Wet Snow Avalanches Research Articles

Retrieval of snow liquid water content from radiative transfer model, field data and PRISMA satellite data

The amount of liquid water (LWC) present in the snowpack is critical for predicting wet snow avalanches, forecasting meltwater release, and assessing water availability in river basins. However, measuring this variable using traditional in situ methods is challenging. Space imaging spectroscopy is emerging as a promising approach to map the spatial and temporal variations of snow parameters. While some studies suggest the potential of hyperspectral remote sensing to infer liquid water content, field validation is still lacking. In this context, we propose a new spectral index, namely Snow Surficial Water Index (SSWI), which is designed to be sensitive to the percentage of surficial liquid water content in snow. We developed the index using the BioSNICAR radiative transfer model and then we tested it on both field spectral data and satellite PRISMA imagery. Validation was performed using field data collected with a Snow Sensor during four campaigns in alpine environments, one of which simultaneously with PRISMA. Through a k-fold cross-validation analysis, we achieved a coefficient of determination of 0.7 and a Root Mean Square Error equal to 3%, demonstrating the effectiveness of the proposed index in retrieving LWC from field data and mapping LWC from PRISMA data. A spatial analysis at the catchment level reinforced the results, showing an LWC distribution consistent with orography. The proposed method can be easily applied to other space imaging spectroscopy missions.

A Study on Avalanche-Triggering Factors and Activity Characteristics in Aerxiangou, West Tianshan Mountains, China

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

Comparison of two 2-D numerical models for snow avalanche simulation

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. 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.

Automated prediction of wet-snow avalanche activity in the Swiss Alps

AbstractWet-snow avalanches are triggered by the infiltration of liquid water which weakens the snowpack. Wet-snow avalanches are among the most destructive avalanches, yet their release mechanism is not sufficiently understood for a process-based prediction model. Therefore, we followed a data-driven approach and developed a random forest model, depending on slope aspect, to predict the local wet-snow avalanche activity at the locations of 124 automated weather stations distributed throughout the Swiss Alps. The input variables were the snow and weather data recorded by the stations over the past 20 years. The target variable was based on manual observations over the same 20-year period. To filter out erroneous reports, we defined the days with wet-snow avalanches in a stringent manner, selecting only the most extreme active or inactive days, which reduced the size of the dataset but increased the reliability of the target variable. The model was trained with weather variables and variables computed from simulated snow stratigraphy in 38$^\circ$ slopes facing the 4 cardinal directions. While model development and validation were done in nowcast mode, we also studied model performance in 24-hour forecast mode by using input variables computed from a numerical weather prediction (NWP) model. Overall, the performance was good in both nowcast and forecast mode (f1-score around 0.8). To assess model performance beyond the stringent definition of wet-snow avalanche days, we compared model predictions to wet-snow avalanche activity over the entire Swiss Alps, based on the raw data over 8 winters. We obtained a Spearman correlation coefficient of 0.71. Hence, our model represents a step toward the application of support tools in operational wet-snow avalanche forecasting.

Can Saharan dust deposition impact snowpack stability in the French Alps?

Abstract. Saharan dust deposits can turn snow-covered mountains into a spectacular orange landscape. When avalanches release, a formerly buried dust layer can become apparent, possibly marking the failure plane. This appearance may suggest a relation between avalanche release and the previously deposited dust, which found mention among recreationists and avalanche professionals alike. While dust deposition affects the absorption of solar energy altering snowpack temperatures and melt rates, to date, there is no clear scientific evidence that dust deposition can significantly modify snow stability. Here we investigate, using an ensemble snow cover model, the impact of dust deposition on snow properties and mechanical stability by comparing simulations with and without dust deposition for synthetic and observed dust deposition events. The study focuses on two typical avalanche situations: artificial triggering on persistent weak layers and natural release of wet-snow avalanches. We study several situations with and without dust deposition and demonstrate how sensitive the impact of dust deposition is to the deposited dust mass, the slope aspect, the elevation and the meteorological conditions following the dust deposition. The additional energy absorbed by the dust layer speeds up warming and may advance surface wetting to ease the formation of a melt-freeze crust. If the crust is buried, the phenomenon of a strong temperature gradient close to the crust may promote the formation of persistent weak layers inside the snowpack. On the other hand, the melt-freeze crust may also lead to an increase in snowpack stability by redistributing the stress applied to weak layers buried below. Regarding wet-snow instability, we show that dust deposition can advance the onset of wet-snow avalanche activity by up to 1 month in spring, as hypothesized in previous studies. Thus, the impact of Saharan dust deposition on snowpack stability can be either neutral, positive or negative, depending on the topographical, snow and meteorological conditions. Even though not all physical processes are implemented, state-of the art snow cover models are able to mimic the speed-up of crust formation, and snow instability models can point out relevant situations for avalanche forecasting.

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.

Three-dimensional and real-scale modeling of flow regimes in dense snow avalanches

Snow avalanches cause fatalities and economic loss worldwide and are one of the most dangerous gravitational hazards in mountainous regions. Various flow behaviors have been reported in snow avalanches, making them challenging to be thoroughly understood and mitigated. Existing popular numerical approaches for modeling snow avalanches predominantly adopt depth-averaged models, which are computationally efficient but fail to capture important features along the flow depth direction such as densification and granulation. This study applies a three-dimensional (3D) material point method (MPM) to explore snow avalanches in different regimes on a complex real terrain. Flow features of the snow avalanches from release to deposition are comprehensively characterized for identification of the different regimes. In particular, brittle and ductile fractures are identified in the different modeled avalanches shortly after their release. During the flow, the analysis of local snow density variation reveals that snow granulation requires an appropriate combination of snow fracture and compaction. In contrast, cohesionless granular flows and plug flows are mainly governed by expansion and compaction hardening, respectively. Distinct textures of avalanche deposits are characterized, including a smooth surface, rough surfaces with snow granules, as well as a surface showing compacting shear planes often reported in wet snow avalanche deposits. Finally, the MPM modeling is verified with a real snow avalanche that occurred at Vallée de la Sionne, Switzerland. The MPM framework has been proven as a promising numerical tool for exploring complex behavior of a wide range of snow avalanches in different regimes to better understand avalanche dynamics. In the future, this framework can be extended to study other types of gravitational mass movements such as rock/glacier avalanches and debris flows with implementation of modified constitutive laws.

Validation of Landsat-8 satellite-derived radiative energy fluxes using wireless sensor network data over Beas River basin, India

ABSTRACT In the present paper, spatio-temporal variability in surface radiative energy fluxes has been estimated for snow/ice-covered Beas River basin, lower Western Himalaya, India using remote sensing technology and evaluated with wireless sensor network (WSN) collected data at various elevation levels. Surface energy fluxes are derived using Landsat-8 satellite images and digital elevation model (DEM) at fine spatial resolution (~30-m) during clear-sky days of the year 2016–2017. RMSE have been estimated in incoming shortwave, incoming longwave, outgoing longwave, net shortwave and net radiation flux and observed to be ~5%, ~ 6.3%, ~2.4%, ~18.9%, and ~28.3% of the respective mean values. Landsat-8 images in conjunction with DEM have shown the potential to retrieve variability in surface radiative energy fluxes at fine resolution in mountain terrain. Additionally, temporal variability in radiative radiation fluxes have also been explored on different elevation ranges and aspects slopes. The high energy fluxes have been estimated on southern slopes during analysis as compared to northern slopes in the present study. The paper appears to be the first for reporting net radiation flux for Himalayan snow cover at 30-m spatial resolution. The satellite-derived energy fluxes may be valuable in various applications associated with climatology, hydrology, wet-snow avalanche detection, land use land cover, mass balance, ecology, etc., especially in the absence of in situ data.

Characteristics and hazards of different snow avalanche types in a continental snow climate region in the Central Tianshan Mountains

Snow avalanches are a common natural hazard in many countries with seasonally snow-covered mountains. The avalanche hazard varies with snow avalanche type in different snow climate regions and at different times. The ability to understand the characteristics of avalanche activity and hazards of different snow avalanche types is a prerequisite for improving avalanche disaster management in the mid-altitude region of the Central Tianshan Mountains. In this study, we collected data related to avalanche, snowpack, and meteorology during four snow seasons (from 2015 to 2019), and analysed the characteristics and hazards of different types of avalanches. The snow climate of the mid-altitude region of the Central Tianshan Mountains was examined using a snow climate classification scheme, and the results showed that the mountain range has a continental snow climate. To quantify the hazards of different types of avalanches and describe their situation over time in the continental snow climate region, this study used the avalanche hazard degree to assess the hazards of four types of avalanches, i.e., full-depth dry snow avalanches, full-depth wet snow avalanches, surface-layer dry snow avalanches, and surface-layer wet snow avalanches. The results indicated that surface-layer dry snow avalanches were characterized by large sizes and high release frequencies, which made them having the highest avalanche hazard degree in the Central Tianshan Mountains with a continental snow climate. The overall avalanche hazard showed a single peak pattern over time during the snow season, and the greatest hazard occurred in the second half of February when the snowpack was deep and the temperature increased. This study can help the disaster and emergency management departments rationally arrange avalanche relief resources and develop avalanche prevention strategies.

Snow cornice and snow avalanche monitoring using automatic time lapse cameras in Tasiapik Valley, Nunavik (Québec) during the winter of 2017–2018

A series of automatic time-lapse cameras installed along the southwestern side of Tasiapik Valley, near the village of Umiujaq, Nunavik (northern Québec) documented several departure modes and types of snow involved in snow avalanches during winter 2017–2018. These included cornice–avalanche dynamics, slab and loose snow avalanches, and clean and dirty snow avalanches. At the top of the selected slope, a camera monitored the development of a snow cornice beginning in November 2017, detecting multiple cornice failures over the winter and spring. The track and deposition area of the runout paths were monitored from two cameras downslope, revealing the concomitance of snow–cornice fall and snow avalanche triggering. Snow avalanche activity remained relatively infrequent until the end of May 2018. Spring snow avalanche activity is characterized by wet and dirty snow avalanches carrying debris to the foot of the slope and by runout zones located near the road along the slope.

Application of a Magnetic Resonance Imaging Method for Nondestructive, Three-Dimensional, High-Resolution Measurement of the Water Content of Wet Snow Samples

The infiltration of melted snow water and rainwater into snow can drastically change the form of snow layers. This process is an important factor affecting wet snow avalanches. Accordingly, numerous field surveys and cold room experiments have been conducted to investigate the distribution of water in snow. The common methods of water content measurement (calorimetric and dielectric methods) are implemented by disturbing snow samples to measure them. However, the resolutions obtained are of the order of several centimeters, which hinders the continuous measurement of the water content of a particular sample. Magnetic resonance imaging (MRI), which is typically used in the medical field, can be used to generate a high-resolution three-dimensional (3D) image of the water distribution in samples without destructing them. The luminance of images produced by MRI depends on the volumetric water content of the sample, with luminance increasing with volumetric liquid water content. Therefore, the volumetric liquid water content of the sample can be estimated from its luminance value. Considering this concept, we developed a method to measure the volumetric liquid water content of wet snow samples using MR images. To evaluate the developed method, we prepared several wet snow samples and measured their various volumetric liquid water contents using MRI (θMRI) and the calorimetric method (θcal). θMRI and θcal showed good correlation when compared, with values in the range 0.02–0.46. Therefore, our system can accurately and nondestructively measure water content. The developed method using MRI can measure 3D volumetric liquid water contents with a high resolution (2 mm). Using the developed method, we investigated the hysteresis of the water retention curve of snow based on the measurements of a wetting process (boundary wetting curve) and a drying process (boundary drying curve) of the water retention curve for each sample. Our results indicate the existence of hysteresis in the snow water retention curves and the possibility of modeling it by adopting contexts of soil physics.

Estimation of Avalanche Development and Frontal Velocities Based on the Spectrogram of the Seismic Signals Generated at the Vallée de la Sionne Test Site

The changes in the seismic signals generated by avalanches recorded at three sites along a path at the Vallée de la Sionne (VdlS) experimental site are presented. We discuss and correlate the differences in the duration, signal amplitudes, and frequency content of the sections (Signal ONset (ON), Signal Body (SBO), and Signal TAil and Signal ENd STA-SEN) of the spectrograms with the evolution of the powder, transitional and wet snow avalanches along a path. The development of the avalanche front was quantified using the exponential function in time F (t) = K’ exp (β t) fitted to the shape of the signal ONset (SON section of the spectrogram. The speed of the avalanche front is contained in β. To this end, a new method was developed. The three seismic components were converted into one seismic component (FS), when expressing the vector in polar coordinates. We linked the theoretical function of the shape of the FS-SON section of the spectrogram to the numerical coefficients of its shape after considering the spectrogram as an image. This allowed us to obtain the coefficients K’ and β. For this purpose, the Hough Transform (HT) was applied to the image. The values of the resulting coefficients K’ and β are included in different ranges in accordance with the three types of avalanche. Curves created with these coefficients enable us to estimate the development of the different avalanche types along the path. Our results show the feasibility of classifying the type of avalanche through these coefficients. Average speeds of the avalanches approaching the recording sites were estimated. The speed values of wet and transitional avalanches are consistent with those derived from GEODAR (GEOphysical Doppler radAR) measurements, when available. The absence of agreement in the speed values obtained from seismic signals and GEODAR measurements for powder snow avalanches indicates, for this type of avalanche, a different source of the measured signal. Hence, the use of the two measuring systems proves to be complementary.

Use of Sentinel-1 radar observations to evaluate snowmelt dynamics in alpine regions

Abstract. Knowing the timing and the evolution of the snow melting process is very important, since it allows the prediction of (i) the snowmelt onset, (ii) the snow gliding and wet-snow avalanches, (iii) the release of snow contaminants, and (iv) the runoff onset. The snowmelt can be monitored by jointly measuring snowpack parameters such as the snow water equivalent (SWE) or the amount of free liquid water content (LWC). However, continuous measurements of SWE and LWC are rare and difficult to obtain. On the other hand, active microwave sensors such as the synthetic aperture radar (SAR) mounted on board satellites are highly sensitive to LWC of the snowpack and can provide spatially distributed information with a high resolution. Moreover, with the introduction of Sentinel-1, SAR images are regularly acquired every 6 d over several places in the world. In this paper we analyze the correlation between the multitemporal SAR backscattering and the snowmelt dynamics. We compared Sentinel-1 backscattering with snow properties derived from in situ observations and process-based snow modeling simulations for five alpine test sites in Italy, Germany and Switzerland considering 2 hydrological years. We found that the multitemporal SAR measurements allow the identification of the three melting phases that characterize the melting process, i.e., moistening, ripening and runoff. In particular, we found that the C-band SAR backscattering decreases as soon as the snow starts containing water and that the backscattering increases as soon as SWE starts decreasing, which corresponds to the release of meltwater from the snowpack. We discuss the possible reasons of this increase, which are not directly correlated to the SWE decrease but to the different snow conditions, which change the backscattering mechanisms. Finally, we show a spatially distributed application of the identification of the runoff onset from SAR images for a mountain catchment, i.e., the Zugspitze catchment in Germany. Results allow us to better understand the spatial and temporal evolution of melting dynamics in mountain regions. The presented investigation could have relevant applications for monitoring and predicting the snowmelt progress over large regions.

On the relation between avalanche occurrence and avalanche danger level

Abstract. In many countries with seasonally snow-covered mountain ranges warnings are issued to alert the public about imminent avalanche danger, mostly employing an ordinal, five-level danger scale. However, as avalanche danger cannot be measured, the characterization of avalanche danger remains qualitative. The probability of avalanche occurrence in combination with the expected avalanche type and size decide on the degree of danger in a given forecast region (≳100 km2). To describe avalanche occurrence probability, the snowpack stability and its spatial distribution need to be assessed. To quantify the relation between avalanche occurrence and avalanche danger level, we analyzed a large data set of visually observed avalanches (13 918 in total) from the region of Davos (eastern Swiss Alps, ∼300 km2), all with mapped outlines, and we compared the avalanche activity to the forecast danger level on the day of occurrence (3533 danger ratings). The number of avalanches per day strongly increased with increasing danger level, confirming that not only the release probability but also the frequency of locations with a weakness in the snowpack where avalanches may initiate from increase within a region. Avalanche size did not generally increase with increasing avalanche danger level, suggesting that avalanche size may be of secondary importance compared to snowpack stability and its distribution when assessing the danger level. Moreover, the frequency of wet-snow avalanches was found to be higher than the frequency of dry-snow avalanches for a given day and danger level; also, wet-snow avalanches tended to be larger. This finding may indicate that the danger scale is not used consistently with regard to avalanche type. Even though observed avalanche occurrence and avalanche danger level are subject to uncertainties, our findings on the characteristics of avalanche activity suggest reworking the definitions of the European avalanche danger scale. The description of the danger levels can be improved, in particular by quantifying some of the many proportional quantifiers. For instance, based on our analyses, “many avalanches”, expected at danger level 4-High, means on the order of at least 10 avalanches per 100 km2. Whereas our data set is one of the most comprehensive, visually observed avalanche records are known to be inherently incomplete so that our results often refer to a lower limit and should be confirmed using other similarly comprehensive data sets.

Snow avalanche susceptibility of the Circo de Gredos (Iberian Central System, Spain)

ABSTRACT We present a detailed snow avalanche susceptibility map at scale 1:20,000 of the Circo de Gredos in the Sierra de Gredos (Iberian Central System, Spain). This cirque-shaped landscape is one of the most popular spots for winter sports in the region. However, no snow avalanche activity assessment has been conducted to date. We have, therefore, produced a snow avalanche susceptibility map based on aerial and satellite imagery, newspaper reviews and field work, including avalanche features recognition and interviews with frequent backcountry users. We extracted the spatial distribution of necessary and enhancer factors for triggering slab, wet and loose snow avalanches from a digital elevation model. Finally, calculations to evaluate each snow avalanche type susceptibility were performed using a Geographical Information System. By combining our map collection, we concluded that most of the area in the Circo de Gredos is highly susceptible to snow avalanches, especially slab and wet snow types.

Determining forest parameters for avalanche simulation using remote sensing data

Mountain forests offer effective, natural and cost-efficient protection against avalanches. Trees reduce the probability of an avalanche formation and can significantly decelerate small to medium size avalanches. Remote sensing methods enable an efficient assessment of forest structural parameters on large scale and therefore determine the protective capacity of a specific forest. The aims of this study are: (i) to evaluate the quality of forest structural parameters obtained from remote sensing data using two different methods; and (ii) to determine how forest parameters and forest cover changes influence avalanche runout. We compared the control assessment of maximum tree height and crown coverage in 107 plots (50 in evergreen and 57 in deciduous forests). The same parameters were analysed using (i) a photogrammetry-based vegetation height model (VHMP) and (ii) a LiDAR-based vegetation height model (VHML). The control assessment of surface roughness was compared to the analysis of a digital terrain model (DTM). We then simulated two avalanche case studies near Davos (Switzerland) with forest parameters estimated by the remote sensing and control methods. Tree height and crown coverage assessed with both remote sensing methods (VHMP and VHML) did not differ significantly from the control measurements. However, surface roughness was underestimated. This had a significant influence on simulation results. For the first case study, a wet-snow avalanche, the simulated runout distances did not differ significantly, when using forest parameters from either of the two tested remote sensing methods. The simulated runout distance increased for an avalanche scenario with less forest cover in the release area and/or less forest cover after forest destruction by a preceding avalanche event. For the second case study, a dry-snow avalanche, the forest cover was underestimated by the VHMP, which led to longer simulated runout distances. Our study indicates that available remote sensing methods are increasingly suitable for the determination of forest parameters which are relevant for avalanche simulation models. However, more research is needed on the precise estimation of forest cover in release areas and understanding how forest cover changes affect avalanche runout.

Near-Real Time Automatic Snow Avalanche Activity Monitoring System Using Sentinel-1 SAR Data in Norway

Knowledge of the spatio-temporal occurrence of avalanche activity is critical for avalanche forecasting. We present a near-real time automatic avalanche monitoring system that outputs detected avalanche polygons within roughly 10 min after Sentinel-1 SAR data are download. Our avalanche detection algorithm has an average probability of detection (POD) of 67.2% with a false alarm rate (FAR) averaging 45.9, with a maximum POD of over 85% and a minimum FAR of 24.9% compared to manual detection of avalanches. The high variability in performance stems from the dynamic nature of snow in the Sentinel-1 data. After tuning parameters of the detection algorithm, we processed five years of Sentinel-1 images acquired over a 150 × 100 km large area in Northern Norway, with the best setup. Compared to a dataset of field-observed avalanches, 77.3% were manually detectable. Using these manual detections as benchmark, the avalanche detection algorithm achieved an accuracy of 79% with high POD in cases of medium to large wet snow avalanches. For the first time, we present a dataset of spatio-temporal avalanche activity over several winters from a large region. Currently, the Norwegian Avalanche Warning Service is using our processing system for pre-operational use in three regions in Norway.

A large wet snow avalanche cycle in West Greenland quantified using remote sensing and in situ observations

On 11 April 2016 we observed high slushflow and wet snow avalanche activity at the environmental monitoring station Kobbefjord in W-Greenland. Snow avalanches released as a result of snow wetting induced by rain-on-snow in combination with a strong rise in air temperature. We exploit high-resolution satellite imagery covering pre- and post-event conditions for avalanche quantification and show that nearly 800 avalanches were triggered during this cycle. The nature of this extraordinary event is put into a longer temporal context by analysing several years of meteorological data and time-lapse imagery. We find that no event of similar size has occurred during the past 10 years of intense environmental monitoring in the study area. Meteorological reanalysis data reveal consistent relevant weather patterns for potential rain-on-snow events in the study area being warm fronts from Southwest with orographic lifting processes that triggered heavy precipitation.

Coming down the tracks

Piece by piece, scientists are gathering evidence of the growing threat of wet snow avalanches in a warmer world.

Wet avalanches: long-term evolution in the Western Alps under climate and human forcing

Abstract. Understanding wet avalanche intensity and the role of past environmental changes on wet avalanche occurrence is a main concern especially in the context of a warming climate and accelerated environmental mutations. Avalanches are closely related to fast cryosphere changes and may cause major threats to human society. Here, we used the sedimentary archive of the Alpine Lake Lauvitel (Lac du Lauvitel; western French Alps) to establish the first long-term avalanche record in this Alpine region. For this purpose, we used a novel CT-scan methodology that allows the precise identification of coarse material – from sand to pebble – transported to the lake and embedded within the finer continuous sedimentation. We identified a total of 166 deposits over the last 3300 yr cal. BP. In parallel, a detailed pollen analysis gave an independent record of environmental changes. Based on modern observation, lake monitoring, seismic investigations and sedimentological evidences, coarse material deposits were attributed to wet avalanche events. Our results highlight the effect of vegetation cover on the avalanche hazard while a period of strong frequency increase occurred after 780 yr cal. BP. In Lake Lauvitel, this period corresponds to a major forest clearance induced by the rise of human land use. Climate forcing on the avalanche hazard was investigated before and after the vegetation shift. On a multicentennial scale, wet avalanches preferably occur during periods of larger glacier extent, in which higher winter precipitation probably generates a sufficiently thick snow cover. On a sub-centennial scale, avalanches are more frequent during periods of relative warming, resulting in a destabilization of the same snow cover in spring season. Our results highlight as well the role of forest cover in mitigating wet snow avalanches' occurrence. In the context of predicted warmer temperatures, this study raises the question of whether a wet avalanche hazard increase may be expected in the near future especially at higher altitudes.