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A common technique for simulating non–Newtonian fluid dynamics, such as snow avalanches, is to solve the Shallow Water Equations (SWE), together with a rheological model describing the momentum dissipation by shear stresses. Friction and cohesion terms are commonly modelled using the Voellmy friction model and, recently, the Bartelt cohesion model. Here, an adaptation of the Roe scheme that ensures the balance between the flux and pressure gradients and the friction source term is presented. An upwind scheme was used for the discretisation of the SWE numerical fluxes and the non–velocity-dependent terms of the friction–cohesion model, whereas a centred scheme was used for the velocity-dependent source terms. The model was tested in analytically solvable settings, laboratory experiments and real cases. In all cases, the model performed well, avoiding numerical instabilities and achieving stable and consistent solution even for an avalanche stopping on a sloping terrain.

Unmanned aerial vehicle (UAV) path planning is critical for autonomous flight operations, yet existing algorithms face challenges in balancing optimization accuracy and computational efficiency. In this paper, we proposed a Multi-Strategy Snow Avalanches Algorithm (MSAA) to address the limitations of the original Snow Avalanches Algorithm (SAA), which suffered from low convergence precision and local optima entrapment. The MSAA integrated three key enhancements: a chaos theory-based initialization method to enhance population diversity, a stochastic central learning mechanism for dynamic exploration–exploitation balance, and an improved sine–cosine operator to prevent premature convergence. Ultimately, the MSAA was successfully applied to the challenge of 3D UAV path planning. By integrating cubic B-spline interpolation for smooth trajectory generation, the proposed method achieved impressive results, with mean path error rates of 0.5% in low-dimensional terrains and 0.4% in high-dimensional environments. Experimental validations demonstrated superior convergence speed and computational efficiency compared to benchmark algorithms, confirming its practical value for 3D UAV navigation in complex scenarios.

Wet-snow avalanches constitute a major geomorphic hazard in southeastern Tibet, where warm, humid climatic conditions and a steep, high-relief terrain generate failure mechanisms that are distinct from those in cold, dry snow environments. This study investigates the snowpack conditions underlying avalanche initiation in this region by integrating UAV-based multi-sensor surveys with field validation. Ground-penetrating radar (GPR), infrared thermography, and optical imaging were employed to characterize snow depth, stratigraphy, liquid water content (LWC), snow water equivalent (SWE), and surface temperature across an inaccessible avalanche channel. Calibration at representative wet-snow sites established an appropriate LWC inversion model and clarified the dielectric properties of avalanche-prone snow. Results revealed SWE up to 1092.98 mm and LWC exceeding 13.8%, well above the critical thresholds for wet-snow instability, alongside near-isothermal profiles and weak bonding at the snow–ground interface. Stratigraphic and UAV-based observations consistently showed poorly bonded, water-saturated snow layers with ice lenses. These findings provide new insights into the hydro-thermal controls of wet-snow avalanche release under monsoonal influence and demonstrate the value of UAV-based surveys for advancing the monitoring and early warning of snow-related hazards in high-relief mountain systems.

The disease that causes a large number of deaths annually across the world is brain cancer and it has become an important research topic in the field of medical image processing in recent times. There are various techniques for the detection of brain tumors (BT) but magnetic resonance imaging (MRI) diagnosing techniques show superior performance in the prognosis and examination of brain tumors in the early stages. The manual detection of brain tumors by radiologists leads to many limitations like errors and lack of detection accuracy. Hence, there is a need for computer-aided diagnostic techniques to help radiologists in detecting brain tumors accurately from the MRI images. To make this process more effective, the implementation of an automated technique is a preferred choice. In this paper, an effective detection and classification technique Adaptive convolutional Autoencoder-based Snow Avalanches (ACAE-SA) Algorithm is proposed. This algorithm comprises an Adaptive CNN component and an Autoencoder to detect and categorize BT from the MRI images. To mitigate the computational complexities in these components a Snow Avalanches algorithm is integrated into this work as an optimization technique. For the validation of the proposed architecture two MRI image datasets namely figshare and BraTS 2018 are used. The proposed technique proved its effectiveness in the detection and classification of brain tumors from the MRI images and outperformed the state-of-the-art techniques.

As an introduction for non-specialists to the Special Issue on snow avalanche dynamics, this paper first outlines how understanding the dynamics of snow avalanches can contribute to reducing risk for settlements and infrastructure. The main knowledge gaps in this field of research concern (i) the properties of the flow regimes and the transitions between them, and (ii) the dynamics of mass change due to erosion and deposition. These two aspects are intertwined and determine not only the reach of an avalanche, but also its velocity, course and impact pressure. Experimental studies described in this Special Issue comprise a wide range of scales from small rotating drums to real snow avalanches. In addition, several papers describe post-event field surveys of specific avalanches and analyze them using different methods and techniques, demonstrating how valuable qualitative insight can be gained in this way. The theoretical developments range from exploratory studies of fluid–particle interactions to a comprehensive review of half a century of avalanche flow modeling in Russia.

Observations of the deposits, flow marks, and damages of three mixed-snow avalanches of widely different size were analyzed with regard to flow regimes, velocities, pressures, densities, flow depths, erosion modes, and mass balance. Three deposit types of different density and granulometry could be clearly discerned in these avalanches. They are attributed to dense, fluidized, and suspension flow regimes, respectively. Combining observations, we estimated the density in the fluidized layer as 35–100 kg m − 3 , in good agreement with inferences from pressure measurements. Upper bounds for the suspension layer density, arising from the run-up height, velocity, and damage pattern, are about 5 kg m − 3 at the valley bottom. An approximate momentum balance of the dense layer suggests that the snow cover was eroded to considerable depth, but only partly entrained into the flow proper. The suspension layer had largely lost its erosive power at the point where it separated from the denser parts of the avalanche. Our estimates shed doubt on collisions between snow particles and aerodynamic forces at the head of the avalanche as sole mechanisms for creating and upholding the fluidized layer. We conjecture that the drag from air escaping from the snow cover as it is being compressed by the overriding avalanche could supply the missing lift force.

Ongoing climate warming has been demonstrated to impact the cryosphere in the Indian Himalayas, with substantial consequences for the risk of disasters, human well-being, and terrestrial ecosystems. Here, we present evidence that the warming observed in recent decades has been accompanied by increased snow avalanche frequency in the Western Indian Himalayas. Using dendrogeomorphic techniques, we reconstruct the longest time series (150 y) of the occurrence and runout distances of snow avalanches that is currently available for the Himalayas. We apply a generalized linear autoregressive moving average model to demonstrate linkages between climate warming and the observed increase in the incidence of snow avalanches. Warming air temperatures in winter and early spring have indeed favored the wetting of snow and the formation of wet snow avalanches, which are now able to reach down to subalpine slopes, where they have high potential to cause damage. These findings contradict the intuitive notion that warming results in less snow, and thus lower avalanche activity, and have major implications for the Western Himalayan region, an area where human pressure is constantly increasing. Specifically, increasing traffic on a steadily expanding road network is calling for an immediate design of risk mitigation strategies and disaster risk policies to enhance climate change adaption in the wider study region.

Continuum numerical modeling of dynamic crack propagation has been a great challenge over the past decade. This is particularly the case for anticracks in porous materials, as reported in sedimentary rocks, deep earthquakes, landslides, and snow avalanches, as material inter-penetration further complicates the problem. Here, on the basis of a new elastoplasticity model for porous cohesive materials and a large strain hybrid Eulerian–Lagrangian numerical method, we accurately reproduced the onset and propagation dynamics of anticracks observed in snow fracture experiments. The key ingredient consists of a modified strain-softening plastic flow rule that captures the complexity of porous materials under mixed-mode loading accounting for the interplay between cohesion loss and volumetric collapse. Our unified model represents a significant step forward as it simulates solid-fluid phase transitions in geomaterials which is of paramount importance to mitigate and forecast gravitational hazards. Anticrack propagation in snow results from the mixed-mode failure and collapse of a buried weak layer and can lead to slab avalanches. Here, authors reproduce the complex dynamics of anticrack propagation observed in field experiments using a Material Point Method with large strain elastoplasticity.

Snow is highly sensitive to atmospheric warming. However, because of the lack of sufficiently long snow avalanche time series and statistical techniques capable of accounting for the numerous biases inherent to sparse and incomplete avalanche records, the evolution of process activity in a warming climate remains little known. Filling this gap requires innovative approaches that put avalanche activity into a long-term context. Here, we combine extensive historical records and Bayesian techniques to construct a 240-y chronicle of snow avalanching in the Vosges Mountains (France). We show evidence that the transition from the late Little Ice Age to the early twentieth century (i.e., 1850 to 1920 CE) was not only characterized by local winter warming in the order of +1.35 °C but that this warming also resulted in a more than sevenfold reduction in yearly avalanche numbers, a severe shrinkage of avalanche size, and shorter avalanche seasons as well as in a reduction of the extent of avalanche-prone terrain. Using a substantial corpus of snow and climate proxy sources, we explain this abrupt shift with increasingly scarcer snow conditions with the low-to-medium elevations of the Vosges Mountains (600 to 1,200 m above sea level [a.s.l.]). As a result, avalanches migrated upslope, with only a relict activity persisting at the highest elevations (release areas >1,200 m a.s.l.). This abrupt, unambiguous response of snow avalanche activity to warming provides valuable information to anticipate likely changes in avalanche behavior in higher mountain environments under ongoing and future warming.

The mitigation of rapid mass movements involves a subtle interplay between field surveys, numerical modelling, and experience. Hazard engineers rely on a combination of best practices and, if available, historical facts as a vital prerequisite in establishing reproducible and accurate hazard zoning. Full-scale field tests have been performed to reinforce the physical understanding of debris flows and snow avalanches. Rockfall dynamics are - especially the quantification of energy dissipation during the complex rock-ground interaction - largely unknown. The awareness of rock shape dependence is growing, but presently, there exists little experimental basis on how rockfall hazard scales with rock mass, size, and shape. Here, we present a unique data set of induced single-block rockfall events comprising data from equant and wheel-shaped blocks with masses up to 2670 kg, quantifying the influence of rock shape and mass on lateral spreading and longitudinal runout and hence challenging common practices in rockfall hazard assessment. The awareness of rock shape dependence in rockfall hazard assessment is growing, but experimental and field studies are scarce. This study presents a large data set of induced single block rockfall events quantifying the influence of rock shape and mass on its complex kinematic behaviour.