As climate change reshapes hazard profiles, strengthening public preparedness for natural hazards has received growing attention worldwide. Although substantial research has examined behavioural drivers of preparedness and the operational constraints of emergency management systems, these dimensions are often studied separately, limiting understanding of how individual and institutional factors operate together.This study examines both dimensions in the Squamish–Lillooet Regional District, a rural, multi-hazard region in southwestern British Columbia, Canada. Using a mixed-methods approach, a resident survey investigated individual hazard perceptions, preparedness behaviours, and associated factors, while interviews with local emergency managers examined current strategies and operational challenges. Although survey participants demonstrated relatively high hazard awareness and willingness to comply with official guidance, notable gaps remained in preparedness, hazard knowledge, and satisfaction with government performance. Examination of psychological and experiential factors also showed that hazard experience, worry, and self-efficacy sometimes had counterintuitive associations with individual preparedness. Interview findings further indicated that institutional and systemic constraints, such as fragmented responsibilities and limited capacity, might indirectly hamper public preparedness outcomes.These findings suggest that strengthening public preparedness for natural hazards depends not only on individual behavioural factors but also on broader institutional systems and legislative frameworks. Practical recommendations include regular baseline surveys, tailored communication strategies, and parallel investment in both informal partnerships and formal governance capacity. Together, this study provides a more comprehensive understanding of preparedness and offers practical insights for similarly resource-constrained jurisdictions facing increasing multi-hazard risks internationally.

Avalanche forecasting is a human judgment process with the goal of describing the nature and severity of avalanche hazard based on the concept of distinct avalanche problems. Snowpack simulations can help improve forecast consistency and quality by extending qualitative frameworks of avalanche hazard with quantitative links between weather, snowpack, and hazard characteristics. Building on existing research on modeling avalanche problem information, we present the first spatial modeling framework for extracting the characteristics of storm and persistent slab avalanche problems from distributed snowpack simulations. The grouping of simulated layers based on regional burial dates allows us to track them across space and time and calculate insightful spatial distributions of avalanche problem characteristics. We applied our approach to 10 winter seasons in Glacier National Park, Canada, and compared the numerical predictions to human hazard assessments. Despite good agreement in the seasonal summary statistics, the comparison of the daily assessments of avalanche problems revealed considerable differences between the two data sources. The best agreements were found in the presence and absence of storm slab avalanche problems and the likelihood and expected size assessments of persistent slab avalanche problems. Even though we are unable to conclusively determine whether the human or model data set represents reality more accurately when they disagree, our analysis indicates that the current model predictions can add value to the forecasting process by offering an independent perspective. For example, the numerical predictions can provide a valuable tool for assisting avalanche forecasters in the difficult decision to remove persistent slab avalanche problems. The value of the spatial approach is further highlighted by the observation that avalanche danger ratings were better explained by a combination of various percentiles of simulated instability and failure depth than by simple averages or proportions. Our study contributes to a growing body of research that aims to enhance the operational value of snowpack simulations and provides insight into how snowpack simulations can help address some of the operational challenges of human avalanche hazard assessments.

Following best practices from risk communication research, there are growing efforts to better understand avalanche forecast users and find opportunities to improve the effectiveness of avalanche safety information products and services. While motivations are a well-established topic in the recreation literature, the avalanche safety community has so far only paid limited attention to this dimension of their target community. The objective of this study is to examine 1) the importance of different motivations related to the types of experiences recreationists seek in the winter backcountry, 2) whether we can identify distinct motivation profiles among our sample of backcountry recreationists, and 3) how these motivations relate to other measures of backcountry and avalanche safety practices such as preferred backcountry terrain, level of formal avalanche safety training, and primary winter backcountry activity.Drawing from the avalanche forecast research panels of the Euregio and Swiss Avalanche Warning Services, we examined patterns in 15 motivation items among 2121 European winter backcountry recreationists. Overall, we found a clear hierarchy of motivations from least to most important. While the motivations vary on a continuum, we identified six clusters with distinct motivation patterns using a k-means approach: the Overall Keeners, Less Enthusiastic, Serenity Seekers, Skills Focused, Summit Focused, and Challenge & Risk Minded. Our analyses of the relationships between motivations, terrain preferences, avalanche safety training level, and primary winter backcountry activity show the expected patterns but also highlight the wide range of motivational patterns that exist across the community and warrant a more comprehensive characterization beyond the traditional focus on backcountry activity and avalanche safety training level. Our results provide initial practical insights into how knowledge of recreationists’ motivations and preferences can be leveraged to design more targeted avalanche safety products and services intended to resonate better with the motivations, needs, and capabilities of different audiences.

Snow avalanches are the primary mountain hazard for mechanized skiing operations. Helicopter and snowcat ski guides are tasked with finding safe terrain to provide guests with enjoyable skiing in a fast-paced and highly dynamic and complex decision environment. Based on years of experience, ski guides have established systematic decision-making practices that streamline the process and limit the potential negative influences of time pressure and emotional investment. While this expertise is shared within guiding teams through mentorship, the current lack of a quantitative description of the process prevents the development of decision aids that could strengthen the process. To address this knowledge gap, we collaborated with guides at Canadian Mountain Holidays (CMH) Galena Lodge to catalogue and analyze their decision-making process for the daily run list, where they code runs as green (open for guiding), red (closed), or black (not considered) before heading into the field. To capture the real-world decision-making process, we first built the structure of the decision-making process with input from guides and then used a wide range of available relevant data indicative of run characteristics, current conditions, and prior run list decisions to create the features of the models. We employed three different modeling approaches to capture the run list decision-making process: Bayesian network, random forest, and extreme gradient boosting. The overall accuracies of the models are 84.6 %, 91.9 %, and 93.3 % respectively compared to a testing dataset of roughly 20 000 observed run codes. The insights of our analysis provide a baseline for the development of effective decision support tools for backcountry avalanche risk management that can offer independent perspectives on operational terrain choices based on historic patterns or as a training tool for newer guides.

This study presents a statistical clustering method that allows avalanche forecasters to explore patterns in simulated snow profiles. The method uses fuzzy analysis clustering to group small regions into larger forecast regions by considering snow profile characteristics, spatial arrangements, and temporal trends. We developed the method, tuned parameters, and present clustering results using operational snowpack model data and human hazard assessments from the Columbia Mountains of western Canada during the 2022–2023 and 2023–2024 seasons. The clustering results from simulated snow profiles closely matched actual forecast regions, effectively partitioning areas based on major patterns in avalanche hazard, such as varying danger ratings or avalanche problem types. By leveraging the uncertain predictions of fuzzy analysis clustering, this method can provide avalanche forecasters with a practical approach to interpreting complex snowpack model output and identifying regions of uncertainty. We provide practical and technical considerations to help integrate these methods into operational forecasting practices.

Snowshoeing and winter hiking have grown substantially in the last decade. To provide this community with better avalanche safety messages, it is critical to understand their existing avalanche awareness and safety practices. To address this knowledge gap, we conducted intercept interviews with snowshoers and winter hikers at a popular backcountry trailhead outside of Vancouver, British Columbia, Canada and surveyed students of introductory avalanche safety courses targeted at snowshoers. Study participants shared their typical trip destinations, which we used to determine their general exposure to avalanche terrain based on the Avalanche Terrain Exposure Scale (ATES). Despite the fact that all but one participant expose themselves to avalanche terrain, we found very low levels of avalanche awareness, formal training, and use of avalanche information products like the public avalanche forecast. Participants’ main reason for not using existing avalanche safety products and services was their belief that they do not expose themselves to avalanche terrain. This highlights that initiatives raising awareness of what constitutes avalanche terrain, how to recognize it and when it is safe to travel into are key starting points for improving avalanche safety practices in this community. Comparisons between study participants without formal avalanche safety training, current course students, and participants with training highlight the value of formal training and offer insights on potential pathways for raising avalanche awareness among snowshoers and winter hikers.

The Avalanche Terrain Exposure Scale (ATES) is a system for classifying mountainous terrain based on the degree of exposure to avalanche hazard. The intent of ATES is to improve backcountry recreationist's ability to make informed risk management decisions by simplifying their terrain analysis. Access to ATES has been largely limited to manually generated maps in high-use areas due to the cost and time to generate ATES maps. Automated ATES (AutoATES) is a chain of geospatial models which provides a path towards developing ATES maps on large spatial scales for relatively minimal cost compared to manual maps. This research validates and localizes AutoATES using two ATES benchmark maps which are based on independent ATES maps from three field experts. We compare the performance of AutoATES in two study areas with unique snow climate and terrain characteristics: Connaught Creek in Glacier National Park, British Columbia, Canada, and Bow Summit in Banff National Park, Alberta, Canada. Our results show that AutoATES aligns with the ATES benchmark maps in 74.5 % of the Connaught Creek study area and 84.4 % of the Bow Summit study area. This is comparable to independently developed manual ATES maps which on average align with the ATES benchmark maps in 76.1 % of Connaught Creek and 84.8 % of Bow Summit. We also compare a variety of DEM types (lidar, stereo photogrammetry, Canadian National Topographic Database) and resolutions (5–26 m) in Connaught Creek to investigate how input data type affects AutoATES performance. Overall, we find that DEM resolution and type are not strong indicators of accuracy for AutoATES, with a map accuracy of 74.5 % ± 1 % for all DEMs. This research demonstrates the efficacy of AutoATES compared to expert manual ATES mapping methods and provides a platform for large-scale development of ATES maps to assist backcountry recreationists in making more informed avalanche risk management decisions.

Avalanche warning services increasingly employ snow stratigraphy simulations to improve their current understanding of critical avalanche layers, a key ingredient of dry slab avalanche hazard. However, a lack of large-scale validation studies has limited the operational value of these simulations for regional avalanche forecasting. To address this knowledge gap, we present methods for meaningful comparisons between regional assessments of avalanche forecasters and distributed snowpack simulations. We applied these methods to operational data sets of 10 winter seasons and 3 forecast regions with different snow climate characteristics in western Canada to quantify the Canadian weather and snowpack model chain's ability to represent persistent critical avalanche layers. Using a recently developed statistical instability model as well as traditional process-based indices, we found that the overall probability of detecting a known critical layer can reach 75 % when accepting a probability of 40 % that any simulated layer is actually of operational concern in reality (i.e., precision) as well as a false alarm rate of 30 %. Peirce skill scores and F1 scores are capped at approximately 50 %. Faceted layers were captured well but also caused most false alarms (probability of detection up to 90 %, precision between 20 %–40 %, false alarm rate up to 30 %), whereas surface hoar layers, though less common, were mostly of operational concern when modeled (probability of detection up to 80 %, precision between 80 %–100 %, false alarm rate up to 5 %). Our results also show strong patterns related to forecast regions and elevation bands and reveal more subtle trends with conditional inference trees. Explorations into daily comparisons of layer characteristics generally indicate high variability between simulations and forecaster assessments with correlations rarely exceeding 50 %. We discuss in depth how the presented results can be interpreted in light of the validation data set, which inevitably contains human biases and inconsistencies. Overall, the simulations provide a valuable starting point for targeted field observations as well as a rich complementary information source that can help alert forecasters about the existence of critical layers and their instability. However, the existing model chain does not seem sufficiently reliable to generate assessments purely based on simulations. We conclude by presenting our vision of a real-time validation suite that can help forecasters develop a better understanding of the simulations' strengths and weaknesses by continuously comparing assessments and simulations.

Danger ratings are used across many fields to convey the severity of a hazard. In snow avalanche risk management, danger ratings play a prominent role in public bulletins by concisely describing existing and expected conditions. While there is considerable research examining the accuracy and consistency of the production of avalanche danger ratings, far less research has focused on how backcountry recreationists interpret andapply the scale. We used 3195 responses to an online survey to provide insight into howrecreationists perceive the North American Public Avalanche Danger Scale and how they use ratings to make trip planning decisions. Using a latent class mixed-effect model, our analysis shows that 65 % of our study participants perceive the avalanche danger scale to be linear, which is different from the scientific understanding of the scale, which indicates an exponential-like increase in severity between levels. Regardless ofperception, most respondents report avoiding the backcountry at the twohighest ratings. Using conditional inference trees, we show thatparticipants who recreate fewer days per year and those who have lowerlevels of avalanche safety training tend to rely more heavily on the dangerrating to make trip planning decisions. These results provide avalanchewarning services with a better understanding of how recreationists interactwith danger ratings and highlight how critical the ratings are forindividuals who recreate less often and who have lower levels of training.We discuss opportunities for avalanche warning services to optimize thedanger scale to meet the needs of these users who depend on the ratings themost.

To provide guidance to the general public, clinicians, and avalanche professionals about best practices, the Wilderness Medical Society convened an expert panel to revise the evidence-based guidelines for the prevention, rescue, and resuscitation of avalanche and nonavalanche snow burial victims. The original panel authored the Wilderness Medical Society Practice Guidelines for Prevention and Management of Avalanche and Nonavalanche Snow Burial Accidents in 2017. A second panel was convened to update these guidelines and make recommendations based on quality of supporting evidence.

Recreationists are responsible for developing their own risk management plans for travelling in avalanche terrain. To help recreationists mitigate their exposure to avalanche hazard, many avalanche warning services include explicit travel and terrain advice (TTA) statements in their daily avalanche bulletins where forecasters offer guidance about what specific terrain to avoid and what to favour under the existing conditions. However, the use and effectiveness of this advice has never been tested to ensure it meets the needs of recreationists developing their risk management approach for backcountry winter travel. We conducted an online survey in Canada and the United States to determine which user groups are paying attention to the TTA in avalanche bulletins, what makes these statements useful, and if modifications to the phrasing of the statements would improve their usefulness for users. Our analysis reveals that the core audience of the TTA is users with introductory-level avalanche awareness training who integrate slope-scale terrain considerations into their avalanche safety decisions. Using a series of proportional-odds ordinal mixed-effect models, we show that reducing the jargon used in the advice helped users with no or only introductory-level avalanche awareness training understand the advice significantly better and adding an additional explanation made the advice more useful for them. These results provide avalanche warning services with critical perspectives and recommendations for improving their TTA so that they can better support recreationists who are at earlier stages of developing their avalanche risk management approach and therefore need the support the most.

Snow avalanches pose a serious threat to people recreating in the mountainous backcountry during the wintertime. To help recreationists manage their risk from avalanches, local avalanche warning services publish daily bulletins to inform the public about the existing hazard conditions. While the correct application of this information is crucial for avoiding potentially deadly accidents, recreationists’ ability to develop their skills through practical experience alone is limited due to the wicked nature of the backcountry learning environment where feedback is not always reliable.The present study explores the idea of improving recreationists’ ability to apply the hazard information to terrain by adding interactive exercises with feedback directly into the daily avalanche bulletins. To examine this idea, we conducted an online survey that included a route ranking exercise. Our analysis dataset included responses from 2278 backcountry recreationists with a variety of backgrounds and avalanche safety training levels. Using a series of generalized linear mixed effects models and conditional inference tree analyses, our results highlight that including interactive self-assessment exercises in avalanche bulletins has potential for enhancing their effectiveness and education value, especially for individuals who might not have the skills to properly understand the hazard information well enough to make informed decisions about personal risk but are willing to learn.Management implicationsAvalanche warning services should consider integrating application exercises with feedback into their bulletins to give users opportunities to assess their understanding and practice their information processing skills. Integrating such exercises directly into the bulletin takes advantage of recreationists' frequent interaction with the product and provides them with just-in-time education when they use the bulletin for personal trip planning. Enhancing bulletins this way will turn them from pure condition reports into a critical component of the overall avalanche awareness education system. Recreationists’ performance in these exercises can provide warning services with valuable insights into skill levels of their users.

Abstract. The combination of numerical weather prediction and snowpack models has potential to provide valuable information about snow avalanche conditions in remote areas. However, the output of snowpack models is sensitive to precipitation inputs, which can be difficult to verify in mountainous regions. To examine how existing observation networks can help interpret the accuracy of snowpack models, we compared snow depths predicted by a weather–snowpack model chain with data from automated weather stations and manual observations. Data from the 2020–2021 winter were compiled for 21 avalanche forecast regions across western Canada covering a range of climates and observation networks. To perform regional-scale comparisons, SNOWPACK model simulations were run at select grid points from the High-Resolution Deterministic Prediction System (HRDPS) numerical weather prediction model to represent conditions at treeline elevations, and observed snow depths were upscaled to the same locations. Snow depths in the Coast Mountain range were systematically overpredicted by the model, while snow depths in many parts of the interior Rocky Mountain range were underpredicted. These discrepancies had a greater impact on simulated snowpack conditions in the interior ranges, where faceting was more sensitive to snow depth. To put the comparisons in context, the quality of the upscaled observations was assessed by checking whether snow depth changes during stormy periods were consistent with the forecast avalanche hazard. While some regions had high-quality observations, other regions were poorly represented by available observations, suggesting in some situations modelled snow depths could be more reliable than observations. The analysis provides insights into the potential for validating weather and snowpack models with readily available observations, as well as for how avalanche forecasters can better interpret the accuracy of snowpack simulations.

Abstract. Potential avalanche release area (PRA) modelling is critical for generating automated avalanche terrain maps which provide low-cost large scale spatial representations of snow avalanche hazard for both infrastructure planning and recreational applications. Current methods are not applicable in mountainous terrain where high-resolution elevation models are unavailable and do not include an efficient method to account for avalanche release in forested terrain. This research focuses on expanding an existing PRA model to better incorporate forested terrain using satellite imagery and presents a novel approach for validating the model using local expertise, thereby broadening its application to numerous mountain ranges worldwide. The study area of this research is a remote portion of the Columbia Mountains in southeastern British Columbia, Canada which has no pre-existing high-resolution spatial data sets. Our research documents an open source workflow to generate high-resolution DEM and forest land cover data sets using optical satellite data processing. We validate the PRA model by collecting a polygon dataset of observed potential release areas from local guides, using a method which accounts for the uncertainty of human recollection and variability of avalanche release. The validation dataset allows us to perform a quantitative analysis of the PRA model accuracy and optimize the PRA model input parameters to the snowpack and terrain characteristics of our study area. Compared to the original PRA model our implementation of forested terrain and local optimization improved the percentage of validation polygons accurately modelled by 11.7 percentage points and reduced the number of validation polygons that were underestimated by 14.8 percentage points. Our methods demonstrate substantial improvement in the performance of the PRA model in forested terrain and provide means to generate the requisite input datasets and validation data to apply and evaluate the PRA model in vastly more mountainous regions worldwide than was previously possible.

Snowpack models can provide detailed insight about the evolution of the snow stratigraphy in a way that is not possible with direct observations. However, the lack of suitable data aggregation methods currently prevents the effective use of the available information, which is commonly reduced to bulk properties and summary statistics of the entire snow column or individual grid cells. This is only of limited value for operational avalanche forecasting and has substantially hampered the application of spatially distributed simulations, as well as the development of comprehensive ensemble systems. To address this challenge, we present an averaging algorithm for snow profiles that effectively synthesizes large numbers of snow profiles into a meaningful overall perspective of the existing conditions. Notably, the algorithm enables compiling of informative summary statistics and distributions of snowpack layers, which creates new opportunities for presenting and analyzing distributed and ensemble snowpack simulations.

Abstract. Snowpack models can provide detailed insight about the evolution of the snow stratigraphy in ways that is not possible with direct observations. However, the lack of suitable data aggregation methods currently prevents the effective use of the available information, which is commonly reduced to bulk properties and summary statistics of the entire snow column or individual grid cells. This is only of limited value for operational avalanche forecasting. To address this challenge, we present an averaging algorithm for snow profiles that can effectively synthesize large numbers of snow profiles into a meaningful overall perspective of the existing conditions. Notably, the algorithm enables compiling of informative summary statistics and distributions of snowpack layers, which creates new opportunities for presenting and analyzing distributed and ensemble snowpack simulations.

To mark the 20th anniversary of Natural Hazards and Earth System Sciences (NHESS), an interdisciplinary and international journal dedicated to the public discussion and open-access publication of high-quality studies and original research on natural hazards and their consequences, we highlight 11 key publications covering major subject areas of NHESS that stood out within the past 20 years. The papers cover all the topics contemplated in the European Geosciences Union (EGU) Division on Natural Hazards including dissemination, education, outreach and teaching. The selected articles thus represent excellent scientific contributions in the major areas of natural hazards and risks and helped NHESS to become an exceptionally strong journal representing interdisciplinary areas of natural hazards and risks. At its 20th anniversary, we are proud that NHESS is not only used by scientists to disseminate research results and novel ideas but also by practitioners and decision-makers to present effective solutions and strategies for sustainable disaster risk reduction.