Comment on egusphere-2023-646

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 Here, we analyze these (yet unpublished) sub-levels, comparing them with data representing the three contributing factors of avalanche hazard: snowpack stability, the frequency distribution of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.

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%.

Even today, the assessment of avalanche danger is by large a subjective, yet data-based decision-making process. Human experts analyze heterogeneous data volumes, diverse in scale, and conclude on the avalanche scenario based on their experience. Nowadays, modern machine learning methods and the rise in computing power in combination with physical snow cover modelling open up new possibilities for developing decision support tools for operational avalanche forecasting. Therefore, we developed a fully data-driven approach to predict the regional avalanche danger level, the key component in public avalanche forecasts, for dry-snow conditions in the Swiss Alps. Using a large data set of more than 20 years of meteorological data measured by a network of automated weather stations, which are located at the elevation of potential avalanche starting zones, and snow cover simulations driven with these input weather data, we trained two random forest (RF) classifiers. The first classifier (RF #1) was trained relying on the forecast danger levels published in the avalanche bulletin. Given the uncertainty related to a forecast danger level as a target variable, we trained a second classifier (RF #2), relying on a quality-controlled subset of danger level labels. We optimized the RF classifiers by selecting the best set of input features combining meteorological variables and features extracted from the simulated profiles. The accuracy of the danger level predictions ranged between 74 % and 76 % for RF #1, and between 72 % and 78 % for RF #2, with both models achieving better performance than previously developed methods. To assess the accuracy of the forecast, and thus the quality of our labels, we relied on nowcast assessments of avalanche danger by well-trained observers. The performance of both models was similar to the accuracy of the current experience-based Swiss avalanche forecasts (which is estimated to 76 %). The models performed consistently well throughout the Swiss Alps, thus in different climatic regions, albeit with some regional differences. A prototype model with the RF classifiers was already tested in a semi-operational setting by the Swiss avalanche warning service during the winter 2020-2021. The promising results suggest that the model may well have potential to become a valuable, supplementary decision support tool for avalanche forecasters when assessing avalanche hazard.

Abstract. Consistent estimates of avalanche size are crucial for communicating not only among avalanche practitioners but also between avalanche forecasters and the public, for instance in public avalanche forecasts. Moreover, applications such as risk management and numerical avalanche simulations rely on accurately mapped outlines of past avalanche events. Since there is not a widely applicable and objective way to measure avalanche size or to determine the outlines of an avalanche, we need to rely on human estimations. Therefore, knowing about the reliability of avalanche size estimates and avalanche outlines is essential as errors will impact applications relying on this kind of data. In the first of three user studies, we investigate the reliability in avalanche size estimates by comparing estimates for 10 avalanches made by 170 avalanche professionals working in Europe or North America. In the other two studies, both completed as pilot studies, we explore reliability in the mappings of six avalanches from oblique photographs from 10 participants and the mappings of avalanches visible on 2.9 km2 of remotely sensed imagery in four different spatial resolutions from 5 participants. We observed an average agreement of 66 % in the most frequently given avalanche size, while agreement with the avalanche size considered “correct” was 74 %. Moreover, European avalanche practitioners rated avalanches significantly larger for 8 out of 10 avalanches, compared to North Americans. Assuming that participants are equally competent in the estimation of avalanche size, we calculated a score describing the factor required to obtain the observed agreement rate between any two size estimates. This factor was 0.72 in our dataset. It can be regarded as the certainty related to a size estimate by an individual and thus provides an indication of the reliability of a label. For the outlines mapped from oblique photographs, we noted a mean overlapping proportion of 52 % for any two avalanche mappings and 60 % compared to a reference mapping. The outlines mapped from remotely sensed imagery had a mean overlapping proportion of 46 % (image resolution of 2 m) to 68 % (25 cm) between any two mappings and 64 % (2 m) to 80 % (25 cm) when compared to the reference. The presented findings demonstrate that the reliability of size estimates and of mapped avalanche outlines is limited. As these data are often used as reference data or even ground truth to validate further applications, the identified limitations and uncertainties may influence results and should be considered.

<p>Avalanche forecasting implies predicting current and future snow instability in time and space. In Switzerland, avalanche bulletins are issued daily during the winter season to warn the public about the avalanche hazard, described by region with one of five danger levels. Assessing avalanche danger is by large a data-driven, yet experience-based decision-making process. It involves analysing a multitude of data diverse in scale – time and space, and concluding by expert judgment on the avalanche scenario. Numerous statistical models were developed in the past, but rarely applied due to limited usefulness in operational forecasting. Modern machine learning techniques open up new possibilities for developing support tools for operational avalanche forecasting. With this aim, we developed a data-driven approach based on the supervised Random Forest (RF) classifier to automatically predict the danger level for dry-snow avalanche conditions in the Swiss Alps. A large database of more than 20 years of meteorological data and modelled snow stratigraphy data obtained with the numerical snow cover model SNOWPACK were used to train the RF algorithm. We optimized the model and selected the best set of input features that combine meteorological variables and features extracted from the simulated profiles, resampled at the same daily resolution as the forecasts. Our target variable was the regional danger level forecast in the public bulletin. We evaluated the predictive performance of the RF model with an independent test set with data of two winter seasons (2018-2019 and 2019-2020). The test set accuracy was 72 %, which is slightly lower than the accuracy estimate of the public forecasts (about 76 %). Given this uncertainty in our target variable, we trained an optimized RF model on a subset containing so-called verified avalanche danger levels. The test set accuracy then increased to 80 %. During the winter season 2020-2021, both RF models were tested in operational setting and automatically predicted a ‘nowcast’ and a ‘forecast’ in real-time.  In parallel, we also tested a deep recurrent neural network model, which used a 7-days time series with 3-hours time resolution as input and also predicted the avalanche danger level. We present a comparison of the performance of the three models. This is one of the first times that a data-driven approach is tested in real-time as a feasible tool for operational avalanche forecasting.</p>

Abstract. In recent years, physics-based snowpack models combined with machine-learning techniques have gained momentum in public avalanche forecasting. When integrated with spatial interpolation methods, these approaches enable fully model-driven predictions of snowpack stability or avalanche danger at any location. This raises a key question: are such spatially detailed model predictions sufficiently accurate for operational use? We evaluated the performance of three spatially interpolated model-driven forecasts of snowpack stability and avalanche danger in Switzerland over three winters. As a benchmark, we used the official public avalanche-danger forecasts, specifically focusing on the forecast danger level that includes the sub-levels. We assessed the ability of both model and human forecasts to discriminate between reference distributions of conditions – typically not associated with avalanche activity – and actual avalanche events that were either naturally released or triggered by humans by calculating event ratios as proxies for release probability. Our results show that event ratios clearly increased with higher predicted avalanche probability, lower snowpack stability, or higher forecast sub-level. Overall, both model predictions and human forecasts showed a comparable ability to discriminate between reference and event conditions, with the event ratio increasing exponentially with increasing model-predicted probabilities or forecast sub-levels. However, the human forecasts – which incorporate model output – achieved a small but statistically significant advantage in discriminatory skill. This indicates that while the models alone have not yet reached the full discriminatory power of human forecasters, their performance is already approaching operational usefulness in a setup such as that used in Switzerland. As model quality is expected to improve further in coming years, it is essential to ensure optimal integration into the operational forecasting workflow to realize the full potential of model-based support. Further research should explore how to implement this effectively, how to integrate real-time avalanche occurrence data into model prediction pipelines, and how to validate increasingly high-resolution avalanche forecasts.

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.

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Abstract. The surface energy balance is a key factor influencing the ground thermal regime. With ongoing climate change, it is crucial to understand the interactions of the individual heat fluxes at the surface and within the subsurface layers, as well as their relative impacts on the permafrost thermal regime. A unique set of high-altitude meteorological measurements was analysed to determine the energy balance at three mountain permafrost sites in the Swiss Alps (Murtèl–Corvatsch, Schilthorn and Stockhorn), where data have been collected since the late 1990s in the framework of the Swiss Permafrost Monitoring Network (PERMOS). All stations are equipped with sensors for four-component radiation, air temperature, humidity, and wind speed and direction, as well as ground temperatures and snow height. The three sites differ considerably in their surface and ground material composition, as well as their ground ice contents. The energy fluxes were calculated based on two decades of field measurements. While the determination of the radiation budget and the ground heat flux is comparatively straightforward (by the four-component radiation sensor and thermistor measurements within the boreholes), larger uncertainties exist for the determination of turbulent sensible and latent heat fluxes. Our results show that mean air temperature at Murtèl–Corvatsch (1997–2018, 2600 m a.s.l.) is −1.66 ∘C and has increased by about 0.8 ∘C during the measurement period. At the Schilthorn site (1999–2018, 2900 m a.s.l.) a mean air temperature of −2.60 ∘C with a mean increase of 1.0 ∘C was measured. The Stockhorn site (2003–2018, 3400 m a.s.l.) recorded lower air temperatures with a mean of −6.18 ∘C and an increase of 0.5 ∘C. Measured net radiation, as the most important energy input at the surface, shows substantial differences with mean values of 30.59 W m−2 for Murtèl–Corvatsch, 32.40 W m−2 for Schilthorn and 6.91 W m−2 for Stockhorn. The calculated turbulent fluxes show values of around 7 to 13 W m−2 using the Bowen ratio method and 3 to 15 W m−2 using the bulk method at all sites. Large differences are observed regarding the energy used for the melting of the snow cover: at Schilthorn a value of 8.46 W m−2, at Murtèl–Corvatsch 4.17 W m−2 and at Stockhorn 2.26 W m−2 are calculated, reflecting the differences in snow height at the three sites. In general, we found considerable differences in the energy fluxes at the different sites. These differences help to explain and interpret the causes of a warming atmosphere. We recognise a strong relation between the net radiation and the ground heat flux. Our results further demonstrate the importance of long-term monitoring to better understand the impacts of changes in the surface energy balance components on the permafrost thermal regime. The dataset presented can be used to improve permafrost modelling studies aiming at, for example, advancing knowledge about permafrost thaw processes. The data presented and described here are available for download at the following site: https://doi.org/10.13093/permos-meteo-2021-01 (Hoelzle et al., 2021).

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.

The surface energy balance is a key factor influencing the ground thermal regime. With ongoing climate change, it is crucial to understand the interactions of the individual heat fluxes at the surface and within the subsurface layers as well as their relative impacts on permafrost thermal regime. A unique set of high-altitude meteorological measurements has been analysed to determine the energy balance at three mountain permafrost sites in the Swiss Alps (Murtèl-Corvatsch, Schilthorn and Stockhorn), where data is being collected since the late 1990s in collaboration with the Swiss Permafrost Monitoring Network (PERMOS). All stations are equipped with sensors for four-component radiation, air temperature, humidity, wind speed and direction as well as ground temperatures and snow height. The three sites differ considerably in their surface and ground material composition as well as their ground ice contents. The energy fluxes are calculated based on two decades of field measurements. While the determination of the radiation budget and the ground heat flux is comparatively straightforward (by the four-component radiation sensor and thermistor measurements within the boreholes), larger uncertainties exist for the determination of turbulent sensible and latent heat fluxes. Our results show that mean air temperature at Murtèl-Corvatsch (1997–2018, 2600 m asl.) is −1.66 °C and has increased by about 0.7 °C during the measurement period. At the Schilthorn site (1999–2018, 2900 m asl.) a mean air temperature of −2.60 °C with a mean increase of 1.0 °C was measured. The Stockhorn site (2003–2018, 3400 m asl.) recorded lower air temperatures with a mean of −6.18 °C and an increase of 0.7 °C. Measured net radiation, as the most important energy input at the surface, shows substantial differences with mean values of 30.59 W m−2 for Murtèl-Corvatsch, 32.40 W m−2 for Schilthorn and 6.91 W m−2 for Stockhorn. The calculated turbulent fluxes show values of around 7 to 13 W m−2 using the Bowen ratio method and 3 to 15 W m−2 using the bulk method at all sites. Large differences are observed regarding the energy used for melting of the snow cover: at Schilthorn a value of 8.46 W m−2, at Murtèl-Corvatsch of 4.17 W m−2 and at Stockhorn of 2.26 W m−2 is calculated reflecting the differences in snow height at the three sites. In general, we found considerable differences in the energy fluxes at the different sites. These differences may help to explain and interpret the causes of the varying reactions of the permafrost thermal regime at the three sites to a warming atmosphere. We recognize a strong relation between the net radiation and the ground heat flux. Our results further demonstrate the importance of long-term monitoring in order to better understand the impacts of changes in the surface energy balance components on the permafrost thermal regime. The dataset presented can be used to improve permafrost modelling studies aiming at e.g. advancing knowledge about permafrost thaw processes. The data presented and described in this study is available for download at the following site http://dx.doi.org/10.13093/permos-meteo-2021-01 (Hoelzle et al., 2021).

Global near infrared models to predict lignin and cellulose content of pine wood were developed using 517 samples for lignin and 457 samples for cellulose. Samples came from seven different pine species, including tropical species ( Pinus caribaea, P. oocarpa, P. maximinoi, P. patula and P. tecunumanii) and temperate species ( P. radiata and P. taeda) from five different countries (Brazil, Colombia, Chile, South Africa and the USA). The global models were tested on an independent validation data set and had excellent fits for lignin [correlation coefficient ( r2)=0.97 and standard error of prediciton ( SEP) = 0.44] and good fits for cellulose ( r2 = 0.82 and SEP = 1.08). Subsets of the data were used to develop smaller multi-species, multi-site calibrations that could be tested on independent datasets containing different species not included in the calibration model. For calibrations based on four or more species, predictions from those models on independent datasets were generally good, with only slight degradation in r2 and SEP relative to the calibration R2 and SECV. The results suggest that global calibrations could be valuable in tree breeding programmes to rank species and genotypes for lignin and cellulose content. Species-specific models were developed for two species ( P. tecunumanii and P. taeda) which had sufficient numbers of observations; the global calibrations gave predictions as good as the species-specific calibrations.

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.

ABSTRACTCompositional and functional alterations to the gut microbiota during aging are hypothesized to potentially impact our health. Thus, determining aging-specific gut microbiome alterations is critical for developing microbiome-based strategies to improve health and promote longevity in the elderly. In this study, we performed a meta-analysis of publicly available 16S rRNA gene sequencing data from studies investigating the effect of aging on the gut microbiome in mice. Aging reproducibly increased gut microbial alpha diversity and shifted the microbial community structure in mice. We applied the bioinformatic tool PICRUSt2 to predict microbial metagenome function and established a random forest classifier to differentiate between microbial communities from young and old hosts and to identify aging-specific metabolic features. In independent validation data sets, this classifier achieved an area under the receiver operating characteristic curve (AUC) of 0.75 to 0.97 in differentiating microbiomes from young and old hosts. We found that 50% of the most important predicted aging-specific metabolic features were involved in carbohydrate metabolism. Furthermore, fecal short-chain fatty acid (SCFA) concentrations were significantly decreased in old mice, and the expression of the SCFA receptor Gpr41 in the colon was significantly correlated with the relative abundances of gut microbes and microbial carbohydrate metabolic pathways. In conclusion, this study identified aging-specific alterations in the composition and function of the gut microbiome and revealed a potential relationship between aging, microbial carbohydrate metabolism, fecal SCFA, and colonic Gpr41 expression.IMPORTANCE Aging-associated microbial alteration is hypothesized to play an important role in host health and longevity. However, investigations regarding specific gut microbes or microbial functional alterations associated with aging have had inconsistent results. We performed a meta-analysis across 5 independent studies to investigate the effect of aging on the gut microbiome in mice. Our analysis revealed that aging increased gut microbial alpha diversity and shifted the microbial community structure. To determine if we could reliably differentiate the gut microbiomes from young and old hosts, we established a random forest classifier based on predicted metagenome function and validated its performance against independent data sets. Alterations in microbial carbohydrate metabolism and decreased fecal short-chain fatty acid (SCFA) concentrations were key features of aging and correlated with host colonic expression of the SCFA receptor Gpr41. This study advances our understanding of the impact of aging on the gut microbiome and proposes a hypothesis that alterations in gut microbiota-derived SCFA-host GPR41 signaling are a feature of aging.

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