Avalanche size estimation and avalanche outline determination by experts: reliability and implications for practice

<strong class="journal-contentHeaderColor">Abstract.</strong> Accurately predicting the location, timing and size of natural snow avalanches is crucial for local and regional decision-makers, but remains one of the major challenges in avalanche forecasting. So far, forecasts are generally made by human experts, interpreting a variety of data, and drawing on their knowledge and experience. Using avalanche data from the Swiss Alps and one-dimensional physics-based snowpack simulations, we developed a model predicting the probability of dry-snow avalanches occurring in the vicinity of automated weather stations based on the output of a recently developed instability model. This new avalanche day predictor was compared to benchmark models related to the amount of new snow. Evaluation on an independent data set demonstrated the importance of snow stratigraphy for natural avalanche release, as the avalanche day predictor outperformed the benchmark model based on the three-day sum of new snow (F1 scores: 0.71 and 0.65, respectively). The averaged predictions of both models resulted in the best performance (F1 score: 0.75). In a second step, we derived functions describing the probability for certain avalanche size classes. Using the 24-hour new snow height as proxy of avalanche failure depth yielded the best estimator of typical (median) observed avalanche size, while the depth of the deepest weak layer, detected using the instability model, provided the better indicator regarding the largest observed avalanche size. Validation of the avalanche size estimator on an independent data set of avalanche observations confirmed these findings. Furthermore, comparing the predictions of the avalanche day predictors and avalanche size estimators with a 21-year data set of re-analysed regional avalanche danger levels showed increasing probabilities for natural avalanches and increasing avalanche size with increasing danger level. We conclude that these models may be valuable tools to support forecasting the occurrence of natural dry-snow avalanches.

Abstract. Forecasting avalanche danger at a regional scale is a largely data-driven yet also experience-based decision-making process by human experts. In the case of public avalanche forecasts, this assessment process terminates in an expert judgment concerning summarizing avalanche conditions by using one of five danger levels. This strong simplification of the continuous, multi-dimensional nature of avalanche hazard allows for efficient communication but inevitably leads to a loss of information when summarizing the severity of avalanche hazard. Intending to overcome the discrepancy between determining the final target output in higher resolution while maintaining the well-established standard of assessing and communicating avalanche hazard using the avalanche danger scale, avalanche forecasters at the national avalanche warning service in Switzerland used an approach that combines absolute and relative judgments. First, forecasters make an absolute judgment using the five-level danger scale. In a second step, a relative judgment is made by specifying a sub-level describing the avalanche conditions relative to the chosen danger level. This approach takes into account the human ability to reliably estimate only a certain number of classes. Here, we analyze these (yet unpublished) sub-levels, comparing them with data representing the three contributing factors of avalanche hazard: snowpack stability, the frequency distribution of snowpack stability, and avalanche size. We analyze both data used in operational avalanche forecasting and data independent of the forecast, going back 5 years. Using a sequential analysis, we first establish which data are suitable and in which part of the danger scale they belong by comparing their distributions at consecutive danger levels. In a second step, integrating these findings, we compare the frequency of locations with poor snowpack stability and the number and size of avalanches with the forecast sub-level. Overall, we find good agreement: a higher sub-level is generally related to more locations with poor snowpack stability and more avalanches of larger size. These results suggest that on average avalanche forecasters can make avalanche danger assessments with higher resolution than the five-level danger scale. Our findings are specific to the current forecast set-up in Switzerland. However, we believe that avalanche warning services making a hazard assessment using a similar temporal and spatial scale as currently used in Switzerland should also be able to refine their assessments if (1) relevant data are sufficiently available in time and space and (2) a similar approach combining absolute and relative judgment is used. The sub-levels show a rank-order correlation with data related to the three contributing factors of avalanche hazard. Hence, they increase the predictive value of the forecast, opening the discussion on how this information could be provided to forecast users.

Dry‐snow slab avalanches are the most fatal type of avalanches, beginning with the failure of a weak snow layer below cohesive slabs. This failure can propagate within the weak layer, causing the overlying slab to fracture and slide. Avalanche forecasters are interested in evaluating crack propagation propensity and potential avalanche sizes. This study tests the hypothesis that two factors may stop dynamic crack propagation: snowpack heterogeneity and terrain variations. We develop a depth‐averaged Material Point Method, which combines MPM with shallow water assumptions for efficient elastic‐brittle modeling of avalanche release. We analyze two scenarios: pure‐elastic and brittle slabs. In the pure‐elastic case, we observe a significant decrease in slab tensile stress with increasing crack speed and provide an analytical formulation for this phenomenon. We evaluate the impacts of weak layer heterogeneity and fracture energy on crack stopping. In the brittle scenario, we explore the interaction between weak layer heterogeneity and slab fracture, quantifying their combined effects on crack arrest. Our results reveal a scaling law that relates crack arrest distance to dimensionless numbers indicative of weak layer and slab strength. The model is applied in case studies to predict release sizes based on field data, and also on synthetic 3D topographies, enhancing the understanding of factors influencing avalanche size and aiding future mitigation strategies.

ABSTRACTAs forest cover in mountain areas impacts headwater properties like habitat extent and downstream water resources, it is important to assess and understand the changes that occur across forest transition zones like the alpine treeline ecotone (ATE). Such changes occur slowly and manifest at decadal to century time scales; however, the present benchmark spatial resolution for land cover analysis from oblique repeat photographs, 100 m, is insufficient to analyze anticipated changes in ATE over a century-scale. In this research note, fractional cover classification of the ATE is achieved through oblique photography analysis with the WSL Monoplotting Tool. Seven oblique photographs of the West Castle Watershed (Alberta, Canada), collected by the Mountain Legacy Project, were gridded to a 20-m resolution and assigned canopy cover classes by manual interpretation. Four canopy cover classes (i.e., no cover, low vegetation, partial canopy, full canopy) were compared to lidar-derived fractional cover. The extraction of canopy cover information from oblique photography at a resolution of 20 m introduces the ability to assess and quantify changes in the ATE using century-scale oblique photographic records.

Effects of the underlying topology on perturbation spreading in the Axelrod model for cultural dissemination

Avalanche occurrences are unambiguous indicators of unstable snow conditions. Information on past and current avalanche activity is therefore crucial for avalanche forecasting. To continuously assess avalanche activity, automatic detection systems are required. In recent years, technological and signal processing advances have led to the development of operational infrasound avalanche detection systems. We evaluated the detection performance of four operationally running infrasound detection systems installed at three different sites in the Swiss Alps during two entire winter seasons. To this end, we collected a comprehensive data set of avalanche activity using a network of automatic cameras and supplementary field observations by local observers. The events automatically identified by the systems were then compared to the data set of visually observed avalanches. Only 3% of the 839 observed avalanches were associated with automatic detections and 21% of the automatic detections were confirmed by field observations. However, the majority of observed avalanches were small and most automatic detections occurred during periods of poor visibility. Furthermore, the probability of detection (POD) increased with avalanche size, decreased with distance, and varied with avalanche type. Dry-snow avalanches were generally better detected than wet-snow and mixed-type avalanches. Large avalanches (on the order of 300 m wide and 1000 m long) within a distance of 3 km from the array were typically well detected (POD ≈ 90%). The false alarm ratio was estimated to 13–30%.

Purpose– While innovation has many similarities to other forms of projects it is characterised by a high failure rate and the need to stimulate creativity. More explicit risk management could help in achieving success in innovation projects. However, too much or inappropriate risk management might stifle the creativity that is core to innovation. So, what project risk management should be applied and where in the innovation project?Design/methodology/approach– A theoretical framework is proposed which combines the generic innovation process with project risk management. The framework was used to analyse the current attitudes to managing innovation risk in a series of companies.Findings– The decision points of the stage-gate innovation process model provide an effective interface for incorporating project risk concepts. The general concepts appear most relevant to innovation management though it is useful to customise them to emphasise the particular characteristics of innovation projects. The experience of using the resultant combined model in a number of diverse case studies indicates the relevance of the model in understanding attitudes towards risk management in innovation. The analysis of the case study companies suggested that risk management needs to be applied in differential manner: simple, unobtrusive techniques early in the innovation life cycle with more substantial, quantitative methods being considered for later stages.Research limitations/implications– It would be useful to extend this research by examining more case studies from other countries and industries.Practical implications– The combined innovation and risk management model provides a framework that diverse companies can appreciate. The framework offers a basis for discussing the most appropriate form of risk management in different innovation-based industries.Originality/value– Although there are many separate models for innovation and project risk management described in the literature, there is very little discussion about explicitly combining these theories. This paper aims to help fill this gap in the knowledge.

Purpose – This research aims to describe and analyze integrated Risk Management and Public Service at Public Sector Organizations Methodology/approach – The Study is a quantitative research approach that uses online-based data collection methods (e_ questionnaires) combined with interviews to get their views on integration risk management implementation and public service. The sample in this study was 94 respondents from three (3) public organizations, consisting of the Papua Provincial Government (Finance Staff &amp; APIP), Higher Education Internal Audit (SPI), and Financial Supervisory Institution (BPKP &amp; BPK). Findings – The results of the analysis show that there are a few key points on how risk management is important and has a close relationship with public service, like this protecting public interest, Enhancing Service Delivery, Allocating Resources Efficiently, Ensuring Compliance and Accountability, Emergency Preparedness and Response, Reputation Management, and Long-term Sustainability. The integration of risk management in public services shows a positive relationship and can contribute to service reliability, better financial management, minimizing fraud and increasing compliance with regulations, maximizing the utilization of assets owned by the government, and having a speed of response to emergency conditions or events, and being able to mitigate risks that may occur. Practical implications – Risk management in public service helps ensure efficient resource allocation, protect public interests, enhance service delivery, and maintain the integrity and reputation of public organizations. Public service agencies can better serve their communities and fulfill their mandate by proactively managing risks. Originality/value – This research combines behavioral and psychological approaches to test the occurrence of Risk Management in the Public Sector.

Elastic disordered interfaces driven through a heterogenpeous landscape respond via intermittent avalanches. In the overdamped limit, avalanche sizes follow a scale-free power-law distribution. Here, we investigate how inertial and dynamical terms beyond the overdamped approximation modify this behavior, focusing on the combined effects of interaction range, dimensionality, and dissipation. Using large-scale numerical simulations, we show that these additional terms robustly generate system-spanning events-so-called kings-responsible for a characteristic bump in the avalanche size distribution, which arise only in the presence of dissipation. We identify two distinct mechanisms underlying this phenomenon. In two-dimensional systems with long-range elasticity, we observe genuine kings, which persist in the thermodynamic limit and are consistent with mean field predictions. In contrast, one-dimensional systems with long-range elasticity and systems with short-range interactions in both 1D and 2D exhibit ghost kings: failed synchronization events producing large, but finite, ballistically spreading avalanches. Our findings clarify the origin of bump features in avalanche statistics and establish the conditions under which true system-spanning events emerge in realistic, dissipative systems.

Abstract. There are distinctive methodological and conceptual challenges in rare and severe event (RSE) forecast verification, that is, in the assessment of the quality of forecasts of rare but severe natural hazards such as avalanches, landslides or tornadoes. While some of these challenges have been discussed since the inception of the discipline in the 1880s, there is no consensus about how to assess RSE forecasts. This article offers a comprehensive and critical overview of the many different measures used to capture the quality of categorical, binary RSE forecasts – forecasts of occurrence and non-occurrence – and argues that of skill scores in the literature there is only one adequate for RSE forecasting. We do so by first focusing on the relationship between accuracy and skill and showing why skill is more important than accuracy in the case of RSE forecast verification. We then motivate three adequacy constraints for a measure of skill in RSE forecasting. We argue that of skill scores in the literature only the Peirce skill score meets all three constraints. We then outline how our theoretical investigation has important practical implications for avalanche forecasting, basing our discussion on a study in avalanche forecast verification using the nearest-neighbour method (Heierli et al., 2004). Lastly, we raise what we call the “scope challenge”; this affects all forms of RSE forecasting and highlights how and why working with the right measure of skill is important not only for local binary RSE forecasts but also for the assessment of different diagnostic tests widely used in avalanche risk management and related operations, including the design of methods to assess the quality of regional multi-categorical avalanche forecasts.

The winners and losers of El Niño

Abstract. Recreational activities in snow-covered mountainous terrain in the backcountry account for the vast majority of avalanche accidents. Studies analyzing avalanche risk mostly rely on accident statistics without considering exposure (or the elements at risk), i.e., how many, when and where people are recreating, as data on recreational activity in the winter mountains are scarce. To fill this gap, we explored volunteered geographic information on two social media mountaineering websites – bergportal.ch and camptocamp.org. Based on these data, we present a spatiotemporal pattern of winter backcountry touring activity in the Swiss Alps and compare this with accident statistics. Geographically, activity was concentrated in Alpine regions relatively close to the main Swiss population centers in the west and north. In contrast, accidents occurred equally often in the less-frequented inner-alpine regions. Weekends, weather and avalanche conditions influenced the number of recreationists, while the odds to be involved in a severe avalanche accident did not depend on weekends or weather conditions. However, the likelihood of being involved in an accident increased with increasing avalanche danger level, but also with a more unfavorable snowpack containing persistent weak layers (also referred to as an old snow problem). In fact, the most critical situation for backcountry recreationists and professionals occurred on days and in regions when both the avalanche danger was critical and when the snowpack contained persistent weak layers. The frequently occurring geographical pattern of a more unfavorable snowpack structure also explains the relatively high proportion of accidents in the less-frequented inner-alpine regions. These results have practical implications: avalanche forecasters should clearly communicate the avalanche danger and the avalanche problem to the backcountry user, particularly if persistent weak layers are of concern. Professionals and recreationists, on the other hand, require the expertise to adjust the planning of a tour and their backcountry travel behavior depending on the avalanche danger and the avalanche problem.

We investigate the properties of boundary avalanches in 2D Abelian sandpile model (ASM). We construct the one-to-one correspondence of boundary avalanches and two-rooted spanning trees. Using the connection between the obtained graph representation and lattice Green functions we calculate the exact values of critical exponents for size and lifetime distributions of avalanches starting at the open boundary that forms an angle alpha . We find that the probability of a boundary avalanche of the size s varies as s-1- pi 2 alpha / for large s and the probability of an avalanche of lifetime t varies as t-1-4 pi 5 alpha / for large t. The obtained values are verified by numerical simulations.

Purpose – This article aims to focus on raising awareness of the limitations of traditional “enterprise‐centric” views of enterprise risk management that ignore the risks that are inherited from key business and supply chain partners. In essence, enterprise systems implementations have allowed organizations to couple their operations more tightly with other business partners, particularly in the area of supply chain management, and in the process enterprise systems applications are redefining the boundaries of the entity in terms of risk management concerns and the scope of financial audits. Design/methodology/approach – The prior literature that has begun to explore aspects of assessing key risk components in these relationships is reviewed with an eye to highlighting the limitations of what is understood about risk in interorganizational relationships. This analysis of the prior research establishes the basis for the logical formation of a framework for future enterprise risk management research in the area of e‐commerce relationships. Findings – Conclusions focus on the overall framework of risks that should be considered when interorganizational relationships are critical to an enterprise's operations and advocate an “extended‐enterprise” view of enterprise risk management. Research limitations/implications – The framework introduced in this paper provides guidance for future research in the area of interorganizational systems control and risk assessment. Practical implications – The framework further highlights areas of risk that auditors and corporate risk managers should consider in assessing the risk inherited through interorganizational relationships. Originality/value – The paper highlights the need to shift from an enterprise‐centric view of risk management to an extended‐enterprise risk management view.

Purpose – This study analyzes the influence of cooperative behavior and organizational risk management on knowledge sharing in Brazilian agricultural cooperatives. Complementarily, it analyzes the interaction of organizational risk management in the relationship between cooperative behavior and knowledge sharing. Theoretical framework – The research is based on the literature on cooperative behavior, organizational risk management, and knowledge sharing with variables derived from previous literature. Methodology – A survey was conducted with strategic level managers in Brazilian agricultural cooperatives, with the sample being composed of 104 valid responses. To test the hypotheses, the structural equation modeling technique was applied. Findings – The results show that cooperative behavior positively and significantly influences risk management and knowledge sharing; however, no statistical significance was found in the relationship between risk management and knowledge sharing. This indicates that higher levels of cooperative behavior are reflected in greater risk management, which helps to mitigate risks, and in greater knowledge sharing in the cooperatives studied. Practical &amp; social implications of research – It presents new information relating to cooperative behavior and risk management in knowledge sharing in cooperatives that integrate strategic alliances. As practical implications, it is highlighted that knowledge sharing should not be perceived as an immediate risk, but as being inherent to cooperation and collaboration between parties. Originality/value – It contributes to the literature by presenting new implications of cooperative behavior and risk management, perceived as enablers of knowledge sharing. Keywords – cooperative behavior; risk management; knowledge sharing; cooperatives.