Avalanches result from an interaction of weather and terrain, where past weather and internal snow cover processes play important roles. So far, climatology was mainly based on weather data, as regional snow instability information, such as avalanche activity, is scarce on climatological time scales. We present a new approach to create a snow avalanche climatology from simulations of avalanche problem types based on snow cover simulations of reanalysis data and a cluster analysis. Analyzing the winters between 1958 and 2020 in the French Alps, wet-snow situations dominated natural release. Dry-snow situations with non-persistent and persistent weak layers occurred each on at least one third of the days. Four typical patterns of avalanche problem types were identified. They follow the main orography with more new snow situations in the northern regions and more cases of persistent weak layers in inner-Alpine regions. In the front-ranges and in southern regions wet-snow situations occurred early in winter – typical for coastal snow climates. Agreement with the standard snow climate classification and the geography of the French Alps suggests that mountain regions with similar conditions can now be outlined. This method for snow avalanche climatology will inform avalanche forecasting and facilitate climate change impact studies.

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

Physically-based avalanche propagation models must still be locally calibrated to provide robust predictions, e.g. in long-term forecasting and subsequent risk assessment. Friction parameters cannot be measured directly and need to be estimated from observations. Rich and diverse data are now increasingly available from test-sites, but for measurements made along flow propagation, potential autocorrelation should be explicitly accounted for. To this aim, this work proposes a comprehensive Bayesian calibration and statistical model selection framework. As a proof of concept, the framework was applied to an avalanche sliding block model with the standard Voellmy friction law and high rate photogrammetric images. An avalanche released at the Lautaret test-site and a synthetic data set based on the avalanche are used to test the approach and to illustrate its benefits. Results demonstrate (1) the efficiency of the proposed calibration scheme, and (2) that including autocorrelation in the statistical modelling definitely improves the accuracy of both parameter estimation and velocity predictions. Our approach could be extended without loss of generality to the calibration of any avalanche dynamics model from any type of measurement stemming from the same avalanche flow.

The medium-high mountain ranges of Western Europe are undergoing rapid socio-environmental changes. The aim of this study is to show that their landscape can be strongly shaped by the actions of avalanche activity, humans and climate. The study area is the Rothenbachkopf-Rainkopf complex, a site representative of avalanche-prone areas of the Vosges Mountains (France). A geo-historical approach documents regular avalanche activity over more than 200 years on 13 paths. A diachronic analysis of historical maps and photographs demonstrates substantial afforestation (from 60 to 80% of the total surface since 1832). LIDAR data and field surveys highlight the existence of a longitudinal and transversal structure of trees. Hence, the avalanche, human and climate activity footprints are retained by the landscape, which rapidly adapts to these changing drivers. Specifically, the pattern of tree species and heights results from a near equilibrium with regular avalanche activity sometimes disturbed by major avalanches that induce quasi-cyclic changes in the landscape mosaic. The afforestation trend is attributable to profound changes in silvo-pastoral practices, supplemented by the impact of climate change. The wider relevance of the results in relation to the local context is discussed, as well as outlooks that can refine our understanding of this complex system.

This paper aims to show how recent knowledge developed in the field of avalanche research can be used for a real case study, the Taconnaz avalanche path, where passive structures already existed but had to be improved. First a morphological analysis of the site is done and historical data are analysed. Second, each recorded event is back-calculated using a numerical model of dense-flow avalanches. For each surveyed avalanche, parameters at the entry of the runout zone upstream of the defence structures are defined. Third, a statistical analysis of these parameters allows characterization of 100 year return period events. Fourth, physical and numerical models of dense avalanches interacting with defence structures are combined in order to design the most effective passive structure able to contain the reference scenarios. Finally, physical and numerical modelling of the interaction between the powder avalanche and the designed defence structure is performed, to show that the proposed improvements do not increase the residual risk due to the powder part in areas downstream of the defence structures.

The values of the Voellmy friction parameters of 735 historical avalanches that have occurred along 26 paths in the Chamonix valley, France, since 1958 are back-analyzed with a depth-averaged hydraulic model, including sub-models for erosion, entrainment and deposition. For each path, the longitudinal and crosswise topographic profiles were derived from a high-resolution digital elevation model acquired by laser scanning. The initial snow depth and snow cohesion, as well as various physical properties of snow, were computed from numerical simulations of the detailed snowpack model Crocus fed by the SAFRAN meteorological analysis. For each event, the full ranges of the two friction parameters were scanned and the pairs of friction parameters for which the run-out altitude is found close enough to the observed one (with an uncertainty of ±5 m) were retained. Statistical class analysis was used to investigate the correlation between the obtained friction coefficients and the snow physical properties. No evident trend with the snow parameters was found for the inertial friction coefficient. For the static friction coefficient, an increasing trend with temperature and density was observed, as well as a decreasing trend with liquid water content and initial snow depth. Although modeling assumptions and limitations regarding data and the calibration procedure should be kept in mind, these trends are worth noting, allowing avalanche simulations to be refined to take into account prevailing weather and snow conditions.

The analysis of historical avalanche data is important when developing accurate hazard maps. The record of snow-avalanche disasters on Sakhalin and the Kuril Islands is incomplete, due to the historical division into periods of Japanese and Russian rule. Here we combine and analyze data from Japanese and Russian sources to reconstruct a continuous record of avalanche catastrophes in the region from 1910 to 2010. Despite the relatively small scale of the majority of catastrophic avalanches, with a total vertical drop < 200 m, we document evidence that places the region among the most avalanche-affected areas in the world. In total, 756 fatalities and > 238 injuries have occurred in 275 incidents over a 100 year period (two-thirds of those killed were Japanese). This death toll is higher than that in Canada, New Zealand or Iceland, or non-recreational fatalities in France. A wave of avalanche disasters (1930s–60s) following intense colonization of Sakhalin and the Kuril Islands is evident. Although this ‘wave’ could be considered a local issue of the past, many presently developing countries may face similar situations. The fatality rate has decreased over time, due to social factors, and differs from that of any other region, in its absence of deaths through recreational activities. Although in recent years the fatality rate is lower than that of Iceland or the USA, the per capita avalanche casualty rate on Sakhalin and the Kuril Islands remains among the highest in the world.

The evaluation of avalanche release depths constitutes a great challenge for risk assessment in mountainous areas. This study focuses on slab avalanches, which generally result from the rupture of a weak layer underlying a cohesive slab. We use the finite-element code Cast3M to build a mechanical model of the slab/weak-layer system, taking into account two key ingredients for the description of avalanche release: weak-layer heterogeneity and stress redistribution via slab elasticity. The system is loaded by increasing the slope angle until rupture. We first examine the cases of one single and two interacting weak spots in the weak layer, in order to validate the model. We then study the case of heterogeneous weak layers represented through Gaussian distributions of the cohesion with a spherical spatial covariance. Several simulations for different realizations of weak-layer heterogeneity are carried out and the influence of slab depth and heterogeneity correlation length on avalanche release angle distributions is analyzed. We show, in particular, a heterogeneity smoothing effect caused by slab elasticity. Finally, this mechanically based probabilistic model is coupled with extreme snowfall distributions. A sensitivity analysis of the predicted distributions enables us to determine the values of mechanical parameters that provide the best fit to field data.

In snow avalanche long-term forecasting, existing risk-based methods remain difficult to use in a real engineering context. In this work, we expand a quasi analytical decisional model to obtain simple formulae to quantify risk and to perform the optimal design of an avalanche dam in a quick and efficient way. Specifically, the exponential runout model is replaced by the Generalized Pareto distribution (GPD), which has theoretical justifications that promote its use for modelling the different possible runout tail behaviours. Regarding the defence structure/flow interaction, a simple law based on kinetic energy dissipation is compared with a law based on the volume stored upstream of the dam, whose flexibility allows us to cope with various types of snow. We show how a detailed sensitivity study can be conducted, leading to intervals and bounds for risk estimates and optimal design values. Application to a typical case study from the French Alps, highlights potential operational difficulties and how they can be tackled. For instance, the highest sensitivity to the runout tail type and interaction law is found at abscissas of legal importance for hazard zoning (return periods of 10–1000 a), a crucial result for practical purposes.