WSL Institute for Snow and Avalanche Research SLF

Plain Language Summary Powder snow avalanches (PSAs) generate low‐frequency sound waves below the threshold of human hearing, called infrasound. Infrasound can travel long distances, which allows for simple and effective avalanche monitoring. However, it is unclear where and how infrasound is produced, limiting the use of infrasound detection systems for avalanche risk management. To address this, we analyze data from a large naturally occurring PSA. We match the activity of suspended snow particles in the airborne layers, captured by a high‐speed camera, with the recorded infrasound. We show that infrasound may originate from particle clusters carried by turbulent eddies or expelled from the denser regions of the PSA. Using radar measurements, we map the spatial distribution of these clusters, revealing an infrasound source extending hundreds of meters behind the avalanche front. Furthermore, we find a connection between infrasound and the kinetic energy of suspended particles, offering a new approach to assess the destructive potential of PSAs. These findings deepen our understanding of infrasound generation during avalanches, providing crucial information for avalanche detection, early warning systems, and understanding the mechanisms behind PSA generation. Moreover, the findings could also be valuable for understanding and managing similar types of gravitational flows, such as pyroclastic surges during volcanic eruptions.

The climate in Continental Chile is marked by strong latitudinal and elevation gradients, exacerbated by diverse geographical features, such as the Andes. Despite previous studies projecting warmer and dryer conditions for most of the territory, there is concern about the robustness (i.e. level of agreement among models) of changes projected for its magnitude, not only for the impact on climate indices across this domain but also to identify changes in the spatial distribution of climate classes. Hence, we statistically downscaled and bias-corrected daily CMIP6 model outputs for continental Chile, using a multivariate bias correction method, to project climate changes under the SSP5-8.5 scenario. The results reveal that General Circulation Models (GCMs) project increased dryness across the study domain by the end of the 21st century, especially in Central Chile (−30% in precipitation), with notable sensitivities of precipitation projections to the implementation of bias correction methods in the northern and austral macrozones. Temperature projections show less dispersion, with higher increments in northern Chile and the Andes (4 ∘C–5 ∘C). Notable shifts in the extension of Köppen–Geiger climate classes are projected for the next decades, with the expansion of deserts in northern Chile and the prevalence of temperate climates with dry summers in central Chile. The Andes subdomain is expected to face the most dramatic changes in Köppen–Geiger classes (inter-model agreement >70%). Surprisingly, despite the large spread in GCM projections, there is high agreement among models regarding spatial changes in climate classes. Additionally, our results project drastic reductions in snowfall across the Andes, with higher freezing level heights that may exacerbate flooding and landslide risk across the country.

Plain Language Summary This study addresses the challenge of accurately computing the amount of water stored in snow (known as snow water equivalent or SWE) in mountainous areas, which is important for managing water resources. Typically, there are no tools that can measure SWE across large areas in complex high alpine surroundings, only at specific points. However, at Mt. Zugspitze at the border of Germany and Austria, a special device called a superconducting gravimeter can detect changes in gravity caused by the snow, providing a way to estimate SWE over large areas. We used data from this gravimeter to test two versions of a snow model called Alpine3D. In the first version, the model relied only on weather station data. In the second version, we improved the model by using satellite images to adjust the amount and spatial distribution of precipitation (snowfall) in the model. The results showed that the model gets more accurate by using satellite data to predict SWE changes during the melting season. This finding suggests that satellite images could be a useful tool for analyzing SWE in mountainous regions with limited infrastructure.

Episodic deposition of light absorbing impurities on glaciers reduces albedo and exacerbates snow melt. In 2019/2020 a devastating Australian bushfire and desert dust event combined with favorable meteorological conditions transported an unprecedented mass of impurities across the Tasman Sea turning the Southern Alps of Aotearoa New Zealand red. Here we use time lapse cameras, airmass back trajectories, snow impurity geochemistry, and remote sensing to quantify the timing, provenance, and mass deposition of the event. Deposited in late November 2019, the impurities were dominated by mineral dust with a distinct southeastern Australian geochemical fingerprint. The event deposited ∼4,500 ± 500 tons of red dust to Southern Alps permanent snow and ice with a mean dust mass concentration of 6.5 ± 0.7 g m⁻². A southeast Australian desert dust storm generated by the same type of meteorological conditions as the 2020 New Year bushfires was the main driver of the glacier discoloration.

Mountain permafrost, constituting 30% of the global permafrost area, is sensitive to climate change and strongly impacts mountain ecosystems and communities. This study examines 21st century permafrost warming in European mountains using decadal ground temperature data from sixty-four boreholes in the Alps, Scandinavia, Iceland, Sierra Nevada and Svalbard. During 2013–2022, warming rates at 10 metres depth exceed 1 °C dec⁻¹ in cases, generally surpassing previous estimates because of accelerated warming and the use of a comprehensive data set. Substantial permafrost warming occurred at cold and ice-poor bedrock sites at high elevations and latitudes, at rates comparable to surface air temperature increase. In contrast, latent heat effects in ice-rich ground near 0 °C reduce warming rates and mask important changes of mountain permafrost substrates. The warming patterns observed are consistent across all sites, depths and time periods. For the coming decades, the propagation of permafrost warming to greater depths is largely predetermined already.

Snow plays a crucial role in the water balance of mountainous regions by affecting the timing and magnitude of runoff and, thus, water availability and flood hazards. However, estimating snow water equivalent (SWE) in mountainous regions is challenging due to its substantial spatial variability, the lack of accurate distributed measurements, and the uncertainties of snow models. Model uncertainties are primarily bound to uncertainties in the meteorological forcings. This study proposes an assimilation scheme to identify and correct spatiotemporal error patterns in the meteorological forcing data. Using a particle filter, we assimilated in situ snow depth observations from 444 stations across Switzerland into an ensemble simulated by the multi‐layer, physics‐based snow model FSM2OSHD. The ensemble is created by applying traceable, fixed perturbations to the energy input and the amount and phase of precipitation. This allows us to identify and correct errors in the meteorological forcing data for each station site and each 3‐day assimilation window. Leveraging spatial correlation in these errors, we distribute the corrections across the entire model domain using a weighted three‐dimensional spatial interpolation method. The refined meteorological data then serve as forcing for improved model runs, allowing unobserved grid points to benefit from the point assimilation. A leave‐one‐station‐out cross‐validation shows marked improvements in root‐mean‐squared error and bias for estimates of snow depth and SWE over the entire elevation range and multiple winter seasons. The proposed scheme is a promising step in developing comprehensive data assimilation solutions for large‐scale, fully distributed, near real‐time snow modeling applications, taking into account operational constraints and practical considerations.

Due to insufficient climate action over the past decade, it is increasingly likely that 1.5 °C of global warming will be exceeded – at least temporarily – in the 21st century. Such a temporary temperature overshoot carries additional climate risks which are poorly understood. Earth System Model climate projections are only available for a very limited number of overshoot pathways, thereby preventing comprehensive analysis of their impacts. Here, we address this issue by presenting a novel dataset of spatially resolved emulated annual temperature projections for different overshoot pathways. The dataset was created using the FaIR and MESMER emulators. First, FaIR was employed to translate ten different emission scenarios, including seven that are characterised by overshoot, into a large ensemble of forced global mean temperatures. These global mean temperatures were then converted into stochastic ensembles of local annual temperature fields using MESMER. To ensure an optimal tradeoff between accurate characterization of the ensemble spread and storage requirements for large ensembles, this procedure was accompanied by testing the sensitivity of sample quantiles to different ensemble sizes. The resulting dataset offers the unique opportunity to study local and regional climate change impacts of a range of overshoot scenarios, including the timing and magnitude of temperature thresholds exceedance.

Electrical resistivity tomography (ERT) is widely used to map, characterize, and monitor the ground in alpine and periglacial environments, where coarse blocky surfaces are often ubiquitous. ERT measurements typically use conventional steel electrodes in combination with a water‐soaked sponge. However, ensuring an optimum contact resistance between the electrodes and the ground to obtain high‐quality data is often challenging and requires considerable logistical, physical, and time commitment. To overcome this, we tested a promising sand‐filled, fist‐sized conductive textile electrode. We conducted ERT measurements using both steel and textile electrodes at a landslide and two rock glaciers in the European Alps with coarse blocky surfaces and performed statistical analyses to test the accuracy and precision of the proposed textile electrodes. Our results show that the textile electrodes can be used as an alternative to the conventional steel electrodes without limitations, as they ensure good galvanic contact with the ground and accurate resistivity measurements. The use of textile electrodes also resulted in lower contact resistances, less time invested, physical and logistical advantages, and reduced risk of injury. In the future, this will enable applications such as enhanced ERT monitoring and/or faster (quasi‐3D) imaging of the interior of entire landforms.

The observed temperature record, which combines sea surface temperatures with near-surface air temperatures over land, is crucial for understanding climate variability and change1–4. However, early records of global mean surface temperature are uncertain owing to changes in measurement technology and practice, partial documentation5–8, and incomplete spatial coverage⁹. Here we show that existing estimates of ocean temperatures in the early twentieth century (1900–1930) are too cold, based on independent statistical reconstructions of the global mean surface temperature from either ocean or land data. The ocean-based reconstruction is on average about 0.26 °C colder than the land-based one, despite very high agreement in all other periods. The ocean cold anomaly is unforced, and internal variability in climate models cannot explain the observed land–ocean discrepancy. Several lines of evidence based on attribution, timescale analysis, coastal grid cells and palaeoclimate data support the argument of a substantial cold bias in the observed global sea-surface-temperature record in the early twentieth century. Although estimates of global warming since the mid-nineteenth century are not affected, correcting the ocean cold bias would result in a more modest early-twentieth-century warming trend¹⁰, a lower estimate of decadal-scale variability inferred from the instrumental record³, and better agreement between simulated and observed warming than existing datasets suggest².

Spatially compounding drought events affect multiple locations simultaneously, severely affecting food, water, energy, human health, and infrastructure sectors. Despite the cascading impacts and challenges compound droughts impose on society, we still lack an in‐depth understanding of spatially connected drought occurrences. Given the complexity and costs of droughts in Brazil, identifying regions prone to co‐experiencing droughts is critical for developing effective adaptation measures. Here, we develop a novel framework to assess the spatial co‐occurrence of hydrological drought events, which can be adapted for global applications to evaluate spatially compounding drought. This framework involves extracting drought data from individual catchments and calculating the co‐occurrence of droughts across all catchments. We apply this method to investigate the spatial connectedness of droughts in 511 Brazilian catchments over 39 years (1983–2022). Additionally, we classify catchments based on drought duration, intensity, deficit, number of events, and spatial connectedness to identify regions with similar drought behavior. Our findings reveal significant variability in drought characteristics and connectedness across Brazil, with the Central‐Northeast and Northwest Amazon regions being most affected by multiple and widespread droughts. We identify five distinct regions in Brazil that exhibit common drought behaviors, sharing attributes such as aridity, catchment area, and precipitation seasonality. These regions hold the potential to guide future adaptation plans for managing hydrological compound extremes at both the catchment and regional scales, including the development of risk pool networks. Our results underscore the importance of considering the interactions of spatially compounding hydrological droughts in risk assessments and adaptation strategies.

The near-surface boundary layer over patchy snow is highly heterogeneous and dynamic. Layers of opposing stability coexist within only a few horizontal meters. Conventional experimental methods to investigate this layer suffer from limitations related to the fixed positions of eddy covariance sensors. To overcome these difficulties, we set up a centimeter-resolution large eddy simulation of flow across an idealised transition from bare ground to snow. We force the simulation with high-frequency eddy covariance data recorded during a field campaign. We show that the model can represent the real flow by comparing it to independent eddy covariance data. However, the simulation underestimates vertical wind speed fluctuations, especially at high frequencies. Sensitivity analyses show that this is influenced by grid resolution and surface roughness representation but not much by subgrid-scale parameterization. Nevertheless, the model can reproduce the experimentally observed plumes of warm air intermittently detaching from bare ground and being advected over snow. This process is highly dynamic, with time scales of only a few seconds. We can show that the growth of a stable internal boundary layer adjacent to the snow surface can be approximated by a power law. With low wind speeds, deeper stable layers develop, while strong wind speeds limit the growth. Even close to the surface, the buoyancy fluxes are heterogeneous and driven by terrain variations, which also induce the frequent decoupling of a thin layer adjacent to the snow surface. Our simulations point the path towards generalizing point-based and aerial measurements to three dimensions.

Water temperature extremes can pose serious threats to the aquatic ecosystems of mountain rivers. These rivers are influenced by snow and glaciermelt, which change with climate. As a result, the frequency and severity of water temperature extremes may change. While previous studies have documented changes in non‐extreme water temperature, it is yet unclear how extreme water temperatures change in a warming climate and how their hydro‐meteorological drivers differ from those of non‐extremes. This study aims to assess temporal changes and spatial variability in water temperature extremes and enhance our understanding of the driving processes across European mountain rivers in the current climate, at both a regional and continental scale. First, we describe the characteristics of extreme events and explore their relationships with catchment characteristics. Second, we assess trends in water temperature extremes and compare them with trends in mean water temperature. Third, we use random forest models to identify the main driving processes of water temperature extremes. Last, we conduct a co‐occurrence analysis to examine the relationship between water temperature extremes and hydro‐climatic extremes. Our results show that mean water temperature has increased by +0.38±0.14 ${+}0.38\pm 0.14$°C per decade, leading to more extreme events at high elevations in spring and summer. While non‐extreme water temperatures are mainly driven by air temperature, water temperature extremes are also importantly influenced by soil moisture, baseflow, and meltwater. Our study highlights the complexity of water temperature dynamics in mountain rivers at the regional and continental scale, especially during water temperature extremes.

The rapid melting of glaciers and thawing of permafrost in mountainous regions have heightened the danger of rock‐ice avalanches. These avalanches pose a severe threat due to their potential to transform into water‐saturated debris flows. The catastrophic event in Chamoli, India, on 7 February 2021, illustrates the devastating consequences of such processes. Developing a model capable of predicting the dynamics and extent of these events is imperative for natural hazard science and disaster mitigation. In response, we propose a depth‐averaged rock‐ice avalanche model encompassing four distinct materials: rock, ice, snow, and water. The model integrates crucial physical processes, including frictional heating, phase changes, ground material entrainment, and air‐blast hazards. Through a system of mass and momentum balance equations extended with grain flow and internal energy equations, the model captures heat exchanges and resulting phase changes as the fragmented material flows. Focusing on identifying the primary water source in the flow and testing the model on the 2021 Chamoli event, we quantify water's influence on flow dynamics and regime transitions. However, uncertainties persist in heat transfer physics and quantifying the hydro‐meteorological state of the flow path. Our thermo‐mechanical model enables the simulation of complex avalanches and identifies key flow transitions: powder cloud formation and potential debris flow transformation. The study underscores the pivotal role of water in avalanche dynamics and the challenge of accurately quantifying water content within the flow, necessitating comprehensive ground assessments for effective disaster management.

The dynamic behaviour of granular media can be observed widely in nature and in many industrial processes. Yet, the modelling of such media remains challenging as they may act with solid-like and fluid-like properties depending on the rate of the flow and can display a varying apparent friction, cohesion and compressibility. Over the last two decades, the μ(I)-rheology has become well established for modelling granular liquids in a fluid mechanics framework where the apparent friction μ depends on the inertial number I. In the geo-mechanics community, modelling the deformation of granular solids typically relies on concepts from critical state soil mechanics. Along the lines of recent attempts to combine critical state and the μ(I)-rheology, we develop a continuum model based on modified cam-clay in an elastoplastic framework which recovers the μ(I)-rheology under flow. This model permits a treatment of plastic compressibility in systems with or without cohesion, where the cohesion is assumed to be the result of persistent inter-granular attractive forces. Implemented in a two-and three-dimensional material point method, it allows for the trivial treatment of the free surface. The proposed model approximately reproduces analytical solutions of steady-state cohesionless flow and is further compared with previous cohesive and cohesionless experiments. In particular, satisfactory agreements with several experiments of granular collapse are demonstrated, albeit with shear bands which can affect the smoothness of the surface. Finally, the model is able to qualitatively reproduce the multiple † Email address for correspondence: lars.blatny@slf.ch Published online by Cambridge University Press L. Blatny, J.M.N.T. Gray and J. Gaume steady-state solutions of granular flow recently observed in experiments of flow over obstacles.

The maximum amount of water rivers can transport before flooding is known as the bankfull discharge, an essential threshold for flood risk and biogeochemical cycles. Current Global Flood Models rely on an untested assumption of a spatially-invariant, 2-year bankfull recurrence. Here, based on observations and machine learning, we deliver the first global estimation of bankfull discharge in different climates along a new bifurcating river network at ~ 1 km spatial resolution. In contrast to the 2-year assumption, we find rivers flood more frequently in tropical and temperate regions (median return periods of 1.5 and 1.8 years; IQR 2.5 and 3.2y, respectively), and less frequently in cold and arid regions (2.8/4.3 years; IQR 4.8/6.0y). Relative to observations, the 2-year assumption overestimates bankfull discharge in the tropics (54%±78%, mean ± std) and underestimates it in arid regions (10%±51%). This new understanding will transform our ability to make accurate global flood predictions.

Human land-use intensification threatens arthropod (for example, insect and spider) biodiversity across aquatic and terrestrial ecosystems. Insects and spiders play critical roles in ecosystems by accumulating and synthesizing organic nutrients such as polyunsaturated fatty acids (PUFAs). However, links between biodiversity and nutrient content of insect and spider communities have yet to be quantified. We relate insect and spider richness to biomass and PUFA-mass from stream and terrestrial communities encompassing nine land uses. PUFA-mass and biomass relate positively to biodiversity across ecosystems. In terrestrial systems, human-dominated areas have lower biomass and PUFA-mass than more natural areas, even at equivalent levels of richness. Aquatic ecosystems have consistently higher PUFA-mass than terrestrial ecosystems. Our findings reinforce the importance of conserving biodiversity and highlight the distinctive benefits of aquatic biodiversity.

The lack of accurate information on the snow water equivalent (SWE) including its spatio-temporal variations in mountain catchments remains a key problem in snow hydrology and water resources management. This is partly because there is no sensor to measure SWE beyond local scale. At Mt. Zugspitze, Germany, a superconducting gravimeter senses the gravity effect of the seasonal snow, reflecting the temporal evolution of SWE in a few kilometer scale radius. We used this new observation to evaluate two configurations of the Alpine3D distributed snow model. In the default run, the model was forced with meteorological station data. In the second run, we applied precipitation correction based on an 8 m resolution snow depth image derived from satellite observations. The snow depth image strongly improved the simulation of the snowpack gravity effect during the melt season. This result suggests that satellite observations can enhance SWE analyses in mountains with limited infrastructure.