Occurrence Of Avalanches Research Articles

Snow Avalanche Susceptibility Mapping of Transportation Corridors Based on Coupled Certainty Factor and Geodetector Models

Avalanche susceptibility assessment is a core aspect of regional avalanche early warning and risk analysis and is of great significance for disaster prevention and mitigation on proposed highways. Using sky–ground integration investigation, 83 avalanche points within the G219 Wen Quan to Horgos transportation corridor were identified, and the avalanche hazard susceptibility of the transportation corridor was partitioned using the certainty factor (CF) model and the coupled coefficient of the certainty factor–Geodetector (CF-GD) model. The CF model analysis presented nine elements of natural conditions which influence avalanche development; then, by applying the Geodetector for each of the factors, a weighting coefficient was given depending on its importance for avalanche occurrence. The results demonstrate the following: (1) According to the receiver operating characteristic (ROC) curve used to verify the accuracy, the area under the ROC curve (AUC) value for the CF-GD coupled model is 0.889, which is better than the value of 0.836 of the CF model’s evaluation accuracy, and the coupled model improves the accuracy by about 6.34% compared with the single model, indicating that the coupled model is more accurate. The results provide avalanche prevention and control recommendations for the G219 Wen Quan to Horgos transportation corridor. (2) The slope orientation, slope gradient, and mean winter temperature gradient are the main factors for avalanche development in the study area. (3) The results were validated based on the AUC values. The AUCs of the CF-GD coupled model and the CF model were 0.889 and 0.836, respectively. The accuracy of the coupled model was improved by about 6.34% compared to the single model, and the coupled CF-GD model was more accurate. The results provide avalanche control recommendations for the G219 Wen Quan to Horgos transportation corridor.

Snowpack variations and their hazardous effects under climate warming in the central Tianshan Mountains

Climate change alters snowpack evolution, which in turn influences the likelihood of snow avalanches and flood risks. The lack of systemic observational data on key snow characteristics in high mountains remains a scientific challenge in terms of systematically elucidating the dynamic chain of variations in climate–snowpack–snow disasters. This restricts our understanding and poses challenges in the prediction of snow-related disaster risks. As such, this study analysed the variations of temperature and snowfall and the physical characteristics of snowpacks based on ground-based observations from the Kunse River Valley situated in the Tianshan Mountains from 1967 to 2021. The results reveal that the temperature increased significantly by 0.32 °C per decade (p < 0.01) during the snow season, along with more extreme snowfall events. The snow-cover duration was observed to have been shortened by 4.77 d per decade (p < 0.01) from 1967 to 2021, which is characterised by later snow-cover onset and earlier snowmelt. Concurrently, average and maximum snow depths increased along with an increase in peak snow water equivalent, thus indicating a higher frequency of extremely scarce or abundant snow years. The low snowpack temperature gradient and earlier snowmelt dates in spring lead to earlier occurrences of snowmelt floods and wet avalanches. As the risks of these events increase, they pose greater threats to farmlands, road transportation, water–electricity infrastructure and several other human activities. Therefore, these insights are critical for providing vital information that can deepen our understanding of the impact of climate change on snowpack characteristics and improve management strategies for snow-related disaster prevention and mitigation.

Load cascades in spatial networks: A sandpile model approach

In the real world, the construction of infrastructure networks is influenced by spatial factors. For such complex networks, We refer to them as spatial networks. This study investigates, based on the Bak–Tang–Wiesenfeld sandpile model, the impact of spatial factors on sandpile cascading avalanches. Through simulations, we discover that spatial factors not only facilitate the occurrence of moderate-scale avalanches in the system but also have a suppressing effect on the occurrence of the largest cascading avalanches. Using a multiplicative branching process, we analyze the influence of triangular structures composed of nodes on sandpile avalanches and find that nodes within triangular structures are relatively resistant to topples. Because of the relatively stable nature within triangular structures, more load tends to stay within this kind of structures. Once a cascading avalanche occurs in the system, the avalanche scale is often larger than that in the non-spatial counterpart. This increase in avalanche scale results in more load being released by the system, making the spatial networks more relaxed. And their probability of very-large-scale cascading avalanches is lower than that of non-spatial networks.

On the conditions for the occurrence of crystal avalanches during alloy solidification

Experimental studies on the solidification of ammonium-chloride-water alloys in relatively large containments reveal conditions that lead to the formation of numerous crystal avalanches. Columnar segments that occasionally slide downwards along a vertical mushy zone further fragmentate and so crystal multiplication occurs. As a condition for this phenomenon solidification-induced solutal buoyancy that leads to a rising interdendritic flow was identified for the present case. The interaction with sedimentation-induced downward flow ahead of a vertical columnar region results in a redirection of the interdendritic flow and thus, to local conditions that slow down further solidification or even lead to remelting. Gravity is then pulling loose segments downwards. In larger containment, the flow in the bulk melt is generally unsteady and even turbulent. Thus, the outlined flow-solidification/melting interplay happens frequently at numerous positions but in a stochastic manner.

Spatial heterogeneity and temporal tendency of channeled snow avalanche activity retrieved from Landsat images in the maritime snow climate of the Parlung Tsangpo catchment, southeastern Tibet

The spatial heterogeneity and temporal tendency of snow avalanche (hereafter called avalanche) activities are responses to variations in the inducing environment under climate warming and epitomize the repercussions of the global cryosphere on climate warming. This paper focuses on channeled avalanches in the marine snow climate region of the Parlung Tsangpo catchment in southeast Tibet as an example. Through field investigations, we identify the spatial extents of avalanche paths, while historical avalanche flow paths from the past 35 years (1986/1987–2021/2022) within these paths are interpreted using Landsat optical images. Statistical analysis is then employed to derive spatial heterogeneity and temporal tendencies within and between years. The results indicate that the runout altitudes of historical avalanches primarily range from 3468 m to 4051 m, with average flow directions predominantly following the northwest, north and northeast directions. Avalanche activity peaks between February and April, with longer runout distances observed during this period. Regarding interannual regularity, the evolution of the runout distance, runout altitude, and height difference of avalanche flow paths over the past 35 years demonstrates a cyclic evolution akin to a sine function. Additionally, a moderate correlation between the interannual variation in historical avalanche activity intensity and the Southern Oscillation Index was observed. These findings strengthen the understanding of the temporal evolutionary pattern of avalanches in response to climate warming, providing valuable insights into the channeled avalanche occurrences in the maritime snow climate of southeastern Tibet.

Abrupt increase of stochastic behavior in domain-wall motion near depinning field

The domain-wall motion in ferromagnetic films exhibits stochastic behavior due to thermal agitation with quenched disorders. The stochasticity is an obstacle in the sense of consistent repeatability of domain-wall position control in magnetic domain-wall devices. In general, the level of stochasticity is expected to decrease as driving force increases. This property suggests that the magnetic domain-wall devices are capable of simultaneously achieving both high operational speed and decrease in the level of stochasticity. However, we report here an observation of stochasticity anomaly, which involves a significant increase in relative speed dispersion near the depinning field. Domain-wall motion measurements were performed in ferromagnetic wires with perpendicular magnetic anisotropy over the same position to measure the relative speed dispersion. The stochasticity in domain-wall motion is intertwined with the number of pinning–depinning events throughout the domain-wall motion. The size of cluster events, which leads to the number of events, reflects the trend in the relative speed dispersion. The observed anomaly is provided by occurrence of large avalanches of cluster events. The anomaly occurs within the tech-relevant speed range of 1–100 m/s, signaling the necessity of material engineering to mitigate its effects.

Concurrently achieving strength-ductility combination and robust anti-wear performance in an in-situ high-entropy bulk metallic glass composite

High-entropy bulk metallic glasses (HE-BMGs) with desired thermal stability often exhibit limited plasticity due to the occurrence of shear localization avalanches. The present study reports the fabrication of a novel composite TiZrHfNb0.5Cu0.5Be0.5, consisting of a high entropy crystalline phase (TiZrHfNb) and an amorphous matrix (TiZrHfCuBe). The composite exhibits a distinctive combination of strength and ductility, surpassing that of traditional BMG composites, along with a notable capacity for work-hardening. Furthermore, it demonstrates exceptional wear resistance under varying normal loads or frequencies. The deformation and wear mechanisms are attributed to the solid-solution strengthening and stress-induced β→α" martensitic transformation in the high entropy crystalline phase, as well as the deformation-induced crystallization in HE-BMG matrix. These findings would provide a new strategy for preparing advanced HE-BMGs composite with unique properties.

Possibly seismically triggered avalanches after the S1222a Marsquake and S1000a impact event

Ground motion from seismic events detected by the SEIS/InSight seismometer on Mars could potentially trigger dust avalanches. Our research strongly suggests that the seismic event S1000a may have triggered a significant number of dust avalanches. In contrast, following the seismic event S1222a, there was only a modest increase in avalanche occurrences. Orbital observations of the area surrounding the projected location of the S1222a quake reveal notable topographic features, such as North-South ridges and impact craters. We utilize orbital imagery to evaluate the rate of avalanches and explore how the S1222a event might have influenced this rate. The S1222a event appears to be a plausible factor contributing to the observed increase in avalanches. Our further analysis of the epicenter location aims to clarify how it aligns with the avalanches’ spatial distribution, offering insights into the regional topography.

Stability of a tilted granular monolayer: How many spheres can we pick before the collapse?

The triggering of avalanches is investigated using discrete element simulations for a process of random extraction of spheres. A monolayer, formed by identical spheres in a hexagonal configuration, is placed on a tilted plane surrounded by a small fence that sustains the spheres, mimicking the disposal of fruits in the market. Then, a random continuous extraction process of spheres is imposed until the collapse. For this simple numerical experiment, a phase diagram was obtained to visualize the occurrence of avalanches triggered by vacancies as a function of the tilting angle, system size, and friction coefficient. More importantly, a subzone was found where we can predict the critical number of extractions until the avalanche takes place. The prediction is made from an evolution model of the average coordination number based on statistical considerations. The theoretical prediction also gives a constant critical void fraction of spheres, which implies the system collapses at a critical packing fraction.

Snow Avalanches as a Driver of Large Wood Dynamics in Mountain Streams

AbstractSnow avalanches are thought to influence large wood dynamics within mountain streams, but studies that explicitly quantify their impact are minimal. In three headwater catchments of the Frying Pan River, Colorado, USA, we found that wood loads in stream reaches intersected by avalanche pathways ranged from 220 to 8,500 m3/ha and were significantly greater than in non‐avalanched reaches. Inter‐catchment differences in the number of avalanche occurrences were not reflected in control reach wood loads, suggesting that elevated loads may be restricted to the general vicinity of the avalanche pathway. However, the sizable wood volumes in avalanche reaches generated notable geomorphic impacts, indicating that avalanches can create hotspots of in‐stream geomorphic complexity and influence the overall habitat mosaic of the stream ecosystem. Loads in avalanche reaches were moreover significantly greater than literature values for similar streams and recruitment processes, emphasizing the importance of snow avalanches as a wood recruitment mechanism.

Prediction of natural dry-snow avalanche activity using physics-based snowpack simulations

Abstract. Predicting the timing and size of natural snow avalanches is crucial for local and regional decision makers but remains one of the major challenges in avalanche forecasting. So far, forecasts are generally made by human experts interpreting a variety of data and drawing on their knowledge and experience. Using avalanche data from the Swiss Alps and one-dimensional physics-based snowpack simulations for virtual slopes, we developed a model predicting the probability of dry-snow avalanches occurring in the region surrounding automated weather stations based on the output of a recently developed instability model. This new avalanche day predictor was compared with benchmark models related to the amount of new snow. Evaluation on an independent data set demonstrated the importance of snow stratigraphy for natural avalanche release, as the avalanche day predictor outperformed the benchmark model based on the 3 d sum of new snow height (F1 scores: 0.71 and 0.65, respectively). The averaged predictions of both models resulted in the best performance (F1 score: 0.75). In a second step, we derived functions describing the probability for certain avalanche size classes. Using the 24 h new snow height as proxy of avalanche failure depth yielded the best estimator of typical (median) observed avalanche size, while the depth of the deepest weak layer, detected using the instability model, provided the better indicator regarding the largest observed avalanche size. Validation of the avalanche size estimator on an independent data set of avalanche observations confirmed these findings. Furthermore, comparing the predictions of the avalanche day predictors and avalanche size estimators with a 21-year data set of re-analysed regional avalanche danger levels showed increasing probabilities for natural avalanches and increasing avalanche size with increasing danger level. We conclude that these models may be valuable tools to support forecasting the occurrence of natural dry-snow avalanches.

Snow avalanche synchronicity derived from a multi-path tree-ring reconstruction in the Făgăraș Mountains (Southern Carpathians, Romania)

Snow avalanches pose a constant threat to human lives, property, and infrastructure in mountainous regions worldwide. To efficiently manage avalanche-related hazards and risks, knowledge of past occurrences is crucial. In many mountain regions, archival records on past events are scarce or even completely missing. Therefore, natural archives, such as tree-rings, are widely used as proxies to build century-long snow avalanche chronologies. However, most dendrogeomorphic studies focus on a small number of sites, providing only path-scale reconstructions with limited significance for a larger area. To overcome this problem, in the present study, we analyzed 17 avalanche paths in a small and relatively homogeneous region located in the Făgăraș Mountains, Southern Carpathians, and documented past snow avalanche activity with dendrogeomorphic techniques. Increment cores extracted from 933 Picea abies trees allowed the reconstruction of a total of 174 snow avalanche years across all investigated paths. At the same time, widespread snow avalanche occurrence was identified in the study area. In years such as 1923, 1929, 1988, 1992, 1995, 1997, 2002, and 2005, more than half of the investigated paths experienced at least a major snow avalanche event. Regarding the frequency of event years, we conclude that the mean recurrence interval of widespread/extreme events is 4.1 years. Finally, this study highlights the importance of investigating multiple avalanche paths in a small, homogeneous area to create a better understanding of snow avalanche activity at path- and mountain range-scale.

Criticality of neuronal avalanches in human sleep and their relationship with sleep macro- and micro-architecture

Sleep plays a key role in preserving brain function, keeping brain networks in a state that ensures optimal computation. Empirical evidence indicates that this state is consistent with criticality, where scale-free neuronal avalanches emerge. However, the connection between sleep architecture and brain tuning to criticality remains poorly understood. Here, we characterize the critical behavior of avalanches and study their relationship with sleep macro- and micro-architectures, in particular, the cyclic alternating pattern (CAP). We show that avalanches exhibit robust scaling behaviors, with exponents obeying scaling relations consistent with the mean-field directed percolation universality class. We demonstrate that avalanche dynamics is modulated by the NREM-REM cycles and that, within NREM sleep, avalanche occurrence correlates with CAP activation phases-indicating a potential link between CAP and brain tuning to criticality. The results open new perspectives on the collective dynamics underlying CAP function, and on the relationship between sleep architecture, avalanches, and self-organization to criticality.

Combining OBIA, CNN, and UAV photogrammetry for automated avalanche deposit detection and characterization

Avalanche activities in the snow-covered mountainous slopes are very frequent and stochastically distributed over time and geographical extents. Conventional methods of avalanche occurrence reporting in these areas are inefficient and incomplete due to inhospitable weather and inaccessible topographic conditions. This paper presents a novel framework for the detection and analysis of avalanche deposits using UAV (Unmanned Aerial Vehicle) RGB images of snow-bound avalanche-prone areas. In the first part of the proposed framework, an OBIA-CNN (Object Based Image Analysis-Convolution Neural Network) method is applied to UAV images for the detection of avalanche deposits. Initially, the OBIA-based multi-scale multi-resolution segmentation technique is used for UAV image segmentation, then the classification of these image segments was performed using a CNN classifier to detect the avalanche deposits. The CNN-based classifier was created, trained, validated, and applied to generate the class probability maps of different classes present in the images. The post-classification refinements were then applied to the detected deposits. In the second step the detailed accuracy analysis of the detected deposits based on their count, area, size similarity, and shape similarity with respect to reference deposits has been carried out to establish the suitability of the proposed method. The avalanche deposits detected using the proposed method were found in good correlation with manually delineated deposits in terms of all four accuracy criteria. The precision of the model was found 1.0, the value of recall was 0.88 and the F-1 score was 0.93. The overall object shape matching accuracy of the detected deposits was also found high (0.92). The results of the proposed deposit detection method were also compared with the classification results of Support Vector Machine (SVM), Random Forest (RF), and UNET to evaluate the performance of the method. Finally, the surface area and snow volume-based characterization of the detected deposits was performed. The proposed framework will be useful for the automated detection of avalanche deposits and their characterization for the regions of specific interest. The study suggests that regular monitoring of avalanche activities using UAV images provides a good solution for the operational monitoring of important corridors of snowbound regions.

Large-scale risk assessment on snow avalanche hazard in alpine regions

Abstract. Snow avalanches are recurring natural hazards that affect the population and infrastructure in mountainous regions, such as in the recent avalanche winters of 2018 and 2019, when considerable damage was caused by avalanches throughout the Alps. Hazard decision makers need detailed information on the spatial distribution of avalanche hazards and risks to prioritize and apply appropriate adaptation strategies and mitigation measures and thus minimize impacts. Here, we present a novel risk assessment approach for assessing the spatial distribution of avalanche risk by combining large-scale hazard mapping with a state-of-the-art risk assessment tool, where risk is understood as the product of hazard, exposure and vulnerability. Hazard disposition is modeled using the large-scale hazard indication mapping method RAMMS::LSHIM (Rapid Mass Movement Simulation::Large-Scale Hazard Indication Mapping), and risks are assessed using the probabilistic Python-based risk assessment platform CLIMADA, developed at ETH Zürich. Avalanche hazard mapping for scenarios with a 30-, 100- and 300-year return period is based on a high-resolution terrain model, 3 d snow depth increase, automatically determined potential release areas and protection forest data. Avalanche hazard for 40 000 individual snow avalanches is expressed as avalanche intensity, measured as pressure. Exposure is represented by a detailed building layer indicating the spatial distribution of monetary assets. The vulnerability of buildings is defined by damage functions based on the software EconoMe, which is in operational use in Switzerland. The outputs of the hazard, exposure and vulnerability analyses are combined to quantify the risk in spatially explicit risk maps. The risk considers the probability and intensity of snow avalanche occurrence, as well as the concentration of vulnerable, exposed buildings. Uncertainty and sensitivity analyses were performed to capture inherent variability in the input parameters. This new risk assessment approach allows us to quantify avalanche risk over large areas and results in maps displaying the spatial distribution of risk at specific locations. Large-scale risk maps can assist decision makers in identifying areas where avalanche hazard mitigation and/or adaption is needed.

Combining modelled snowpack stability with machine learning to predict avalanche activity

Abstract. Predicting avalanche activity from meteorological and snow cover simulations is critical in mountainous areas to support operational forecasting. Several numerical and statistical methods have tried to address this issue. However, it remains unclear how combining snow physics, mechanical analysis of snow profiles and observed avalanche data improves avalanche activity prediction. This study combines extensive snow cover and snow stability simulations with observed avalanche occurrences within a random forest approach to predict avalanche situations at a spatial resolution corresponding to elevations and aspects of avalanche paths in a given mountain range. We develop a rigorous leave-one-out evaluation procedure including an independent evaluation set, confusion matrices and receiver operating characteristic curves. In a region of the French Alps (Haute-Maurienne) and over the period 1960–2018, we show the added value within the machine learning model of considering advanced snow cover modelling and mechanical stability indices instead of using only simple meteorological and bulk information. Specifically, using mechanically based stability indices and their time derivatives in addition to simple snow and meteorological variables increases the probability of avalanche situation detection from around 65 % to 76 %. However, due to the scarcity of avalanche events and the possible misclassification of non-avalanche situations in the training dataset, the predicted avalanche situations that are really observed remains low, around 3.3 %. These scores illustrate the difficulty of predicting avalanche occurrence with a high spatio-temporal resolution, even with the current data and modelling tools. Yet, our study opens perspectives to improve modelling tools supporting operational avalanche forecasting.

Acoustic emission monitoring and analyses for avalanche release and slab fracturing events observed in Great Himalaya

ABSTRACT The acoustic emission (AE) monitoring of failures, fractures and instabilities evolving within a snowpack may provide vital information about the avalanche formation preceding release processes. In this work, AE activity was collected continuously over five years from the snowpack built on avalanche starting zone in Great Himalaya, and the analyses are presented in the context of an avalanche release and two slab fracturing events detected successfully during this study period. For non-invasive detection of AE activity from snow, a specialised sensor-arrestor array was deployed over the avalanche slope. In our investigations, an abnormal AE activity was observed both during the slab fracturing as well as the avalanche occurrence periods. For AE-based direct assessment of snow instability, a method is proposed to estimate instability index (βin), based on AE count and amplitude values of a hit, and it is applied for analyses of avalanche formation preceding release and slab fracturing cases. The prominent AE attributes including βin-variations are also analysed at appropriate window scales for a smooth representation of the ongoing instability and failure processes underneath the slab. The AE spectral analyses of avalanche release and slab fracturing reveal the dominant frequencies around 45–47 kHz and 34–43 kHz, respectively. A progressively increasing behaviour was observed with an abrupt increase in the relevant AE parameters and also the βin-values, nearly 13 hours before the avalanche release, and it continued until the avalanche released which can be associated to the instability development prior to avalanche release.

Impact of climate change on snowpack and avalanches in Slovenia: The Soča Valley case study

The article discusses avalanche occurrence in the Slovenian Alps (SE Alps) in the context of climate change. It analyses the relationship between the North Atlantic Oscillation and maximum snow depth over the last two centuries, and the relationships between maximum snow depth and avalanches over the last three decades. We argue that higher temperatures lead to precipitation in the form of rain at higher elevations even in winter, so that major wet avalanches occur already in winter rather than in early spring, as was more common in the past. A case study of extreme avalanches in January 2021 is presented to support the hypothesis.

Analysis of Seismic Impact on Hailuogou Glacier after the 2022 Luding Ms 6.8 Earthquake, China, Using SAR Offset Tracking Technology

An Ms 6.8 earthquake struck Luding County, Ganzi Prefecture, Sichuan Province on 5 September 2022, with the epicenter about 10 km away from Hailuogou Glacier. How Hailuogou Glacier was affected by the earthquake was of major concern to society. Sentinel-1 SAR satellite imaging was used to monitor the glacier surface velocity during different periods before and after the Luding earthquake based on pixel offset tracking (POT) technology, which applies a feature-tracking algorithm to overcome the phase co-registration problems commonly encountered in large displacement monitoring. The results indicated that the velocity had a positive correlation with the average daily maximum temperature and the slope gradient on the small-slope surfaces. The correlation was not apparent on the steeper surfaces, which corresponded spatially with the identified ice avalanche region in the Planet images. It was deduced that this may be because of the occurrence of ice avalanches on surfaces steeper than 25°, or that the narrower front channel impeded the glacier’s movement. The Luding earthquake did not cause a significant increase in the velocity of Hailuogou Glacier within a large range, but it disturbed the front area of the ice cascade, where the maximum velocity reached 2.5 m/d. Although the possibility of directly-induced destruction by ice avalanches after the earthquake was low, and the buffering in the downstream glacier tongue further reduced the risk of ice avalanches, the risk of some secondary hazards such as debris flow increased. The proposed method in this study might be the most efficient in monitoring and evaluating the effects of strong earthquakes on glaciers because it would not be limited by undesirable weather or traffic blockage.

Dimensional crossover in driving-rate induced criticality on the hysteresis-loop of disordered ferromagnetic systems

We study the effect of finite driving rate on the nonequilibrium hysteresis-loop criticality with the systems’ nonequilateral geometry constraints allowing the dimensional crossover from three-dimensional to two-dimensional disordered ferromagnetic systems . For each system’s thickness, the disorder is fixed above the critical line for adiabatic driving, such that the emergent critical behaviors are solely attributed to the increased driving rates of the external field.We demonstrate it by computing the pertaining magnetizations and coercive fields, changes in avalanche distributions and shapes, and correlation functions of spin-flip events where the pace of the field increments are varied in a broad range. Our results reveal that the driving field rate profoundly affects system evolution attaining three distinct regimes of the induced criticality at the proper thickness-dependent rate values. In particular, these regimes are characterized by the occurrence of non-spanning avalanches (slow driving), the first appearance of spanning avalanches (intermediate regime), and being overwhelmed by system-spanning avalanches (fast driving regime) for a given thickness. Our results will bring some critical insights to the theoretical and experimental studies on the field-driven nonequilateral systems both for conceptual reasons and the versatility of their applications..