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The erosion‐deposition propagation of granular avalanches is prevalent and may increase their destructiveness. However, this process has rarely been reported for debris flows on gentle slopes, and the contribution of momentum hidden under the surge front to debris‐flow destructiveness is ambiguous. Therefore, the momentum carried by the apparent surge front is often used to indicate debris‐flow destructiveness. In this study, the erosion‐deposition propagation is confirmed by surge‐depth hydrographs measured at the Jiangjia Ravine (Yunnan Province, China). Based on simple hydraulic jump equations, the eroded deposition depth of surge flow is quantified, and the erosion pattern can be divided into two patterns (shallow and deep erosion). For surge flows with erosion‐deposition propagation, significant downward erosion potential is confirmed, and debris‐flow surge erosion is considered the deep erosion. The total momentum carried by surge flow is further quantified by two Froude numbers (surge‐front and rearward Froude numbers) and verified through the field observation of surge flows. The total momentum of surge flow not only originates from the apparent surge front, but also includes the momentum within the eroded deposition layer. This study provides a theoretical approach for quantifying the upper limit of erosion depth and revealing the destructiveness of debris‐flow surges. A perspective on the importance of substrate deposition for debris‐flow erosion on gentle slopes is emphasized, as this approach can improve the reliability of debris‐flow risk assessment. Plain Language Summary For flow‐type mass movements consisting of multiple surges, a subsequent surge would entrain the deposition of previous surges. The subsequent surge continues to move forward until it deposits again. This deposition is in turn carried away by the subsequent surges. This process is termed erosion‐deposition propagation. The erosion‐deposition propagation widely occurs in snow avalanches and enhances destructiveness by amplifying the scale and mobility of avalanches. For debris flows on gentle slopes, erosion‐deposition propagation has not been reported, and the effect of this process on debris‐flow destructiveness is unclear. In this study, the erosion‐deposition propagation of debris flows is confirmed by the field observation of surge flows at the Jiangjia Ravine (Yunnan Province, China). Based on simple hydraulic jump equations, the erosion into deposition of surge flow is quantified. The erosion patterns and momentum hidden under debris‐flow surges are revealed. The deep erosion pattern means that the apparent debris‐flow surge is merely “the tip of the iceberg,” and there is a large portion underneath. This study proposes a theoretical approach for quantifying the eroded deposition depth and the total momentum carried by debris‐flow surges, which is conducive to a precise risk assessment and mitigation of debris‐flow surges. Key Points The erosion‐deposition propagation of debris flow is confirmed by surge‐depth hydrographs measured at the Jiangjia Ravine, Yunnan Province, China Shallow and deep erosion patterns are revealed by hydraulic jump equations. The debris‐flow surges at the Jiangjia Ravine fall into the deep erosion The destructiveness of debris‐flow surges is quantified by considering the momentum hidden under the surge front and confirmed by field observation

Surging debris flows are among the most destructive natural hazards, and elucidating the interaction between coarse‐grained fronts and the trailing liquefied slurry is key to understanding these flows. Here, we describe the application of high‐resolution and high‐frequency 3D LiDAR data to explore the dynamics of a debris flow at Illgraben, Switzerland. The LiDAR measurements facilitate automated detection of features on the flow surface, and construction of the 3D flow depth and velocity fields through time. Measured surface velocities (2–3 m s−1) are faster than front velocities (0.8–2 m s−1), illustrating the mechanism whereby the flow front is maintained along the channel. Further, we interpret the relative velocity of different particles to infer that the vertical velocity profile varies between plug flow and one that features internal shear. Our measurements provide unique insights into debris‐flow motion, and provide the foundation for a more detailed understanding of these hazardous events. Plain Language Summary Debris flows are surging flows of soil, wood, and water that can impact people and infrastructure far downstream of their initiation zone. Work by others has identified that debris flows tend to develop a distinct segregation between large particles concentrated at the front of the flow and small particles at the tail; however, the formation process and implications of this for debris‐flow motion have remained vague. In this work, we present measurements from laser scanners, originally developed for autonomous vehicles, that provide insight into this process. The scanners provide 10 scans per second, which can be used to measure the velocity of objects (rocks and woody debris) on the surface of the flow. We show that different objects in the flow move at different speeds, which results in many destructive features of debris flows. These measurements, and the resulting process understanding, are important for predicting debris‐flow hazard and reducing the associated risk. Key Points High‐resolution 3D LiDAR scans at subsecond intervals demonstrate a novel method for exploring debris‐flow dynamics LiDAR‐derived front and surface velocities allow identification of processes forming and maintaining the debris‐flow front Observations of individual particle motion place constraints on the vertical velocity profile and temporal variation in flow regimes

On 13 August 2010, significant debris flows were triggered by intense rainfall events in Wenchuan earthquake-affected areas, destroying numerous houses, bridges, and traffic facilities. To investigate the impact force of debris flows, a fluid–structure coupled numerical model based on smoothed particle hydrodynamics is established in this work. The debris flow material is modeled as a viscous fluid, and the check dams are simulated as elastic solid (note that only the maximum impact forces are evaluated in this work). The governing equations of both phases are solved respectively, and their interaction is calculated. We validate the model with the simulation of a sand flow model test and confirm its ability to calculate the impact force. The Wenjia gully and Hongchun gully debris flows are simulated as the application of the coupled smoothed particle hydrodynamic model. The propagation of the debris flows is then predicted, and we obtain the evolution of the impact forces on the check dams.

Runoff‐generated debris flows are a potentially destructive and deadly response to wildfire until sufficient vegetation and soil‐hydraulic recovery have reduced susceptibility to the hazard. Elevated debris‐flow susceptibility may persist for several years, but the controls on the timespan of the susceptible period are poorly understood. To evaluate the connection between vegetation recovery and debris‐flow occurrence, we calculated recovery for 25 fires in the western United States using satellite‐derived leaf area index (LAI) and compared recovery estimates to the timing of 536 debris flows from the same fires. We found that the majority (>98%) of flows occurred when LAI was less than 2/3 of typical prefire values. Our results show that total vegetation recovery is not necessary to inhibit runoff‐generated flows in a wide variety of regions in the western United States. Satellite‐derived vegetation data show promise for estimating the timespan of debris‐flow susceptibility. Plain Language Summary Debris flows caused by excessive surface‐water runoff during intense rainfall can be a deadly and destructive hazard in mountainous areas after wildfire. In some cases, debris flows have only occurred in the burned area in the weeks to months after the fire, while, in other cases, debris flows occurred over several years. Though the recovery of vegetation is important for stabilizing sediment and reducing debris‐flow likelihood, uncertainty remains about how much recovery is needed to inhibit debris flows and about how much time is needed to reach this level of recovery. Knowing for how long debris flows are likely to be a hazard is important for managing risks to residents and infrastructure. To investigate this issue, we assembled a data set of 536 debris flows from the western United States and used satellite‐derived vegetation data to calculate the recovery condition of the burned area when each debris flow occurred. We found that the vast majority of the debris flows initiated when the burned area had not yet reached two‐thirds of its prefire vegetation condition. Burned areas that were slower to recover tended to experience debris flows over more protracted timescales. Key Points Majority (>98%) of western United States postfire debris flows occurred when leaf area index was less than 2/3 of typical prefire values Total recovery of vegetation not necessary to inhibit debris flows Remotely sensed postfire vegetation state useful to evaluate elevated debris‐flow susceptibility with time

This paper presents a new, generalized two‐phase debris flow model that includes many essential physical phenomena. The model employs the Mohr‐Coulomb plasticity for the solid stress, and the fluid stress is modeled as a solid‐volume‐fraction‐gradient‐enhanced non‐Newtonian viscous stress. The generalized interfacial momentum transfer includes viscous drag, buoyancy, and virtual mass. A new, generalized drag force is proposed that covers both solid‐like and fluid‐like contributions, and can be applied to drag ranging from linear to quadratic. Strong coupling between the solid‐ and the fluid‐momentum transfer leads to simultaneous deformation, mixing, and separation of the phases. Inclusion of the non‐Newtonian viscous stresses is important in several aspects. The evolution, advection, and diffusion of the solid‐volume fraction plays an important role. The model, which includes three innovative, fundamentally new, and dominant physical aspects (enhanced viscous stress, virtual mass, generalized drag) constitutes the most generalized two‐phase flow model to date, and can reproduce results from most previous simple models that consider single‐ and two‐phase avalanches and debris flows as special cases. Numerical results indicate that the model can adequately describe the complex dynamics of subaerial two‐phase debris flows, particle‐laden and dispersive flows, sediment transport, and submarine debris flows and associated phenomena. Key Points This paper presents a new, generalized and unified two‐phase debris flow model Includes non‐Newtonian viscous stress, virtual mass, generalized drag, buoyancy New model adequately describes complex two‐phase debris flow, sediment transport

This study aimed to investigate the formation and propagation processes of a landslide-generated debris flow within a small catchment while considering the effects of vegetation. This process is divided into three stages: rainfall infiltration, slope failure, and debris flow routing, according to their different mechanisms. Existing models that involve the effect of vegetation for each stage, including Richards’s model, infinite slope stability model, and the enhanced two-phase debris flow model (Pudasaini 2012), were coupled. The tridiagonal matrix algorithm and finite volume method were applied to solve these equations, respectively. Finally, the approach was tested by application to the 2018 debris flow event in the Yindongzi catchment, China. The results showed that the proposed comprehensive model could effectively describe the behaviors of each stage during the formation and propagation processes of landslide-generated debris flows in vegetated area. The roles of vegetation on each stage, such as root water uptake and root soil reinforcement, were also analyzed by performing several scenarios.

Debris flows have been always a serious problem in the mountain areas. Research on the assessment of debris flows susceptibility (DFS) is useful for preventing and mitigating debris flow risks. The main purpose of this work is to study the DFS in the Shigatse area of Tibet, by using machine learning methods, after assessing the main triggering factors of debris flows. Remote sensing and geographic information system (GIS) are used to obtain datasets of topography, vegetation, human activities and soil factors for local debris flows. The problem of debris flow susceptibility level imbalances in datasets is addressed by the Borderline-SMOTE method. Five machine learning methods, i.e., back propagation neural network (BPNN), one-dimensional convolutional neural network (1D-CNN), decision tree (DT), random forest (RF), and extreme gradient boosting (XGBoost) have been used to analyze and fit the relationship between debris flow triggering factors and occurrence, and to evaluate the weight of each triggering factor. The ANOVA and Tukey HSD tests have revealed that the XGBoost model exhibited the best mean accuracy (0.924) on ten-fold cross-validation and the performance was significantly better than that of the BPNN (0.871), DT (0.816), and RF (0.901). However, the performance of the XGBoost did not significantly differ from that of the 1D-CNN (0.914). This is also the first comparison experiment between XGBoost and 1D-CNN methods in the DFS study. The DFS maps have been verified by five evaluation methods: Precision, Recall, F1 score, Accuracy and area under the curve (AUC). Experiments show that the XGBoost has the best score, and the factors that have a greater impact on debris flows are aspect, annual average rainfall, profile curvature, and elevation.

Heavy rainfall in mountainous terrain can trigger numerous landslides in hill slopes. These landslides can be deadly to the community living downslope with their fast pace, turning failures into catastrophic debris flows and avalanches. Active tectonics coupled with rugged topography in a complex geoenvironment multiplies this likelihood. The available hazard maps are usually helpful in mitigating disasters. However, fool-proof predicting landslide susceptibility identification remains a challenge in landslide discipline. Recently, ensemble machine learning (ML) techniques have proved the potential to provide a more accurate and efficient solution in spatial modeling. The main purposes of the current study are to examine and evaluate the predictive capability of support vector machine hybrid ensemble ML algorithms, i.e., the bagging, boosting, and stacking for modeling the catastrophic rainfall-induced landslide occurrences in the Northern parts of Kyushu Island, at the watershed scale in Japan. In this study, a landslide inventory map containing 265 landslide polygons was first interpreted from the aerial photographs and fieldwork after the September 2017 rainfall event. The raw data were randomly separated into two parts using a 70/30 sampling strategy for training and validating the landslide models. Then, 13 predisposing factors were prepared as predictors and dependent variable. The landslide susceptibility maps (LSM) were validated by the area under the receiver operating characteristic curve (AUC). The results of validation showed that the AUC values of the four models (SVM-Stacking, SVM, SVM-Bagging, and SVM-Boosting) varied from 0.74 to 0.91. The SVM-boosting model outperformed the other models, while SVM-stacking model has found to be the lowest performance. The outcome suggests that an ensemble ML model does not necessarily mean good performance. It is always preferable to select an appropriate model, such as the one proposed the hybrid novel ensemble SVM-boosting model, which could significantly improve the accuracies of LSM. Also, from Information Gain Ratio (IGR) we found that the rainfall factor mainly affects the results, that agrees with the analogy of present study.

Mountainous areas surrounding the Campanian Plain and the Somma-Vesuvius volcano (southern Italy) are among the most risky areas of Italy due to the repeated occurrence of rainfall-induced debris flows along ash-fall pyroclastic soil-mantled slopes. In this geomorphological framework, rainfall patterns, hydrological processes taking place within multi-layered ash-fall pyroclastic deposits and soil antecedent moisture status are the principal factors to be taken into account to assess triggering rainfall conditions and the related hazard. This paper presents the outcomes of an experimental study based on integrated analyses consisting of the reconstruction of physical models of landslides, in situ hydrological monitoring, and hydrological and slope stability modeling, carried out on four representative source areas of debris flows that occurred in May 1998 in the Sarno Mountain Range. The hydrological monitoring was carried out during 2011 using nests of tensiometers and Watermark pressure head sensors and also through a rainfall and air temperature recording station. Time series of measured pressure head were used to calibrate a hydrological numerical model of the pyroclastic soil mantle for 2011, which was re-run for a 12-year period beginning in 2000, given the availability of rainfall and air temperature monitoring data. Such an approach allowed us to reconstruct the regime of pressure head at a daily time scale for a long period, which is representative of about 11 hydrologic years with different meteorological conditions. Based on this simulated time series, average winter and summer hydrological conditions were chosen to carry out hydrological and stability modeling of sample slopes and to identify Intensity-Duration rainfall thresholds by a deterministic approach. Among principal results, the opposing winter and summer antecedent pressure head (soil moisture) conditions were found to exert a significant control on intensity and duration of rainfall triggering events. Going from winter to summer conditions requires a strong increase of intensity and/or duration to induce landslides. The results identify an approach to account for different hazard conditions related to seasonality of hydrological processes inside the ash-fall pyroclastic soil mantle. Moreover, they highlight another important factor of uncertainty that potentially affects rainfall thresholds triggering shallow landslides reconstructed by empirical approaches.

Debris flows are frequently occurring natural processes in geologically complex terrains of the Indian Himalayas. Debris flow runout modelling leading to hazard assessment is essential for planning, designing, and execution of mitigation measures. In the present context, debris flow hazard assessment has been carried out for the Tangni debris flow in Garhwal Himalayas, India. Runout modelling was carried out using a Voellmy model-based 3D numerical simulation for estimation of flow intensity parameters such as runout distance, flow velocity, height and pressure along the propagation path. For calibration of the model inputs, back analysis of Tangni debris flow event that occurred in 2013, with its known runout length and deposition volume as the criteria, has been conducted. The best calibrated values of frictional parameters are obtained at μ = 0.10 and ξ = 400 m/s2. Using the best calibrated values of frictional parameters, hazard assessment was carried out for two potential release areas separately, with different initial volumes, and in combination to derive the probable runout distance along with other flow intensity parameters for different scenarios that may happen in the future. It has been observed that this debris flow scenario including the combination of two potential release areas will block the Alaknanda River, forming a landslide dam with a probable height of 6 m. Debris flow runout modelling-based hazard assessment will be helpful in determining quantitative information on flow intensity parameters, where complete data on past events are generally not available or were not possible to capture for the Indian Himalayan region.