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This paper aims to introduce a numerical technique for forecasting the hazard caused by the disaster chain of landslide blocking river-dam breach floods through an integration of the distinct element method (DEM) and a well-balanced finite volume type shallow water model (SFLOW). A toppling slope in a reservoir area, the southeastern Tibetan Plateau, was chosen for the study. Creep has been observed in the potential instability area, and a possible sliding surface was identified based on the data collected from adits and boreholes. Catastrophic rock avalanches may be triggered after reservoir impoundment, and the associated landslide disaster chain needed to be predicted. First, the landslide blocking river process was modeled by the DEM using the three-dimensional particle flow code (PFC 3D). The landslide duration, runout distance, and kinematic characteristics were obtained. In addition, the landslide dam and barrier lake were constructed. Then, the cascading dam breach flood propagation was simulated using the self-developed SFLOW. The flow velocity, inundation depth, and area were obtained. The hazard maps derived from the combined numerical technique provided a quantitative reference for risk mitigation. The influences of two involved parameters on the final hazard-affected area are discussed herein. It is expected that the presented model will be applied in more prediction cases. [ABSTRACT FROM AUTHOR]

The mapping unit is the smallest indivisible unit of landslide susceptibility mapping. The mapping unit considerably affects the effect of landslide susceptibility mapping. Among many mapping units, the slope unit is one of the most suitable units for landslide susceptibility mapping. The methods available for classifying slope unit are not uniform, and the dividing effects differ. In this study, the hydrological method and curvature watershed method were used to classify the slope units in the study area on the upper Jinsha River, south-western China. Lithology, slope angle, slope aspect, normalized difference vegetation index (NDVI), land cover, rainfall, curvature, distance-to-river, distance-to-fault, were selected as the landslide conditioning factors. Support vector machine were applied to the landslide susceptibility modeling. Statistical indexes, 5-flod cross validation, Kappa coefficient, and AUC values were introduced to validate the prediction accuracy of the landslide susceptibility model. By comparing the shape characteristics of slope units classified by the two methods, we found that the slope unit classified based on curvature watershed method has a uniform size, shape between circle and equilateral triangle, and small internal terrain variation. As for the mean prediction accuracy, Kappa coefficient and AUC values of landslide susceptibility model, in the training stage, were 81.62%, 63.23%, and 89.72%, respectively, for the hydrological method, while 84.26%, 68.51%, and 90.80%, respectively, for the curvature watershed method; in the testing stage, were 80.70%, 61.40%, and 88.08%, respectively, for the hydrological method, while 83.24%, 66.49%, and 88.96%, respectively, for the curvature watershed method, which means that the curvature watershed method is more effective to produce landslide susceptibility map of the study area. [ABSTRACT FROM AUTHOR]

The objective of this study was to produce a landslide susceptibility map along the rapidly uplifting section of the upper Jinsha River. Firstly, a total of 40 landslides were identified in the study area from the interpretation of remote sensing (RS) and field survey data. Following landslide identification, ten variables including slope angle, slope aspect, curvature, land use, normalised difference vegetation index (NDVI), rainfall, lithology, distance to river, distance to fault, and Strahler's integral value were selected as the influencing factors in landslide susceptibility mapping. All of the influencing factors were extracted by the slope unit. The Strahler's integral value was used to represent the relationship between the rate of uplift and rate of denudation in each slope unit. Furthermore, three methods, including logistic regression, a support vector machine, and an artificial neural network, were applied to landslide susceptibility modelling. Five-fold cross validation, a statistical analysis method, and the area under the receiver operating characteristic curve (AUC) were used to compare the evaluation results of the three models. Finally, the variance-based method was used to find the key factors associated with landslides in the study area. The results show that the mean prediction accuracies of the logistic regression model, artificial neural network model, and support vector machine model were 80.47%, 87.30%, and 83.94% in the training stage, respectively, and 81.08%, 82.16%, and 83.51% in the validating stage, respectively. The mean AUCs of the three models were 88.16%, 93.96%, and 89.68% in the training stage, respectively, and 87.68%, 92.60%, and 89.88% in the validating stage, respectively. These results show that the artificial neural network model is the best model for evaluating landslide susceptibility in this study. The landslide susceptibility map produced by the artificial neural network model was divided into five classes, including very low, low, moderate, high, and very high, and the percentages of the areas of the five susceptibility classes were 17.23%, 28.32%, 22.73%, 16.73%, and 15.00%, respectively. Furthermore, the distance to river, slope aspect, lithology, and distance to fault are the most important influencing factors for landslide susceptibility mapping in the study area. Consequently, this study will be a useful guide for landslide prevention, mitigation, and future land planning in the study area. [ABSTRACT FROM AUTHOR]

To simulate debris flow run-out, the governing equations for free-surface shallow flow are corrected by setting the basal flow resistance coefficients with the quadratic rheological friction model. A well-balanced numerical scheme is developed for its run-out simulation over irregular topography. A linear reconstruction is adopted for improving the spatial accuracy of the numerical scheme. Considering the complex friction terms of governing equations of debris flow, they are estimated with a full implicit scheme for ensuring stability of the numerical scheme. The validity check of run-out simulation is implemented based on general knowledge of fluid, and a well-studied case occurred in the Xiezi Gully in Yingxiu Town, Sichuan Province of China. For practical purpose, the present numerical scheme is used for run-out prediction of debris flow in Xiaojia Gully of Panzhihua City, Sichuan Province of China. Our work aims to present a well-balanced numerical scheme for debris flow run-out simulation prediction, which can be applied quite conveniently to solve other kinds of debris flow models and helpful to promote the development in debris flow numerical calculation. [ABSTRACT FROM AUTHOR]

Catastrophic landslides and flowslides usually occur at large waste dumps. Compared to normal slopes, waste dumps have unique topographical conditions and material properties that cause considerable challenges associated with hazard prediction and mitigation. This paper describes one of the largest and highest waste dumps in China, the Zhujiabaobao waste dump, which covers an area of 4 km2 and has a volume of 30,000 × 104 m3 mine waste. A field investigation in April 2016 indicated that the waste dump was unstable. To assess the stability of the slope and predict its hazard scale, the authors use a 3D slope model based on a discrete element method to achieve it. The model is first constructed based on field investigations, including reconnaissance, as well as geomorphological analysis and laboratory experiments. Then, the strength reduction technique is used to calculate the safety factor of a slope. The results show that the safety factor of the slope is 2.3, and compared to the normal limit equilibrium method, the safety factor calculated by the DEM simulation is more accurate and approximately about 15% larger, because it considers more realistic boundary conditions and material properties. During the calculation of the safety factor, the corresponding run-out process can be presented as well. The simulation shows that sliding masses at different parts have different mobility performances, which the upper and front particles presenting the potential to move farther and faster during the run-out process due to force conditions. Additionally, the proposed method shows that topography is a crucial factor for mass movement that requires special attention for the prevention and prediction of landslide hazards. [ABSTRACT FROM AUTHOR]