Individual Avalanche Research Articles

Detachment and flow behaviour of anode slimes in high nickel copper electrorefining

Most of the world’s copper is produced via copper electrorefining, where nickel is the most abundant impurity in the process. Previously it has been suggested that nickel affects the adhesion of anode slimes on the anode as well as the porosity of the slime layer that forms. This paper investigates the effects of nickel, oxygen, sulphuric acid and temperature on the detachment of anode slimes from the anode surface. The detachment of particles as a function of both anode and electrolyte composition was studied on laboratory scale using a camera connected to a Raspberry Pi, and particle detection and movement analysed using TrackPy. The results revealed four different slime detachment mechanisms: cloud formation, individual particle detachment, cluster detachment and avalanche. These were found to be dependent on the electrolyte (0, 10, 20, 30 g/dm3 Ni2+ & 100, 200 g/dm3 H2SO4), with increasing nickel concentration promoting cluster detachment and increasing sulphuric acid concentration favouring detachment of individual particles. Anode composition (0.05-0.44 wt% O and 0.07-0.64 wt% Ni) was shown to affect the flow direction of anode slimes, with increasing nickel leading to more upward-flowing slimes. Typical particle movement velocities were from -0.5 to 1.0 mm/s regardless of the electrolyte and anode composition, and regardless of the operating temperature (25 °C & 60 °C) for small particles (<0.5 mm). The results also support previous findings that increasing the nickel concentration of the electrolyte leads to a more porous anode slime layer on the anode.

Characterizing vegetation and return periods in avalanche paths using lidar and aerial imagery

ABSTRACT Snow avalanches are a hazard and ecological disturbance across mountain landscapes worldwide. Understanding how avalanche frequency affects forests and vegetation improves infrastructure planning, risk management, and avalanche forecasting. We implemented a novel approach using lidar, aerial imagery, and a random forest model to classify imagery-observed vegetation within avalanche paths in southern Glacier National Park, Montana, USA. We calculated spatially explicit avalanche return periods using a physically based spatial interpolation method and characterized the vegetation within those return period zones. The automated vegetation classification model differed slightly between avalanche paths, but the combination of lidar and spectral signature metrics provided the best accuracy (88–92 percent) for predicting vegetation classes within complex avalanche terrain rather than lidar or spectral signature metrics alone. The highest frequency avalanche return periods were broadly characterized by grassland and shrubland, but the influence of topography greatly influences the vegetation classes as well as the return periods. Furthermore, statistically significant differences in lidar-derived vegetation canopy height exist between categorical return periods. The ability to characterize vegetation within various avalanche return periods using remote sensing data provides land use planners and avalanche forecasters a tool for assessing the spatial extent of large-magnitude avalanches in individual avalanche paths.

Development and evaluation of a method to identify potential release areas of snow avalanches based on watershed delineation

Abstract. Snow avalanches are a prevalent threat in mountain territories. Large-scale mapping of avalanche-prone terrain is a prerequisite for land-use planning where historical information about past events is insufficient. To this aim, the most common approach is the identification of potential release areas (PRAs) followed by numerical avalanche simulations. Existing methods for identifying PRAs rely on terrain analysis. Despite their efficiency, they suffer from (i) a lack of systematic evaluation on the basis of adapted metrics and past observations over large areas and (ii) a limited ability to distinguish PRAs corresponding to individual avalanche paths. The latter may preclude performing numerical simulations corresponding to individual avalanche events, questioning the realism of resulting hazard assessments. In this paper, a method that accurately identifies individual snow avalanche PRAs based on terrain parameters and watershed delineation is developed, and confusion matrices and different scores are proposed to evaluate it. Comparison to an extensive cadastre of past avalanche limits from different massifs of the French Alps used as ground truth leads to true positive rates (recall) between 80 % and 87 % in PRA numbers and between 92.4 % and 94 % in PRA areas, which shows the applicability of the method to the French Alps context. A parametric study is performed, highlighting the overall robustness of the approach and the most important steps/choices to maximize PRA detection, among which the important role of watershed delineation to identify the right number of individual PRAs is highlighted. These results may contribute to better understanding avalanche hazard in the French Alps. Wider outcomes include an in-depth investigation of the issue of evaluating automated PRA detection methods and a large data set that could be used for additional developments, and to benchmark existing and/or new PRA detection methods.

Altered spreading of neuronal avalanches in temporal lobe epilepsy relates to cognitive performance: A resting-state hdEEG study.

Large aperiodic bursts of activations named neuronal avalanches have been used to characterize whole-brain activity, as their presence typically relates to optimal dynamics. Epilepsy is characterized by alterations in large-scale brain network dynamics. Here we exploited neuronal avalanches to characterize differences in electroencephalography (EEG) basal activity, free from seizures and/or interictal spikes, between patients with temporal lobe epilepsy (TLE) and matched controls. We defined neuronal avalanches as starting when the z-scored source-reconstructed EEG signals crossed a specific threshold in any region and ending when all regions returned to baseline. This technique avoids data manipulation or assumptions of signal stationarity, focusing on the aperiodic, scale-free components of the signals. We computed individual avalanche transition matrices to track the probability of avalanche spreading across any two regions, compared them between patients and controls, and related them to memory performance in patients. We observed a robust topography of significant edges clustering in regions functionally and structurally relevant for the TLE, such as the entorhinal cortex, the inferior parietal and fusiform area, the inferior temporal gyrus, and the anterior cingulate cortex. We detected a significant correlation between the centrality of the entorhinal cortex in the transition matrix and the long-term memory performance (delay recall Rey-Osterrieth Complex Figure Test). Our results show that the propagation patterns of large-scale neuronal avalanches are altered in TLE during the resting state, suggesting a potential diagnostic application in epilepsy. Furthermore, the relationship between specific patterns of propagation and memory performance support the neurophysiological relevance of neuronal avalanches.

On-Chip Quantum Communication Devices

We present here results of the Quantum Technology Flagship project UNIQORN in the area of integrated photonics for quantum communication applications. Three distinct integration platforms, namely indium phosphide based monolithic integration, polymer-based hybrid integration and the CMOS-compatible silicon platform, have been employed to manufacture components and sub-systems on chip for quantum communication devices. The choice of different platforms was made to exploit the best characteristics of each platform for the intended quantum communication device. The indium phosphide platform was employed to manufacture a transmitter chip for quantum key distribution featuring laser, modulators, and attenuators. The transmitter chip was evaluated in a QKD experiment achieving a secure rate of 1 kbit/s. The polymer platform was investigated for engineering non-classical light sources. Entangled and heralded single-photon sources, based on non-linear optics, were assembled on the polymer in a hybrid fashion together with waveguides and other passive micro-optical elements. A quantum random number generator, featuring a 70% randomness extraction efficiency, was also fabricated using the polymer integration technique. An array of 32 individual single-photon avalanche diodes, operating at room temperature and featuring an onboard coincidence logic, was coupled to the chip to demonstrate direct detection of photons on the polymer. Finally, a transimpedance amplifier based on gallium arsenide high electron mobility transistors was produced with an exceptional large electrical noise clearance of 28 dB at 100 MHz.

Statistical Analysis of Silicon Photomultiplier Output Signals

Silicon photomultipliers are relatively new devices designed as a matrix of single-photon avalanche detectors, which have become popular for their miniature dimensions and low operating voltage. Their superior sensitivity allows detecting low-photon-count optical pulses, e.g., in ranging and LIDAR applications. The output signal of the photomultiplier is a non-stationary stochastic process, from which a weak periodic pulse can be extracted by means of statistical processing. Using the double-exponential approximation of output avalanche pulses the paper presents a simple analytical solution to the mean and variance of the stochastic process. It is shown that even for an ideal square optical pulse the rising edge of the statistically detected signal is longer than the edge of individual avalanche pulses. The knowledge of the detected waveform can be used to design an optimum laser pulse waveform or algorithms for estimating the time of arrival. The experimental section demonstrates the proposed procedure.

Where are the avalanches? Rapid SPOT6 satellite data acquisition to map an extreme avalanche period over the Swiss Alps

Abstract. Accurate and timely information on avalanche occurrence is key for avalanche warning, crisis management and avalanche documentation. Today such information is mainly available at isolated locations provided by observers in the field. The achieved reliability, considering accuracy, completeness and reliability of the reported avalanche events, is limited. In this study we present the spatially continuous mapping of a large avalanche period in January 2018 covering the majority of the Swiss Alps (12 500 km2). We tested different satellite sensors available for rapid mapping during the first avalanche period. Based on these experiences, we tasked SPOT6 and SPOT7 for data acquisition to cover the second, much larger avalanche period. We manually mapped the outlines of 18 737 individual avalanche events, applying image enhancement techniques to analyze regions in the shade as well as in brightly illuminated ones. The resulting dataset of mapped avalanche outlines, having unique completeness and reliability, is evaluated to produce maps of avalanche occurrence and avalanche size. We validated the mapping of the avalanche outlines using photographs acquired from helicopters just after the avalanche period. This study demonstrates the applicability of optical, very high spatial resolution satellite data to map an exceptional avalanche period with very high completeness, accuracy and reliability over a large region. The generated avalanche data are of great value in validating avalanche bulletins, in completing existing avalanche databases and for research applications by enabling meaningful statistics on important avalanche parameters.

Coupled Snow Cover and Avalanche Dynamics Simulations to Evaluate Wet Snow Avalanche Activity

We present physics‐based snowpack simulations for four snow seasons with detailed wet snow avalanche activity records. The distributed, spatially explicit simulations using the Alpine3D and SNOWPACK model show that the simulated snowpack in the release areas of documented wet snow avalanches often exhibits its first wetting of the season on the release day. This first wetting is accompanied in the simulations by liquid water accumulating on capillary barriers, often formed by depth hoar layers. The strongest water accumulations and largest increases in percolation depth are found on the day of avalanche release. For individual avalanche paths, however, this only holds in 25%–30% of the cases. Assuming that the depth of the strongest water accumulation corresponds to the avalanche fracture depth, the avalanche dynamics model RAMMS‐Extended was run using simulated snowpack properties as initial conditions in the release area and boundary conditions along the avalanche path. On average, the simulated affected area by the avalanche and runout distance for the release day are statistically significant in closer agreement with the observations than two days before the release. This does not hold for the simulations of 1 day before and 1 and 2 days after the release. This suggests that fracture depths and the temporal evolution of percolation depths are adequately simulated within a ±1‐day period. The results show a large potential for distributed snow cover and avalanche dynamics simulations to assess wet snow avalanche hazards, although predictions for individual avalanche paths remain challenging.

A comprehensive characterization of the time resolution of the Philips Digital Photon Counter

Photodetectors with excellent time resolution are becoming increasingly important in many applications in medicine, high energy- and nuclear physics applications, biology, and material science. Silicon photomultipliers (SiPM) are a novel class of solid-state photodetectors with good timing properties. While the time resolution of analog SiPMs has been analyzed by many groups, the time resolution of the digital photon counter (DPC) developed by Philips has not yet been fully characterised. Here, the timing capabilities of the DPC are studied using a femtosecond laser. The time resolution is determined for complete dies, single pixels, and individual single photon avalanche diodes (SPADs). The measurements cover a broad dynamic range, from intense illumination down to the single-photon level, and were performed at various temperatures between 0°C and 20°C. The measured single photon time resolution (SPTR) ranges from 101 ps FWHM for the DPC3200 sensor pixel to 247 ps FWHM for the DPC6400 sensor die. An extensive study of the single-SPAD time resolution, ranging from single photon to very high laser intensities (∼1000 photons per pulse), yielded a time resolution of 48 ps FWHM at the single-photon level.

Risk assessment in the North Caucasus ski resorts

Abstract. Avalanches pose a significant problem in most mountain regions of Russia. The constant growth of economic activity, and therefore the increased avalanche hazard, in the North Caucasus region lead to demand for the development of large-scale avalanche risk assessment methods. Such methods are needed for the determination of appropriate avalanche protection measures as well as for economic assessments.The requirement of natural hazard risk assessments is determined by the Federal Law of the Russian Federation (Federal Law 21.12.1994 N 68-FZ, 2016). However, Russian guidelines (SNIP 11-02-96, 2013; SNIP 22-02-2003, 2012) are not clearly presented concerning avalanche risk assessment calculations. Thus, we discuss these problems by presenting a new avalanche risk assessment approach, with the example of developing but poorly researched ski resort areas. The suggested method includes the formulas to calculate collective and individual avalanche risk. The results of risk analysis are shown in quantitative data that can be used to determine levels of avalanche risk (appropriate, acceptable and inappropriate) and to suggest methods to decrease the individual risk to an acceptable level or better. The analysis makes it possible to compare risk quantitative data obtained from different regions, analyze them and evaluate the economic feasibility of protection measures.

The morphodynamics of slopes within the snow avalanche starting zones in the Tatras

The upper sections of avalanche paths (avalanche starting zones) in the Tatras are being transformed by morphogenetic processes, both secular and rapid mass movements, which include avalanches. The erosion niches and scars occurring within the avalanche starting zones are most exposed to these processes. A three-year monitoring of the impacts of these processes leads to the conclusion that, in the absence of dirty avalanches, the efficiency of secular geomorphological processes is low (max. a few cm/year). Such avalanches can clearly transform the relief as is demonstrated by the results of analyses of erosion niches and scars on orthophotomaps. Furthermore, it was found that the direction of relief changes (accumulation or erosion) in the Tatras is spatially and temporally varied, as well as varying within individual avalanche starting zones, and sometimes within individual erosion niches or scars.

Which is more hazardous: avalanche, landslide, or mudslide?

Gravity erosion is a dominant geomorphic process on the steep loess slopes. Here, we conducted rainfall simulation experiments to monitor occurrence and behavior of the mass failure on steep loess slopes. The results show that the quantity of soil loss caused by avalanche and landslide was much more than that caused by mudslide, and the avalanche was the most violent gravity erosion. As the slopes were eroded with five runs of rainfalls each at an amount of 48 mm, the total volume of avalanche, landslide, and mudslide were 150.9, 82.5, and 3.9 × 103 cm3/m, and their maximum individual amounts were 369.9, 177.6, and 24.6 × 103 cm3, respectively. The amount of avalanche, landslide, and mudslide accounted for 62, 36, and 2 % of the total gravity erosion in a rainfall experiment of the model test. Furthermore, the slope height and gradient had a remarkable impact on the erosion amount. When the slope height was increased from 1.0 to 1.5 m, the total amount of avalanche was increased by 22 %, and the maximum volume of individual avalanche was augmented by 165 %. When the slope gradient was increased from 70° to 80°, the total amount of landslide was enlarged by 52 %, and the maximum amount of individual avalanche was magnified by 65 %. As a result, avalanche and landslide, especially the former, played a crucial role of soil erosion on steep slope compacted by hand with loess.

Classification trees as a tool for operational avalanche forecasting on the Seward Highway, Alaska

The Seward Highway is located in coastal Alaska and is subject to an extreme maritime climate, with strong winds, and large storms that can bring several meters of snow to the start zones and total snow in the start zones often exceeding 10m per year. The highway extends for over 200km through steep glacially carved valleys, from Seward to Anchorage, Alaska. Along its route, from mileposts 18 (29km) to 107 (171km), avalanche paths threaten the road and in many cases these avalanches flow down from their starting zones in excess of 1000m above the road.Using a classification tree, we examined 28years (1983–2011) of snowpack, weather and avalanche data. This suite of data contained more than 4500 individual avalanche events on over 100 paths, with 20 paths seeing regular activity. We used this wealth of data to train our classification tree model for days with significant avalanche activity. We tested trees with both equal and unequal misclassifications costs. The equal tree using only three parameters; the sum of 72h of water, the 24h high temperature, and the 72h average high temperature, managed to obtain a probability of detection of 0.77 with 422 of the 545 avalanche days correctly predicted. The unequal tree using only two parameters; the sum of 72h of water and 24h high temperature, managed to obtain a probability of detection of 0.94 with 510 of the 545 avalanche days correctly predicted, but at the expense of a high false alarm rate. Testing these trees in a hindcast mode outside of their training period results in a drop in the model performance metrics considered. However when used in a forecasting mode in an operational setting no further reduction in model performance is observed. We conclude with a demonstration and test of a simple approach to use these trees in an operational avalanche forecasting program. We show how these trees have been used in a combined approach as a tool to assist avalanche forecasters with reasonable success.

THEORY AND PRACTICE OF INDIVIDUAL SNOW AVALANCHE RISK ASSESSMENT IN THE RUSSIAN ARCTIC

In recent years, the Government of the Russian Federation considerably increased attention to the exploitation of the Russian Arctic territories. Simultaneously, the evaluation of snow avalanches danger was enhanced with the aim to decrease fatalities and reduce economic losses. However, it turned out that solely reporting the degree of avalanche danger is not sufficient. Instead, quantitative information on probabilistic parameters of natural hazards, the characteristics of their effects on the environment and possibly resulting losses is increasingly needed. Such information allows for the estimation of risk, including risk related to snow avalanches. Here, snow avalanche risk is quantified for the Khibiny Mountains, one of the most industrialized parts of the Russian Arctic: Major parts of the territory have an acceptable degree of individual snow avalanche risk ( 1×10 -4 ) degree of individual snow avalanche risk (0.5 and 2% of the total area) correspond to the Southeast of the Khibiny Mountains where settlements and mining industries are situated. Moreover, due to an increase in winter tourism, some traffic infrastructure is located in valleys with an admissible or unacceptable degree of individual snow avalanches risk.

Spatio-temporal avalanche forecasting with Support Vector Machines

Abstract. This paper explores the use of the Support Vector Machine (SVM) as a data exploration tool and a predictive engine for spatio-temporal forecasting of snow avalanches. Based on the historical observations of avalanche activity, meteorological conditions and snowpack observations in the field, an SVM is used to build a data-driven spatio-temporal forecast for the local mountain region. It incorporates the outputs of simple physics-based and statistical approaches used to interpolate meteorological and snowpack-related data over a digital elevation model of the region. The interpretation of the produced forecast is discussed, and the quality of the model is validated using observations and avalanche bulletins of the recent years. The insight into the model behaviour is presented to highlight the interpretability of the model, its abilities to produce reliable forecasts for individual avalanche paths and sensitivity to input data. Estimates of prediction uncertainty are obtained with ensemble forecasting. The case study was carried out using data from the avalanche forecasting service in the Locaber region of Scotland, where avalanches are forecast on a daily basis during the winter months.

Avalanche risk assessment for mountain roads: a case study from Iceland

This paper presents an assessment of the avalanche hazard potential and the resulting risks on mountain roads for a 38.7-km-long section of road no 76 (Siglufjarðarvegur) in northern Iceland following a regional scale approach developed in the Alps. The assessment of the individual avalanche death risk proved applicable to distinguish areas of avalanche hazard with a risk above the accepted level, which should be given priority in following detailed investigations and the planning of possible protective measures, from road sections where the avalanche death risk is low and accepted according to international practice. The cumulative individual and collective avalanche death risks in the investigated road section provide a comparable measure for assessing the avalanche hazard both within the Icelandic public road network and on an international scale. The case study on road no 76 in northern Iceland shows that a standardised regional scale risk-based approach is practical to determine, analyse and assess the avalanche hazard situation on mountain roads in Iceland and guarantees comprehensible, reproducible and comparable results as a basis for a sustainable planning of measures.

Real Time Coincidence Detection Engine for High Count Rate Timestamp Based PET

Coincidence engines follow two main implementation flows: timestamp based systems and AND-gate based systems. The latter have been more widespread in recent years because of its lower cost and high efficiency. However, they are highly dependent on the selected electronic components, they have limited flexibility once assembled and they are customized to fit a specific scanner's geometry. Timestamp based systems are gathering more attention lately, especially with high channel count fully digital systems. These new systems must however cope with important singles count rates. One option is to record every detected event and postpone coincidence detection offline. For daily use systems, a real time engine is preferable because it dramatically reduces data volume and hence image preprocessing time and raw data management. This paper presents the timestamp based coincidence engine for the LabPET?, a small animal PET scanner with up to 4608 individual readout avalanche photodiode channels. The engine can handle up to 100 million single events per second and has extensive flexibility because it resides in programmable logic devices. It can be adapted for any detector geometry or channel count, can be ported to newer, faster programmable devices and can have extra modules added to take advantage of scanner-specific features. Finally, the user can select between full processing mode for imaging protocols and minimum processing mode to study different approaches for coincidence detection with offline software.

On forecasting large and infrequent snow avalanches

Snow avalanches that threaten a highway or a residential area are often large avalanches that have a return period > 1 year. Danger assessment strongly relies on precipitation data since these avalanches are typically triggered by major snow storms. Given the extensive protection work that is in place in the European Alps, the avalanche control service (also called avalanche commission) responsible for danger assessment will usually monitor the avalanche situation throughout the winter, but only become active in case of a major snow fall. Related safety concepts describing the procedures and measures to be taken in a given danger situation are therefore often based on threshold values for new snow. By analysing the avalanche occurrence of a major avalanche path, we show that forecasting based on new snow amounts involves high uncertainty. Whereas the return period of an avalanche to, for example, the road was about 5 years, the return period for the corresponding new snow depth was substantially smaller, in our case slightly less than 2 years. Similar proportions were found for a number of other avalanche paths with different snow climate. The return period of the critical new snow depth was about 2–5 times smaller than the return period of the avalanche. This proportion is expected to increase with increasing return period. Hence, based on the return period of an avalanche path a first estimate for the critical new snow depth can be made. With a return period of the critical new snow depth of 1–2 years, avalanche prediction for individual avalanche path becomes very challenging since the false alarm ratio is expected to be high.

Avalanche crown‐depth distributions

The literature disagrees about the statistical distribution of snow avalanche crown depths. Large datasets from Mammoth Mountain, California and the Westwide Avalanche Network show that the three‐parameter generalized extreme value distribution provides the most robust fit, followed by a two‐parameter variation, the Fréchet distribution. The most parsimonious explanation is neither self‐organized criticality nor other complex cascades, but the maximum domain of attraction, implying that distributions of individual avalanche crown depths are scaling. We also show that crown depths do not have a universal tail index. Rather, they range from 2.8 to 4.6 over different avalanche paths, consistent with other geophysical phenomena such as wildfires, which show similar variability.

Assessment and mapping of snow avalanche risk in Russia

AbstractThe term ‘risk’ can be defined as the probability of unfavourable consequences or negative effects. Risk can be expressed by means of various indices, such as collective or social risk (possible number of dead), individual risk (probability of a person’s death within a certain territory during 1 year), probability of losses, etc. This paper is a case study of the small-scale assessment and mapping of individual avalanche risk focused on the two regions of Russia with the highest levels of avalanche activity: the northern Caucasus and the mountainous parts of Sakhalin island. The basic indices applied for individual avalanche risk estimation are: recurrence interval of avalanches (avalanche frequency), percentage of the whole investigated territory that is occupied by avalanche-prone areas, duration of avalanche danger period, probability of a person’s stay in an avalanche-prone area during 1 day (24 hours) and during 1 year, total population of the area and its density. The results of individual avalanche risk assessment, undertaken for the territory of Russia as a whole, show that its values generally do not exceed the admissible level (from 1×10–6 to 1×10–4). However, some areas of the northern Caucasus, including famous alpine skiing resorts (Krasnaya Poliana, Dombai, the Mount Elbrus region, etc.), and of Sakhalin, including the environs of towns (Kholmsk, Nevel’sk) and other smaller human settlements, are characterized by an unacceptable level of risk. In the aggregate, areas with an unacceptable (>1×10–4) level of individual avalanche risk comprise about 7% of the whole avalanche-prone territory of the northern Caucasus, those with an admissible level comprise 52% and those with an acceptable level (<1×10–6) 41%. The corresponding values for Sakhalin are 0.1%, 14.8% and 85.1%.