The snow depth (SD) is an excellent indicator of climate, yet a poorly monitored variable in many mountain ranges. A novel integrated approach is proposed for optimal monitoring of SD dynamics in the 5 National Parks located in Alpine (NPA) zones of Spain (i.e., Sierra Nevada, Guadarrama, Picos de Europa, Ordesa y Monte Perdido, and Aigüestortes i Estany de Sant Maurici). It will leverage the existing infrastructure of snow poles installed by the Snow Monitoring National Program in Spain (ERHIN). This program obtains SD measurements by direct observation from helicopter flights (1-3 per year). This monitoring activity has been drastically reduced in some mountain ranges during the economic crisis. The objective of this current work is to avoiding potential gaps in the valuable long-term SD timeseries of the pole measurements. An innovative Citizen Science Activity (CSA) methodology is being implemented to engage volunteers to collect the maximum number of photos of the snow poles. It is designed as a sports challenge, in which ranking and awards will be given to the most active participants. It aims to enhance the project with a minimum economic cost, and has the additional objective of raising awareness and encouraging responsible visits to these NPA. It has been tested in Sierra Nevada National Park, where we have identified the necessity to combine the information obtained from this CSA with other approaches to maximize the amount of useful information collected, and in order to reduce the uncertainty in snow distribution.

A number of automatic point sensors have been installed to collect additional snow depth data at snow poles with a high number of days with snow, as identified from a historical analyses of snow cover area (SCA). These locations also have higher uncertainty SD measurements, and thus far, there have been less opportunity for the citizen science collection of photos. In order to identify the most relevant snow poles, we have used a regression model that estimates the spatial distribution of snow depth and its uncertainty from snow cover area and snow depth data. since the high cost of this complementary monitoring actions needs to be considered. a multi-sensors experiment is being performed to identify the best cost-benefit automatic sensors (ultrasound sensors, time-lapse cameras, etc). Drone field campaigns will be also performed, together with distributed information from airborne LIDAR and high resolution Pléiades satellite imagery. Such field campaigns there are costly, and thus the CSA has been also extended to the other 4 NPA. We are using a variety of media (e.g., social networks, TV, radio, and newspapers) to disseminate and communicate the CSA activity in order to maximize participation.

Acknowledgements:
This research has been partially supported by the projects: STAGES-IPCC (TED2021-130744B-C21), SIGLO-PRO (PID2021-128021OB-I00), from the Spanish Ministry of Science, Innovation and Universities, SER-PM (2908/22) from the National Park Research Program, RISKYEARTH (Recovery funds), and SIERRA-CC (PID2022-137623OA-I00) funded by MICIU/AEI/10.13039/501100011033 and by FEDER, UE.

How to cite: Pulido-Velazquez, D., Collados_Lara, A., Sánchez, P., Baena-Ruiz, L., Pardo-Iguzquiza, E., Lorenzo-Carnicero, C., García-Davalillo, J. C., Carcavilla, L., Fassnatch, S., Herrero, J., Hidalgo, J. D., Cruz Gallegos, V., Gomez Gomez, J. D. D., Meléndez, M. L., Heredia, N., Lopez-Moreno, I., Revuelto, J., Flynn, H., Romero, A., de la Hera Portillo, Á., Jódar, J., and Diaz-Losada, E.: Monitoring snow depth by Integrating in an optimal way citizen science and other techniques, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20320, https://doi.org/10.5194/egusphere-egu24-20320, 2024.

To maintain computational efficiency and avoid adding too many uncertainties into Land Surface Models (LSMs) with fine-scale parameterization, many efforts have been made to improve sub-grid representations of heterogeneous landscapes. HydroBlocks LSM stands out as a model that employs advanced hierarchical clustering methods, utilizing field-scale satellite-derived data to construct sub-grid tiles or clusters. The Noah-MP land surface model is applied within each tile. Unlike conventional tiling approaches, knowing the spatial location of the clusters provides the opportunity to incorporate the interactions across the distinct clusters. Presently, they interact through the subsurface flow processes. Despite the comprehensiveness of these models, both Noah-MP and HydroBlocks lack consideration for the wind-induced snow transport which plays a pivotal role in snow-related hazards. Other than that, the sublimation and redistribution of wind-blown snow in exposed environments contributes significantly to variations in snow depth. It not only exerts local influence on surface water and energy balance, but also can have expansive impact since the snowmelt is critical for the water availability of downstream basins. To address this limitation, we propose the integration of a blowing snow module into HydroBlocks. This module, inspired by the Prairie Blowing Snow Model, consists of physical-based wind transport and sublimation algorithms. Clusters will be categorized into source and sink regions considering their topography and vegetation. The redistribution of snow mass at every timestep will be calculated based on the wind condition and the adjacent borders between clusters. This research seeks to pave the way for modeling other mass transport processes between tiles which considers the complex interactions within heterogeneous landscapes.

How to cite: Cai, J. and Chaney, N.: Integrating a Blowing Snow Module for Enhanced Representation of Snow Dynamics and Surface in the HydroBlocks modeling framework, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13274, https://doi.org/10.5194/egusphere-egu24-13274, 2024.

How to cite: Engbers, R., González-Herrero, S., Wever, N., Gerber, F., and Lehning, M.: Spatiotemporal variability of turbulent fluxes over snow in mountain regions , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8976, https://doi.org/10.5194/egusphere-egu24-8976, 2024.

Snow precipitation frequently occurs under moderate to strong wind conditions, resulting in drifting and blowing snow. Processes like particle fragmentation and airborne metamorphism during snow transport result in microstructural modifications of the ultimately deposited snow. Despite the relevance (optically and mechanically) of surface snow for alpine and polar environments, this effect of wind on the snow microstructure remains poorly understood and quantified. Available descriptions of snow densification due to wind are exclusively derived from field measurements where conditions are difficult to control. Information on the effect of wind on the specific surface area (SSA) is basically nonexistent. The goal of this experimental study was to systematically quantify the influence of wind on the surface snow density and SSA for various atmospheric conditions (temperature, wind speed, precipitation intensity), and to identify the relevant processes. 

We conducted experiments in a cold laboratory using a closed-circuit ring wind tunnel with an infinite fetch to investigate wind-induced microstructure modifications under controlled atmospheric, flow and snow conditions. Artificially produced dendritic nature-identical snow was manually poured into the ring wind tunnel for simulating precipitation during the experiments. Airborne snow particles are characterized by high-speed imaging, and deposited snow is characterized by density and SSA measurements resulting in a comprehensive dataset.

            The high-speed images support a snow particle transport scheme in the saltation layer similar to natural conditions. We measured an increase of the densification rate with increasing wind speed which differs from available model parameterizations. The SSA was found to decrease under the influence of wind, while increasing wind velocities intensified the SSA decrease. For higher air temperatures (T_a > -5°C), both the densification and SSA rates significantly differ from the rather constant rates at lower temperatures. We attribute this to the effects of enhanced cohesion or sintering (density) and intensified airborne snow metamorphism (SSA) at higher air temperatures. A sensitivity experiment revealed a strong influence of airborne snow metamorphism on the SSA decrease. Our results provide a first step towards an improved understanding and modeling of the effect of aeolian snow transport on optically and mechanically relevant microstructural properties of surface snow.

How to cite: Walter, B., Wahl, S., Weigel, H., and Löwe, H.: Wind tunnel experiments to characterize snow densification and SSA reduction caused by aeolian snow transport, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15202, https://doi.org/10.5194/egusphere-egu24-15202, 2024.

How to cite: Wahl, S., Walter, B., Aemisegger, F., Bianchi, L., and Lehning, M.: Aeolian snow transport induces airborne snow metamorphism with implications for snowpack physical properties, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6861, https://doi.org/10.5194/egusphere-egu24-6861, 2024.

Mass loss of snow packs due to recrystallization processes and subsequent vapor fluxes are inherently difficult to measure experimentally. Present numerical advances enable new simulation tools to explore the otherwise invisible mass fluxes due to diffusive and convective water vapor transport. In this study we calculate the effective vapor fluxes as a function of the local mass transfer coefficient, snow depth, and a range of microstructure parameters given by porosity and specific surface area. A set of flow, heat transport and vapor transport equations re developed. Heat transport is characterized by the Rayleigh number while vapor transport depends on the Péclet and Damkhöler numbers. The latter measures the relative importance of vapor transfer to advective fluxes. For low Rayleigh numbers, the system behaves in a purely diffusive manner. however, convective transport mechanisms dominate for high Rayleigh values. Convection is found to enhance vapor transport. This is in agreement with previously unexplained mass losses in field observations. The effect of vapor mass transfer between the solid and gas phase is also analyzed. The results can be used for macroscale snow pack models to predict large scale mass loss due to sublimation for snow covered areas such as Antarctica, Greenland and seasonally covered Tundra.

How to cite: Hidalgo, J. J. and Krol, Q.: Effective vapor transport in snow: The role of convection and the local mass transfer coefficient, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3827, https://doi.org/10.5194/egusphere-egu24-3827, 2024.

While the complex nonlinear rheology of ice is well known and often discussed, for example in the context of large-scale ice sheet modeling, calving, and isotropy occurring at shear margins, the rheology of firn is often considered to be rather simple. According to Truesdell’s first metaphysical principle, which states that ”all properties of a mixture must be mathematical consequences of properties of the constituents” (Truesdell, C. (1984), Rational Thermodynamics, Springer-Verlag, p. 221), the material behavior of firn should be related to that of ice, since firn is primarily a mixture of ice and air. What distinguishes firn from ice is its microstructure. The field of continuum mechanics provides methods to relate the microstructural properties of a material to its macroscopic material behavior.

Here we review a homogenization method developed for the densification of nonlinear creeping metallic powders and first applied to the simulation of firn densification by Gagliardini and Meyssonnier (1997, Annals of Glaciology, 24, pp. 242–248). The method links the rheology of ice to that of firn by describing firn as a porous medium with an ice matrix. The advantage of this approach is that it is formulated in all three spatial dimensions, allowing the inclusion of horizontal divergence due to ice flow without additional parameterization. A large database of dated firn cores allows the determination of the governing model parameters using an optimization approach. We discuss the results, advantages, and limitations of this approach, as well as validation strategies.

How to cite: Schultz, T., Humbert, A., and Müller, R.: A continuum mechanics perspective on the rheology of firn in the context of firn densification, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19751, https://doi.org/10.5194/egusphere-egu24-19751, 2024.

Mountain snowpack serves as a vital water source for both high-altitude regions and adjacent lowlands, significantly impacting local economies through its influence on tourism, communication, logistics, and recreational risks. However, mid-elevation snow cover is diminishing due to climate change (IPCC-2021), emerging as a critical concern in water management. Despite its importance, a lack of comprehensive understanding stems from a scarcity of well-distributed mountain snowpack observations and specific simulation tools. This knowledge gap is more pronounced in Mediterranean mountainous regions, where intricate processes of growth and ablation, high spatial variability, and a high inter-annual variability pose obstacles for models. To address these challenges, hyper-high resolution models (<1 km) have been developed, but they often come with significant computational expenses. As an alternative, SnowCast has been introduced, which nests ERA5 atmospheric reanalysis (ECMWF), the Intermediate Atmospheric Research model (ICAR, NCAR), and the Flexible Snow Model (FSM2, University of Edinburgh), incorporating custom parametrizations and high-resolution topographic forcing models. This approach enables highly parallelized computations, enhancing the efficiency of simulating multiple years. This capability allows the application of such resolution for climate studies while managing computational costs effectively. Validation through extensive fieldwork, automated snowpack monitoring, and satellite imagery shows that the model provides a realistic temporal and spatial representation of snow cover. In-depth analysis of model performance will be presented, along with discussions on potential new processes for implementation, exploration of additional validation techniques, and future prospects for coupling with a hydrological model.

How to cite: González Cervera, Á. and Durán, L.: SnowCast: Hyper-high resolution downscaling model. Snowpack simulation in a mountainous region in Central Spain (Peñalara Massif), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15828, https://doi.org/10.5194/egusphere-egu24-15828, 2024.

The snowpack plays a fundamental role in regulating the global climate thanks to its high albedo and thermal insulation properties, and for many regions of the world it also has very local and important impacts. Indeed, the snow is an important water reservoir, storing the water in solid state during cold months, and releasing it in liquid state during warmer months. But the snow is also the necessary condition for the development of rural places which base their economy on winter sports. However, a certain risk is always associated with snow when it deposits on the ground, since the snow can slide down, creating avalanches which may cause several damages to the local flora, fauna, buildings and infrastructures. Typically, the conditions that allow the occurrence of snow avalanches span from the point scale to the slope scale, and depend on the snowpack properties. Kilometer-resolution numerical models are not able to reproduce the slope-scale variability of the snowpack properties because of the complex interaction between the atmospheric flows and the topography at finer scale. To address this limitation, we apply several algorithms to downscale 1 km horizontal resolution WRF atmospheric simulations to 500 m horizontal resolution in order to force the snow cover model Alpine3D with more representative weather data. Additionally, we train a fully convolutional neural network to downscale 10 km resolution IMERG precipitation data to 1 km horizontal resolution, further downscaled to 500 m. Furthermore, 2m temperature point observations are interpolated at 500 m resolution using geostatistical techniques. Finally, we force Alpine3D with a combination of forecasted and observed data, obtaining improved simulation results compared to using only forecasted weather data. This implies that the use of a combination of simulated and observed weather data is particularly promising for the estimation of the snowpack properties at slope-scale resolution in regions characterized by complex topography, providing more reliable information for risk mitigation, and sustainable development of snow-prone areas.

How to cite: Raparelli, E. and Tuccella, P.: Improving snowpack simulation at slope-scale resolution with machine learning and geostatistical downscaling of observed and forecasted weather data., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15809, https://doi.org/10.5194/egusphere-egu24-15809, 2024.

Accurate characterization of surface meteorological distributions over coastal areas and complex terrain, especially the relationship between temperature and altitude, is essential for the accurate simulation of snowpack dynamics. This becomes increasingly difficult at spatial resolutions smaller than common gridded meteorological forcing datasets due to the sparsity of long-term temperature measurements and the influence of local factors like cool air pooling and inversions. Near-surface air temperatures (T_a) are often assumed to decrease with elevation at a constant rate of 6.5^oC km^-1, which could lead to large model errors in snow evolution and other processes key to snow hydrology, water resource management, and other applications. This study evaluates the impact of local dynamical adjustments to downscaled T_a on snow simulations over two coastal mountainous terrains using the Noah-MultiParameterization (NoahMP) land surface model. Forcings are derived from remote sensing and reanalysis precipitation products and the (Modern-Era Retrospective Analysis for Research and Applications, version 2) MERRA-2 atmospheric products (including T_a) at the downscaled 1-km resolution. Hourly lapse rates at each grid cell are calculated by applying linear regression to T_a and elevation from surrounding grid cells (within one grid lengths in the x or y direction) at the T_a native MERRA-2 resolution and applied to the downscaled 1-km T_a product. We will present the impact on simulated snow water equivalent, snow cover, and snow depth across simulations forced with the downscaled T_a (1) without lapse rate correction, (2) corrected with a constant lapse rate (6.5^oC km^-1), and (3) corrected with the dynamic hourly lapse rate. Results will be compared against remote sensing-based products.

How to cite: Whitney, K., Kumar, S., Bolten, J., Pflug, J., Maina, F., Hain, C., Mocko, D., and Wrzesien, M.: Quantifying the Impact of Dynamic Lapse Regimes on Spatially-Distributed Snow Simulations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12854, https://doi.org/10.5194/egusphere-egu24-12854, 2024.