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Simulation and Analysis of a Snow Avalanche Accident in Lower Western Himalaya, India
Abstract
In Western Himalaya, an average 49 person die every year due to snow avalanche activities and this death rate is very high as compared to other Asian countries. A snow avalanche accident was observed on 5 January 2018 on Chowkibal–Tangdhar (CT) road axis at avalanche site number CT-8 located near Chowkibal village in Kupwara district, union territory Jammu and Kashmir, India. In the present paper, we discuss snow avalanche simulation, the climatic condition, avalanche debris height and length, and suggested solutions to handle avalanche situations. Rapid Mass MovementS numerical model in combination with digital elevation model and potential release area has been used to simulate avalanche accident occurred on 5 January 2018 at CT-8. The analysis demonstrates maximum snow avalanche velocity, impact of pressure and height of flow to be ~ 25 ms−1, ~ 9.39 × 104 kgm−1 s−2, and ~ 3.0 m respectively on 5 January 2018 at CT-8. Further simulated avalanche debris height and length form road has been validated with ground observed data. Ground reconnaissance of the location was conducted by a team of Snow and Avalanche Study Establishment, Chandigarh and it has been observed that lack of ‘avalanche awareness and Standard Operating Procedures during movement in avalanche prone areas’ among the travellers on the road cause accident
. The present paper seems to be first to investigate snow avalanche accident in Western Himalaya and recommend that proper campaigning of avalanche awareness among the people residing in avalanche prone areas of Himalaya could reduce such accidents significantly.
... In recent years, Rapid Mass Movements Simulation (RAMMS) has been widely used in analyses of the dynamic process of geological disasters such as debris flows, snow avalanches, and rockfalls in high mountain regions (Christen et al. 2010;Aydin 2014;Frank et al. 2015Frank et al. , 2017. The RAMMS runout model uses the 2-D depth-averaged shallow water equations for granular flows in three dimensions, which is already a widely used model for practical (Gan and Zhang 2019;Singh et al. 2020). ...
... Based on a two-dimensional numerical calculation model, RAMMS can quickly simulate debris avalanches, rockfalls, and debris flows, and obtain motion parameters including motion distance, velocity impact pressure, and flow path, in three-dimensional terrain (Chirsten et al. 2010a, b). In recent years, Rapid Mass Movement Simulation (RAMMS) has been widely used to analyze the dynamic process of geological disasters such as mudslides, avalanches, and rock slides in high mountains (Singh et al. 2020; Fig. 6 Dam burst and road inundation photos Content courtesy of Springer Nature, terms of use apply. Rights reserved. ...
On October 17 and 29, 2018, two rock and ice avalanches occurred on the western slope of the Sedongpu Basin upstream of the Yarlung Zangbo River in the Tibetan Plateau. Both avalanches formed the disaster chains and damaged many bridges and roads. Both avalanches on high mountain slope at an elevation of 6000 m asl above sea level triggered long-runout disaster chains, including debris flow, river blocking and flood. In this study, the disaster characteristics and dynamic process were analyzed by multitemporal satellite imagery. The results show that both of the initial sliding bodies were composed of rock and ice. Due to the large elevation difference, the initial sliding bodies rapidly descended into valley floor and immediately transformed into a debris flow after impact and fragmentation. And then, this study divided the disaster chain into four zones by satellite images and field observation, including source zone, dynamic erosion zone, deposition and damming zone, and flash flood zone. This study also carried out the numerical simulation of the disaster by RAMMS. The numerical results reproduced the dynamical process of the debris flow. Furthermore, the potential causes of disaster, evolution process, and the geohazard tendency are discussed.
... The objective of this study is to test the performance of the twodimensional model FLO-2D in simulating snow avalanche dynamics and compare its results with the two-dimensional avalanche dynamic simulation tool RAMMS::AVALANCHE (Christen et al., 2010b). Since RAMMS:: AVALANCHE is one of the most widely used two-dimensional snow avalanche simulation tools and has been successfully applied in the Alps (Christen et al., 2010a;Dreier et al., 2016), Carpathians (Košová et al., 2022), Pyrenees (Riba Porras et al., 2018), Himalayas Singh et al., 2020), and in the Andes (Janeras et al., 2013), it represents a good reference to evaluate the performances of FLO-2D. Furthermore, considering the uncertainties in the estimation of snow avalanche flow velocity (Fischer et al., 2014;Gauer, 2014), this investigation could help in the drawing up of composite hazard maps resulting from merged numerical modelling. ...
Snow avalanches are gravitational processes characterised by the rapid movement of a snow mass, threatening inhabitants and damaging infrastructure in mountain areas. Such phenomena are complex events, and for this reason, different numerical models have been developed to reproduce their dynamics over a given topography. In this study, we focus on the two-dimensional numerical simulation tools RAMMS::AVALANCHE and FLO-2D, aiming to compare their performance in predicting the deposition area of snow avalanches. We also aim to assess the employment of the FLO-2D simulation model, normally used in water flood or mud/debris flow simulations, in predicting the motion of snow avalanches. For this purpose, two well-documented avalanche events that occurred in the Province of Bolzano (IT) were analyzed (Knollgraben, Pichler Erschbaum avalanches). The deposition area of each case study was simulated with both models through back-analysis processes. The simulation results were evaluated primarily by comparing the simulated deposition area with the observed one through statistical indices. Subsequently, the maximum flow depth, velocity and deposition depth were also compared between the simulation results. The results showed that RAMMS::AVALANCHE generally reproduced the observed deposits better compared to FLO-2D simulation. FLO-2D provided suitable results for wet and dry snow avalanches after a meticulous calibration of the rheological parameters, since they are not those typically considered in avalanche rheology studies. However, the results showed that FLO-2D can be used to study the propagation of snow avalanches and could also be adopted by practitioners to define hazard areas, expanding its field of application.
... Physical models require variations in snow characteristics to assess snow stability in terms of avalanche dynamics (Reuter et al., 2022). Specifically, snow processes associated with the changes in temperature, density, and moisture content gradients in a snowpack are simulated to determine the parameters of the weak layer (Singh et al., 2020;Komatsu and Nishimura, 2020). Then, various instability metrics were evaluated to predict the avalanche hazards (Reuter et al., 2022;Richter et al., 2021;Komatsu and Nishimura, 2020). ...
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.
... Physical models require variations in snow characteristics to assess snow stability in terms of avalanche dynamics (Reuter et al., 2022). Specifically, snow processes associated with the changes in temperature, density, and moisture content gradients in a snowpack are simulated to determine the parameters of the weak layer (Singh et al., 2020;Komatsu and Nishimura, 2020). Then, various instability metrics were evaluated to predict the avalanche hazards (Reuter et al., 2022;Richter et al., 2021;Komatsu and Nishimura, 2020). ...
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 icrowave Daily Brightness Temperature), Meteorological data (e.g., WRF simulation), topography, and limited observation in Tianshan Mountains, China. There are 21 causative factors in the se�lection, 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.
... Long records of avalanches including observations, characterisation of morphometry, time of release as well as their weather 545 conditions have been compiled (Gidrometeoizdat, 1984) and used for mitigation infrastructures construction, especially on Kamchik Pass in the Tian Shan (Semakova and Bühler, 2017;Semakova et al., 2018Semakova et al., , 2009). In Western Himalaya and the Karakoram some preliminary studies for specific avalanche events employing RAMMS exist as well, however hampered in their quality by the lack of accurate snow depth data, general snow properties as well as local meteorological data (Gilany and Iqbal, 2019;Mahboob et al., 2015;Singh et al., 2020). 550 ...
The cryosphere in high mountain Asia (HMA) not only sustains livelihoods of people residing downstream through its capacity to store water but also holds potential for hazards. One of these hazards, avalanches, so far remains poorly studied as the complex relationship between climate and potential triggers is poorly understood due to lack of long-term observations, inaccessibility, severe weather conditions, and financial and logistic constraints. In this study, available literature was reviewed covering the period from the late 20th century to June 2022 to identify research and societal gaps and propose future directions of research and mitigation strategies. Beyond scientific literature, technical reports, newspapers, social media and other local sources were consulted to compile a comprehensive, open access and version controlled database of avalanche events and their associated impacts. Over 681 avalanches with more than 3131 human fatalities were identified in eight countries of the region. Afghanistan has the highest recorded avalanche fatalities (1057) followed by India (952) and Nepal (508). Additionally, 564 people lost their lives while climbing peaks above 4500 m a.s.l., one third of which were staff employed as guides or porters. This makes it a less deadly hazard than in the less populated European Alps for example, but with a considerably larger number of people affected who did not voluntarily expose themselves to avalanche risk. Although fatalities are significant, and local long-term impacts of avalanches may be considerable, so far, limited holistic adaptation or mitigation measures exist in the region. These measures generally rely on local and indigenous knowledge adapted with modern technologies. Considering the high impact avalanches have in the region we suggest to further develop adaptation measures including hazard zonation maps based on datasets of historic events and modelling efforts. This should however happen acknowledging the already existing knowledge in the region and in close coordination with communities and local government and civil society stakeholders. More research studies should also be attempted to understand trends and drivers of avalanches in the region.
... In addition to snow, avalanches often contain other materials (rock debris, soil, plants) which are transported and accumulated in the lower areas. The aftermaths of avalanches include loss of human lives and impact on the human environment, settlements and transport infrastructure, biodiversity, landscape, etc. [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23]. A large number of human casualties have been reported in Switzerland, Austria, Italy, Türkiye, Afghanistan, Pakistan, Tajikistan and Canada [14,[24][25][26][27][28][29]. ...
Snow avalanches are one of the most devastating natural hazards in the highlands that often cause human casualties and economic losses. The complex process of modeling terrain susceptibility requires the application of modern methods and software. The prediction of avalanches in this study is based on the use of geographic information systems (GIS), remote sensing, and multicriteria analysis—analytic hierarchy process (AHP) on the territory of the Šar Mountains (Serbia). Five indicators (lithological, geomorphological, hydrological, vegetation, and climatic) were processed,
where 14 criteria were analyzed. The results showed that approximately 20% of the investigated area is highly susceptible to avalanches and that 24% of the area has a medium susceptibility. Based on the results, settlements where avalanche protection measures should be applied have been singled out. The obtained data can help local self-governments, emergency management services, and mountaineering services to mitigate human and material losses from the snow avalanches. This is the first research in the Republic of Serbia that deals with GIS-AHP spatial modeling of snow avalanches,
and methodology and criteria used in this study can be tested in other high mountainous regions.
... In addition to snow, avalanches often contain other materials (rock debris, soil, plants) which are transported and accumulated in the lower areas. The aftermaths of avalanches include loss of human lives and impact on the human environment, settlements and transport infrastructure, biodiversity, landscape, etc. [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23]. A large number of human casualties have been reported in Switzerland, Austria, Italy, Türkiye, Afghanistan, Pakistan, Tajikistan and Canada [14,[24][25][26][27][28][29]. ...
Snow avalanches are one of the most devastating natural hazards in the highlands that often cause human casualties and economic losses. The complex process of modeling terrain susceptibility requires the application of modern methods and software. The prediction of avalanches in this study is based on the use of geographic information systems (GIS), remote sensing, and multicriteria analysis—analytic hierarchy process (AHP) on the territory of the Šar Mountains (Serbia). Five indicators (lithological, geomorphological, hydrological, vegetation, and climatic) were processed, where 14 criteria were analyzed. The results showed that approximately 20% of the investigated area is highly susceptible to avalanches and that 24% of the area has a medium susceptibility. Based on the results, settlements where avalanche protection measures should be applied have been singled out. The obtained data can will help local self-governments, emergency management services, and mountaineering services to mitigate human and material losses from the snow avalanches. This is the first research in the Republic of Serbia that deals with GIS-AHP spatial modeling of snow avalanches, and methodology and criteria used in this study can be tested in other high mountainous regions.
... In recent years, the software rapid mass movement simulation (RAMMS) has been widely used in analyses of the dynamic process of geological disasters such as debris flows, snow avalanches and rockfalls in high mountain regions (Christen et al., 2010a, b;Aydin et al., 2014). The RAMMS runout model uses the two-dimensional (2D) depth-averaged shallow water equations for granular flows in three dimensions, which has been widely used in practice (Mahboob et al., 2015;Gan and Zhang, 2019;Singh et al., 2020). Compared with other numerical simulation software, RAMMS with advanced three-dimensional (3D) visualization interface can be used to combine the results with digital elevation models, aerial images, and topographic maps. ...
A massive rock and ice avalanche occurred on the western slope of the Ronti Gad valley in the northern part of Chamoli, Indian Himalaya, on 7 February 7, 2021. The avalanche on the high mountain slope at an elevation of 5600 m above sea level triggered a long runout disaster chain, including rock mass avalanche, debris avalanche, and flood. The disaster chain had a horizontal travel distance of larger than 17,600 m and an elevation difference of 4300 m. In this study, the disaster characteristics and dynamic process were analyzed by multitemporal satellite imagery. The results show that the massive rock and ice avalanche was caused by four large expanding discontinuity planes. The disaster chain was divided into five zones by satellite images and field observation, including source zone, transition zone, dynamic entrainment zone, flow deposition zone, and flood zone. The entrainment effect and melting water were recognized as the main causes of the long-runout distance. Based on the seismic wave records and field videos, the time progress of the disaster was analyzed and the velocity of frontal debris at different stages was calculated. The total analyzed disaster duration was 1247 s, and the frontal debris velocity colliding with the second hydropower station was approximately 23 m/s. This study also carried out the numerical simulation of the disaster by rapid mass movement simulation (RAMMS). The numerical results reproduced the dynamic process of the debris avalanche, and the mechanism of long-runout avalanche was further verified by parametric study. Furthermore, this study discussed the potential causes of disaster and flood and the roles of satellite images and seismic networks in the monitoring and early-warning.
Glacier-related geohazards threaten societies in mountain regions. Massive rock-ice avalanches/rockslides/glacier debris flows frequently occurred and triggered secondary geohazard chains in the Yarlung Zangbo River downstream, which pose a serious threat to nearly residents and engineering facilities and can be linked to climate change as glaciers generally retreated after 2000. This paper provides an interdisciplinary and insight analysis for geohazard modes, monitoring and evolutions, deposits features and dynamic process of glacier geohazards by combining satellite images, field investigation, InSAR, UAVs, and simulation. It is revealed that more and more ice crevasses of various size, which may expand and further decrease the integrity of rock mass due to meltwater, earthquakes, and rainfall infiltration, appear on avalanche sources through cases analysis. The temperature rise accelerates glacier retreat and results in glacier movement. Hereafter, the slope gradually becomes unstable and starts to fail. It also analyzes historic events and long-runout process of rock-ice avalanches in the Sedongpu gully. Geomorphology plays an important role in high mobility and volume amplification, such as terrain gradient, valley twist, and moraines. The volume amplification is not only connected with the substrate entrainment, but also the riverbanks failure. This work mainly provides a scientific basis for disaster risk assessment and government policy formulation in this region.
Mountains, being fragile, act as a vital repository for water and biodiversity. The Himalaya, in specific, the “roof of the world” is endowed with a magnificent and scenic view with temperate green forests, alpine meadows, agricultural fields, gorges, waterfalls, cascades of river valleys, and a human settlement in the unstable slopes or at the perennial streams of major rivers. The vulnerability of mountain ecosystems is being disproportionately influenced by climate change-induced disasters and is poorly understood as well. Cascading effect of temperature change can melt the snow and ice, thereby exhibiting a noticeable impact on the availability of water, biodiversity, boundary shift in ecosystem, agriculture, and on human well-being. Furthermore, several climate-induced disasters, like flash floods, mass movements, debris flows, and landslides, have occurred in the Himalayas. Specifically, this has happened a lot in the recent past, resulting in numerous deaths and property damage. This insecurity is due to the region’s unplanned, unscientific and unregulated practices as well as a massive rise in population. This underlines the necessity for a Mountain Specific Risk Management Framework (MSMRMF) and the incorporation of spatial specificities for risk reduction. The three dimensions of vulnerability, namely, adaptive capacity, exposure, and sensitivity, are greatly governed by livelihood strategies, access to water, food, and hygiene. The best available research on disaster risk reduction (DRR) and climate change adaptation must be incorporated in deciding disaster resilience. This chapter sheds light on various climate-induced and geological disasters in mountain regions, their impact, and risk management strategies. The significance of regional climate models, development of alternative technologies, people’s understanding regarding the social construction of risk, the role of local stakeholders, and enhancing the governance capacity and participation to manage the disaster risk is as well briefly discussed.
Accurate snow depth observations are one of the most important inputs in the process of avalanche hazard estimation, forecasting and mitigation. This study presents the high-resolution snow depth mapping of avalanche prone regions of Manali-Dhundi area, Himachal Pradesh (HP), India, using the repeated UAV Photogrammetric surveys. The images captured by a RGB camera mounted in a fixed wing UAV (e-Bee X) were used to generate the high-resolution Digital Surface Model (DSM) and Orthophotos of the study area. Mapping of the snow depth of the study area is crucial for avalanche hazard assessment and safe movement of the personnel and vehicles passing through the highways in this area during winters. A total 12 km 2 area covering six major avalanche sites was surveyed. Snow depth map was generated by differencing co-registered snow-covered DSM and bare surface DSM. UAV-derived snow depth values have been validated with the point snow depth measurements of Wireless Sensor Networks (WSN), manual observation station and snow depth around a structure of known height in the survey area. The UAV-retrieved snow depth values were found in good correlation with field measured snow depth (R=0.95). The maximum and minimum observed snow depth values at different locations on open slope and flat area were 1.62 m and 0.79 m. The estimated Mean Error (ME) and RMSE based on these measured values were found 0.15 m and 0.21 m respectively. The study reveals that snow depth map generated from UAV survey provides reliable estimates of snow depth values for flat areas and open slopes but method is not suitable for forested areas. Study also infers that the statistical measures (i.e., , , RMSE etc.) derived from complete survey dataset without considering topographic factors and features of the region will not provide realistic estimates of snow depth and its accuracy. The study suggests that regular monitoring (before and after every snow storm) of the snow depth using UAV-based method is quick, cost effective, and provides accurate snow conditions for operational use and planning in snow bound regions.
The forecast of snow avalanches in the Himalayan region has critical importance due to repetitive hazard scenarios and huge loss of property and lives. In recent years, avalanche forecast models have been developed using meteorological data collected from surface observatories (SO) at Defence Geoinformatics Research Establishment (erstwhile Snow and Avalanche Study Establishment (SASE) and now DGRE), India. For operational forecasting, outputs of a Weather Research Forecasting (WRF) model are used in real-time. The objective of this study is to determine a suitable bias correction approach for three key variables: mean temperature (T), relative humidity (RH), and wind speed (WS) which can be implemented in the operational forecast services at four observatory stations in Himachal Pradesh, India. We consider data of seven cold seasons (November–April) from 2011 to 2018 obtained from SO and WRF model output. Three quantile-based bias-correction approaches: Quantile Mapping (QM), Quantile–Quantile Mapping (QQ), and Quantile–Delta Mapping (QD), have been applied. In applying the QD method, two types of delta terms: multiplicative and additive, have been experimented according to the density distribution of the data. For evaluation, a leave-one-season-out-cross-validation approach is used. The results indicate that the QM and QD methods significantly reduced the bias associated with the mean temperature, relative humidity, and wind speed, whereas the performance of QQ method has limitations for all the variables. Furthermore, to meet the objective of generating bias-corrected data for operational forecasting services, uniform parameter set is generated for each variable associated with all the observatory stations. This approach is effective only for temperature, and for other variables, the improvements were not significant, mainly because of the high topographical variability in the Himalayan terrain.
The Himalayas is massive mountainous hills of altitude about 2000m to 6500m garlanding India in the north extending about 2500km arc-shaped snowy glaciers covering parts of Afghanistan, Pakistan, India, Nepal, China, and Bhutan at various heights in sub-tropics. Snow avalanches in winter with floods and landslides cause maximum fatalities with increasing vulnerability in West Indian Himalayas (WIH) dropping temperature to ≈ -60°C. The ignored calamity has encountered unplanned relief to inaccessible areas that warrant a disaster risk reduction (DRR) approach with modern structural interventions under downsizing glacier due to Anthropocene stresses. The present study envisions the various avalanche occurrences in the three most pretentious states Jammu & Kashmir, Himachal Pradesh, and Uttarakhand, in WIH. Correlation between geological vs meteorological responsibilities such as temperature, earthquake, snowfalls, and Indian summer monsoon with avalanche sorted out and causes depicted. Present investigation is about vulnerability, risk, track, impact, forecasting procedures, early warning system (EWS), disaster risk reduction processes, moderating strategies and other factors. The inferred results are the cause of avalanche formation, the decline in the glacier area, the increase in frequency, and intensity, and the disaster risk reduction processes including the disaster management action plan (DMAP). The vulnerability area designation, awareness among people, disaster mitigation by public private partnership (PPP) mode, as combined effort on war footing basis by the line departments.
On October 17 and 29, 2018, two rock and glacier avalanches occurred on the western slope of the Sedongpu Basin upstream of the Yarlung Zangbo River in the Tibetan Plateau, forming the disaster chains and causing damage to many bridges and roads. Based on the comparative analysis of multiple pre-and post-remote sensing images, the initial sliding body, which was composed of rock and glacial material, was located on a steep slope above an elevation of 6000 m. Under the coupling effect of multiple factors such as gravity, rainfall, and weather changes, the initial sliding body detached from the source zone and then transformed into a debris flow after impact and fragmentation. The debris flow traveled downstream and scraped loose glacial till in its path, causing the volume of the sliding body to increase. In addition, the debris flow traveled 10 km under low frictional resistance, as a result of the lubrication via early rainfall and glacial meltwater. Eventually, the debris flow rushed out onto the valley floor, forming a landslide dam and blocking the Yarlung Zangbo River. The deposit volumes on October 17 and 29 were 20.4 million m ³ and 10.1 million m ³ , respectively, with a total mean thickness of ~22m. This study provides an insight into the dynamic process as they unfolded, through multitemporal satellite imagery and numerical simulation. Furthermore, we also discuss the potential cause of rock/ice avalanche and disaster scenarios, as well as the tendency of the rock and glacier avalanches are discussed.
The aim of this study is to generate a reliable dynamic snow avalanche hazard map using analytical hierarchy process method based on multisource geo-spatial data for the Chowkibal–Tangdhar (C–T) road axis in Jammu and Kashmir (J&K), India. Avalanche-prone areas of C–T axis have been demarcated using three causative parameters, i.e., terrain, ground cover and meteorological parameters. Terrain parameters, e.g., elevation, slope, aspect and curvature, have been estimated from Advanced Spaceborne Thermal Emission and Reflection Radiometer, Global Digital Elevation Model Version 2. Ground cover information has been extracted from Landsat-8 data. Meteorological parameters maps, i.e., snow depth, relative humidity and air temperature, have been generated using geo-spatial interpolation techniques of in situ data. All the parameters have been incorporated in Geographic Information System environment to generate the hazard map. Validation of hazard map was accomplished with the area under the curve method. The prediction rate was observed to be 93.2%. Further, 20% of the study area was estimated having no hazard, 55% as low hazard, 12% as moderate hazard and 13% as high hazard on April 13, 2015. Dynamic hazard map thus generated using remote sensing and in situ data will be useful for mitigation of snow avalanche hazard, regional planning for development of infrastructure, transportation, troops movement, army deployments and communication network.
Snow is an essential component of the cryosphere and spatio-temporal variability of snow cover over large Himalayan region is important for climate change studies, regional and global energy budget, aquatic cycles and managing water resources, etc. In the present paper, analysis of snow cover area (SCA) variation for more than a decade period from 2001 to 2016 in North-West Himalaya (NWH) and its climatic zones, i.e., lower Himalayan zone (LHZ), middle Himalayan zone (MHZ), and upper Himalayan zone (UHZ), has been presented. SCA has been estimated using 10-day maximum snow cover product derived from Moderate Resolution Imaging Spectroradiometer (MODIS) sensor images. Large inter and intra-annual variation in snow cover of NWH and its climatic zones have been observed during the data period. SCA in NWH varied from ~ 13,180 km² (August, 2001) to ~ 2, 11,000 km² (February, 2004) during the data period. Mean of annual and seasonal SCA has been estimated for entire NWH and its climatic zones. Mean annual SCA of NWH, LHZ, MHZ, and UHZ were estimated to be ~ 92,482 km², ~ 8150 km², ~ 35,078 km², and ~ 21,190 km² respectively. SCA was observed to be decreasing in NWH, LHZ, and MHZ at the rate of 840 km² year⁻¹, 31 km² year⁻¹, and 74 km² year⁻¹ respectively during 2001–2010, although the trend was statistically non-significant. Statistically significant increasing trend in SCA has been observed in UHZ at the rate of 241 km² year⁻¹ during 2001–2016. The paper highlights a shift in SCA trends after 2010 in NWH, LHZ, and MHZ and slowdown in snow/ice cover shrinkage during recent years. Additionally, variation in snowline elevation, snow cover duration and effect of topography on snow cover has been explored during different seasons in the NWH and its climatic zones.
Hidden Markov Model (HMM) has been developed for avalanche warning on 10 different road sectors in Pir-Panjal and Great Himalayan mountain ranges of North-West Himalaya. The model uses a data set of nine snow and meteorological variables—average air temperature, snow temperature index, snow drift index, snowfall in 24 h, snowfall in 48 h, snow water equivalent, snowfall intensity, standing snow and snowpack settlement collected during past 20 winters (1992–2012). The HMM is composed of four observations derived from the model input variables and four state variables. The state variables of the model are four levels of avalanche danger (No, Low, Medium and High). Single HMM has been developed to provide avalanche warning for both direct and delayed/wet avalanches with a lead time of two days. The HMM has been validated with (Case-1) and without (Case-2) incorporating delayed/wet avalanches using data collected during four winters (2012–2016) and compared with official Avalanche Warning Bulletin issued by Snow and Avalanche Study Establishment during these winters. The model has been validated through computation of accuracy measures such as percent correct (PC), bias, false alarm rate, probability of detection and Heidke Skill Score. The PC of the HMM for different stations for Case-1 varies from 80.1 to 98.6% for day-1 and 81.2 to 98.3% for day-2 and that for Case-2 from 82.2 to 98.6% for day-1 and 83.3 to 98.3% for day-2.
Avalanche accidents, particularly those resulting in fatalities, attract substantial attention from policy makers and organizations, as well as from the media and the public. Placing fatal accidents in a wider context requires long-term and robust statistics. However, avalanche accident statistics, like most other accident statistics, often rely on relatively small sample sizes, with single multi-fatality events and random effects having a potentially large influence on summary and trend statistics. Additionally, trend interpretation is challenging because statistics are generally explored at a national level, and studies vary in both the period covered and the methods. Here, we addressed these issues by combining the avalanche fatality data from the European Alps (Austria, France, Germany, Liechtenstein, Italy, Slovenia, and Switzerland) for three different periods between 1937 and 2015 and applying the same data analysis methodology. During the last four decades, about 100 people lost their lives each year in the Alps. Despite considerable inter-annual variation, this number has remained relatively constant in the last decades. However, exploring fatality numbers by the location of the victims at the time of the avalanche revealed two partly opposing trends. The number of fatalities in controlled terrain (settlements and transportation corridors) has decreased significantly since the 1970s. In contrast to this development, the number of fatalities in uncontrolled terrain (mostly recreational accidents) almost doubled between the 1960s and 1980s and has remained relatively stable since then, despite a strong increase in the number of winter backcountry recreationists. Corresponding to these trends, the proportion of fatalities in uncontrolled terrain increased from 72 to 97 %. These long-term trends were evident in most national statistics. Further, the temporal correlation between subsets of the Alpine fatality data, and between some of the national statistics, suggests that time series covering a longer period may be used as an indicator for missing years in shorter-duration datasets. Finally, statistics from countries with very few incidents should be compared to, or analysed together with, those from neighbouring countries exhibiting similar economical and structural developments and characteristics.
In this paper, an algorithm is proposed for generation of snow depth maps. The efficacy of the algorithm has been established through a case study in lower and middle Himalayas, India. The algorithm is a modified version of the spatial interpolation method proposed earlier in Swiss Alps. The method uses discrete point data supplemented with remotely sensed derived information data to create snow depth maps at spatial resolution of 0.5 km. In situ snow depth observations from 14 locations, Automatic Weather Station (AWS) recorded snow depth from 9 locations, Moderate Resolution Imaging Spectroradiometer (MODIS) images and Shuttle Radar Topographic Mission (SRTM) DEM form the database. The algorithm is based on the dependency of snow depth on elevation above mean sea level, which is later adjusted through the in situ snow depth observations to represent the local and regional characteristics of the snow distribution. The algorithm has been validated for different days of the winter season 2012–13 using leave one out station cross validation method. The mean absolute error (MAE) and Root Mean Square Error (RMSE) in estimation of snow depth have been observed as ~ 34 cm and ~ 42 cm respectively during the season. The snow depth maps generated from the proposed algorithm may be useful in assessment of snow avalanche hazards as well as in various hydrological and glaciological studies in the inaccessible cryospheric region of the Western Himalaya.
With the exception of northern India, there are few, if any, consistent data records relating to avalanche activity in the high mountains of Asia. However, records do exist of avalanche fatalities in the region, contained in mountaineering expedition reports. In this paper, I review and analyze statistics of avalanche fatalities (both snow and ice) in the high mountains of Asia (Himalaya, Karakoram, Pamir, Hindu Kush, Tien Shan, Dazu Shan) from 1895 to 2014. The data are stratified according to accident cause, geographical region (Nepal-Tibet (Xizang), Pakistan, India, China, Central Asia), mountain range, personnel (hired or expedition members) and terrain. The character of the accidents is compared with data from North America and Europe. The data show that the important risk components are the temporal and spatial exposure probabilities. It is shown that human actions and decisions govern the pattern of fatal avalanches in the high mountains of Asia.
In the northern environments of Quebec (eastern Canada), snow avalanche hazards have been ignored for a long time because no major incident was recorded before the tragedies of Blanc-Sablon (Lower North Shore of the St. Lawrence River) in 1995 and Kangiqsualujjuaq (Nunavik) in 1999. To enhance risk reduction at these sites, this research on process characteristics describes prone terrain, run-out distance and triggering factors, and prompted efforts (permanent and temporary measures) made to mitigate and prevent future snow avalanche tragedy from short, steep slopes. Considering the high vulnerability of these communities related to the growing population of Nunavik and the lack of knowledge of avalanches on the Lower North Shore, acceptable risk was based on the implementation of a snow avalanche forecasting and warning program over 3 years, the first one in eastern Canada. Community participation and the involvement of the municipal and provincial authorities have enabled the efficient operation of the program and accentuate the sensitivity and resilience of the populations to avalanche hazard and risk, as evidenced by the subsequent identification of avalanche sites by the communities themselves. These case studies demonstrate the importance of adequate and safe land planning, notably in the context of climate change, and particularly for isolated northern communities.
A numerical avalanche prediction scheme using Hidden Markov Model (HMM) has been developed for Chowkibal–Tangdhar road axis in J&K, India. The model forecast is in the form of different levels of avalanche danger (no, low, medium, and high) with a lead time of two days. Snow and meteorological data (maximum temperature, minimum temperature, fresh snow, fresh snow duration, standing snow) of past 12 winters (1992–2008) have been used to derive the model input variables (average temperature, fresh snow in 24 hrs, snow fall intensity, standing snow, Snow Temperature Index (STI) of the top layer, and STI of buried layer). As in HMMs, there are two sequences: a state sequence and a state dependent observation sequence; in the present model, different levels of avalanche danger are considered as different states of the model and Avalanche Activity Index (AAI) of a day, derived from the model input variables, as an observation. Validation of the model with independent data of two winters (2008–2009, 2009–2010) gives 80% accuracy for both day-1 and day-2. Comparison of various forecasting quality measures and Heidke Skill Score of the HMM and the NN model indicate better forecasting skill of the HMM.
Western Himalayan region of India was mainly delineated into three principle zones: Lower, Middle and Upper. These zones were broadly categorized on the basis of snow meteorological parameters and cover a large snowy area of thousands of square kilometer. Weather parameters, snowpack properties, snowpack structure and avalanche activities were observed non-homogeneous in these zones. In the present study these large zones were first divided into small snowy areas covering main road axes used for transportation during winter. Snow-meteorological data, stratigraphy data and avalanche occurrences of past 15 to 35 years of these small snowy areas were analyzed and zones having similar meteorological conditions and gross snow cover properties were identified. Identification of these small snow zones are of great help for operational avalanche forecaster to issue area specific avalanche warning. GIS (Geographic Information System) technique is used for obtaining detailed terrain information e.g. slope, aspect, elevation, surface roughness and vegetation etc. of these snow zones. Climate and snow-pack information has been accumulated from 42 observation points spread around major road axis of these small snowy areas in Western Himalaya. The snow climate of these snow zones were also examined by the snow climate classification scheme given by Mock and Birkeland.
Conventional as well as numerical techniques are being widely used for the prediction of snow avalanches. The present approach combines both the techniques and delivers avalanche danger warning for 24 h in advance. Initially different levels of avalanche danger have been decided by observing fresh snow of 24 h and standing snow from a snow-meteorological database of the past ten winters (1992-2002) along with a database of avalanche warning and occurrences. Finally these levels have been characterized by a critical range calculated by using a discriminant function, which is a function of all the significant snow and meteorological parameters. The significant snow and meteorological parameters have been selected by correlation analysis. For the selection of significant parameters and to calculate the critical range for each of the danger levels, a new term, i.e. index of avalanche has been introduced and its variation in different ranges of snow and meteorological parameters has been discussed. For the winter of 2003-2004, model outcome has been compared with the actual avalanche occurrences. Out of total 122 days during winter, there were 27 avalanche days and 95 non-avalanche days. The accuracy of the model for avalanche occurrence is 67% and for nonoccurrence it is 84%.
The snow cover model SNOWPACK simulates snow stratigraphy for the locations of automatic snow and weather stations. Based on the stratigraphy, snow stability is predicted by calculating three stability indices. We verified the performance of the skier stability index SK38 in order to develop a supporting tool for avalanche warning services. Since stability depends on snow stratigraphy, modelled grain type, grain size, hardness and density were first validated. The skier stability index SK38 performed poorly in terms of identifying potential weak layers. By introducing a new stability formulation (SSI) that combined the SK38 with differences of hardness and grain size across layer interfaces - known indicators of structural instability - the model performance was substantially improved. For manually observed flat field profiles, the SSI was significantly related with stability test results. At the regional scale, a statistically significant relation between predicted and verified stability was found. Stability patterns at the mountain range scale were reproduced. With these improvements the snow cover model SNOWPACK will develop into a valuable supporting tool for stability evaluation as done by avalanche warning services.
heuristic mismatches in avalanche accidents involving victims with and without avalanche training. In a review of 41 avalanche accidents involving avalanche-aware victims, Atkins (2001) found that 34 accidents (83%) were due to decision-making errors rather than subtleties of the terrain or snowpack. These and other results have fostered a growing emphasis on decision-making skills and human factors in avalanche educa-tion (see, for example, Tremper, 2001). In this paper, I present evidence that four heuristic traps – familiarity, social proof, commitment and scar-city – have played key roles in recreational avalanche accidents. For each trap, I examine its statistical signif-icance, the influences of group size and level of ava-lanche training, and how reliable or unreliable the underlying heuristic might be for making decisions in avalanche terrain. Data for this study came from acci-dent records maintained by the Colorado Avalanche Information Center, published accounts in the Snowy Torrents (Williams and Armstrong, 1984; Logan and Atkins, 1996), and various internet and newspaper resources. Over the course of the study, I reviewed 622 recreational avalanche incidents involving 1180 indi-viduals in the United States between 1972 and 2001. 2. Methods: Quantifying decision making in avalanche terrain In order to examine the effects of heuristic traps in avalanche accidents, I used a simple quantitative approach described in an earlier study (McCammon, 2001). Each accident was assigned a hazard score equal to the sum of the number of hazard indicators present at the time of the accident (Table A1). In effect, the hazard score approximated the level of ava-lanche risk that the victims had exposed themselves to at the time of the accident. To minimize reporting Abstract: Even though people are capable of making decisions in a thorough and methodical way, it appears that most of the time they don't. A growing body of research suggests that people unconsciously use simple rules of thumb, or heuristics, to navigate the routine complexities of modern life. In this paper, I examine evidence that four of these heuristics – familiarity, social proof, commitment and scarcity – have influenced the decisions of avalanche victims. Using a quantitative method to define the level of hazard exposure in 598 avalanche accidents in the United States, I compare the behavior of the victims when heuristic cues were present to their behavior when these cues were absent. Key findings of this study include: 1) evidence that social proof, commitment, and scarcity traps were significant in many accidents, 2) evidence that group size influenced susceptibility to certain heuristic traps, and 3) evidence that the level of avalanche training in victims influenced their susceptibility to heuristic traps. These findings strongly support the idea that tools for managing heuristic traps are essential for effective avalanche education.
1] Mountains are important sources of freshwater for the adjacent lowlands. In view of increasingly scarce freshwater resources, this contribution should be clarified. While earlier studies focused on selected river systems in different climate zones, we attempt here a first spatially explicit, global typology of the so-called ''water towers'' at the 0.5° Â 0.5° resolution in order to identify critical regions where disproportionality of mountain runoff as compared to lowlands is maximum. Then, an Earth systems perspective is considered with incorporation of lowland climates, distinguishing four different types of water towers. We show that more than 50% of mountain areas have an essential or supportive role for downstream regions. Finally, the potential significance of water resources in mountains is illustrated by including the actual population in the adjacent lowlands and its water needs: 7% of global mountain area provides essential water resources, while another 37% delivers important supportive supply, especially in arid and semiarid regions where vulnerability for seasonal and regional water shortage is high.
This paper is based on a study for the New Zealand Mountain Safety Council which investigated the circumstances contributing to the deaths of 128 people in avalanches between 1863 and 1999. The study identified a trend of high fatalities during European settlement followed by a lull in fatalities early last century and then an increase in recent decades similar to other recently colonized countries. Similar to other studies, most victims were in their twenties and shift from work-to recreation-based activities has occurred from a century ago to recent times. Comparison with other studies of more specific activities involved in recent decades showed that alpine climbing, people on training courses and in area skiers and patrollers were over-represented while out of area ski/boarders and snowmobilers were under-represented. The geographic distribution of fatalities is concentrated in the South Island reflecting the preponderance of terrain for climbing and skiing.
Numerical avalanche dynamics models have become an essential part of snow engineering. Coupled with field observations and historical records, they are especially helpful in understanding avalanche flow in complex terrain. However, their application poses several new challenges to avalanche engineers. A detailed understanding of the avalanche phenomena is required to construct hazard scenarios which involve the careful specification of initial conditions (release zone location and dimensions) and definition of appropriate friction parameters. The interpretation of simulation results requires an understanding of the numerical solution schemes and easy to use visualization tools. We discuss these problems by presenting the computer model RAMMS, which was specially designed by the SLF as a practical tool for avalanche engineers. RAMMS solves the depth-averaged equations governing avalanche flow with accurate second-order numerical solution schemes. The model allows the specification of multiple release zones in three-dimensional terrain. Snow cover entrainment is considered. Furthermore, two different flow rheologies can be applied: the standard Voellmy–Salm (VS) approach or a random kinetic energy (RKE) model, which accounts for the random motion and inelastic interaction between snow granules. We present the governing differential equations, highlight some of the input and output features of RAMMS and then apply the models with entrainment to simulate two well-documented avalanche events recorded at the Vallée de la Sionne test site.
The numerical formulation of a one-dimensional physical snowpack model is presented. The model is operationally employed on a day-to-day basis by avalanche warners to predict snowpack settlement, layering, surface energy exchange and mass balance. Meteorological data obtained from automatic weather stations positioned near avalanche starting zones is used as model input. In this paper, the one-dimensional equations governing the heat transfer, water transport, vapour diffusion and mechanical deformation of a phase changing snowpack are stated. New snow, wind drift and snow ablation are treated as special mass boundary conditions. Snow is modelled as a three-component (ice, water, air) porous material capable of undergoing large irreversible viscous deformations. Phase changes between the components are simulated. Snow layers are defined not only in terms of height and density, but also microstructure. That is, by the size, shape and bonding of the grains composing the ice lattice. The governing differential equations are solved using a fully implicit Lagrangian Gauss–Seidel finite-element method. Example calculations from the catastrophic avalanche winter 1999 are presented to document model performance. The overall mass balance evaluation shows that the model accurately predicts the build-up and ablation of the seasonal alpine snowcover.
The snow cover model SNOWPACK includes a detailed model of snow microstructure and metamorphism. In SNOWPACK, the complex texture of snow is described using the four primary microstructure parameters: grain size, bond size, dendricity and sphericity. For each parameter, rate equations are developed that predict the development in time as a function of the environmental conditions. The rate equations are based on theoretical considerations such as mixture theory and on empirical relations. With a classification scheme, the conventional snow grain types are predicted on the basis of those parameters. The approach to link the bulk constitutive properties, viscosity and thermal conductivity to microstructure parameters is novel to the field of snow cover modeling. Expanding on existing knowledge on microstructure-based viscosity and thermal conductivity, a complete description of those quantities applicable to the seasonal snow cover is presented. This includes the strong coupling between physical processes in snow: The bond size, which changes not only through metamorphic processes but also through the process of pressure sintering (included in our viscosity formulation), is at the same time the single most important parameter for snow viscosity and thermal conductivity. Laboratory results are used to illustrate the performance of the formulations presented. The numerical implementation is treated in the companion paper Part I. A more complete evaluation for the entire model is found in the companion paper Part III.
The development of the seasonal snow cover is entirely driven by atmospheric forcing. SNOWPACK uses measured snow depths to determine snow precipitation rates via the calculated settling rates. This requires a rigid data control algorithm. A new statistical model is used to estimate fresh snow density as a function of the measured atmospheric conditions. A statistical model is also derived for the snow albedo, which is necessary to determine the absorbed radiation. The surface sensible and latent heat flux parameterizations are derived from Monin–Obukhov similarity and include a formulation for wind pumping. The formulations will also adapt to drifting snow conditions. The new suggestion is consistent with the observation of different roughness lengths for scalars and momentum over snow. An accurate formulation, especially for the latent heat exchange, is crucial because latent heat exchange determines the formation of surface hoar, a very important weak layer. We also account for the effect of wind pumping on the thermal conductivity in the uppermost snow layers. The surface energy and mass exchange formulations are evaluated by looking at the formation of the important thin layers surface hoar and melt–freeze crusts in SNOWPACK. Those layers are well simulated. In addition, the complete snow profile development is modeled successfully for the parameters grain type, temperature, density, grain size and liquid water content. An overall score between 0 and 1 is used to describe the profile agreement with observations and an average score of over 0.8 is reached.
Release of snow avalanche from a mountain slope depends on various parameters such as snow cover, terrain and meteorological conditions of the region. The precise information of avalanche occurrence in terms of its location and extent is essentially important for hazard mapping and for avalanche occurrence feedback. In the present study, various techniques have been explored for automatic detection and mapping of snow avalanche debris for a part of Western Himalayan region using Sentinel-2 satellite data. Spectral signatures of avalanche and non-avalanche snow collected from the field spectroradiometer survey are used for identifying suitable spectral bands of Sentinel-2 for avalanche debris detection. Techniques such as Ratio Method (RM), Gray Level Co-occurrence Matrix (GLCM), a new proposed index i.e. Avalanche Debris Index (ADI) and Object Based Image Analysis (OBIA) are applied on satellite images to retrieve the avalanche debris. Retrieved avalanche debris are further compared with the manually digitized avalanche occurred boundaries. The OBIA method has been found the most suitable for avalanche debris detection and mapping using the medium resolution satellite data.
Aims:
This retrospective study investigated the epidemiology of summer avalanche accidents that occurred in Switzerland and caused at least one fatality between 1984 and 2014. Summer avalanche accidents were defined as those that occurred between June 1st and October 31st.
Results:
Summer avalanches caused 21 (4%) of the 482 avalanches with at least one fatality occurring during the study period, and 40 (6%) of the 655 fatalities. The number of completely buried victims per avalanche and the proportion of complete burials among trapped people were lower in summer than in winter. Nevertheless, the mean number of fatalities per avalanche was higher in summer than in winter: 1.9 ± 1.2 (standard deviation; range 1-6) versus 1.3 ± 0.9 (range 1-7; p < 0.001). Trauma was the presumed cause of death in 94% (33 of 35) in summer avalanche accidents. Sixty-five percent of fully buried were found due to visual clues at the snow surface.
Conclusions:
Fatal summer avalanche accidents caused a higher mean number of fatalities per avalanche than winter avalanches, and those deaths resulted mostly from trauma. Rescue teams should anticipate managing polytrauma for victims in summer avalanche accidents rather than hypothermia or asphyxia; they should be trained in prehospital trauma life support and equipped accordingly to ensure efficient patient care.
Rule-based systems are widely being used in decision making, control systems and forecasting. In the real world much of the knowledge is imprecise, uncertain, ambiguous and inexact in nature. Fuzzy logic offers a better way to represent complicated situations in terms of simple natural language. Here an attempt has been made to develop a rule-base for prediction of direct action avalanches of Chowkibal-Tangdhar road axis (Jammu and Kashmir) in Indian Himalaya using fuzzy logic. The condition attributes of the rule-based system are six snow-related parameters selected from the past dataset of the representative observatory 'Stage-II' in the axis. Different fuzzy sets are defined for each parameter on the basis of their distribution with four danger labels of avalanche activity. A total of 101 composite rules are developed for different danger labels of avalanche activity. The results show good agreement with the danger classification for avalanche activity and prediction of non-avalanche activities.
Avalan~hes caused. c?nsi~erab~e damage. initially to !he inhabitants of Himalayan belt but lately only a small propomon of the ongmal inhabitants of Himalayan regIOn have suffered due to avalanche menace. The inhabitants, after initial suffering settled their dwellings at safe places through logic as well as trial and error method. Th hazard, however, increased with the movement of persons in the inner belts of Himalayan region. The movem~ this time was not for the settlement but for carrying out the various tasks in the high altitude snow bound regions. The paper analyses the evolution and progress of avalanche accidents in Western Himalayan region. The cause of some of the major avalanche disasters was found to be unprecedented weather situation that caught people off-guard. The authors opine that with the increasing population and increased activity in the Himalaya belt like winter sports, adventure tourism, refuge in hills etc., the threat is likely to assume greater proportion in near future. While avalanche forecasting models are being refmed and fine-tuned and better equipment are being developed for safe travel through avalanche terrain, emphasis in the Western Himalayan region should also be given to awareness and quality education programme. This will help the pedestrians to take adequate precautionary measures while negotiating avalanche slopes.
The nearest-neighbour method is widely used independently as well as in combination with other methods for avalanche forecasting. Though, the nearest-neighbour method has proven fairly helpful, forecasters may often face difficulty with its output in determining the number of real neighbours of the present day. Values of geometrical distances, defined by distance metric used, help only in ranking the past days in terms of their nearness with present day. A situation, where sufficient real neighbours of the present day do not exist in sample database, usual practice of considering 10 nearest-neighbours for decision making may lead to unrealistic conclusions. A method is offered as a supplement to the popular nearest-neighbour method for avalanche forecasting. The method is applied on the same database and parameter vector space as being used for nearest-neighbour method. Forecaster defines the ranges of various parameters around the parametric values of the present day. A geometrical closed volume is generated in the parametric vector space according to the specified ranges, and the set of past days falling within this volume is the output. The output is an explicit evidence of uniqueness (or commonness) of the present day within the specified ranges. This output, when analysed in the backdrop of nearest-neighbour method output on the same data, helps forecasters in fine-tuning the decision. The proposed method was tested for its potential along Chowkibal-Tangdhar (CT) road axis, a stretch of about 20 km with 17 prominent avalanche sites, in Pir Panjal ranges of Indian Western Himalaya. With the prediction of avalanche days with mean probability 0.80 and standard deviation 0.38 along CT axis, model holds promise as a potential supplement tool for avalanche forecasting.
Shortly after 1300 hrs on wednesday March 5, 1986, a snow avalanche released from Storebalak, a mountain located at Vassdalen in Nordland county. The avalanche struck 31 men from the North Norway Brigade while they were moving snowmobiles along a stream valley on the north side of Storebalak. Thirty men (3 sergeants and 27 soldiers) originated from Platoon 2, Engineer Company, and one soldier had been borrowed from Infantry Battalion 3. All of the men were swept along by the avalanche, and to a greater or lesser extent, buried in the snow mass. A total of 16 of the 31 men were killed in the accident, and there were 15 survivors.The accident took place in association with the Nato winter exercise, Anchor Express. According to the battle plan, Brigade North was to advance northward along the highway E6 from the Bjerkvik area near Narvik. One battalion, Infantry Battalion 3 was to carry out a flank operation through the valleys Vassdalen, Bukkedalen and Rauddalen to Bonnes, in order to come behind the enemy and cut off the E6 at Salangsdalen.The opening of a snowmobile track through the abovementioned valleys as early as possible, was essential to carrying out this operation. Platoon 2, from the Engineer Company, Brigade North, was in the process of preparing this track when the accident took place.
The identification of snow avalanche release areas is a very difficult task. The release mechanism of snow avalanches depends on many different terrain-, meteorological-, snow pack- and triggering parameters and their interactions, which are very difficult to assess. In many alpine regions such as the Indian Himalaya, nearly no information on avalanche release areas exists mainly due to the very rough and poorly accessible terrain, the vast size of the region and the lack of avalanche records. However avalanche release information is urgently required for numerical simulation of avalanche events to plan mitigation measures, for hazard mapping and to secure important roads. The Rohtang tunnel access road near Manali, Himachal Pradesh, India is such an example. By far the most reliable way to identify avalanche release areas is using historic avalanche records and field investigations accomplished by avalanche experts in the formation zones. But both methods are not feasible for this area due to the rough terrain, its vast extent and lack of time. Therefore, we develop an operational, easy to use automated potential release area (PRA) detection tool in Python/ArcGIS which uses high spatial resolution digital elevation models (DEMs) and forest cover information derived from airborne remote sensing instruments as input. Such instruments can acquire spatially continuous data even over inaccessible terrain and cover large areas. We validate our tool using a database of historic avalanches acquired over 56 years in the neighborhood of Davos, Switzerland and apply this method for the avalanche tracks along the Rohtang tunnel access road. This tool, used by avalanche experts, delivers valuable input to identify focus areas for more detailed investigations on avalanche release areas in remote regions such as the Indian Himalaya and is a precondition for large scale avalanche hazard mapping.
The integration of a nearest-neighbours method based avalanche forecast model with a mesoscale weather forecast model (MM5) has been attempted for avalanche forecasting in Indian Himalaya. The MM5 model simulates weather parameters up to day-4 over the entire western Himalaya. The paper describes the methodology of using MM5 model predictions and some empirical relations, to find the probability of avalanche occurrence up to day-4 at a spatial resolution of 5 km by applying the nearest-neighbours method. The nearest-neighbours model uses Euclidean weighted distance metric to find 10 nearest neighbours from the past data in terms of snow and weather parameters. Based on the avalanche occurrences associated with nearest-neighbours, an a priori probability of avalanche occurrence is derived. This approach has been tested for forecasting of avalanches in Chowkibal–Tangdhar road axis in Indian western Himalaya.
The avalanche handbook, mountaineers, 1001SW Klickitat Way
- D Mcclung
- P Schaerer