PICO
Most processes occurring in the snow cover depend on the vertical and horizontal distribution of its physical properties, which are primarily controlled by the microstructure of snow (e.g., density and specific surface area). In turn, snow metamorphism changes the microstructure, leading to feedback loops that affect the snow cover on coarser scales. This can have far-reaching implications for a wide range of applications, including snow hydrology and rapid runoff and flooding, weather forecasting, climate modelling, avalanche hazard forecasting, and the remote sensing of snow. The characterization of snow thus demands synergistic investigations of the hierarchy of processes across the scales, ranging from explicit microstructure-based studies to sub-grid parameterizations for unresolved processes in large-scale phenomena (e.g., albedo and drifting snow).
This session is therefore devoted to modelling and measuring snow processes across scales. The aim is to gather researchers from various disciplines to share their expertise on snow processes in seasonal and perennial snowpacks. We invite contributions ranging from “small” scales, as encountered in microstructure studies, over “intermediate” scales typically relevant for 1D snowpack models, up to “coarse” scales, that typically emerge for spatially distributed modelling over mountainous or polar snow- and ice-covered regions. Specifically, we welcome contributions reporting results from field, laboratory, and numerical studies of the physical and chemical evolution of snowpacks as well as of rain-on-snow (ROS) events rapidly altering snow properties and changing snow microstructure. We also welcome contributions reporting statistical or dynamic downscaling methods of atmospheric driving data, assimilation of in-situ and remotely sensed observations, representation of sub-grid processes in coarse-scale models, and evaluation of model performance and associated uncertainties.
PICO: Fri, 8 May, 08:30–12:30 | PICO spot 1a
In mountain ecosystems, snow is a critical resource that regulates hydrological processes, ecosystem dynamics, economical activities and downstream water availability. Accurately estimating snow at these highly heterogeneus environments remains a challenge, due to the strong spatial and temporal variability. The combination of snowdrift-permitting models and snowpack remote sensing observations can improve the accuracy of snowpack estimations across scales. The Canadian Hydrological Model (CHM) is a novel snow modeling framework that explicitly represents lateral snow transport processes over an irregular mesh. This study analyzes the impact of modelling spatial scales over three domains in the Pyrenees between 2019 and 2025 using CHM: the Izas Experimental Cathment (~10 km²), a portion of the Tena Valley (~100 km²), and a larger section of the mountain range (~1200 km²) using a snowdrift permitting model. Each domain is modelled using a different horizontal resolution, relative to the domain area, and driven by downscaled meteorological forcings. We analyze several snowpack properties, including snow covered area and snow depth, across the spatial scales, using point-scale snow survey stations, UAV-derived snow depth distribution maps at the catchment scale, Pléiades-derived snow depth maps at the valley scale and Sentinel 2 imagery at the mountain range scale. Error statistics, spatial efficiency metrics and scale breaks derived from semi variograms are used to evaluate the model performance. Preliminary results show that higher resolution simulations have a better representation of snow depth variograms and their scale breaks, and lower mean snow depth biases over the Izas catchment. However, snow depth is overestimated during the accumulation period and underestimated during the ablation season, and differences between the observed and simulated spatial snow distribution can be seen. This study improves our understanding of snowpack dynamics across spatial scales and of the horizontal resolution required for accurate snow simulations. Finally, this study enables the development of a remote sensing–based monitoring framework for the Pyrenees to improve snowpack simulation, which open new insights and allow more reliable forecasts.
How to cite: Courard, M., Marsh, C., Gouttevin, I., Merzisen, H., López Moreno, J. I., Deschamps-Berger, C., Izaguirre, E., and Revuelto, J.: Multi-scale snowpack modeling in the Pyrenees using the Canadian Hydrological Model, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18027, https://doi.org/10.5194/egusphere-egu26-18027, 2026.
Seasonal snowpack is one of the primary sources of freshwater for rivers in the Indian Himalaya. It plays a vital role in regional hydrology, climate variability, and water resource management. To understand these processes and their impact on the community, spatial and temporal monitoring of snow is essential. Snow depth is a key parameter for monitoring snow. However, in the Himalayas, due to accessibility challenges and logistical constraints, limited snow depth observations are available. To address this gap and estimate snow depth at high spatial and temporal resolution, we develop a model using polarimetric parameters derived from Sentinel-1 SAR data, topographic and auxiliary data, integrated with field-based observations in the European Alps and Grand Mesa, USA. Field observations are filtered to match the Sentinel-1 pass, ensuring consistency between field-based observations and satellite acquisition. Our model employs topographic data (e.g., elevation, slope, and aspect) from the Copernicus 30 m digital elevation model, auxiliary parameters (such as day of the season (DoS)), Forest cover fraction from MODIS, and Sentinel-1 SAR-based polarimetric parameters (cross-ratio, entropy, Stokes parameters, alpha), ensuring a topographically dependent snow depth distribution. Sensitivity analysis is performed using SHAP (SHapley Additive Explanations) to identify the most critical parameters for estimating snow depth. The model shows a Mean Absolute Error (MAE) of 0.04m, a root mean square error (RMSE) of 0.15m, with a test R-squared (R2) of 0.95 and a cross-validation correlation coefficient (R) of 0.98 in the European Alps. We transfer the model to the mountains in the Chandra Bagha basin (33°01′N°, 76°40′E) of the Indian Himalayas. Our transferred model highlights the potential of estimating snow depth in data-scarce regions while resolving the spatial and temporal details.
How to cite: Sharma, P. and Vijay, S.: Snow depth estimation model calibration and validation for high-altitude glacier valleys in the Indian Himalaya., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-738, https://doi.org/10.5194/egusphere-egu26-738, 2026.
Sublimation of snow represents an often important but poorly constrained component of the hydrological cycle, especially at the global scale. Studies that estimate snow sublimation at point or catchment scales demonstrate a range of uncertainties in the contribution of sublimation to total winter snowfall, ranging from 5% to 90%. Although it is well established that dry, windy and clear-sky conditions favor snow sublimation, a modern, global-scale assessment of the climatic controls and regions where sublimation occurs and is relevant for snowpack evolution, glacier mass balance and water resources is lacking. Existing global efforts are limited by coarse resolution (~250 km) reanalysis data, leaving a critical gap in our understanding of sublimation’s contribution to the water balance across climates and regions. Here we present a global analysis of snow surface sublimation hotspots, using ERA5-Land reanalysis at 0.1° (~10 km) resolution from 1980 to the present. Comparisons with sublimation observations from eddy-covariance flux towers demonstrate that ERA5-Land underestimates sublimation rates, but performs favorably compared to estimates from other reanalysis (GLDAS, GLEAM, MERRA-2) products. Comparisons with station observations also demonstrate that ERA5-Land correctly reproduces global patterns of seasonal snow variability.
Preliminary results show clear latitudinal, elevation and climatic controls on global surface sublimation. Hotspots of snow sublimation (>80 mm/year) are identified in the higher elevations of South America, North America and Asia, with contributions to total snow ablation ranging mostly from 10 to 20%. Hotspots of lower total annual surface sublimation (30 to 60 mm/year) lie in latitudes between 40 and 60 °N in dry climates, where the contributions to total snow ablation mostly range from 20% to 60%. The strongest surface sublimation hotspots in absolute and relative terms are identified in parts of Greenland and coastal Antarctica, where uncertainty is high as no sublimation observations from flux towers are available to compare with. We also investigate historical (1980-2025) changes in sublimation fluxes in response to warming and changing snow cover patterns.
Our results highlight regions where surface sublimation may be a significant component of the hydrological cycle, with implications for water resources, glacier mass balance and snow–atmosphere interactions. Important uncertainties remain, particularly in complex mountain regions where the resolution of ERA5-Land data may not fully capture sublimation processes such as boundary layer warming and drying. Furthermore, drifting and blowing snow sublimation are not resolved in ERA5-Land. Future efforts should refine these global estimates by using higher-resolution simulations and improved representations of snow–atmosphere interactions to identify sublimation hotspots over complex terrain.
How to cite: Fontrodona-Bach, A., Beria, H., Groshev, D., Shaw, T. E., Fyffe, C., Anglin, I., Lehning, M., and Pellicciotti, F.: Climatic controls on global snow surface sublimation based on ERA5-Land, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20575, https://doi.org/10.5194/egusphere-egu26-20575, 2026.
Snow-dominated montane watersheds provide critical ecological function, water storage, and water supply for downstream population centers across the globe. Recent literature suggests that hydrologic model uncertainty in these watersheds is largely driven by meteorological forcing uncertainty. Additionally, few models simulate lateral snow transport processes such as blowing snow and avalanche, meaning that the impact of forcing uncertainty on snowpack redistribution is unknown. This pair of limitations presents a distinct challenge for modelers in both identifying accurate model structures and identifying the drivers of simulated results. In this study, we ask how uncertainty in windspeed and precipitation forcing affects modeled lateral redistribution of snow in mountain basins. We hypothesize that windspeeds and precipitation from downscaled meteorological datasets require numerical correction for effective snow redistribution, and that the magnitude of these corrections will vary across geographic regions. Analyzing the impacts of these uncertainties allows us to determine how influential windspeed and precipitation forcings are on snow transport processes and on the spatial patterns of snow accumulation and melt dynamics.
We use the Canadian Hydrologic Model (CHM), to simulate snow accumulation and melt over five water years within a set of basins in the Sierra Nevada and Rocky Mountains in the United States that have extensive airborne lidar observations from the Airborne Snow Observatory (ASO). CHM runs over a triangular mesh with a six-layer snowpack energy balance model and lateral transport through blowing snow and avalanche. We use two climate forcing datasets with different underlying resolutions to evaluate the effects of windspeed and precipitation on modeled snowpack in mountainous terrain. ERA5-Land, a 9-km resolution dataset, is selected because its global coverage is advantageous for geographic generalizability. The CONUS404 product, a 4-km resolution dynamically downscaled dataset from ERA5 over the contiguous US, is selected to test a higher resolution product over the areas of interest. In each basin, windspeed and precipitation are perturbed to assess sensitivity and the resulting snowpack distribution.
We use observed SWE, snow cover, and derived snow disappearance date from SNOTEL, snow courses, and MODSCAG to evaluate model results using a standardized benchmarking process. This enables us to decipher whether corrections to windspeed and precipitation yield similar metrics despite different underlying redistribution processes. By evaluating models across two climatically distinct regions, we can assess whether numerical precipitation and windspeed adjustments improve snow simulations, and whether they are transferable or region-specific. We present an overview of the study and results demonstrating how uncertainty in meteorologic forcing propagates into lateral snow transport processes, which can provide guidance for improving snowpack simulations across complex mountainous terrain.
How to cite: Corrigan, R., Marshall, A., Marsh, C. B., and Wood, A. W.: Assessing the effects of uncertainty in windspeed and precipitation forcings on lateral snow redistribution in mountainous basins, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15731, https://doi.org/10.5194/egusphere-egu26-15731, 2026.
Mass and energy exchanges between the cryosphere and the atmosphere affect the state of both systems, motivating the development of two-way coupled cryosphere-atmosphere models. For snow-atmosphere models, traditional atmospheric models are coupled to multilayer physics-based snow models and run in a large-eddy mode when simulating horizontal resolutions approaching 100m. For the CRYOWRF model in particular, the atmospheric model WRF was coupled to the snow model SNOWPACK. This approach has enabled detailed studies of snow-atmosphere feedbacks such as sublimation of drifting and blowing snow. However, the high computational cost of CRYOWRF limits its application to short spatio-temporal domains at scales relevant to drifting and blowing snow (<100m). This excludes research questions such as the role that blowing snow may play as an ice nucleation particle. A prior attempt to circumvent this experimental constraint by coupling the intermediate complexity atmospheric model HICAR and the snowpack model FSM2Trans yielded promising results but showed clear shortcomings when simulating drifting and blowing snow, as well as radiation-driven spatial melt patterns. This echoes work highlighting the importance of prognostic, physics-based models of surface albedo and blowing snow schemes which include vertical advection.
These considerations lead to the development of a two-way coupling between the physics-based SNOWPACK snow model and the intermediate-complexity atmospheric model HICAR. To capture mass exchange between the snow and atmosphere, blowing and drifting snow schemes similar to those in the CRYOWRF model are implemented. The resultant 2-way coupling of SNOWPACK to HICAR yields the Snow-Cover and High-resolutioN Atmospheric Processes System (SCHNAPS). Here we detail the coupling strategy, including a revised interface for SNOWPACK. Benchmarking runs at a 50m resolution are performed, showing the fractional increase in runtime attributed to using a snow model of higher physical complexity. A preliminary validation of SCHNAPS using distributed snow height measurements is presented. The improved representation of ice physics in SNOWPACK relative to NoahMP is also shown to improve the surface energy balance over a mountain glacier. Additionally, we present a comparison of blowing and drifting snow totals between SCHNAPS and CRYOWRF, as well as HICAR coupled to the intermediate complexity snow model FSM2Trans. SCHNAPS demonstrates how different representations of snowpack processes in a coupled snow-atmosphere model impacts snowpack evolution over the course of a season. This work sets the foundation for future studies of snow-atmosphere interactions in High Mountain Asia and the Antarctic via the SnowShifts Project.
How to cite: Reynolds, D., Viaro, S., Wever, N., and Lehning, M.: Modeling with SCHNAPS: the Snow Cover and High-resolutioN Atmospheric Processes System , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12973, https://doi.org/10.5194/egusphere-egu26-12973, 2026.
Recent permafrost temperature observations show warming, likely due to the combined impacts of more snow insulation and increased air temperatures. Depth hoar refers to coarse, faceted snow crystals that form near the bottom of the snowpack due to a strong temperature gradient that induces a vapor gradient. The thin and sparse connection between depth hoar crystals results in lower snow density. The depth hoar formed in a snowpack likely enhances permafrost warming during the winter season, and the impact could be sequentially fed back to CO2 fluxes from the permafrost soil during the next growing season. However, little quantitative assessments have been made on the impact of depth hoar on permafrost temperature and the associated feedback to CO2 fluxes. To address this deficiency, we coupled the depth hoar process to the land surface model CHANGE. The model assessed the impact of the depth hoar on permafrost and the associated greenhouse gases, based on two experiments that included or excluded the depth hoar process, over the pan-Arctic scale for the period 1979–2019. The differences between the two experiments illustrated that the depth hoar induced lower snow density and the resultant warmer permafrost temperature was linked to both larger vegetation photosynthesis and decomposition of soil organic carbon. These results strongly suggest that these snow processes improvement should be included in land surface models for better simulations and future projections on the Arctic environmental changes.
How to cite: Park, H., Suzuki, K., and Fassnacht, S.: Modeling depth hoar snow and its impact on permafrost and greenhouse gas fluxes, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8497, https://doi.org/10.5194/egusphere-egu26-8497, 2026.
In the Alpine region, forests cover about 2/3 of the ground, yet surface and/or snow models designed for hydrological applications, often represent them in a very coarse way.
In the current study, we present and evaluate new developments in the physics-based ISBA/MEB-Crocus model that enables a detailed representation of snow cover and processes in interaction with an above-lying 1-layer canopy and atmosphere, and with a litter layer on top of the ground. While the canopy representation within this model demonstrated an added value for climate modeling, due to a better representation of snowpack in subarctic regions characterised by boreal forests, ISBA/MEB-Crocus failed to reproduce the observed snowpack at a mid-altitude alpine forest site, systematically overestimating the snowpack in terms of depth and duration.
With a view of correcting for these biases, we use detailed snowpack and meteorological measurements available at the Col de Porte research site in the Chartreuse massif, France, at both open and forested sites. In addition to conventional measures, indirect interception measurements and tree and soil temperatures are recorded.
The use of this dataset enables the improvement of the MEB-Crocus model for alpine forests. This enhancement is achieved through an adaptation of the interception scheme, a revision of the melt parametrization for intercepted snow, and of the unloading scheme. The meteorological forcing is also adapted to align with the top-of-canopy conditions. We demonstrate that these adjustements enable the snowpack model to replicate the observations for the Col de Porte forest site without degrading the results for Artic regions. Furthermore, we characterize the influence of the various parameters employed for the representation of the forest and their physical consistency.
This detailed, point-scale evaluation paves the way for the use of this model for distributed simulations enabling an insight into the role of snow and snow-forest interactions in the hydrological regime of mid-altitude alpine catchments.
How to cite: Pauze, T., Bouchet, A., Boone, A., Lafaysse, M., Fructus, M., Rivière, A., Yves, L., and Isabelle, G.: Improvements of a subcanopy snow model conveyed by observations from a mid-altitude alpine site, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6561, https://doi.org/10.5194/egusphere-egu26-6561, 2026.
We present a 20-year timeseries of key snow properties measured in Sodankylä, northern Finland. Systematic snow pit observations began in 2006, and the range of measured variables and instruments has expanded substantially over time. Initially, observations included snow depth, stratigraphy, grain size, and temperature, recorded twice per week at a forest opening site. Snow water equivalent (SWE) measurements were added in 2007, density profiles and liquid water content in 2009, and specific surface area (SSA) measurements in 2012. Since 2010, snow pit observations have been conducted once per week.
The monitored locations have varied over the years. A forest opening site was observed from 2006 to 2018, a wetland site from 2009 to 2015 and again from 2019 onward, and a forest site has been included since 2018. Additional snow pits were dug at Lake Orajärvi between 2009 and 2014. Currently, routine observations are carried out at two sites: a wetland and a forest.
The present snow pit protocol includes definition of stratigraphy, a temperature profile measured every 10 cm, and estimation of grain size and grain type, complemented by macrophotography of grain samples from each layer. Density measurements are performed at the surface and at 5 cm vertical intervals using a rectangular cutter. When snow is wet, liquid water content is measured with a WISe instrument at the same heights as the density samples. SSA is measured using InfraSnow for the surface and ice layers, while other layers are measured with IceCube. For thicker layers, IceCube samples are taken every 5 cm. Penetration resistance is measured with SnowScope. Finally, bulk SWE is measured using a snow tube, and snow depth is measured at three points around each pit.
This 20-year dataset provides a unique opportunity to examine long-term changes in snowpack structure and properties, and it illustrates the impacts of a changing climate in snow conditions in northern Finland.
How to cite: Leppänen, L., Kontu, A., Hannula, H.-R., Rimali, A., and Rytkönen, H.: Two decades of snow pit measurements in Sodankylä, Finland, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-951, https://doi.org/10.5194/egusphere-egu26-951, 2026.
Snow cover is a highly sensitive indicator of climate change and plays a crucial role in hydrological processes, including groundwater recharge, runoff generation, and flood dynamics. Reliable long-term information on snow cover depth, extent, duration, and variability is therefore essential for climate monitoring, hydrological modeling, and impact assessments.
This study presents a comprehensive climatology of snow cover dynamics in Germany from 1950 to the present. The analysis is based on daily snow depth observations from the dense monitoring network of the Deutscher Wetterdienst (DWD) complemented by partner networks in Germany and neighbouring countries. All station data underwent rigorous quality control and homogeneity testing. The cleaned observational dataset was then interpolated onto a regular 1 × 1 km² grid using an optimal interpolation scheme that forms an important part of the operational DWD snow-melt forecast model SNOW4.
A suite of snow-related parameters was derived, including mean and maximum snow depth, snow cover duration, onset and disappearance dates, length of the main continuous winter snowpack, timing of peak snow depth, snow cover persistence, and winter snowpack stability.
The results reveal a widespread, statistically significant decline in almost all snow-related parameters across Germany over the last seven decades. The magnitude of the negative trends is strongly elevation-dependent: while lowlands and mid-elevation regions show pronounced reductions in snow cover duration and depth, high-altitude ridge and summit areas exhibit substantially weaker or – in the highest zones – partly insignificant trends.
This new high-resolution snow climatology provides a robust, consistent dataset for hydrological applications, climate change impact studies, water resource management, and the development of future climate services in the field of snow and water resources in Central Europe.
How to cite: Drüke, M., Castino, F., Machui-Schwanitz, G., Wichura, B., Künzel, A., Fiedler, A., and Rauthe, M.: Long-term changes in snow cover dynamics across Germany (1950–present), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16310, https://doi.org/10.5194/egusphere-egu26-16310, 2026.
The Arctic is undergoing rapid environmental changes, with profound implications for snowpack dynamics, hydrology, and regional climate feedbacks. Ny-Ålesund, Svalbard (79°N), serves as an important site for documenting these changes due to its unique geographic location in the Arctic and its year-round research infrastructure. Here, we present the results of a comprehensive snowpack monitoring at Ny-Ålesund during five consecutive winter seasons (2018–2023).
Manual in-situ measurements of snow stratigraphy—including layer thickness, density, temperature, and hardness—were performed in weekly snow pits. While the region is traditionally considered as dominated by cold, shallow, and wind-affected "tundra” snow, recent winters exhibit increasing occurrences of snow characteristics not attributed to tundra snow, such as melt-freeze layers, internal ice accumulation, or wet snow. These anomalies are linked to rising temperatures, increased precipitation, and episodic winter rainfall events, which contrast sharply with the historical tundra snow regime. While the winter of 2019–2020 displayed classic tundra snow conditions, others winter seasons showed dominant maritime snow features. The statistical analysis of the observed physical snow parameters reveals a high variability of the snowpack characteristics. Such variability underscores the sensitivity of Arctic snowpack to local changes and highlights the challenges in predicting seasonal snowpack evolution. Simulating this enhanced variability will likely require snow models with enhanced capabilities.
This research emphasizes the importance of long-term, high-resolution observations in the remote Arctic. As the Arctic continues to warm, understanding these dynamics is essential for assessing broader environmental impacts, from permafrost degradation to shifts in regional water and biogeochemical cycles. The results call for sustained monitoring efforts and adaptive research strategies to address the evolving challenges posed by climate change in the Arctic.
How to cite: Jacobi, H.-W., Larose, C., and Dedieu, J.-P.: Interannual variability of the arctic snowpack: Results from long-term observations at Ny-Ålesund, Svalbard, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18196, https://doi.org/10.5194/egusphere-egu26-18196, 2026.
Accurate observation of seasonal snow depth (SD) across spatial scales remains a major challenge in mid-latitude regions, particularly over complex terrain where sub-footprint heterogeneity and scale mismatch strongly affect satellite-based retrievals. Although ICESat-2 has demonstrated high potential for SD estimation in high-latitude regions, its performance in mid-latitude areas is constrained by the limited availability of snow-free digital elevation models (DEMs) with centimeter-level vertical accuracy and by the scarcity of reliable ground-based validation due to ground-track shifting.
To address these challenges, we established a multi-scale “ground-airborne-satellite” synergistic observation framework within a controlled study area in northern Xinjiang, China. To reconcile spatial scale mismatches among the different observational platforms, UAV-LiDAR data were employed as a validated intermediate-scale bridge (RMSE = 6.03 cm against in-situ measurements). Based on this framework, we conducted an error propagation analysis to quantify ICESat-2 SD uncertainty under varying terrain conditions.
Results indicate that ICESat-2 achieves excellent accuracy over flat, open terrain (slope < 5°), with an RMSE of 6.69 cm. In contrast, over complex sub-footprint terrain combining steep slopes and artificial structures, SD deviations increased substantially, ranging from -30 to +60 cm, reflecting the strong influence of sub-footprint terrain heterogeneity on SD retrieval. Across the entire study area, ICESat-2 maintains robust overall performance, yielding a total RMSE of 15.61 cm.
This study demonstrates the feasibility of accurate ICESat-2 SD retrieval in mid-latitude regions and emphasizes the critical influence of sub-footprint terrain complexity on SD uncertainty. The proposed multi-scale observational framework provides a transferable approach for interpreting satellite-derived snow products and for improving the representation of snow processes across scales.
How to cite: zhu, L. and Zheng, L.: Monitoring Snow Depth with ICESat-2 at mid-latitudes: A Synergistic Multi-Scale Framework Integrating Ground-Airborne-Satellite Observations in Northern Xinjiang, China, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10265, https://doi.org/10.5194/egusphere-egu26-10265, 2026.
Warming ground temperatures in the Arctic raise the need to forecast permafrost thaw. Seasonal snow cover is a crucial factor for ground temperatures as it can have a warming or cooling effect on the underlying soil, depending on snow cover timing and its physical properties. Vegetation and topography modulate snow distribution and affect the snow thermal insulation. However, the formation processes and resulting properties of Arctic snowpacks are difficult to represent in snow models and in-situ data is sparse. Further understanding of the interactions between snow, vegetation and permafrost and the deduction of empirical relationships could support the parametrization of snow in permafrost modeling.
We study how the ground thermal regime is influenced by the interplay of snow, vegetation, topography and climatic conditions. In particular, we evaluate the effect of snow density variation on the ground thermal regime. We present a novel dataset that combines air, surface and soil temperature, as well as soil moisture time series recorded from September 2024 to August 2025 with end-of-season snow depth distribution and high-resolution vertical snow density profiles. Temperatures and soil moisture were monitored using 60 TOMST TMS-4 loggers, distributed across different vegetation types and topographic features in the taiga-tundra ecotone (Trail Valley Creek, Northwest Territories, Canada). Snow density profiles were measured in March 2025 next to the TOMST loggers using a SnowMicroPen.
Our data shows several characteristic snowpack types which do not only differ in depth but also have a different layering structure. Low density snowpacks with high depth hoar fractions are most prominent in forested areas that are shielded from the wind, whereas leeward slopes can accumulate thick, high-density wind slab, regardless of vegetation. While snow depth is clearly one of the major drivers of soil temperature, the role of snow density is more complex.
Categorization of different tundra vegetation types with characteristic snow conditions and specific impact on permafrost vulnerability helps to refine permafrost models and constrain predictions of permafrost thaw.
How to cite: Erkens, E., Gruenberg, I., Matthes, H., Rutter, N., and Boike, J.: How snow, vegetation and soil properties influence soil temperatures in a permafrost environment (Trail Valley Creek, Western Canadian Arctic), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22324, https://doi.org/10.5194/egusphere-egu26-22324, 2026.
At high altitudes and latitudes, snow has a large influence on hydrological processes. Large fractions of these regions are covered by forests, which have a strong influence on snow accumulation and melting processes. Trees absorb a large part of the incoming shortwave radiation and this heat load is mostly dissipated as longwave radiation. Trees shelter the snow surface from wind, so sub-canopy snowmelt depends mainly on the radiative fluxes: vegetation attenuates the transmission of shortwave radiation but enhances longwave irradiance to the surface. 13 pyranometers and 11 pyrgeometers were deployed on the snow surface below a coniferous forest at the CEN-MeteoFrance Col de Porte station in the French Alps (1325m asl) during the winters 2016-17 and 2017-18 in order to investigate spatial and temporal variabilities of solar and infrared irradiances in different meteorological conditions. Sky view factors measured with hemispherical photographs at each radiometer location ranged from 1.5 to 3.5. In clear sky conditions, the attenuation of solar radiation by the canopy reached 96% and its spatial variability exceeded 100 W.m-2. Longwave irradiance varied by 30 W.m-2 from dense canopy to gap areas. In overcast conditions, the spatial variabilities of solar and infrared irradiances were reduced and remained closely related to the sky view factor. Comparing the measurements at different radiometer locations, we investigated the dependence of surface net radiation on the overlying canopy density. Of particular interest were the atmospheric conditions that favor an offset between shortwave energy attenuation and longwave irradiance enhancement by the canopy, such that net radiation does not decrease with increasing forest density (situations of “radiation paradox”). It was found that cloud effects on the shortwave transmissivity and longwave emissivity factors of the canopy have a strong impact on the subcanopy radiation fluxes: canopy largely counteracts the effects of clouds on the incoming radiation fluxes. As a result, variations in net surface radiation due to forest cover appear to depend largely on meteorological conditions: “radiative paradox” conditions were more frequent during the winter of 2017 than in 2018, which was cloudier and colder. As a result, variations in surface net surface radiation by canopy cover appear to be largely dependent on weather conditions: “radiative paradox” conditions were more prevalent during the winter of 2017 than in 2018, which was cloudier and colder.
How to cite: Sicart, J.-E., webster, C., Lejeune, Y., Essery, R., and Rutter, N.: Spatial and Temporal Variabilities of Solar and Longwave Radiation Fluxes below a Coniferous Forest in the French Alps, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18218, https://doi.org/10.5194/egusphere-egu26-18218, 2026.
Snow is a major component of the hydrological cycle in cold environments. Snowpacks not only directly contribute to the local water cycle through snowmelt in late winter, but also constantly interact with local atmospheric water vapor through sublimation and vapor exchange throughout the winter. However, the widely used ‘traditional’ snow water isotope sampling method is destructive and temporally discrete, which limits the ability to capture the highly dynamic snow-liquid-vapor process within snowpacks. Therefore, at the Julinia site in Finland, we conducted the first winter field deployment of an innovative in-situ water isotope probe (WIP) system to sample cold and dry water vapor from snowpack layers and ambient air, where WIP was originally designed for use in trees and soils to study tree water uptake during the growing season. Water vapor sampled in-situ based on the direct vapor equilibrium method was continuously measured by a laser spectroscopy isotope analyzer (Picarro). Combined with ‘traditionally’ sampled water isotopes from event-based snowfall and snowpack layers, the temporal variation of δ18O and δ2H in different snowpack layers formed by different snowfall events illustrate the isotopic process of snowpack compaction, vapor exchange within the snowpack, and snowmelt. This approach provides an opportunity to better understand the long-overlooked isotopic difference between snowfall, snowpack, and snowmelt water, which can lead to non-negligible bias in partitioning ‘blue water’ and ‘green water’ in snow-dominated regions when using the stable water isotope techniques.
How to cite: Chen, Z., Marttila, H., and Ala-Aho, P.: In-situ high-resolution stable water isotope measurements of snowpacks in cold environments: opportunities for better understanding dynamic snowpack processes, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18697, https://doi.org/10.5194/egusphere-egu26-18697, 2026.
Under most atmospheric conditions, snowfall is triggered by the freezing of supercooled water droplets in clouds through heterogeneous nucleation on airborne particles. Among the most efficient atmospheric ice-nucleating particles, capable of inducing freezing at temperatures only a few degrees below 0 °C, are feldspar minerals. Certain feldspars are known to initiate ice nucleation very efficiently at relatively warm subzero temperatures [1], which has led to their application in snowmaking [2] and controlled freezing processes [3].
In our group, we study the properties of snow produced with the aid of feldspar ice-nucleating particles under real environmental conditions at a Snow Laboratory located in the La Molina ski resort (Spain). In this work, we present results from field studies conducted during the 2022–2023 and 2023–2024 snow seasons. The Snow Lab consists of two technically identical and independent snow guns installed 25 m apart (see Figure a). Snow was produced under varying environmental conditions. In one snow gun, only reservoir water was used, while in the second gun a feldspar powder with high ice-nucleating efficiency [4] was added to the water supply.
The volume and physical properties of the produced snow, including density and reflectivity, were systematically compared between snow generated with and without feldspar additives. Three-dimensional maps of snow volume and physical properties were constructed from a grid of field measurements. The results show that, for the same amount of water, a larger volume of snow is produced when feldspar particles are introduced. In addition, feldspar-assisted snow exhibits lower surface density and higher reflectivity, indicating a modified crystallographic evolution of ice crystals as water exits the snow gun (see an example in Figure b).
These findings not only demonstrate the potential of feldspar additives to improve the efficiency and sustainability of artificial snowmaking, but also provide valuable insight into the crystallization pathways of supercooled water droplets in the presence of mineral ice-nucleating particles in natural and engineered environments.
Figure: (a) Images of the Snow Laboratory at La Molina. (b) Example snow density maps obtained with and without the use of feldspar additives.
[1] Kanji, Z. A., Ladino, L. A., Wex, H., Boose, Y., Burkert-Kohn, M., Cziczo, D. J., and Krämer, M.: Overview of Ice Nucleating Particles, Am. Meteorol. Soc., 58, 1.1-1.33, https://doi.org/10.1175/amsmonographs-d-16-0006.1, 2017.
[2] ]. Patent: “Artificial Snow Making Method And Product For Implementing The Method “ A. Verdaguer and M. Galvin https://uspto.report/patent/app/20190323753
[3] Daily, M. I., Whale, T. F., Kilbride, P., Lamb, S., John Morris, G., Picton, H. M., and Murray, B. J.: A highly active mineral-based ice nucleating agent supports in situ cell cryopreservation in a high throughput format, J. R. Soc. Interface, 20, 20220682, https://doi.org/10.1098/rsif.2022.0682, 2023
[4] Canet, J., Rodríguez, L., Renzer, G., Alfonso, P., Bonn, M., Meister, K., Garcia-Valles, M., Verdaguer, A.: Measurement report: Ice nucleation ability of perthite feldspar powder, EGU [preprint], https://doi.org/10.5194/egusphere-2025-5014, December 2025.
How to cite: Verdaguer, A., Canet, J., and Rodríguez, L.: Field Studies of Feldspar-Assisted Snowmaking: Effects on Snow Volume, Density, and Reflectivity, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11937, https://doi.org/10.5194/egusphere-egu26-11937, 2026.
Snow density and specific surface area (SSA) are key parameters controlling snowpack stability, hydrological processes, and surface energy balance. Their accurate simulation is therefore essential for applications ranging from avalanche forecasting to climate modeling. However, these parameters are often time consuming to measure and are available at coarse vertical resolution. The SnowMicroPen (SMP) allows for high-resolution measurements of penetration force from which key microstructural parameters for instance snow density and SSA can be derived using parameterizations such as the one from [Proksch et al., 2016] or [Calonne et al., 2020]. At the Weissfluhjoch research site located in the eastern Swiss Alps at 2536 m a.s.l, daily SMP measurements have been conducted by the SLF PhD students continuously since winter 2015–2016, resulting in a unique, long-term dataset documenting the seasonal evolution of alpine snowpack at high temporal (daily) and spatial (vertical) resolution.
Here, we present and analyze ten winters (2015–2025) of daily SMP measurements, combined with complementary manual observations, i.e. bi-weekly snow profile measurements, density cutter data, snow water equivalent (SWE) profiles, IceCube SSA measurements, and automated snow and meteorological observations. Post-processing steps, including the identification and correction of sensor offset effects, were applied to ensure comparability of the derived snow properties across the full multi-year dataset. This was crucial, as the data exhibited a clear offset that showed season-dependent behavior and strongly affected derived snow properties, particularly in low density snow ranges. SMP derived snow density and SSA were then evaluated against independent reference measurements across multiple winters.
Snow density showed good agreement with cutter and SWE-derived densities, with the strongest agreement observed for SWE from the full profile and calibration-period cutter data derived by [Calonne et al., 2020]. The SMP is limited to dry-snow conditions. Larger deviations were observed for fresh snow and under warm conditions. For SSA, SMP-derived values showed systematic deviations relative to IceCube measurements, particularly at higher temperatures.
This multi year, high temporal and vertical resolution dataset provides insight into the seasonal evolution of snow stratigraphy, densification, and microstructural changes in an alpine snow. The data allows for analyzing snow layer evolution across multiple winters, and how density and SSA respond to factors such as temperature gradients and densification processes. These findings highlight the potential of the SMP to improve understanding of snow microstructure which helps to improve representations of snow in climate and snowpack models.
References
Neige Calonne, Bettina Richter, H. L¨owe, C. Cetti, J. ter Schure, A. Van Herwijnen, C. Fierz, M. Jaggi, and M. Schneebeli. The rhossa campaign: multi-resolution monitoring of the seasonal evolution of the structure and mechanical stability of an alpine snowpack. The Cryosphere, 14(6):1829–1848, 2020. doi: 10.5194/tc-14-1829-2020.
M. Proksch, N. Rutter, C. Fierz, and M. Schneebeli. Intercomparison of snow density measurements: bias, precision, and vertical resolution. The Cryosphere, 10(1):371–384, 2016. doi: 10.5194/tc-10-371-2016.
How to cite: Gaillard Festa, L., Richter, B., Mewes, L., Walter, B., and Jaggi, M.: Daily high-resolution SnowMicroPen Snow Stratigraphy measurements at a Swiss mountain site, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12118, https://doi.org/10.5194/egusphere-egu26-12118, 2026.
Dry snow microstructure refers to the complex three-dimensional arrangement of ice and air at the sub-millimeter scale. This microstructure undergoes constant shape transformations known as snow metamorphism. These transformations are driven by variations in equilibrium vapor pressure at the ice-air interface, which depend on the local curvature and temperature gradient. A key descriptor of snow microstructure is the specific surface area (SSA), which is the surface area of the ice and air interfaces normalized per ice volume or mass. This metric is commonly used to quantify the average grain size in snowpack models. Moreover, SSA affects important physical properties of the snowpack, including the spectral albedo of the surface and fluid permeability. Consequently, accurately representing SSA evolution in snowpack models is crucial. Overall, snow SSA decays over time, except in specific conditions where SSA increases, such as high temperature gradients. Current descriptions of SSA in snowpack models, such as CROCUS or SNOWPACK, are not fully satisfying, especially they fail to reproduce SSA increase. It restricts the model’s ability to represent processes under high temperature gradients, as typically occurring in Arctic regions. Recent efforts have been made to derive theoretical relations between SSA and microstructural and growth parameters, but have been applied to a limited number of snow evolution experiments.
In this work, we build upon these previous studies and investigate the physical mechanisms driving SSA evolution for numerous dry snow metamorphism scenarios. We re-derive a relationship between the SSA temporal evolution, the local interface growth velocity, and the local mean curvature. To examine the implications of this relation on different snow microstructures, we acquired 20 time series of 3D X-ray tomographic images of dry snow metamorphism at high temporal and spatial resolution during cold-lab experiments. These experiments span a wide range of thermal boundary conditions and initial snow types. Using this data set, we compute local properties on the grain surface, including interface growth velocity, mean curvature, and temperature gradients. Focusing on a subset of experiments, we present SSA evolution for temperature gradients ranging from 10 to 100 K/m. In particular, we investigate the mechanisms responsible for SSA increase at high temperature gradients. We aim to disentangle the respective contributions of local microstructural shape and local temperature gradients to the overall SSA evolution. A more comprehensive understanding of the mechanisms at stake in the SSA evolution will help develop a robust representation of SSA in snowpack models.
How to cite: Dick, O., Calonne, N., and Hagenmuller, P.: Specific surface area evolution during dry snow metamorphism: insights from interface growth velocity computed on 4D tomographic data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7829, https://doi.org/10.5194/egusphere-egu26-7829, 2026.
Recent studies suggests that drifting snow particles undergo snow metamorphism while being transported by wind, involving concurrent sublimation and vapor deposition, affecting particle size, shape, specific surface area, and isotopic composition [Walter et al., 2024; Wahl et al., 2024]. This newly identified process of airborne snow metamorphism (ASM) is particularly relevant in polar regions, where snow particles in saltation layers may be transported over long distances and durations before final deposition. As a result, this process strongly influences the microstructure of surface snow, with large scale implications for albedo and climate signals. Experimental investigations of ASM under laboratory conditions has so far been constrained by the lack of facilities providing well controlled boundary conditions.
Based on our experience with an exisiting but limitied ring wind tunnel (RWT), we developed a new wind tunnel in a cold laboratory designed to study airborne snow metamorphism under controlled flow and thermal conditions. The obround closed-circuit wind tunnel enables particle transport over long durations while maintaining stable boundary conditions. The facility is installed in a cold laboratory at the WSL Institute for Snow and Avalanche Research SLF, about 2m x 3m x 0.5m (W x L x H) in dimensions, and includes enhanced thermal control, a revised wind turbine integration reducing heating of the air, and snow surface temperature control, allowing independent regulation of air and surface temperatures.
We present a first comprehensive characterization of the flow field, including velocity distributions, spatial flow homogeneity, and turbulence properties across a range of wind speeds relevant for snow saltation and suspension. We further present a characterization of the thermal performance of the RWT, demonstrating improved temperature stability of the air and snow surface. The new ring wind tunnel provides a unique experimental facility for studying aerodynamic and thermodynamic impacts on snow particle evolution during snow transport. Generally, the new RWT facility additionally allows for studying a wide range of particle-flow and flow-surface (ice, snow, or water) interaction processes in turbulent cryospheric environments.
Walter B, Weigel H, Wahl S, Löwe H (2024) Wind tunnel experiments to quantify the effect of aeolian snow transport on the surface snow microstructure, The Cryosphere, 18, 3633-3652, https://doi.org/10.5194/tc-18-3633-2024
Wahl, S., Walter, B., Aemisegger, F., Bianchi, L., & Lehning, M. (2024). Identifying airborne snow metamorphism with stable water isotopes. Cryosphere, 18(9), 4493-4515. https://doi.org/10.5194/tc-18-4493-2024
How to cite: Walter, B., Philippe, V., and Wahl, S.: A new cold ring wind tunnel facility for studying airborne snow metamorphism, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17234, https://doi.org/10.5194/egusphere-egu26-17234, 2026.
The liquid water content (LWC) of snow is a key parameter controlling snowpack stability, runoff generation, and the timing of meltwater release (Vorkauf et al., 2021). With climate warming, rain-on-snow events and earlier snowmelt are becoming more frequent (Beniston et al., 2016), raising challenges for water management, hydropower production, flood warning, and avalanche forecasting. Despite its importance, accurate measurement of LWC in the field remains difficult. Existing methods, such as calorimetry, centrifugal separation, and dielectric sensors (Denoth et al., 1984), provide useful estimates but are limited by relatively high uncertainties (1-2% LWC) and low spatial resolution (> 3 cm). Hyperspectral imaging can resolve LWC variability at millimetre scale but is costly and impractical for routine fieldwork.
In recent years, the Snow Physics group at WSL/SLF has developed the SnowImager, a near-infrared (NIR) imaging instrument capable of capturing snow properties at high spatial resolution (Macfarlane et al., 2023). Using this instrument, we investigated the influence of liquid water on reflectance images by comparing the relative difference between a wet snow surface and its (re)frozen dry reference state. The obtained trend as a function of LWC is consistent with theoretical predictions based on a modified single scattering equation that accounts for both LWC and SSA. Building on this result, we developed a straightforward method to estimate LWC from reflectance images acquired with the SnowImager. Preliminary cold-lab and field tests confirmed the feasibility of this approach and demonstrated its potential to produce quantitative, high-resolution 2D maps of LWC.
We anticipate that the resulting 2D LWC field method will provide cryospheric researchers with a long-needed, practical, and precise tool to characterize the spatiotemporal dynamics of wet snow. This advancement will support improving wet snow avalanche forecasting, melt water runoff modelling, and climate impact assessments, while enhancing the SnowImager’s role as a versatile instrument for the international snow science community.
REFERENCES
Beniston, M., & Stoffel, M. (2016). Rain-on-snow events, floods and climate change in the Alps: Events may increase with warming up to 4 °C and decrease thereafter. Science of the Total Environment, 571, 228–236. https://doi.org/10.1016/j.scitotenv.2016.07.146
Denoth, A., Foglar, A., Weiland, P., Mätzler, C., Aebischer, H., Tiuri, M., & Sihvola, A. (1984). A comparative study of instruments for measuring the liquid water content of snow. Journal of Applied Physics, 56(7), 2154–2160. https://doi.org/10.1063/1.334215
Macfarlane, A. R., Dadic, R., Smith, M. M., Light, B., Nicolaus, M., Henna-Reetta, H., Webster, M., Linhardt, F., Hämmerle, S., & Schneebeli, M. (2023). Evolution of the microstructure and reflectance of the surface scattering layer on melting, level Arctic sea ice. Elementa: Science of the Anthropocene, 11(1), Article 00103. https://doi.org/10.1525/elementa.2022.00103
Vorkauf, M., Marty, C., Kahmen, A., et al. (2021). Past and future snowmelt trends in the Swiss Alps: The role of temperature and snowpack. Climatic Change, 165, Article 44. https://doi.org/10.1007/s10584-021-03027-x
How to cite: Philippe, V., Lombardo, M., Mewes, L., and Walter, B.: Development of a 2D high-resolution field method to measure liquid water content in snow, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12232, https://doi.org/10.5194/egusphere-egu26-12232, 2026.
The specific surface area (SSA) is a crucial parameter to characterize the microstructure of snow. It is one of the main properties controlling the optical and mechanical behavior of snow. Thus, being able to describe the evolution of SSA under the effects of metamorphism is key for detailed numerical snowpack models. This then allows simulating for example the albedo of snow-covered surfaces and its evolution over time. To this end, we propose to derive the law governing the evolution of SSA of snow directly from the physics of water vapor transport at the microstructure scale. We identify the crucial physical parameters for the evolution of the SSA. We show that the evolution of SSA is generally composed of two additive terms: an isothermal contribution and a temperature gradient contribution, each characterized by scalar macroscopic properties relating the evolution of the SSA to the temperature and temperature gradient imposed to the snow. On-going work includes parameterizing these scalar properties in order to obtain a fully-closed and operational law for the evolution of SSA.
How to cite: Fourteau, K., Braun, A., Lehning, M., and Löwe, H.: Deriving the evolution of snow specific surface area from water vapor physics at the microstructure scale, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5591, https://doi.org/10.5194/egusphere-egu26-5591, 2026.
The microstructure of snow undergoes continuous transformation in a process known as snow metamorphism. This evolving microstructure determines meso- and macroscopic optical, mechanical and thermal properties of the snowpack. Therefore, understanding the microstructural evolution on the pore scale is essential to forecast large-scale behavior.
By modeling phase transitions between ice and water vapor, we can treat fully coupled heat and mass transport on an arbitrary microstructure, allowing us to model dry snow metamorphism under temperature gradients and isothermal conditions alike. For this, a multi-phase-field model is used, by which we implicitly track the evolving microscopic ice-air interface. Compared to previous phase field models for dry snow metamorphism, a grand potential formulation is used to simplify the simulation of ice-vapor interfaces, as well as increasing the thermodynamic consistency. Thereby, we can treat various cross couplings between heat and mass transport like the Soret effect as well as surface diffusion and crystal growth dynamics. In this new model, near isothermal snow metamorphism is interpreted as sintering of ice grains. The thermodynamic properties of ice are modeled using CALPHAD data and humid air is modeled as a mixture of ideal gases.
We present our novel phase field model and validate it against semi-analytical solutions of the Stefan-problem and recently published experiments on simple geometries.
How to cite: Jentgens, H., Kaempfer, T., and Plapp, M.: Towards a Thermodynamically Consistent Phase-Field Model for Snow Metamorphism, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16509, https://doi.org/10.5194/egusphere-egu26-16509, 2026.
The mechanical behavior of snow is complex, as it depends on a variety of physical processes occurring at different scales, from the microstructure (sintering, bond breakage, etc.) to the scale of the snowpack and entire slopes. In particular, snow mechanical behavior is highly dependent on strain rate, with a ductile-to-brittle transition occurring at strain rates of about 10-4-10-3 s-1. It is important to develop comprehensive snow mechanical models accounting for this complexity for applications such as avalanche hazard evaluation, snowpack compaction or hydrological studies.
In this work, our objective is to build a continuous numerical model in a finite strain framework, that captures the key mechanical behavior of snow in a large range of strain rates. In particular, this model should be capable of properly retrieving the various deformation patterns observed in experiments, from quasi-homogeneous deformation in the ductile regime to the emergence of unstable localization patterns, such as compaction bands or cracks, typically observed in the brittle regime.
The model is based on an elasto-viscoplastic constitutive law, inspired by the Modified Cam Clay model, which is characterized by an elliptical yield surface. Two specific effects are included in the evolution of this yield surface throughout the deformation process: a hardening effect due to the compaction of the snow, and a viscous effect due to the competition between bond breakage and sintering of the microstructure. This law has been implemented in the software Matter [1] based on the Material Point Method (MPM). This method combines Lagrangian integration points and a fixed background mesh, which allows for computations of large deformations.
We performed 2D simulations of centimeter-scale samples (15mm x 15mm), undergoing uniaxial displacement-controlled compaction, at different strain rates between 1.8x10-6 and 7.5x10-3 s-1. These simulations are meant to reproduce the laboratory experiments of Bernard et al. [2], which were carried out in an X-ray microtomograph, providing reconstructions of the snow microstructure and deformation throughout the compression. Detailed comparisons between numerical and experimental results will be presented to evaluate the robustness of the numerical model.
In addition, a systematic sensitivity analysis was conducted to investigate the impact of the various physical processes considered in the constitutive law on the observed compaction patterns. Of particular interest is the role of sintering on the emergence and propagation speeds of localization bands. Finally, future adaptations of the model to investigate the propagation of instabilities in heterogeneous snowpacks will be discussed.
[1] Blatny, L. and Gaume, J.: Matter (v1): An open-source MPM solver for granular matter, Geosci. Model Dev., 18, 9149–9166, https://doi.org/10.5194/gmd-18-9149-2025, 2025.
[2] Antoine Bernard. Etude multiéchelle de la transition ductile-fragile dans la neige. Science des matériaux [cond-mat.mtrl-sci]. Université Grenoble Alpes, 2023. Français. ⟨NNT : 2023GRALI027⟩. ⟨tel-04145610⟩
How to cite: Bonnetier, M., Blatny, L., Chambon, G., Gaume, J., and Montagnat, M.: Identifying key physical processes in snow compaction at different strain rates, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12655, https://doi.org/10.5194/egusphere-egu26-12655, 2026.
Extensive and reliable ground-truth measurements of snow properties play a crucial role in environmental science to validate models and remote-sensing products. Among the available methods, optics-based approaches offer a good compromise between measurement accuracy and sufficiently large coverage.
We present the technical details of the SnowImager as well as its data products. The SnowImager is a novel, rugged yet portable field instrument that uses near-infrared (NIR) imaging to determine physical snow properties. It enables fast, accurate, and standardized retrieval of two-dimensional specific surface area (SSA) images as well as vertically resolved density profiles, both with millimetre-scale resolution. The SnowImager can be used on vertical snow profiles as well as on the surface scattering layer of sea ice. It was jointly developed by the Swiss federal institute for snow- and avalanche research SLF and Davos Instruments AG.
Providing enhanced snow microstructure characterization, the SnowImager allows better understanding of the processes influenced by the physical properties of snow as well as of their spatial variability. Examples of such fields of use include snow physics, avalanche science and forecasting, meltwater runoff modelling and water storage management, energy balance analysis in climatic models and permafrost studies and albedo observations in systems including snow or a surface scattering layer on sea ice.
How to cite: Zölly, A., Walter, B., Mewes, L.-H., Schneebeli, M., Löwe, H., and Thomi, T.: Optical Determination of Snow Microstructure Parameters with the SnowImager instrument, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13371, https://doi.org/10.5194/egusphere-egu26-13371, 2026.
Rain-on-snow (ROS) events are increasing across the Arctic as the region warms, altering snow microstructure, creating ice crusts, and impacting wildlife and surface conditions. To better document these events, we combine high-resolution passive microwave data, in situ measurements, climate reanalysis, and Inuit knowledge to assess ROS variability across the Canadian Arctic Archipelago from 1987–2019. Using both fixed and variable winter windows, and validating with meteorological stations, we detect a rise in absolute ROS occurrence, especially along coastal regions. A focused analysis on Banks Island shows significantly greater ROS-affected areas in the fall, with coastal zones experiencing the highest frequency. We find that atmospheric rivers and declining autumn sea ice both contribute to increased ROS occurrence and intensity. By integrating remote sensing with Inuit observations, we improve large-scale ROS detection and understanding of their ecological consequences, particularly for Peary caribou and the communities dependent on them.
How to cite: Langlois, A., Langlois, J.-A., Sasseville, V., and Johnson, C. A.: A decade of rain-on-snow detection in the Canadian Arctic: Insights from Remote Sensing and Inuit Knowledge, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1390, https://doi.org/10.5194/egusphere-egu26-1390, 2026.
In this study, we investigate the development of percolation columns in fine-grained snow triggered by the accumulation of liquid precipitation on a cold, dry snowpack during a rain-on-snow (ROS) event in the Southern Taiga. We analyze snow physical properties, stratigraphy, and meteorological conditions before and after the percolation event, documenting changes in snow layering and the formation of percolation columns. Furthermore, we examine how local-scale factors, such as ground surface microtopography and vegetation cover, influence the spatial distribution of these features by comparing snow properties at three adjacent sites with distinctly different surface and vegetation characteristics.
Our results demonstrate that, under certain conditions, percolation columns can form even within fine-grained, low-density snow. Their spatial distribution appears strongly influenced by ground microtopography, with preferential formation between tussocks, while the presence of deciduous vegetation may inhibit their development. Additionally, we discuss the development of preferential flow paths on the adjacent slope that formed simultaneously to the development of percolation columns on flat surfaces and describe the major morphological features we observed. These findings contribute to a deeper understanding of preferential flow in snow and highlight the need to consider localized environmental conditions and evolving climate patterns in future snow hydrology research and hazard forecasting models.
Our observations also provide valuable information for improving the representation of preferential flow processes, which remain a major source of uncertainty in snow models. The distinct vertical icy features associated with percolation columns are also likely to affect radar signal penetration and backscatter, with potential implications for the interpretation of remote sensing observations. Moreover, the fact that such features can be identified from above, for example using drone imagery, offers opportunities for model evaluation and spatial validation under natural conditions.
How to cite: Komarov, A. and Stroeve, J.: Development of Percolation Features After a Rain-on-Snow Event in the Southern Taiga , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4478, https://doi.org/10.5194/egusphere-egu26-4478, 2026.
Climate change alters precipitation patterns and extends the warm season in Arctic and sub-Arctic regions, with direct consequences for river flow dynamics. Snow Water Equivalent (SWE) provides a critical link between climate forcing and streamflow response, as it represents the portion of the snowpack that is released as runoff. However, temporal analysis of SWE is challenged by the discontinuous nature of observations provided by the Finnish Environment Institute (SYKE). In this study, a degree-day model was used to generate daily SWE time series, which were subsequently corrected using observed data, for four non-regulated Finnish catchments. River flow timing was analyzed relative to snowmelt onset over the period 1982–2024. While no clear trend was identified in the calendar-day occurrence of spring peak discharge, analysis relative to snowmelt onset revealed a consistent shift toward later peak flow, indicating an increasing delay between melt initiation and maximum discharge. Temperature analysis during the snowmelt period showed a significant increasing trend, suggesting warmer melt-season conditions that promote intensified melt but also modify the timing of runoff generation. In addition, precipitation analysis indicated an increasing tendency toward rain-on-snow events, as well as a rising frequency of rainfall occurring between maximum SWE and peak discharge. These results indicate a potential shift from predominantly snowmelt-driven to increasingly rain-driven peak flow.
How to cite: Edraki, M. and Torabi Haghighi, A.: Changes in Snowmelt Timing and Peak Flow Generation in Non-Regulated Finnish Catchments, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6663, https://doi.org/10.5194/egusphere-egu26-6663, 2026.
Rain-on-snow (ROS) events are an increasingly prevalent Arctic extreme weather phenomenon, driven by accelerated atmospheric warming. These events create ice layers within the snowpack, which can prevent foraging for ungulates like reindeers, caribou and muskoxen and have been linked to catastrophic herd die-offs. Accurately simulating physical consequences of ROS, specifically development of these ice crusts, is therefore critical for assessing wildlife habitat suitability. However, the performance of detailed snow models in high-latitude environments remains inadequately evaluated, particularly their ability to replicate the snowpack stratigraphy following complex meteorological events.
This study investigates the capacity of the snow model Crocus-SVS2 to simulate the impacts of known, major ROS events on the snowpack of Banks Island, Nunavut. We focus on a case study where a documented ROS event was followed by a severe muskoxen mortality event in the winter of 2003-2004. Our methodology forces Crocus-SVS2 with three meteorological reanalysis datasets: the Canadian Surface Reanalysis version 2.1 (CaSR2.1) and 3.1 (CaSR3.1), and ERA5 reanalysis. This multi-forcing approach allows to assess not only the model's physical fidelity but also the sensitivity of the simulations to different weather inputs, thereby evaluating the ability of reanalysis products to represent ROS in the Arctic accurately.
Model outputs are analysed to determine if Crocus-SVS2 can successfully replicate the formation, thickness, and vertical position of observed ice lenses within the snow profile. The primary outcome is a robust evaluation of whether an operational snow model, when driven by the best available meteorological data, can serve as a reliable tool for retrospectively analysing ROS impacts in data-sparse Arctic regions. This research also provides a framework to identify key meteorological conditions that separate minor ROS events from those causing catastrophic ungulate die-offs.
How to cite: Boisvert-Vigneault, É., Sandells, M., Vionnet, V., Leroux, N., Rutter, N., Langlois, A., and Bloomfield, H.: Snow Modelling Locked Pastures from Rain-on-Snow Events in the Arctic, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2816, https://doi.org/10.5194/egusphere-egu26-2816, 2026.
The Svalbard Archipelago has experienced rapid warming in recent decades, leading to an increased frequency and intensity of Rain-on-Snow (ROS) events. While the physical and ecological impacts of ROS in the Arctic are well documented, their potential role in influencing the atmospheric fate of emerging contaminants remains largely unexplored. This study examines the chemical signature of four ROS events observed during the 2023–24 field campaign in Ny-Ålesund (Kongsfjorden, Svalbard, Norway), with particular attention to the behaviour of emerging pollutants before, during, and after each event. By integrating aerosol and wet deposition measurements with meteorological parameters and air-mass back-trajectory analyses, we assess the capacity of ROS events to act as removal processes for benzothiazole derivatives, tris(2-carboxyethyl)phosphine (TCEP) used as a flame retardant, pesticides, and haloacetic acids. Our results reveal marked variability in contaminant patterns across events, indicating a strong influence of synoptic-scale air mass origins and local meteorological conditions. Diagnostic ratios and inorganic ion tracers further provide insights into potential atmospheric transformation pathways and transport mechanisms. This study presents the first detailed chemical characterisation of aerosols and depositions associated with Rain-on-Snow events, offering a preliminary framework to better understand the interactions between ROS processes and contaminant cycling in a rapidly warming Arctic. This work contributes to ongoing efforts to elucidate atmospheric scavenging mechanisms under changing climate conditions.
How to cite: Spagnesi, A., Gilardoni, S., Salzano, R., Feltracco, M., Ulgelmo, B., Maetzke, R., Ardini, F., Grotti, M., Coppolaro, V., Viglezio, T., Montaguti, S., Scoto, F., Spolaor, A., Gambaro, A., Barbante, C., and Barbaro, E.: Emerging contaminants during Arctic Rain-On-Snow events: insights from the 2023-24 Ny-Ålesund campaign, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5841, https://doi.org/10.5194/egusphere-egu26-5841, 2026.
In an era characterized by urban densification and increasing pressures on urban space, along with the costs and availability of construction materials, the optimal design of infrastructure has become a critical focus. Furthermore, cold climate regions are experiencing the impacts of climate change, which manifest in altered precipitation patterns, resulting in more extreme storm events, including rain-on-snow events and increased freeze-thaw cycles. According to Maurin et al. (2024), rain-on-snow events have been identified as the leading cause of the highest observed runoff from green roofs, presenting significant challenges for urban areas in preventing flooding.
The present study aims to enhance understanding of the effects of climate variability and change on the hydrological performance of nature-based infrastructure, with a particular emphasis on green roofs during winter, especially in relation to snow and rain-on-snow events in cold climate regions. The goal is to develop guidelines that assist stakeholders in optimizing the design of nature-based solutions (NBS) infrastructure, ensuring they are resilient over time and effectively manage stormwater in a changing climate. This initiative addresses the current gap in research, particularly the lack of location-specific regulations that incorporate future climate projections for stormwater infrastructure design, giving decision-makers accurate information regarding the requirements for long-term and robust infrastructure design.
The study uses models of six different green and grey roof configurations developed in the SFI Klima 2050 project, calibrated for the winter season. These models utilize precipitation and temperature time series originated from high-resolution, convection-permitting climate models with hourly resolution and a 3x3 km gridded projection. Simulations for winter event separation (Melt, Rain and Rain-on-snow) are conducted following the methodology outlined in Maurin et al. (2024).
Results indicate that the changing climate will influence stormwater management strategies during winter, including higher runoffs of urban infrastructure due to rain-on-snow event with effects unevenly distributed across Norway (9 different cities studied). This pinpoints the need to combine the local future climate with hydrological models able to capture rain-on-snow events when planning and designing stormwater managements solutions that must remain effective under future climate scenarios. The findings have laid the groundwork for local guidelines aimed at ensuring climate-resilient design of nature-based infrastructure.
Maurin, N., Abdalla, E.H.M., Muthanna, T.M., Sivertsen, E., 2024. Understanding the hydrological performance of green and grey roofs during winter in cold climate regions. Science of The Total Environment 945, 174132. https://doi.org/10.1016/j.scitotenv.2024.174132
How to cite: Maurin, N., Abdalla, E. M. H., Landgren, O., and Sivertsen, E.: Assessing green roof hydrological performance during winter and rain-on-snow events under climate variability and change using high-resolution convection-permitting climate models, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22740, https://doi.org/10.5194/egusphere-egu26-22740, 2026.
