Large magnitude snow avalanches (destructive size ≥ D3) impact settlements, transportation corridors, and public safety worldwide. In Colorado, United States, avalanches have killed more people than any other natural hazard since 1950. In March 2019, a large magnitude avalanche cycle occurred throughout the entire mountainous portion of Colorado resulting in more than 1000 reported avalanches during a two-week period. Nearly 200 of these avalanches were size D4 or larger with at least three D5 avalanches. However, placing this 2019 large magnitude avalanche cycle in historic context requires data prior to the instrumental record. Here, we paired tree disturbance data from dendrochronology (1698 to 2020) with meteorological data from the modeled and instrumental record (1901 to 2020) to understand the frequency and climate drivers of large magnitude snow avalanche cycles. The extensive number of downed trees from the 2019 avalanche cycle allowed us to collect 1,188 cross-sections and cores from 1023 individual trees within 24 avalanche paths across the state. From these samples we identified 4135 avalanche-related growth disturbances. We employed a strategic nested sampling design to spatially aggregate avalanche frequency from individual avalanche paths, to counties, to three major sub-regions (i.e., north, central, and south), and across the entire region (i.e., state of Colorado). Over a period spanning more than three centuries (1698 to 2020), we identified 76 avalanche years within 24 individual avalanche paths. Large magnitude avalanche event frequency varied across paths and sub-regions with several notable region-wide avalanche cycles. Both tree-ring and historical written records highlighted 1899 as a year with widespread and large magnitude avalanche activity similar to the March 2019 avalanche cycle. Since the early-20th century (1900 to 2020) regional avalanche probability declined significantly in parallel with decreasing snowpack throughout Colorado. Similarly, dominant avalanche regimes shifted from large magnitude regional cycles driven by above average snowfall years over most of the record, to regional avalanche cycles occurring more commonly in average to low snow years since 1988. In recent decades, a lack of December precipitation and above average March precipitation characterized years with regional large magnitude avalanche activity. Even with declining snow water equivalent, truly extreme regional large magnitude avalanche cycles remain possible – as demonstrated by the 2019 cycle. This underscores that rare but high-impact events are not eliminated by long-term trends. Understanding the changing snow and weather drivers and subsequent behavior of large magnitude avalanche cycles across multiple spatial scales may improve avalanche forecasting and the products and mitigations strategies developed by structural engineers to mitigate avalanche danger. This can decrease the avalanche risk to the public and improve infrastructure design in avalanche terrain.

Snow avalanches pose a major hazard in mountainous regions, but their sporadic occurrence and remote locations hinder consistent regional monitoring. Automated remote sensing techniques, particularly those using Synthetic Aperture Radar (SAR), offer promising solutions for systematic data collection. However, validating SAR-based avalanche detections remains challenging due to the limited availability of ground truth data, spatial mismatches, temporal inconsistencies between reference datasets, and uncertainties associated with the relatively simple hypotheses underlying detection algorithms. This study assesses the performance and reliability of SAR-based avalanche debris detection across seven massifs in the French Alps over two winter seasons (2017-2018 and early 2020). The SAR-derived detections are evaluated against multiple indicators of avalanche activity, including avalanche inventories, snow cover simulation models, and hazard levels from official French avalanche bulletins. The findings demonstrate that, overall, SAR-based methods effectively capture the spatial and temporal patterns of ground-observed avalanche activity and align well with reported hazard levels, particularly during periods of elevated avalanche risk. Notably, for the Beaufortain massif during the 2020 season, SAR detections achieved a Pearson correlation coefficient of 0.65 with ground-based observations. Nevertheless, performance varies significantly across massifs and seasons, with strong correlations in some areas and weaker associations in others. The topographic characteristics (slope, elevation, aspect) of detected debris also show good agreement with other indicators. Despite inherent biases in each reference dataset, the results highlight the potential of SAR imagery for capturing regional-scale spatiotemporal dynamics of avalanches. While SAR offers valuable insights, detection remains far from perfect, underscoring the continued need for direct field observations and further refinement of detection algorithms to improve accuracy and validation.

Mountain areas are subject to various types of hydrological hazards such as torrential floods and debris flows. They are also particularly sensitive to climate and societal changes that lead to the emergence of new risk situations. To meet the challenges of managing and anticipating these risks, floods must be documented as exhaustively as possible over the long term. However, existing chronologies tend to cover a too large spatial scale, most often outside the mountain context, and do not take sufficient account of the complexity of the processes involved. In this article, a 420-year flood chronology has been reconstructed using data extracted from the Vallouise-Pelvoux multi-risk inventory. The accuracy and regularity of the sources made it possible to (i) identify 278 flood episodes that affected one or more torrents and/or torrential rivers in response to the same synoptic hydrometeorological conditions over the period 1600-2020 in Vallouise area, (ii) analyze their typology and consequences, (iii) identify six "active" periods separated by periods with much fewer floods, whose temporal distribution is broadly consistent with that of existing chronologies in other contexts, (iv) highlight a change in seasonality. The spatio-temporal evolution of the typology of individual events was also studied, showing, over the last 30 years, a shift in the proportion of torrential river and torrent events in favor of the latter.. An initial effort at contextualization allowed these results to be compared with climatic, societal, and source changes in order to formulate hypotheses as to their cause. Approaches for further exploration of the gathered information are proposed.

Due to rapid and intense socio-environmental transformations in rockfall-prone areas, risk assessment accounting for non-stationary conditions becomes a crucial issue for supporting long-term land-use zoning and adapting mitigation strategies. During the past few decades, several studies have examined the impacts of global warming on rockfall activity, especially at high-elevation sites. By contrast, concomitant changes in exposure, such as modifications in road traffic with changes in mountain tourism and social practices, have received little attention, making the overall changes in risks largely unknown. This study therefore proposes a quantitative risk assessment (QRA) of the cantonal road in Balmatten (Zermatt Valley, Swiss Alps), which evaluates the respective and overall effects of rockfall frequency and traffic density changes on rockfall risks at three time steps: 1960, 2010 and 2060. The approach relies on strong, yet reasonable assumptions that minimize computational burden. Results demonstrate the significant impact of road traffic fluctuations (+818%) on risk estimates, which largely outweighs the effects of climate change (+100%) on rockfall risk over the 1960-2060 period. Despite several challenges inherent to the implementation of non-stationary QRA approaches, these findings demonstrate their value to support adaptation of mountain territories in a time of global change.

The record values theory study elements of a time series that exceed all previous observations, which are of particular interest in fields such as sports or climate science. In this paper, we propose a statistical method based on the construction of a Brownian stochastic simulator to reconstruct entire time series solely from such record values, even in a non-stationary case. We then implement a procedure, which can be compared to a Neural-Based Inference (NBI) procedure, to choose the optimal generator hyper parameters. To illustrate our method and motivate its development, we apply it to a glaciological problem. Understanding the past dynamics of glacier fronts is a major challenge to mitigate related mountain hazards, assess water resources, and evaluate contributions to sea-level rise. Field-visible indicators such as moraines provide spatio-temporal evidence of these front position evolution (refered as trajectories) and can be interpreted as the records of a non-stationary process. As a benchmark case, the two hyper parameters of our NBI approach are tuned from the well documented French alpine Glacier des Bossons. Our purely data-based approach offers new perspectives for challenging and further developing physical models of glacier dynamics and inferring the response of glaciers to climate change on centennial to millenial time scales. Beyond the glacier case, it has potential for the various problems for which record series is the sole available data.

Les territoires de montagne sont soumis à différents types d’inondations notamment liées à des crues torrentielles et laves torrentielles. Ils sont également particulièrement sensibles aux changements climatiques et sociétaux qui mènent à l’émergence de nouvelles situations de risque. Pour répondre aux défis de la gestion et de l’anticipation de ces risques dans ces territoires, les inondations doivent être documentées de la manière la plus exhaustive possible sur le long terme. Cependant, les chronologies existantes tendent à couvrir des échelles spatiales trop larges, le plus souvent hors contexte montagnard, et ne prennent pas suffisamment en compte la complexité des processus en jeu. Dans cet article, la reconstruction d’une chronologie de 420 ans d’inondations a été réalisée à partir des données extraites de l’inventaire multirisque de Vallouise-Pelvoux. La précision et la régularité des sources ont permis (i) d’identifier 278 épisodes d’inondations ayant affecté un ou plusieurs torrents et/ou rivières torrentielles en réponse aux mêmes conditions hydrométéorologiques synoptiques sur la période 1600-2020 en Vallouise, (ii) d’en analyser la typologie et les conséquences, (iii) d’identifier 6 périodes « actives » séparées par des périodes « calmes » en inondations dont la répartition temporelle est globalement cohérente avec celle des chronologies existantes dans d’autres contextes, (iv) de mettre en évidence une évolution de la saisonnalité. L’évolution spatio-temporelle de la typologie des événements individuels est également étudiée, montrant une évolution des proportions d’événements entre rivières torrentielles et torrents au bénéfice de ces derniers au cours des 30 dernières années. Un premier effort de contextualisation permet de confronter ces résultats aux évolutions climatiques, sociétales et des sources afin de formuler des hypothèses quant à leur cause. Des pistes sont proposées pour approfondir l’exploitation de l’information rassemblée.

Climate warming forces snow cover changes, which impact natural hazards such as avalanches. While significant changes of observed avalanche activity or runout distance could be documented and linked to climate drivers, little is known about potential changes of conditions in avalanche release areas as data are scarce. Using demonstrated methodology to simulate avalanche problems from reanalysis weather data, we assess how characteristics of avalanche danger evolved between 1958 and 2020 in typical release areas of the French Alps. During this period, the number of situations with natural release and the frequency of new snow situations did not change significantly at typical release area elevation at the scale of the French Alps. The frequency of persistent weak layer problems declined by about 3 days and the simulated onset date of wet-snow activity advanced by about 3 weeks. After a change point at the end of the 1980s, the frequency of new snow and the onset date of wet-snow situations changed significantly, and substantial differences between regions appeared, highlighting regional effects of climatic change. New snow situations increased by about 1 day in 10 years, in particular at higher elevations in the inner-Alpine climate regions and the wet-snow onset date advanced rapidly, by about 12 days in 10 years, in particular in the northern climate regions. Broad and regional patterns of change in avalanche problems are in line with documented atmospheric warming and changes in mean and extreme snowfall in the area. Our findings add to the already existing information on the response of avalanche activity to climate change, describing changes in avalanche danger at different spatio-temporal scales. Moreover, we provide a methodology amenable to other mountain areas of the world, where avalanche observations are lacking but input to snow cover modelling is available. In a logical next step this methodology will be applied to regional climate projections to assess future trends of avalanche danger characteristics.

Databases of natural hazards play a crucial role for assessing related risks and in mitigating their impacts on the environment. In the mountainous regions of France, potentially destructive events are both numerous and diverse, however, only a limited number of databases containing information on past occurrences exist. The database of the RTM service (Restoration of Mountainous Areas service, BD-RTM) consolidates such information through a multi-hazard approach over an extended timeframe, integrating systematic observations and a retrospective compilation of various sources. This article outlines the key features of this unique database. Focusing on the Isère department (38), which records the highest number of events among the 12 areas covered by the BD-RTM, we present (1) the history of the database, particularly the origins of the data and its structural framework, and (2) the richness and diversity of the recorded information. On the 31st of December 2023, the BD-RTM had documented 5,888 events in the Isère department, with nearly 70% consisting of torrential floods and landslides. Furthermore, 85% of the recorded events resulted in damages. A notable increase in the frequency of events is evident from the 2000s onwards, with an average of 85 events recorded annually since that time. All the information is accessible for further spatial and temporal analyses, including statistical and historical assessments, as well as integration with other data

Databases of natural hazards play a crucial role for assessing related risks and in mitigating their impacts on the environment. In the mountainous regions of France, potentially destructive events are both numerous and diverse, however, only a limited number of databases containing information on past occurrences exist. The database of the RTM service (Restoration of Mountainous Areas service, BD-RTM) consolidates such information through a multi-hazard approach over an extended timeframe, integrating systematic observations and a retrospective compilation of various sources. This article outlines the key features of this unique database. Focusing on the Isère department (38), which records the highest number of events among the 12 areas covered by the BD-RTM, we present (1) the history of the database, particularly the origins of the data and its structural framework, and (2) the richness and diversity of the recorded information. On the 31st of December 2023, the BD-RTM had documented 5,888 events in the Isère department, with nearly 70% consisting of torrential floods and landslides. Furthermore, 85% of the recorded events resulted in damages. A notable increase in the frequency of events is evident from the 2000s onwards, with an average of 85 events recorded annually since that time. All the information is accessible for further spatial and temporal analyses, including statistical and historical assessments, as well as integration with other data

To develop efficient mountain risk management strategies, an obvious, yet tremendously difficult prerequisite is the constitution of comprehensive databases of past events and their impacts over long-time frames. However, existing records are often too short and siloed between different data providers and/or as function of hazards. To fill this gap, a methodology based on the combination of scattered pre-existing records with further archival research is proposed and used to populate a well-structured multirisk database covering the period 1600–2020 AD in a municipality of the French Alps – Vallouise-Pelvoux. Results include 2131 events related to rockfall, landslides, snow avalanches, floods (including debris flows) and glacial hazards, with documentation of possible interactions between hazards, their characteristics and detailed impacts. The combined use of different sources – and in particular archival searches – and their cross-referencing therefore provides a detailed record of past events that goes far beyond any inventory existing at the local scale. The analysis suggests that the distribution of events results from the combined effect of hazards, sources and human activities putting assets at risk, with a primary effect of sources. The methodology opens perspective for multirisk assessment in mountain territories and can be usefully transferred to other case studies.

Mountain areas are very sensitive to climate change, which has led to changes in natural hazards that are often linked to disturbances in the cryosphere. In this context, changes in snowfall characteristics and snow cover affect avalanche hazards. Long-term variability can be reconstructed by using historical archives, tree rings and, more rarely, lake sediments. The latter approach is based on the identification of lake sediment consisting of poorly sorted, coarse sediments in a fine matrix, which are often associated with terrestrial organic debris. This sediment is generally brought to the lake within large amounts of wet snow, or via ‘drop stones’ when the ice melts if the avalanche takes place on a frozen surface. Here, we study two high-altitude lakes (Melu and Capitellu) in the Restonica Valley in Corsica, an area where systematic records are lacking, to reconstruct signals related to such large wet snow flows on a millennial scale. The analysis of several sedimentological and geochemical markers enables the characterization of wet avalanche deposits in the two lakes, as well as turbidite-type facies linked to historical earthquakes in Corsica. Age models based on short-lived radionuclides and radiocarbon also make it possible to reconstruct two avalanche chronologies covering 600 and 1750 years in Melu and Capitellu Lakes, respectively, which show similar temporal variations. A comparison with the only long-term chronology available in the Alps (Lake Muzelle, Ecrins) also reveals synchronicity in the secular variability of avalanches, suggesting a common forcing between Corsica and the Alps. Human observations and accident records from recent decades, and snow and weather release conditions reconstructed from a hydrological modeling scheme confirm the ability of the lacustrine avalanche sedimentary method to document local wet snow avalanche activity. The proposed methodology, which is based on paleolimnological studies, may therefore be useful, alone or combined with other avalanche data sources, for tracking changes in avalanche activity and related risks in mountainous areas.

In the rapidly evolving mountain cryosphere, snow avalanches threaten livelihoods, settlements and infrastructure. In this Review, we analyse past and projected impacts of climate change on avalanche activity and the associated risks. The limited availability of comprehensive datasets, the potential confounding factors and the limitations of statistical approaches can make it difficult to identify trends in avalanche activity. However, available data indicate a general decrease in avalanche number, size, seasonality and active paths at low elevations, and an increase in the proportion of wet avalanches relative to dry avalanches. Increased snowfall at high elevations can lead to peaks in avalanche activity and an increase in the number of wet and slush-like avalanches. Activity patterns gradually shift from low to high elevations under continued warming. These changes affect avalanche risk; however, risk is also influenced by factors such as land use and the growth or decline of human settlements. The impact of these factors varies across diverse mountain environments, making it challenging to predict how risk will evolve under a changing climate. Therefore, future research should aim to couple an improved systemic understanding of the impacts of these factors with slope-scale projections of avalanche hazards and risks to support sustainable mountain development and adaptation strategies. Avalanche conditions and related risks are influenced by ongoing changes in temperature and precipitation. This Review synthesizes existing data, approaches and results to highlight dominant patterns of change and how they are linked to climate change and other socio-environmental factors.

Anthropogenic climate change is rapidly altering high mountain environments, including changing the frequency, dynamic behavior, location, and magnitude of alpine mass movements. Here, we review three decades of scientific literature (∼1995 to early 2024) to assess to what degree observational records from the European Alps – as the region with the most comprehensive records – reveal these changes. We do this for the processes that are most common in this region, namely rockfall, rock avalanches, debris flows, ice avalanches, and snow avalanches. The systematic literature search and review yielded 335 publications, of which we omitted publications that did not focus primarily on observational records. The remaining 103 publications used observations from over 100 sites and 30 inventories to investigate the connection between climate change and mass movements. About one third of the relevant studies found a measurable impact of climate change on the investigated alpine mass movement processes (with the exception of large rock avalanches). The clearest climate-controlled trends are (i) increased rockfall frequency in high-alpine areas due to higher temperatures, (ii) fewer and smaller snow avalanches due to scarcer snow conditions at low and mid elevations, and (iii) a shift towards avalanches with more wet snow and fewer powder clouds. While there is (iv) despite a clear increase in debris-flow triggering precipitation, debris-flow activity has not been found to uniformly increase, though there is some evidence for increasing activity above treeline and at locations without historical precedence. The trends for (v) ice avalanches are spatially very variable with no clear direction. Ice temperatures are measurably increasing, but – despite a theoretical expectation – this has not impacted ice avalanche activity to date. The reviewed literature also reveals that quantifying the impact of climate change on these mass movements remains difficult in part due to the complexities of the natural system, but also because of limitations in the available datasets, confounding effects, and existing statistical processing techniques. Better assessments could be achieved if we would more broadly support the compilation and maintenance of large standardized data catalogs, bring together various dispersed datasets (in said catalogs), including from social and citizen science projects, invest in long-term natural observatories, and develop suitable processing techniques. Better observations will additionally support the development and performance of process-based models. If we can advance natural hazard research on these fronts, more quantitative predictions of future change are well within our reach.

Mountain regions are subject to highly damaging hydrological and gravitational hazards. This exposure is due to their biophysical and societal characteristics. It is essential for a sustainable management of these risks to consider the natural risk as the result of complex interactions within the risk system, which is composed of a natural and a societal subsystem. In this way, it is possible to adopt a dynamic approach to the risk system by placing the phenomenon in an evolutionary context. We can therefore consider its trajectory according to the socio-environmental dynamics that influence it.Studying these risk systems over the long term is necessary to understand their evolution and to anticipate future ones in order to guarantee the sustainability of mountain socio-ecosystems, and requires an interdisciplinary approach between geography and history.The study of the trajectory of a multi-hazard system is being carried out in the Commune of Vallouise-Pelvoux, a high Alpine valley in the Écrins massif, France. This territory was chosen because it is subject to various risks that occur over a wide range of altitudes, its recent socio-economic development is mainly based on tourism, and it is marked by glacial recession, but also because we were aware of the availability of several sources allowing the production of event and multirisk chronologies.The first stage of the research consisted in the production of a multi-hazard event chronology over 420 years (1600-2020). This database was built from various resources. On the one hand, from existing databases produced by public services and organizations such as the French Forest Office (ONF) specifically the mountain land restoration service (RTM) or the departmental councils. On the other hand, archival research was carried out in the municipal archives of Vallouise-Pelvoux and the departmental archives of the Hautes-Alpes.After analysis of all available sources, the data collected was processed in various ways. Indeed, sources of different forms and origins requires standardization of the information to make it comparable and usable. The chronology was also subjected to a critical analysis : are the sources authentic? Reliable? What factors might influence them? Once this chronology of events in Vallouise-Pelvoux has been contextualized (changes in the natural and societal systems of the Commune), a first statistical analysis of the risks identified and the damage caused will be presented. In the future the data will be used to analyze the trajectory of the system.

De nombreux territoires font face à des risques grandissants et de plus en plus multiples et interconnectés. Sur la base du matériel rassemblé lors d’un atelier de prospective INRAE et d’une analyse bibliographique et bibliométrique, cet article propose un état des lieux transversal des risques environnementaux en 2020 et de la recherche menée à INRAE sur le sujet. Le périmètre de l’analyse inclut les risques de catastrophe, les risques physicochimiques et les risques pour les écosystèmes. Les leçons de cet état des lieux sont mises en perspective au regard des besoins de la recherche nationale et internationale sur le sujet et du contexte d’urgence environnementale actuelle. L’analyse fait ressortir l’intérêt d’une approche holistique et intégrée des risques environnementaux incluant l’ensemble du périmètre considéré et la nécessité d’inscrire l’appréhension des risques environnementaux dans une démarche de sciences de la durabilité pour surmonter les verrous associés.

Predicting avalanche activity from meteorological and snow cover simulations is critical in mountainous areas to support operational forecasting. Several numerical and statistical methods have tried to address this issue. However, it remains unclear how combining snow physics, mechanical analysis of snow profiles and observed avalanche data improves avalanche activity prediction. This study combines extensive snow cover and snow stability simulations with observed avalanche occurrences within a random forest approach to predict avalanche situations at a spatial resolution corresponding to elevations and aspects of avalanche paths in a given mountain range. We develop a rigorous leave-one-out evaluation procedure including an independent evaluation set, confusion matrices and receiver operating characteristic curves. In a region of the French Alps (Haute-Maurienne) and over the period 1960-2018, we show the added value within the machine learning model of considering advanced snow cover modelling and mechanical stability indices instead of using only simple meteorological and bulk information. Specifically, using mechanically based stability indices and their time derivatives in addition to simple snow and meteorological variables increases the probability of avalanche situation detection from around 65 % to 76 %. However, due to the scarcity of avalanche events and the possible misclassification of non-avalanche situations in the training dataset, the predicted avalanche situations that are really observed remains low, around 3.3 %. These scores illustrate the difficulty of predicting avalanche occurrence with a high spatio-temporal resolution, even with the current data and modelling tools. Yet, our study opens perspectives to improve modelling tools supporting operational avalanche forecasting.

Following the projected increase in extreme precipitation, an increase in extreme snowfall may be expected in cold regions, e.g., for high latitudes or at high elevations. By contrast, in low- to medium-elevation areas, the probability of experiencing rainfall instead of snowfall is generally projected to increase due to warming conditions. Yet, in mountainous areas, despite the likely existence of these contrasted trends according to elevation, changes in extreme snowfall with warming remain poorly quantified. This paper assesses projected changes in heavy and extreme snowfall, i.e., in mean annual maxima and 100-year return levels, in the French Alps as a function of elevation and global warming level. We apply a recent methodology, based on the analysis of annual maxima with non-stationary extreme value models, to an ensemble of 20 adjusted general circulation model–regional climate model (GCM–RCM) pairs from the EURO-CORDEX experiment under the Representative Concentration Pathway 8.5 (RCP8.5) scenario. For each of the 23 massifs of the French Alps, maxima in the hydrological sense (1 August to 31 July) are provided from 1951 to 2100 and every 300 m of elevations between 900 and 3600 m. Results rely on relative or absolute changes computed with respect to current climate conditions (corresponding here to +1 ∘C global warming level) at the massif scale and averaged over all massifs. Overall, daily mean annual maxima of snowfall are projected to decrease below 3000 m and increase above 3600 m, while 100-year return levels are projected to decrease below 2400 m and increase above 3300 m. At elevations in between, values are on average projected to increase until +3 ∘C of global warming and then decrease. At +4 ∘C, average relative changes in mean annual maxima and 100-year return levels, respectively, vary from −26 % and −15 % at 900 m to +3 % and +8 % at 3600 m. Finally, for each global warming level between +1.5 and +4 ∘C, we compute the elevation threshold that separates contrasted trends, i.e., where the average relative change equals zero. This elevation threshold is shown to be lower for higher return periods, and it is projected to rise from 3000 m at +1.5 ∘C to 3350 m at +4 ∘C for mean annual maxima and from 2600 to 3000 m for 100-year return levels. These results have implications for the management of risks related to extreme snowfall.

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.

Snow avalanches are a major component of the mountain cryosphere that frequently create a menace for the road network. Deposit characteristics determine the extent of damage and disruption to communication networks, but the factors controlling snow-deposit volumes remain largely unknown. This study investigates the influence of meteorological and snowpack conditions on snow-avalanche deposits and road-network vulnerability based on 1986 deposit volumes from 182 paths located in two regions of the French Alps between 2003 and 2017: Guil and Haute-Maurienne valleys. During the period, 195 avalanches impacted the road network in these areas, leading to major disruptions. In Haute-Maurienne, correlations between deposit volumes and meteorological and snowpack conditions are high in winter. However, the relationships differ with path elevation and orientation. Results do not show any significant relationship between volumes and meteorological or snowpack conditions for the spring season. Focusing on deposits that disturbed the road network, winter and spring reveal a distinct influence of meteorological and snow variables compared to the overall data set, with snowfall intensity as the predominant control variable of deposit volumes leading to road cuts. When the same analysis is conducted by considering Guil valley separately or by aggregating Haute-Maurienne with Guil valley area data, results do not show any significant relationship, highlighting the specific regional nature of relations between deposit volumes and meteorological and snowpack conditions.