Snow Avalanches Research Articles

Impacts of Climate Change on Snow Avalanche Activity Along a Transportation Corridor in the Tianshan Mountains

Snow avalanches can repeatedly occur along the same track under different snowpack and meteorological conditions during the snow season in areas of snow avalanche activity. The snowfall, air temperature, and snow cover can change dramatically in a warming climate, causing significant changes in the snow avalanche risk. But how the risk of snow avalanche activity during the snow season will change under a warming climate remains an open question. Based on the observed meteorological and snowpack data from 1968 to 2021 and the snow avalanche activity data during the 2011–2021 snow seasons along a transportation corridor in the central Tianshan Mountains that has a typical continental snow climate, we analyzed the temporal distribution of the snow avalanche activity and the impacts of climate change on it. The results indicate that the frequency of the snow avalanche activity is characterized by a Gaussian bimodal distribution, resulting from interactions between the snowfall, air temperature, and snowpack evolution. In addition, the active period of wet snow avalanches triggered by temperature surges and high solar radiation has gradually moved forward from the second half to the first half of March with climate warming. The frequency and size of snowfall-triggered snow avalanches showed only a slight and insignificant increase. These findings are important for rationally arranging snow avalanche relief resources to improve the risk management of snow avalanche disasters, and highlight the necessity to immediately design risk mitigation strategies and disaster risk policies to improve our adaptation to climate change.

Snow avalanche susceptibility mapping using novel tree-based machine learning algorithms (XGBoost, NGBoost, and LightGBM) with eXplainable Artificial Intelligence (XAI) approach

Snow avalanche susceptibility mapping using novel tree-based machine learning algorithms (XGBoost, NGBoost, and LightGBM) with eXplainable Artificial Intelligence (XAI) approach

Ceng Jing Ru Shi (Ago) by Stan Lai

Reviewed by: Ceng Jing Ru Shi (Ago) by Stan Lai Chiayi Seetoo CENG JING RU SHI (AGO). Text and direction by Stan Lai. Theatre Above, Shanghai, China. 9–22 December 2019. Ceng Jing Ru Shi 曾經如是(Ago) is the latest work written and directed by Stan Lai, which was premiered in Shanghai in December, 2019. The play lasted for more than five hours with more than forty speaking characters (including Tibetans, Chinese Han people, Americans, all kinds of animals, a mythical sacred beast, Time, and Chance), stretching the timespan of twenty-five years in the play’s setting. It was presented in a “circular staging” configuration pioneered by Lai in which the stage is built surrounding the audience who would then experience dramatic actions circling around them.1 Alongside playacting were the semi-panoramic backdrop projection and enchanting live singing, creating a feast for the senses as an unusual metropolitan theatre-ritual, which also marked the grand finale of the year for Lai’s own theatre venue, Theatre Above, in Shanghai. The devastating COVID-19 that soon followed, however, caught everyone in shock and halted live theatre for a long time. The pandemic that has resulted in millions of deaths became ominously reminiscent of the major phenomenon featured in Ago—deadly disasters—as if theatrical illusion should foreshadow reality. While the swift spreading of the disease has reminded us of our deeply interconnected conditions, the post-pandemic moment allows us to further contemplate, with renewed hindsight, the eco-cosmopolitan concerns embedded in Ago—present in the cross-cultural and even cross-species backgrounds of its characters and large-scale geographical movements (across a mountain village of China’s southwest, New York, and the Kanchenjunga mountain of the Himalayas). Ago unfolds by way of storytelling. The narrator JJ, also one of the characters, studied history at Beijing University. A disillusioned angry youth, she abandons herself to faraway lands and ends up in a mountain village of Yunnan Province in China, where she befriends the Tibetan residents, among them the principal characters Pema, Dorje, and Tashi. Then, a fatal earthquake changes everyone’s life. Ten years later, these survivors end up in New York, where they are hit once again by the September 11 attack. The first two acts of the play are set in the Yunnan village and New York respectively, with Pema’s noodle shop, a homelike gathering place in both locales, standing unchanged at the same spot onstage. With skillful staging highlighting key repetitions onstage, Lai transmits the feeling of life, recurring almost like mirror images, “as it used to be” (the literal translation of Ceng Jing Ru Shi), be it acts of survival such as Pema’s running of the noodle shop, or unexpected lethal blows preceded by people scurrying about in horror [End Page 200] followed by post-disaster distress and devastation. Along the way, various vignettes of encounters become increasingly entangled and, as if drawn into a centripetal swirl, evolve toward a singular goal in the third act. It involves a group of nine people embarking on a collective journey to the Kanchenjunga mountain in search of the legendary Pure Land (jingtu 淨土a reference to the utopic realm in the Buddhist tradition), only to end in yet another disaster, a snow avalanche from which only JJ and Pema survive. They go back to New York and continue with their lives. Click for larger view View full resolution Figure 1. Pema (Hao Lei) in Ago. (Photo by Wang Kai) Punctuated by the deadly disasters and the anti-climax of not finding the Pure Land, the play is structured like an experimental symphony while exuding a fable-like quality. The impossible journey to the Pure Land is itself a powerful metaphor, while interspersed in the play are also dry-humored quasi-philosophical remarks made by personifications of Time and Chance, who would begin to circle on the stage before the arrival of disasters. The surreal is fused in the realistic plane of action and acting style on Lai’s stage. And as in Aesop’s fables or Zhuangzi’s Daoist fables, the surreal dimensions of Ago resonate with much of the longstanding ways in which moral or philosophical teachings...

Snow avalanche susceptibility mapping from tree-based machine learning approaches in ungauged or poorly-gauged regions

Mapping avalanche susceptibility is essential for disaster management, especially in ungauged or poorly-gauged regions. Most existing machine-learning methods have a shortfall in multi-source, heterogeneous spatial data requirements and overfitting. Faced with these deficiencies, this study has developed four novel tree-based machine-learning models (Catboost, LightGBM, RF, and XGBoost) to predict avalanche susceptibility, along with the data support of remote sensing images (e.g., Superview-1, Sentinel-2, and Calibrated Enhanced-Resolution Passive Microwave Daily Brightness Temperature), Meteorological data (e.g., WRF simulation), topography, and limited observation in Tianshan Mountains, China. There are 21 causative factors in the selection, training, and validation processes. Then, the SHAP value is introduced to quantify the variable-based share of contribution to the possibility of avalanches on both global and local scales. The results demonstrate the following: (1) All four models are potent in assessing avalanche susceptibility due to similar patterns, with Catboost being the best with its eight indices: Accuracy (0.9249), TPR (0.9920), FAR (0.0080), TNR (0.8904), PPR (0.8229), NPR (0.0046), CSI (0.8175), and HSS (0.8404) superior to others; (2) The Catboost provides a reliable susceptibility map illustrating moderate and high susceptible areas up to 54.12% of the total, mainly concentrated in the west and southeast; (3) Topographic factors (distance to rivers, aspect, and relative slope position) and meteorology (precipitation) are the most effective for susceptibility modeling. The results above indicate the significant potential of tree-based machine-learning with multi-source and heterogeneous spatial data in obtaining high-quality susceptibility maps without too much field observation. It is of great importance for avalanche prevention in regions with complex terrain conditions and sparse data.

Disaster-development interface and its impact on emerging vulnerability scenario in Ladakh region of northwestern Himalayas.

The Union Territory of Ladakh, located in the northwestern Himalayan region, is highly vulnerable to natural and anthropogenic hazards like earthquakes, landslides, snow avalanches, flash floods, cloud bursts, and border conflicts. Occurrences of these disasters have significantly influenced the development and vulnerability scenario of Trans-Himalayan Ladakh. Findings reveal that despite suffering losses from natural and human-induced disasters, the region has benefited by grabbing the attention of policymakers at the national level. Consequently, long-term developments were positively impacted, reflecting infrastructural upgradation, improved transportation and communication, profoundly improving the socio-economic well-being of the people. Furthermore, post-disaster developments have managed to showcase the unique physiography and adventurous terrains of Ladakh, promoting tourism as the main economic driver in the region. The exponential growth of tourism and associated sectors have influenced the vulnerability scenario, which was quantified using the multi-criterion-based analytical hierarchical processes (AHP) method, indicating an increase in climate change-related vulnerability, followed by socio-cultural, environmental, and physical vulnerabilities. Specifically, the vulnerabilities with respect to flash floods, landslides, erratic rainfall, haphazard constructions, cultural dilution, water crisis, and changes in land use patterns have been exacerbated across the study area. The study highlights the need for effective management of these emerging vulnerabilities through proper planning to ensure long-term sustainable development goals in this environmentally fragile region.

ProForM: A simulation model for the management of mountain protection forests

Protection from gravitational natural hazards such as snow avalanches and rockfall is among the most important ecosystem services provided by forests in steep mountain terrain such as the European Alps. These so-called “protection forests” often have to be actively managed to ensure a sustainable ecosystem service provision in time and space. For studying these slowly developing ecosystems, dynamic forest models are important tools to assess the effect of management on the protective function. However, existing models focus on the current protective effect and in many cases neglect aspects of stand resistance and resilience to large disturbances, which are important factors for maintaining the long-term protective function. Therefore, we developed ProForM, a new dynamic forest model aimed at (1) reproducing stand dynamics of managed temperate mountain forests and (2) assessing their protective function in detail. ProForM is a structurally simple model that contains mechanistic formulations of basic demographic processes but was developed mainly with empirical data. It is spatially explicit and was developed with a focus on regeneration dynamics. The model is parameterized for four tree species (Picea abies, Abies alba, Fagus sylvatica, and Acer pseudoplatanus) and four elevational zones, ranging from mixed species submontane to spruce-dominated subalpine forests. ProForM was calibrated with and validated against independent data from long-term forest inventories from Switzerland. It exhibited a satisfactory performance in reproducing measured basal area, stem number, diameter distribution and mean crown ratio at the stand level, comparable to the performance of other, typically more complex models. Illustrative example simulations demonstrate the assessment of the protective function of a stand against gravitational hazards according to multiple criteria used in forestry practice in Alpine countries. ProForM is a valuable new tool to study the impacts of management on the protective effect of mountain forests, and to inform forest managers.

Editorial: Snow and ice disaster: Formation mechanism and control engineering

Snow is one of the most active natural elements on the earth. It is an important material source of the 6 polar ice sheet and alpine glaciers. The spatial and temporal distribution and evolution of snow cover 7 profoundly affect the global hydrological cycle, ecosystems, climate evolution, and other natural 8 processes. On the other hand, snow and ice disasters, such as snow avalanches, snowstorms, wind 9 blown snow, snowmelt floods, building collapses, traffic hazards, etc, can cause great damage to 10 human lives and social economies. Thus, studies on the physical processes and formation mechanism 11 of snow/ice disasters, prediction and early warning systems, as well as control engineering are of 12 great significance to preventing snow/ice disasters from damaging society. The research on related 13 disasters also involves multi-physical field coupling, strongly nonlinear, multi-scale, and other 14 scientific common problems. As well as the interaction between atmospheric turbulence and particle The cryosphere is a key component of the Earth system. The distribution of regional snow/ice cover 36 is affected mainly by factors such as turbulent wind field, topography, snowfall, and temperature. As global warming continues, extreme weather and climate events frequently occur, including snow/ice 38 disasters. Therefore, future studies are still needed on the improvement of the snow/ice disaster 39 prediction model and formation mechanism.

Slope Dynamics in Relation to the Occupation and Abandonment of a Mountain Farm in Þistilfjörður, Northeast Iceland

Extreme weather events such as storms, heavy snow accumulation, rapid snowmelt, and heavy rain have been closely related to slope instability in arctic and subarctic regions. In this paper, we investigate the historical activity of slope processes such as snow avalanches and debris flows in Þistilfjörður, northeastern Iceland, and examine their possible role in the occupation and abandonment of three archaeological sites located on slopes of Mt. Flautafell. The study combines geomorphological and stratigraphical surveys with historical records, notably Jarðabók Árna Magnússonar og Páls Vídalíns and Sýslu og sóknarlýsingar Bókmenntafélagsins Svalbarðssókn. Geomorphological surveys show numerous features that are indicative of active slope movement processes in and around the investigated sites. Our results suggest that the slopes experienced periods of instability during the occupation of these sites. The burial or destruction of some parts of the homefield at the Flautafell farm reveals slope activity, which may also be related, at least indirectly, to the abandonment of the farm at Norður Hús sometime before A.D. 1300. Nearby auxiliary farm installations of Stekkur remained untouched by slope processes even though they are situated in a vulnerable area. Further study and dating of slope processes and farm occupation could allow them to be used as proxies for deteriorating environmental conditions affecting the region.

Particle-size segregation in self-channelized granular flows

Geophysical mass flows such as debris flows, dense pyroclastic flows and snow avalanches can self-channelize on shallow slopes. The confinement afforded by formed levees helps to maintain the flow depth, and hence mobility, allowing self-channelized flows to run out significantly farther than unconfined, spreading flows. Levee formation and self-channelization are strongly associated with particle-size segregation, but can also occur in monodisperse flows. This paper uses the monodisperse depth-averaged theory of Rochaet al.(J. Fluid Mech., vol. 876, 2019, pp. 591–641), which incorporates a hysteretic friction law and second-order depth-averaged viscous terms. Both of these are vital for the formation of a travelling wave that progressively deposits a pair of levees just behind the front. The three-dimensional velocity field is reconstructed in a frame moving with the front assuming Bagnold flow. This enables a bidisperse particle-size segregation theory to be used to solve for the large and small particle concentrations and particle paths in three-dimensions, for the first time. The model shows that the large particles tend to segregate to the surface of the flow, forming a carapace that extends over the centre of the channel, as well as along the external sides and base of the levee walls. The small particles segregate downwards, and are concentrated in the main channel and in the inner levee walls. This supports the contention that a low-friction channel lining provides a secondary mechanism for run-out enhancement. It is also shown that the entire theory scales with particle diameter, so experiments with millimetre-sized particles provide important insights into geophysical-scale flows with boulders and smaller rock fragments. The model shows that self-channelization does not need particle-size segregation to occur, but supports the hypothesis that particle-size segregation and the associated frictional feedback can significantly enhance both the flow mobility and the levee strength.

Environmental drivers of paraglacial time duration and intensity for scree slopes dynamics in forested environments

AbstractThe highly fissile lithology of the rockwalls and the diversity of mass‐wasting processes provide a specific character to the active talus slopes of the northern Gaspé Peninsula since deglaciation. At a regional scale, the geology of the rockwalls, the patterns and modalities of deglaciation and the evolution towards a cold temperate morphoclimatic regime in a maritime context still influence the geomorphological dynamics of scree slopes today. At a local scale, the south–north orientation of the main coastal valleys influences insolation and exposure to prevailing winds, which in turn influence the snow cover regime and the occurrence of freeze–thaw cycles. The statistical analyses carried out from the mapping of 43 talus slopes and their geometric variables allowed the identification of significant environmental factors for the characterization of the dominant geomorphic processes: snow avalanches, frost‐coasted clast flows, debris flows and rockfalls. Slope aspect appears to be a key parameter in the nature of the processes acting on the talus slopes. East‐ and north‐facing talus slopes are generally covered by a significant snowpack in winter and the dominant processes are snow avalanches and debris flows. West‐ and south‐facing talus slopes face prevailing winds and insolation and are subject to frost‐coated clast flows, the main driver for forest regression, and rockfalls. However, the evolution of scree slopes in forested environments remains extremely complex due to the multiscale components that affect their evolution in the short, medium and long term.

Internal structure and transformation process from firn to ice in a perennial snow patch nourished by snow avalanches on Mt. Rishiri, Hokkaido

Internal structure and transformation process from firn to ice in a perennial snow patch nourished by snow avalanches on Mt. Rishiri, Hokkaido

Multi-component avalanches for rock- and ice-falls to potential debris flow transition modelling

Rock/ice avalanches are complex gravitational flows involving three important physicalprocesses: entrainment, phase-changes, and flow transitions. The basic complexity of modelling rock/ice avalanches lies in the fact that the flow is a mixture of rock, ice, water, snow, soil sediments. The interaction of these components can lead to flow transitions which is difficult to model with simplified flow rheologies. For example, a flow initially composed of rocks and ice can transition into a long-runout debris flow, dependent on the entrainment of water, water-saturated sediments and/or snow. The dynamic behaviour of rock/ice avalanches is therefore highly difficult to predict because the flow mixture is dependent on the hydrological and geomorphological properties along the avalanche track. These properties can vary from year to year but even from season to season. The problem is intensified by the fact that frictional shearing can produce enough heat energy to melt ice and snow, leading to additional water in the flow. In this contribution, we present a new RAMMS module specifically designed to simulate single- and multicomponent avalanches of rock, ice, water, snow, and ice. The rheology of the flow is treated by using the concept of component activation energy. The model includes both snow and sediment/water/ice entrainment modules. A unique feature of the model is that it tracks the temperature of the rock, ice and water phases and therefore can treat phase changes. We apply the model on the Chamoli case-study to highlight the potential and present limitations of the model..

Diachronic quantitative snow avalanche risk assessment as a function of forest cover changes

This work proposes a holistic quantitative snow avalanche risk assessment that evaluates, at reasonable computational costs and for various types of buildings, the impact of forest cover changes on the probability distribution of runout distances, impact pressures and subsequent risk estimates. A typical case study of the French Alps shows that, from a completely deforested to a completely forested path, avalanche risk for a building located downslope decreases by 53–99%, depending on how forest cover is accounted for in avalanche statistical–dynamical modeling. Local forest cover data inferred from old maps and photographs further demonstrates that a 20–60% risk reduction actually occurred between 1825 and 2017 at the site because of the afforestation dynamics, with significant modulations according to the considered building technology. These results (1) assert the protective role of forests against snow avalanches, (2) highlight the potential of combining nature-based solutions with traditional structural measures to reduce risk to acceptable levels at reasonable costs, (3) suggest a significant decrease in risk to settlements in areas that encountered similar forest cover changes and (4) open the door to the quantification of long-term avalanche risk changes as a function of changes of all its hazard, vulnerability and exposure drivers in various mountain context.

DENDROCHRONOLOGICAL METHODS IN THE STUDY OF GEOMORPHOLOGICAL NATURAL HAZARDS IN THE CARPATHIANS

The specifics of dendrochronological methods in the study of the most common hazardous slope processes are examined: snow avalanches, landslides, rockfalls and debris flows. The dendrogeomorphological methods mainly refer to the study and dating of tree rings of coniferous species (Larix decidua, Picea abies, Pinus cembra, Pinus mugo) and less deciduous species (Fagus sylavatica). In this way, they aim to interpret past events recorded in the structure of the tree rings in terms of their deformations and damage. The most typical signs of response to geomorphological natural hazards are mechanical disturbances including injures, scars and raising ducts on trees, inclination of tree stems, broken brunches and crowns and denudations of roots. Dendrogeomorphological methods are valuable tools for reconstructing hazardous geomorphological processes under conditions of an insufficient stationary monitoring network for natural hazards. Moreover, such methods allow not only dating and thus determining the recurrence of natural hazards, but also spatially determining the coverage of the territory by hazardous geomorphological processes. This function is particularly important This characteristic is particularly important for the assessment of remote regions with complex topography, especially mountainous regions. A retrospective analysis of earlier developments in this field shows the suitability of such methods for the reconstruction of hazardous geomorphological processes in mountainous regions.
 The application of dendrogeomorphological methods in the reconstruction of hazardous geomorphological processes was introduced in geomorphological studies in the 1970s. Most dendrogeomorphological studies on mountain regions are currently being carried out in Switzerland, the Czech Republic, France, Spain, Italy, Poland, Romania and the USA. Dendrogeomorphological methods are among the most important absolute methods for the identification and reconstruction of landslides, especially those that have occurred in recent centuries. Landslides, like other hazardous geomorphological processes, particularly affect the growth of trees, which is reflected in their impaired growth. Dendrogeomorphological methods are also considered reliable to reconstruct, predict and mitigate the consequences of mudflows.
 Czech, Polish and Romanian Carpathians are the common objects of the dendrogeomorphological studies in the Carpathians. Here, the reconstruction of snow avalanches is one of the most common dendrogeomorphological research in the Carpathians. At the same time, while in the parts of the Carpathians located in the other countries, such studies are active, in the Ukrainian Carpathians, still with insufficient possibilities for automated monitoring, such studies are scarce. The authors have already successfully tested such methods for the reconstruction of snow avalanches in the Ukrainian Carpathians (Chornohora and Borzhava massifs) while learning from Romanian experience in the Rodna and Maramuresh massifs of Eastern Carpathians. The dendrochronological results are congruent to stationary snow avalanche observations, evidence of residents and relevant services. The perspective of the authors’ studies in the field is related to the further testing of dendrochronological methods in the reconstruction of other dangerous geomorphological processes (landslides, rockfalls, mudslides) in the Eastern Carpathians on the example of the Chornohora and Poloninsky massifs using samples of damaged tree-rings of Picea abies, Abies alba, and Fagus sylvatica.
 Key words: dendrogeomorhological methods, geomorphological natural hazards, Carpathians, snow avalanches, tree rings.

Powder snow avalanche impact on hanging cables

In this paper we perform a stress wave analysis to determine the forces in hanging cables under the action of powder snow avalanches. Powder avalanches are a serious threat to communities and infrastructure in mountainous regions. They endanger power transmission lines which transport electricity over avalanche prone terrain. Powder avalanches produce an elevated air-blast that induces above ground forces in hanging cables, leading to failure and often the over-turning of the supporting towers. We solve a nonlinear system of three wave equations with appropriate boundary conditions that describes the internal stress state and the cable deformation in three directions. The impact pressures are estimated by simulating powder avalanche motion in three-dimensional terrain. The stress wave model is tested numerically, the obtained results are in good accordance with experimental measurements. We also investigate a possibility of usage of hanging cables as loading sensors to reconstruct powder cloud pressures. For that we formulate an inverse problem, solve it analytically in time-independent case and study numerically the obtained solution.

What weather variables are important for wet and slab avalanches under a changing climate in a low-altitude mountain range in Czechia?

Abstract. Climate change impact on avalanches is ambiguous. Fewer, wetter, and smaller avalanches are expected in areas where snow cover is declining, while in higher-altitude areas where snowfall prevails, snow avalanches are frequently and spontaneously triggered. In the present paper, we (1) analyse trends in frequency, magnitude, and orientation of wet- and slab-avalanche activity during 59 winter seasons (1962–2021) and (2) detect the main meteorological and snow drivers of wet and slab avalanches for winter seasons from 1979 to 2020 using machine learning techniques – decision trees and random forest – with a tool that can balance the avalanche-day and non-avalanche-day dataset. In terms of avalanches, low to medium–high mountain ranges are neglected in the literature. Therefore we focused on the low-altitude Czech Krkonoše mountain range (Central Europe). The analysis is based on an avalanche dataset of 60 avalanche paths. The number and size of wet avalanches in February and March have increased, which is consistent with the current literature, while the number of slab avalanches has decreased in the last 3 decades. More wet-avalanche releases might be connected to winter season air temperature as it has risen by 1.8 ∘C since 1979. The random forest (RF) results indicate that wet avalanches are influenced by 3 d maximum and minimum air temperature, snow depth, wind speed, wind direction, and rainfall. Slab-avalanche activity is influenced by snow depth, rainfall, new snow, and wind speed. Based on the balanced RF method, air-temperature-related variables for slab avalanches were less important than rain- and snow-related variables. Surprisingly, the RF analysis revealed a less significant than expected relationship between the new-snow sum and slab-avalanche activity. Our analysis allows the use of the identified wet- and slab-avalanche driving variables to be included in the avalanche danger level alerts. Although it cannot replace operational forecasting, machine learning can allow for additional insights for the decision-making process to mitigate avalanche hazard.

Characteristics and controls of the runout behaviour of non-Boussinesq particle-laden gravity currents – A large-scale experimental investigation of dilute pyroclastic density currents

One of the most dangerous aspects of explosive volcanism is the occurrence of dilute pyroclastic density currents that move at high velocities of tens to about a hundred of metres per second outwards from volcanic vents. Predicting the runout behaviour of these turbulent flows of hot particles and air is complicated by strong changes in the flow density resulting from entrainment of ambient air, sedimentation of particles, as well as heating and expansion of the gas phase. Current hazard models that are based on the behaviour of aqueous gravity currents cannot capture all aspects of the flow dynamics, and thus pyroclastic density current dynamics remain comparatively poorly understood. Here we interrogate the runout behaviour of dilute pyroclastic density currents in large-scale experiments using hot volcanic material and gas. We demonstrate that the flows transition through four dynamic regimes with distinct density and force characteristics. The first, inertial regime is characterized by strong deceleration under high density differences between the flow and ambient air where suspended particles carry a main proportion of the flows' momentum. When internal gravity waves start to propagate from the flow body into the advancing flow front, the currents transition into a second, inertia-buoyancy regime while flow density continues to decline. In this regime, subsequent arrivals of fast-moving internal gravity waves into the front replenish momentum and lead to sudden short-lived front accelerations. In the third regime, when the density ratio between flow and ambient air decreases closer to a value of unity, buoyancy forces become negligible, but pressure drag forces are large and constitute the main flow retarding force. In this inertia-pressure drag regime, internal gravity waves cease to reach the front. Finally, and with the density ratio decreasing below 1, the current transitions into a buoyantly rising thermal in regime 4.Unlike for aqueous gravity currents, the Froude number is not constant and viscous forces are negligible in these gas-particle gravity currents. We show that, in this situation, existing Boussinesq and non-Boussinesq gravity current models strongly underpredict the front velocity for most of the flow runout for at least half of the flow propagation. These results are not only important for hazard mitigation of pyroclastic density currents but are also relevant for other turbulent gas-particle gravity currents, such as powder snow avalanches and dust storms.

Towards Linking the Sustainable Development Goals and a Novel-Proposed Snow Avalanche Susceptibility Mapping

Towards Linking the Sustainable Development Goals and a Novel-Proposed Snow Avalanche Susceptibility Mapping

Automated snow avalanche release area delineation in data-sparse, remote, and forested regions

Abstract. Potential avalanche release area (PRA) modeling is critical for generating automated avalanche terrain maps which provide low-cost, large-scale spatial representations of snow avalanche hazard for both infrastructure planning and recreational applications. Current methods are not applicable in mountainous terrain where high-resolution (≤5 m) elevation models are unavailable and do not include an efficient method to account for avalanche release in forested terrain. This research focuses on expanding an existing PRA model to better incorporate forested terrain using satellite imagery and presents a novel approach for validating the model using local expertise, thereby broadening its application to numerous mountain ranges worldwide. The study area of this research is a remote portion of the Columbia Mountains in southeastern British Columbia, Canada, which has no pre-existing high-resolution spatial datasets. Our research documents an open-source workflow to generate high-resolution digital elevation models (DEMs) and forest land cover datasets using optical satellite data processing. We validate the PRA model by collecting a polygon dataset of observed potential release areas from local guides, using a method which accounts for the uncertainty in human recollection and variability in avalanche release. The validation dataset allows us to perform a quantitative analysis of the PRA model accuracy and optimize the PRA model input parameters to the snowpack and terrain characteristics of our study area. Compared to the original PRA model our implementation of forested terrain and local optimization improved the percentage of validation polygons accurately modeled by 11.7 percentage points and reduced the number of validation polygons that were underestimated by 14.8 percentage points. Our methods demonstrate substantial improvement in the performance of the PRA model in forested terrain and provide means to generate the requisite input datasets and validation data to apply and evaluate the PRA model in vastly more mountainous regions worldwide than was previously possible.

Snow cover characterization using C-band polarimetric SAR in parts of the Himalaya

Assessment of the snow cover state in the initiation zone of an avalanche is essential for the snow avalanche hazard mapping in the mountainous region. Seasonal snow cover is very dynamic in the Himalaya and changes its state from dry to moist or wet according to temperature during the progression of winter. In this study, a methodology based on decomposition techniques using polarimetric synthetic aperture radar (SAR) data in conjunction with field snow wetness profiles is adapted for characterization of snow cover into dry and moist or wet snow in the Himalayan region. C-band Radarsat-2 full-polarization SAR data is utilized for polarimetric descriptors generation by applying various in-coherent target decomposition techniques, viz., Yamaguchi 4-component, Freeman Durden 3-component and Wishart Unsupervised (H-α). The generated polarimetric descriptors, viz., surface, volume, double-bounce and helix scattering are utilized in the supervised classification and Entropy (H) and mean scattering angle (α) for understanding the underlying scattering process happening within the snowpack. These decomposition models although describes various physical scattering mechanisms but are inadequate to provide quantitative estimates of underlying physical properties (as in our case changes in snowpack wetness). A supervised classification based on support vector machine (SVM) is used to characterize snow cover with changing conditions of snow wetness from dry to moist or wet. Three data sets of snow-accumulation and snow-melt period of winter 2015 are processed and analyzed along with field snow wetness (% by volume) profiles. A total 47 Nos. of snow wetness profiles of the snowpack are collected using snow fork instrument in synchronous with satellite passes at well distributed, identifiable field locations and are used in the training (34 Nos.) and testing (13 Nos.) of the SVM model. Snow being heterogeneous material varies metamorphically within snowpack. Based on changing weather conditions and sun exposure duration, it results into different scattering behavior in microwave region of the electromagnetic spectrum. It has been qualitatively observed from target decomposition results that polarimetric descriptors generated using Yamaguchi 4-component scattering decomposition have better captured the increase in the surface scattering (during February and March imagery, due to wet snow conditions of snow cover) from volume scattering (during January imagery, due to dry or moist snow conditions) from seasonal snowpack over the acquisition period from January to March 2015. Further to understand and comprehend the scattering process in the snowpack metamorphism with time, Wishart unsupervised (H-α) classification has been applied on all Radarsat-2 datasets. It was observed in the Himalayan region that during 29 Jan 2015 under dry snow conditions, anisotropic and multiple scattering processes dominates and during period between 22 Feb 2015 and 18 Mar 2015 under moist or wet snow conditions the dipole and Bragg type scattering are dominating. A confusion matrix has been generated for the accuracy assessment of the SVM based classification results, which shows overall accuracy ∼ 79.6 % (for 29 Jan 2015), 72.4 % (for 22 Feb 2015) and 74.5 % (for 18 Mar 2015), respectively with the testing samples. The C-band polarimetric SAR data shown its capability for snow cover type characterization and can be applied for operational applications in the avalanche zones of the snow bound terrain.