serve as pertinent guidance for regional landslide susceptibility assessments in the Dadu River Basin and beyond. [ABSTRACT FROM AUTHOR]

The "ALOS PULSAR DEM" is incorrect in the original article. The correct one should be "ALOS PALSAR DEM". [ABSTRACT FROM AUTHOR]