Towards a holistic paradigm for long-term snow avalanche risk assessment and mitigation.

In mountain territories, snow avalanches are a prevalent threat. Long-term risk management involves defining meaningful compromises between protection and overall sustainability of communities and their environment. Methods able to (i) consider all sources of losses, (ii) account for the high uncertainty levels that affect all components of the risk and (iii) cope for marked non-stationarities should be employed. Yet, on the basis of a literature review and an analysis of relations to Sustainable Development Goals (SDGs), it is established that snow avalanche risk assessment and mitigation remain dominated by approaches that can be summed up as deterministic, hazard oriented, stationary and not holistic enough. A more comprehensive paradigm relying on formal statistical modelling is then proposed and first ideas to put it to work are formulated. Application to different mountain environments and broader risk problems is discussed.

Parameter importance assessment improves efficacy of machine learning methods for predicting snow avalanche sites in Leh-Manali Highway, India.

Due to ongoing climate change, water mass redistribution and related hazards are getting stronger and frequent. Therefore, predicting extreme hydrological events and related hazards is one of the highest priorities in geosciences. Machine Learning (ML) methods have shown promising prospects in this venture. Every ML method requires training where we know both the output (extreme event) and input (relevant physical parameters and variables). This step is critical to the efficacy of the ML method. The usual approach is to include a wide variety of hydro-meteorological observations and physical parameters, but recent advances in ML indicate that the efficacy of ML may not improve by increasing the number of input parameters. In fact, including unimportant parameters decreases the efficacy of ML algorithms. Therefore, it is imperative that the most relevant parameters are identified prior to training. In this study, we demonstrate this concept by predicting avalanche susceptibility in Leh-Manali highway (one of the most severely affected regions in India) with and without Parameter Importance Assessment (PIA). The avalanche locations were randomly divided into two groups: 70% for training and 30% for testing. Then, based on temporal and spatial sensor data, eleven avalanche influencing parameters were considered. The Boruta algorithm, an extension of Random Forest (RF) ML method that utilizes the importance measure to rank predictors, was used and it found nine out of eleven parameters to be important. Support Vector Machine (SVM) based ML technique is used for avalanche prediction, and to be comprehensive, four different kernel functions were employed (linear, polynomial, sigmoid, and radial basis function (RBF)). The prediction accuracy for linear, polynomial, sigmoid, and RBF kernels, with all the eleven parameters were found to be 80.4%, 81.7%, 39.2%, and 85.7%, respectively. While, when using selected parameters, the prediction accuracy for linear, polynomial, sigmoid, and RBF kernels were 84.1%, 86.6%, 43.0%, and 87.8%, respectively. We also identified locations where occurrences of avalanches are most likely. We conclude that parameter selection should be considered when applying ML methods in geosciences.

The trajectory of symptom burden in exposed and unexposed survivors of a major avalanche disaster: a 30 year long-term follow-up study.

BACKGROUND: Limited research exists concerning the long-term effects of avalanches on survivors' mental health beyond the first years after the accident. The aims of this study were to describe and evaluate possible differences in long-term mental health symptoms after a major avalanche disaster between exposed and unexposed soldiers using a longitudinal design. METHOD: Present mental health symptoms were examined among avalanche exposed (n = 12) and unexposed (n = 9) soldiers by PTSS-10, IES-15 and STAI-12 in four waves (1986-1987 and 2016). RESULTS: Binary logistic regression revealed that the odds to score above the cut-off were significantly lower for both groups after one year compared to baseline for PTSS-10 (p = 0.018) and significantly lower after 30 days compared to baseline for IES-15 (p = 0.005). Data did not reveal significant differences between the exposed and unexposed groups regarding adjusted PTSS-10, IES-15 or STAI-12 mean scores compared. Linear mixed model-analyses revealed significant effects of time. The adjusted mean scores declined over time for both groups: PTSS-10 (p = 0.001), IES-15 (p = 0.026) and STAI-12 (p = 0.001), and the time trajectories for PTSS-10 were significantly different between the groups (p = 0.013). Although not significant (all p > 0.05), results indicated that a larger proportion of soldiers in the exposed group experienced posttraumatic stress symptoms (5/12) (PTSS-10 score ≥ 4) and distress symptoms (6/12) (IES-15 score ≥ 26) above cut-off points, 30 years post-disaster. CONCLUSIONS: The course of mental health symptoms may persist, and even increase, in selected and trained military personnel 30 years after exposure to a natural disaster. These findings may be of great importance for health authorities planning appropriate follow-up.

Non-invasive assessment of animal exercise stress: real-time PCR of GLUT4, COX2, SOD1 and HSP70 in avalanche military dog saliva.

Exercise has been shown to increase mRNA expression of a growing number of genes. The aim of this study was to assess if mRNA expression of the metabolism- and oxidative stress-related genes GLUT4 (glucose transporter 4), COX2 (cyclooxygenase 2), SOD1 (superoxide dismutase 1) and HSP70 (heat shock protein 70) in saliva changes following acute exercise stress in dogs. For this purpose, 12 avalanche dogs of the Italian Military Force Guardia di Finanza were monitored during simulation of a search for a buried person in an artificial avalanche area. Rectal temperature (RT) and saliva samples were collected the day before the trial (T0), immediately after the descent from a helicopter at the onset of a simulated avalanche search and rescue operation (T1), after the discovery of the buried person (T2) and 2 h later (T3). Expressions of GLUT4, SOD1, COX2 and HSP70 were measured by real-time PCR. The simulated avalanche search and rescue operation was shown to exert a significant effect on RT, as well as on the expression of all metabolism- and oxidative stress-related genes investigated, which peaked at T2. The observed expression patterns indicate an acute exercise stress-induced upregulation, as confirmed by the reductions in expression at T3. Moreover, our findings indicate that saliva is useful for assessing metabolism- and oxidative stress-related genes without the need for restraint, which could affect working dog performance.

Risk assessment in winter backcountry travel.

OBJECTIVE: Risk assessment is an important part of safe backcountry travel in avalanche terrain. The purpose of this study was to determine and compare the ability of backcountry travelers to accurately estimate the avalanche danger for their destination and time of travel. METHODS: We surveyed 353 winter backcountry users, asking them to rate the avalanche danger for their destination that day. We then compared this estimation to the Utah Avalanche Center daily advisory for that specific location, aspect, and elevation. Tendency to underestimate the avalanche danger was then compared across 6 different sports (backcountry skiing, backcountry snowboarding, snowshoeing, snowmobiling, out-of-bounds skiing, and out-of-bounds snowboarding) as well as across age, gender, and subject participation in an avalanche safety course. RESULTS: A comparison across different sports, adjusted for age and gender, showed that snowshoers were 7.11 times more likely than skiers to underestimate the avalanche danger (95% CI, 2.95, 17.11). This difference was maintained after adjusting for past education in an avalanche safety course (odds ratio, 5.74; 95% CI, 2.28, 14.46). Snowmobilers were also significantly more likely to underestimate the avalanche danger when compared to skiers (odds ratio, 3.11; 95% CI, 1.12, 8.24), but these differences ceased to be significant when the data were adjusted for avalanche safety course (odds ratio, 2.39; 95% CI, 0.84, 6.74). While there was a trend for women and older age groups to underestimate the avalanche danger when compared to men, these trends were not significant. CONCLUSIONS: Snowshoers and snowmobilers are groups that tend to underestimate avalanche danger when traveling in the backcountry. These groups may be unknowingly assuming a higher risk and should be targeted for avalanche education and awareness.

Dealing with the white death: avalanche risk management for traffic routes.

This article discusses mitigation strategies to protect traffic routes from snow avalanches. Up to now, mitigation of snow avalanches on many roads and railways in the Alps has relied on avalanche sheds, which require large initial investments resulting in high opportunity costs. Therefore, avalanche risk managers have increasingly adopted organizational mitigation measures such as warning systems and closure policies instead. The effectiveness of these measures is, however, greatly dependent on human decisions. In this article, we present a method for optimizing avalanche mitigation for traffic routes in terms of both their risk reduction impact and their net benefit to society. First, we introduce a generic framework for assessing avalanche risk and for quantifying the impact of mitigation. This allows for sound cost-benefit comparisons between alternative mitigation strategies. Second, we illustrate the framework with a case study from Switzerland. Our findings suggest that site-specific characteristics of avalanche paths, as well as the economic importance of a traffic route, are decisive for the choice of optimal mitigation strategies. On routes endangered by few avalanche paths with frequent avalanche occurrences, structural measures are most efficient, whereas reliance on organizational mitigation is often the most appropriate strategy on routes endangered by many paths with infrequent or fuzzy avalanche risk. Finally, keeping a traffic route open may be very important for tourism or the transport industry. Hence, local economic value may promote the use of a hybrid strategy that combines organizational and structural measures to optimize the resource allocation of avalanche risk mitigation.