Search Results (98)

The focus of this study is to introduce a hybrid predictive framework encompassing different meta-heuristic optimization and machine learning techniques to identify the regions susceptible to snow avalanches. To accomplish this aim, the present research sought to acquire the best-performed model among nine different hybrid scenarios encompassing three different meta-heuristics, namely particle swarm optimization (PSO), gravitational search algorithm (GSA), and Cuckoo Search (CS), and three different ML approaches, i.e., support vector classification (SVC), stochastic gradient boosting (SGB), and k-nearest neighbors (KNN), pertaining to different predictive families. According to diligent analysis performed with regard to the blinded testing set, the PSO-SGB illustrated the most satisfactory predictive performance with an accuracy of 0.815, while the precision and recall were found to be 0.824 and 0.821, respectively. The F1-score of the predictions was found to be 0.821, and the area under the receiver operating curve (AUC) was obtained to be 0.9. Despite attaining similar predictive success via the CS-SGB model, the time-efficiency analysis underscored the PSO-SGB, as the corresponding process consumed considerably less computational time compared to its counterpart. The SHapley Additive exPlanations (SHAP) implementation further informed that slope, elevation, and wind speed are the most contributing attributes to detecting snow avalanche susceptibility in the French Alps. Full article

Understanding the dispersion of floating objects and ocean properties at the ocean surface is crucial for various applications, including oil spill management, debris tracking and search and rescue operations. While mesoscale turbulence has been recognized as a primary driver of dispersion, the role of submesoscale processes is poorly understood. This study investigates the largely unexplored mechanism of dispersion by refracted wave fields. In situ observations demonstrate significantly faster and distinct dispersion patterns for objects influenced by wind, waves and currents compared to those solely driven by ocean currents. Numerical simulations of wave fields refracted by ocean eddies corroborate these findings, revealing diffusivities that exceed those of turbulent diffusion at scales up to 10 km during energetic sea states. Our results highlight the importance of ocean waves in dispersing surface material, suggesting that refracted wave fields may play a significant role in submesoscale spreading. As atmospheric forcing at the ocean surface will only strengthen due to anthropogenic contributions, additional research into wave refraction is necessary. This requires concurrent high-resolution measurements of wind, waves and currents to inform the revisions of large-scale coupled models to better include the submesoscale physics. Full article

Snow avalanches, one of the most severe natural hazards in mountainous regions, pose significant risks to human lives, infrastructure, and ecosystems. As climate change accelerates shifts in snowfall and temperature patterns, it is increasingly important to improve our ability to monitor and predict avalanches. This review explores the use of remote sensing technologies in understanding key geomorphological, geobotanical, and meteorological factors that contribute to avalanche formation. The primary objective is to assess how remote sensing can enhance avalanche risk assessment and monitoring systems. A systematic literature review was conducted, focusing on studies published between 2010 and 2025. The analysis involved screening relevant studies on remote sensing, avalanche dynamics, and data processing techniques. Key data sources included satellite platforms such as Sentinel-1, Sentinel-2, TerraSAR-X, and Landsat-8, combined with machine learning, data fusion, and change detection algorithms to process and interpret the data. The review found that remote sensing significantly improves avalanche monitoring by providing continuous, large-scale coverage of snowpack stability and terrain features. Optical and radar imagery enable the detection of crucial parameters like snow cover, slope, and vegetation that influence avalanche risks. However, challenges such as limitations in spatial and temporal resolution and real-time monitoring were identified. Emerging technologies, including microsatellites and hyperspectral imaging, offer potential solutions to these issues. The practical implications of these findings underscore the importance of integrating remote sensing data with ground-based observations for more robust avalanche forecasting. Enhanced real-time monitoring and data fusion techniques will improve disaster management, allowing for quicker response times and more effective policymaking to mitigate risks in avalanche-prone regions. Full article

A practical analysis of the spectrograms of the seismic data generated by gravitational mass movements (GMMs), such as snow avalanches, landslides, lahars, and debris flows recorded on one sensor, is presented. The seismic signal produced by these movements is analyzed in terms of the shape of the initial section of the spectrogram, which corresponds to the start of the movement of the gravitational mass. The shape of the envelope of the spectrogram is a consequence of the progressive reception of high-frequency energy in the signal as the gravitational mass (GM) approaches the sensor because of the attenuation properties of the seismic waves in the ground. An exponential law was used to fit this envelope of the onset signal. The proposed methodology allows us to obtain the propagation characteristics of different types of GMM. The analysis of the adjusted parameters for different types of GMM allows us to assert that differences of one order of magnitude exist in the values of these parameters depending on the type of event. In addition, differences in the values of the exponent were obtained between the events of each type of the analyzed GMM. We present a template of different curves for each type of GMM with the corresponding parameter values that can help professionals characterize a GMM with only one seismic record (one seismic sensor) whenever the mass movement approaches the recording sensor or passes over it. Full article

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. Full article

Most high-mountain regions worldwide are susceptible to snow avalanches during the winter or all year round. In this study, a Universal Snow Avalanche Modeling Index is developed, suitable for determining avalanche hazard in mountain regions. The first step in the research is the collection of data in the field and their processing in geographic information systems and remote sensing. In the period 2023–2024, avalanches were mapped in the field, and later, avalanches as points in geographic information systems (GIS) were overlapped with the dominant natural conditions in the study area. The second step involves determining the main criteria (snow cover, terrain slope, and land use) and evaluating the values to obtain the Snow Avalanche Formation Index (SAFI). Thresholds obtained through field research and the formation of avalanche inventory were used to develop the SAFI index. The index is applied with the aim of identifying locations susceptible to avalanche formation (source areas). The values used for the calculation include Normalized Difference Snow Index (NDSI > 0.6), terrain slope (20–60°) and land use (pastures, meadows). The third step presents the analysis of SAFI locations with meteorological conditions (winter precipitation and winter air temperature). The fourth step is the modeling of the propagation (simulation) of other parts of the snow avalanche in the Flow-R software 2.0. The results show that 282.9 km² of the study area (Šar Mountains, Serbia) is susceptible to snow avalanches, with the thickness of the potentially triggered layer being 50 cm. With a 5 m thick snowpack, 299.9 km² would be susceptible. The validation using the ROC-AUC method confirms a very high predictive power (0.94). The SAFI–Flow-R approach offers snow avalanche modeling for which no avalanche inventory is available, representing an advance for all mountain areas where historical data do not exist. The results of the study can be used for land use planning, zoning vulnerable areas, and adopting adequate environmental protection measures. Full article

Snow avalanche susceptibility (AS) mapping is a crucial step in predicting and mitigating avalanche risks in mountainous regions. The conditioning factors used in AS modeling are diverse, and the optimal set of factors depends on the environmental and geological characteristics of the region. Using a sub-optimal set of input features with a data-driven machine learning (ML) method can lead to challenges like dealing with high-dimensional data, overfitting, and reduced model generalization. This study implemented a robust framework involving the Sequential Backward Selection (SBS) algorithm and a decision-tree based ML model, CatBoost, for the automatic selection of predictive variables for AS mapping. A comprehensive inventory of a large avalanche period, previously derived from satellite images, was used for the investigations in three distinct catchment areas in the Swiss Alps. The integrated SBS-CatBoost approach achieved very high classification accuracies between 94% and 97% for the three catchments. In addition, the Shapley additive explanations (SHAP) method was employed to analyze the contributions of each feature to avalanche occurrences. The proposed methodology revealed the benefits of integrating advanced feature selection algorithms with ML techniques for AS assessment. We aimed to contribute to avalanche hazard knowledge by assessing the impact of each feature in model learning. Full article

Dual-frequency precipitation radar (DPR) on the Core GPM satellite provides spaceborne three-dimensional observations of precipitation fields and surface rainfall rate with quasi-global coverage. The present study evaluates the behavior of liquid precipitation intensity, radar reflectivity factor (Z_Ku and Z_Ka) and drop size distribution (DSD) parameters (weighted mean diameter D_m and intercept parameter N_w) of the GPM DPR-derived products, version 07, from 2014 to 2023. Observations from seven Parsivel disdrometers located in different topographic zones in the Western Mediterranean are taken as ground references. Four matching techniques between satellite estimates and ground level observations were tested, and the best results were found for the so-called optimal comparison approach. Overall, GPM DPR products captured the variability of the observed DSD well at different rainfall intensities. However, overestimation of the mean D_m and underestimation of the mean N_w were observed, being much more sensitive to errors in drop diameters larger than 1.5 mm. Moreover, the lowest errors were found for radar reflectivity factor and D_m, and the highest for N_w and rainfall rate. In addition, the GPM DPR convective and stratiform classification was tested, and a substantial overestimation of stratiform cases compared to disdrometer observations were found. Full article

Accurate snow depth estimation is of significant importance, particularly for preventing avalanche disasters and predicting flood seasons. The predominant approaches for such snow depth estimation, based on deep learning methods, typically rely on passive microwave remote sensing data. However, due to the low resolution of passive microwave remote sensing data, it often results in low-accuracy outcomes, posing considerable limitations in application. To further improve the accuracy of snow depth estimation, in this paper, we used active microwave remote sensing data. We fused multi-spectral optical satellite images, synthetic aperture radar (SAR) images and land cover distribution images to generate a snow remote sensing dataset (SRSD). It is a first-of-its-kind dataset that includes active microwave remote sensing images in high-latitude regions of Asia. Using these novel data, we proposed a multi-scale feature perception and aggregation neural network (MFPANet) that focuses on improving feature extraction from multi-source images. Our systematic analysis reveals that the proposed approach is not only robust but also achieves high accuracy in snow depth estimation compared to existing state-of-the-art methods, with RMSE of 0.360 and with MAE of 0.128. Finally, we selected several representative areas in our study region and applied our method to map snow depth distribution, demonstrating its broad application prospects. Full article

Over the past decades, the cryosphere has changed significantly in High Mountain Asia (HMA), leading to multiple natural hazards such as rock–ice avalanches, glacier collapse, debris flows, landslides, and glacial lake outburst floods (GLOFs). Monitoring cryosphere change and evaluating its hydrological effects are essential for studying climate change, the hydrological cycle, water resource management, and natural disaster mitigation and prevention. However, knowledge gaps, data uncertainties, and other substantial challenges limit comprehensive research in climate–cryosphere–hydrology–hazard systems. To address this, we provide an up-to-date, comprehensive, multidisciplinary review of remote sensing techniques in cryosphere studies, demonstrating primary methodologies for delineating glaciers and measuring geodetic glacier mass balance change, glacier thickness, glacier motion or ice velocity, snow extent and water equivalent, frozen ground or frozen soil, lake ice, and glacier-related hazards. The principal results and data achievements are summarized, including URL links for available products and related data platforms. We then describe the main challenges for cryosphere monitoring using satellite-based datasets. Among these challenges, the most significant limitations in accurate data inversion from remotely sensed data are attributed to the high uncertainties and inconsistent estimations due to rough terrain, the various techniques employed, data variability across the same regions (e.g., glacier mass balance change, snow depth retrieval, and the active layer thickness of frozen ground), and poor-quality optical images due to cloudy weather. The paucity of ground observations and validations with few long-term, continuous datasets also limits the utilization of satellite-based cryosphere studies and large-scale hydrological models. Lastly, we address potential breakthroughs in future studies, i.e., (1) outlining debris-covered glacier margins explicitly involving glacier areas in rough mountain shadows, (2) developing highly accurate snow depth retrieval methods by establishing a microwave emission model of snowpack in mountainous regions, (3) advancing techniques for subsurface complex freeze–thaw process observations from space, (4) filling knowledge gaps on scattering mechanisms varying with surface features (e.g., lake ice thickness and varying snow features on lake ice), and (5) improving and cross-verifying the data retrieval accuracy by combining different remote sensing techniques and physical models using machine learning methods and assimilation of multiple high-temporal-resolution datasets from multiple platforms. This comprehensive, multidisciplinary review highlights cryospheric studies incorporating spaceborne observations and hydrological models from diversified techniques/methodologies (e.g., multi-spectral optical data with thermal bands, SAR, InSAR, passive microwave, and altimetry), providing a valuable reference for what scientists have achieved in cryosphere change research and its hydrological effects on the Third Pole. Full article

Snow failure is the process by which the stability of snow or snow-covered slopes is destroyed, resulting in the collapse or release of snow. Heavy snowfall, low temperatures, and volatile weather typically cause consequences in Antarctica, which can occur at different scales, from small, localized collapses to massive avalanches, and result in significant risk to human activities and infrastructures. Understanding snow damage is critical to assessing potential hazards associated with snow-covered terrain and implementing effective risk mitigation strategies. This review discusses the theoretical models and numerical simulation methods commonly used in Antarctic snow failure research. We focus on the various theoretical models proposed in the literature, including the fiber bundle model (FBM), discrete element model (DEM), cellular automata (CA) model, and continuous cavity-expansion penetration (CCEP) model. In addition, we overview some methods to acquire the three-dimensional solid models and the related advantages and disadvantages. Then, we discuss some critical numerical techniques used to simulate the snow failure process, such as the finite element method (FEM) and three-dimensional (3D) material point method (MPM), highlighting their features in capturing the complex behavior of snow failure. Eventually, different case studies and the experimental validation of these models and simulation methods in the context of Antarctic snow failure are presented, as well as the application of snow failure research to facility construction. This review provides a comprehensive analysis of snow properties, essential numerical simulation methods, and related applications to enhance our understanding of Antarctic snow failure, which offer valuable resources for designing and managing potential infrastructure in Antarctica. Full article

On average, one hundred people die each year under avalanche snow. Despite extensive global research on gas exchange in buried avalanche victims, it remains unclear how the diffusion of respiratory gases affects survival under avalanche snow. This study aims to determine how oxygen and carbon dioxide diffuse through snow, as well as through wet and dry perlite, which may serve as a surrogate for avalanche snow. A custom-made apparatus to study the diffusion of respiratory gases consisted of a plastic cylinder (1200 mm long, ID 300 mm) with 13 gas sampling needles evenly spaced along the axis of the cylinder filled with the tested material. Following 60 min of free diffusion, gas samples were analyzed using a vital signs monitor with a module for respiratory gas analysis (E-CAiOVX, Datex-Ohmeda, GE Healthcare, Chicago, IL, USA). A combination of 16% oxygen, 5% carbon dioxide, and 79% nitrogen was used. The rates of diffusion for both respiratory gases were comparable in snow and both forms of perlite. Oxygen propagated faster than carbon dioxide. Due to similar diffusion characteristics to snow, perlite possesses the potential to stand in as an effective substitute for soft snow for the study of respiratory dynamics, for conducting breathing experiments, and for testing avalanche safety equipment. Full article

Snow avalanches are a type of serious natural disaster that commonly occur in snow-covered mountains with steep terrain characteristics. Susceptibility analysis of avalanches is a pressing issue today and helps decision makers to implement appropriate avalanche risk reduction strategies. Avalanche susceptibility maps provide a preliminary method for evaluating places that are likely to be vulnerable to avalanches to stop or reduce the risks of such disasters. The current study aims to identify areas that are vulnerable to avalanches (ranging from extremely high and low danger) by considering geo-morphological and geological variables and employing an Analytical Hierarchy Approach (AHP) in the GIS platform to identify potential snow avalanche zones in the Karakoram region in Northern Pakistan. The Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM) was used to extract the elevation, slope, aspect, terrain roughness, and curvature of the study area. This study includes the risk identification variable of land cover (LC), which was obtained from the Landsat 8 Operational Land Imager (OLI) satellite. The obtained result showed that the approach established in this study provided a quick and reliable tool to map avalanches in the study area, and it might also work with other glacier sites in other parts of the world for snow avalanche susceptibility and risk assessments. Full article

Through analyzing the triggering factors and activity characteristics of avalanches in Aerxiangou in the Western Tianshan Mountains, the formation and disaster-causing process of avalanches were studied to provide theoretical support and a scientific basis for avalanche disaster prevention. In this paper, based on remote sensing interpretation and field investigation, a spatial distribution map of avalanches was established, and the induced and triggering factors in disaster-prone environments were analyzed using the certainty factor model. The degree of influence (E) of the disaster-causing factors on avalanche triggering was quantified, and the main control conditions conducive to avalanche occurrence in different periods were obtained. The RAMMS-avalanche model was used to analyze the activity characteristics at points where multiple avalanches occurred. Research results: (1) The E values of the average temperature, average snowfall, and surface roughness in February were significantly higher than those of other hazard-causing factors, reaching 1.83 and 1.71, respectively, indicating strong control. The E values of the surface cutting degree, average temperature, and average snow depth in March were all higher than 1.8, indicating that these control factors were more prominent than the other factors. In contrast, there were four hazard-causing factors with E values higher than 1.5 in April: the mean temperature, slope, surface roughness, and mean wind speed, with clear control. (2) Under the influence of the different hazard-causing factors, the types of avalanches from February–April mainly included new full-layer avalanches, surface avalanches, and full-layer wet avalanches. (3) In the RAMMS-avalanche simulation test, considering the deposition effect, compared to the previous avalanche movement path, the secondary avalanche flow accumulation area impact range changes were slight, while the movement area within the avalanche path changes was large, as were the different categories of avalanches and their different movement characteristic values. Overall, wet snow avalanches are more hazardous, and the impact force is larger. The new snow avalanches start in a short period, the sliding rate is fast, and the avalanche sliding surface (full-snow surface and face-snow) of the difference is mainly manifested in the differences in the value of the flow height. Full article

The glacial and torrential basin of Taconnaz (Mont-Blanc massif, France) dominates the Chamonix valley. It is one of the major paths for snow avalanches in the Alps, often triggered by serac falls from the Taconnaz glacier. On 24 November 2018, the basin’s multi-risk nature was further accentuated by a new type of hazard with a rockfall triggered at c. 2700 m a.s.l. It travelled down over a distance of 1.85 km and stopped 165 m away from the construction site of a micro-hydroelectric power station. We studied the triggering conditions at the permafrost lower limit, the effects of the supra-glacial path on the flow patterns, and the fate of the scar and the deposit on torrential activity. By comparing a pre-event Structure from Motion model with a post-event LiDAR model, we estimated the volume of the scar to be 42,900 m³ (±5%). A numerical model was employed to simulate the rapid runout. It revealed the complexity of the flow, attributed to the sequestration of a part of the deposit in crevasses, the incorporation of a significant volume of ice resulting in a transition from a dry granular flow to a mud-like flow, and the presence of numerous deposit zones. Subsequent monitoring of the area after the event allowed for the documentation of the scar’s evolution, including a landslide, as well as the progressive degradation and evacuation of the deposit by the torrent without producing debris flow. The study of the triggering factors indicated glacial retreat as the probable main cause, assisted by the melting of ice lenses left by the permafrost disappearance. Finally, we present replicable methods for managing risks at the site following the event. This event improves the understanding of cascading processes that increasingly impact Alpine areas in the context of climate change. Full article