Snowpack Model Research Articles

The sensitivity of modeled snow accumulation and melt to precipitation phase methods across a climatic gradient

Abstract. A critical component of hydrologic modeling in cold and temperate regions is partitioning precipitation into snow and rain, yet little is known about how uncertainty in precipitation phase propagates into variability in simulated snow accumulation and melt. Given the wide variety of methods for distinguishing between snow and rain, it is imperative to evaluate the sensitivity of snowpack model output to precipitation phase determination methods, especially considering the potential of snow-to-rain shifts associated with climate warming to fundamentally change the hydrology of snow-dominated areas. To address these needs we quantified the sensitivity of simulated snow accumulation and melt to rain–snow partitioning methods at sites in the western United States using the SNOWPACK model without the canopy module activated. The methods in this study included different permutations of air, wet bulb and dew point temperature thresholds, air temperature ranges, and binary logistic regression models. Compared to observations of snow depth and snow water equivalent (SWE), the binary logistic regression models produced the lowest mean biases, while high and low air temperature thresholds tended to overpredict and underpredict snow accumulation, respectively. Relative differences between the minimum and maximum annual snowfall fractions predicted by the different methods sometimes exceeded 100 % at elevations less than 2000 m in the Oregon Cascades and California's Sierra Nevada. This led to ranges in annual peak SWE typically greater than 200 mm, exceeding 400 mm in certain years. At the warmer sites, ranges in snowmelt timing predicted by the different methods were generally larger than 2 weeks, while ranges in snow cover duration approached 1 month and greater. Conversely, the three coldest sites in this work were relatively insensitive to the choice of a precipitation phase method, with average ranges in annual snowfall fraction, peak SWE, snowmelt timing, and snow cover duration of less than 18 %, 62 mm, 10 d, and 15 d, respectively. Average ranges in snowmelt rate were typically less than 4 mm d−1 and exhibited a small relationship to seasonal climate. Overall, sites with a greater proportion of precipitation falling at air temperatures between 0 and 4 ∘C exhibited the greatest sensitivity to method selection, suggesting that the identification and use of an optimal precipitation phase method is most important at the warmer fringes of the seasonal snow zone.

Impact of GPS Processing on the Estimation of Snow Water Equivalent Using Refracted GPS Signals

Global navigation satellite system (GNSS) antennas buried underneath a snowpack have a high potential for in situ snow water equivalent (SWE) estimation. Automated and continuous SWE quantification independent of weather conditions could enhance snow hydrological monitoring and modeling. Accurate and reliable in situ data are needed for the calibration and validation of remote sensing data and snowpack modeling. A relative bias of less than 5% is achieved using sub-snow global positioning system (GPS) antennas (GPS refractometry) during a three full seasons time period in the Swiss Alps. A systematic overview regarding the temporal reliability of the sub-snow GPS derived results is, however, missing for this emerging technique. Moreover, GPS processing impacts the results significantly. Different GPS processing parameters are therefore selected and their influence on the SWE estimation is investigated. The impact of elevation-dependent weighting, the elevation cutoff angles, and the time intervals for SWE estimation are systematically assessed. The best results are achieved using all observations with an elevation-dependent weighting scheme. Moreover, the SWE estimation performance is equally accurate for hourly SWE estimation as for lower temporal resolutions up to daily estimates. The impact of snow on the coordinate solution is furthermore evaluated. While the east and north components are not systematically influenced by the overlying snowpack, the vertical component exhibits a significant variation and strongly depends on the SWE. The biased vertical component therefore provides an additional possibility to estimate SWE.

Sub-kilometer Precipitation Datasets for Snowpack and Glacier Modeling in Alpine Terrain

apturing the spatial and temporal variability of precipitation at fine scale is necessary for high-resolution modeling of snowpack and glacier mass balance in alpine terrain. In this study, we assess the impact of three sub-kilometer precipitation datasets on distributed simulations of snowpack and glacier mass balance with the detailed snowpack model Crocus for winter 2011-2012. The different precipitation datasets at 500-m grid spacing over the northern and central French Alps are coming from (i) the SAFRAN reanalysis specially developed for alpine terrain interpolated at 500-m grid spacing, (ii) the numerical weather prediction (NWP) system AROME at 2.5-km resolution downscaled with a precipitation-elevation adjustment factor and (iii) a version of AROME at 500-m grid spacing. The spatial patterns of seasonal snowfall are first analyzed for the different precipitation datasets. Large differences between SAFRAN and the two versions of AROME are found at high-altitude and in regions of strong orographic precipitation enhancement. Results of Crocus snowpack simulations are then evaluated against (i) point measurements of snow depth, (ii) maps of snow covered areas retrieved from optical satellite data (MODIS) and (iii) field measurements of winter accumulation of six glaciers. The two versions of AROME lead to an overestimation of snow depth and snow-covered area, which are substantially improved by SAFRAN. However, all the precipitation datasets lead to an underestimation of snow depth increase at the daily scale and cumulated over the season, with AROME 500 m providing the best performances at the seasonal scale. The low correlation found between the biases in snow depth and in cumulated snow depth increase illustrates that total snow depth has a limited significance for the evaluation of precipitation datasets. Measurements of glacier winter mass balance showed a systematic underestimation of high-elevation snow accumulation with SAFRAN. The two versions of AROME overestimate the winter mass balance at four glaciers and produce nearly unbiased estimation for two of them. Our study illustrates the need for improvements in the precipitation field from high-resolution NWP systems for snow and glacier modeling in alpine terrain.

Climate controls on snow reliability in French Alps ski resorts

Ski tourism is a major sector of mountain regions economy, which is under the threat of long-term climate change. Snow management, and in particular grooming and artificial snowmaking, has become a routine component of ski resort operations, holding potential for counteracting the detrimental effect of natural snow decline. However, conventional snowmaking can only operate under specific meteorological conditions. Whether snowmaking is a relevant adaptation measure under future climate change is a widely debated issue in mountainous regions, with major implications on the supply side of this tourism industry. This often lacks comprehensive scientific studies for informing public and private decisions in this sector. Here we show how climate change influences the operating conditions of one of the main ski tourism markets worldwide, the French Alps. Our study addresses snow reliability in 129 ski resorts in the French Alps in the 21st century, using a dedicated snowpack model explicitly accounting for grooming and snowmaking driven by a large ensemble of adjusted and downscaled regional climate projections, and using a geospatial model of ski resorts organization. A 45% snowmaking fractional coverage, representative of the infrastructures in the early 2020s, is projected to improve snow reliability over grooming-only snow conditions, both during the reference period 1986–2005 and below 2 °C global warming since pre-industrial. Beyond 3 °C of global warming, with 45% snowmaking coverage, snow conditions would become frequently unreliable and induce higher water requirements.

Uncertainties in Snowpack Simulations—Assessing the Impact of Model Structure, Parameter Choice, and Forcing Data Error on Point‐Scale Energy Balance Snow Model Performance

AbstractIn this study, we assess the impact of forcing data errors, model structure, and parameter choices on 1‐D snow simulations simultaneously within a global variance‐based sensitivity analysis framework. This approach allows inclusion of interaction effects, drawing a more representative picture of the resulting sensitivities. We utilize all combinations of a multiphysics snowpack model to mirror the influence of model structure. Uncertainty ranges of model parameters and input data are extracted from the literature. We evaluate a suite of 230,000 model realizations at the snow monitoring station Kühtai (Tyrol, Austria, 1,920 m above sea level) against snow water equivalent observations. The results show throughout the course of 25 winter seasons (1991–2015) and different model performance criteria a large influence of forcing data uncertainty and its interactions on the model performance. Mean interannual total sensitivity indices are in the general order of parameter choice < model structure < forcing error, with precipitation, air temperature, and the radiative forcings controlling the variance during the accumulation period and air temperature and longwave irradiance controlling the variance during the ablation period, respectively. Model skill is highly sensitive to the snowpack liquid water transport scheme throughout the whole winter period and to albedo representation during the ablation period. We found a sufficiently long evaluation period (>10 years) is required for robust averaging. A considerable interaction effect was revealed, indicating that an improvement in the knowledge (i.e., reduction of uncertainty) of one factor alone might not necessarily improve model results.

Estimating alpine snow depth by combining multifrequency passive radiance observations with ensemble snowpack modeling

This paper presents a physically-based snow depth retrieval algorithm adapted for deep mountainous snowpack and airborne multifrequency (10.7, 18.7, 37.0 and 89.0 GHz) passive microwave (PM) radiance observations from a single flight. The algorithm employs a single forecast-analysis cycle of a traditional sequential assimilation scheme. It uses an ensemble of multi-layer snowpack model runs to resolve snow microstructure and melt-refreeze crusts, and microwave radiative transfer models to relate snow properties to microwave measurements. Snow depth was retrieved at a 120 m spatial resolution over three 1 km2 Intensive Study Areas (ISA) within the Rabbit Ears Meso-Cell Study Area (MSA) from the NASA Cold Land Processes Experiment (CLPX) in Colorado (United States) for one date in February 2003. When evaluated against in situ observations, root mean square error (RMSE) of the snow depth from the assimilation was 13.3 cm for areas with low (<5%) forest cover, which was a reduction of 48% in the RMSE compared with the modeled snow depth when the PM observations were not assimilated, indicating a ~5% relative error of the posterior snow depth with respect to the average snow depth (200 cm) measured at these pixels. For pixels with forest cover ranging from 5 to 15% and 15–30%, results were improved (R2 increased from 0.65 to 0.71 and from 0 to 0.38, respectively) by introducing a forest radiative transfer model during the assimilation.

Flow Analysis at the Snow Covered High Altitude Catchment via Distributed Energy Balance Modeling

Energy budget-based distributed modeling at high-altitude glacio-nival watersheds is essential to accurately describe hydrological processes and quantify the flow rates. In this study, SNOWPACK model and its distributed version Alpine3D are applied for the first time in Pakistan to simulate the runoff response of a high altitude glaciated catchment. The basic aim was to explore the feasibility of this modeling system and its future applications in the region. Final results demonstrated satisfactory performance of the model between measured and modeled discharges with Nash-Sutcliff Efficiency of 0.54. However, total simulated flow volume differs only 1.3 times as compared to measured discharge of the lake, located at the glacier snout. Flow composition analysis revealed that the runoff regime of the study site is strongly influenced by the snow and glacier melt runoff representing 53% snowmelt and 38% glacier melt contribution. Low model efficiency has been observed during glacier melting season due to inaccurate wind speed distribution and biased input met-data. It is concluded that high performance of this model can be achieved if the model is optimized over the catchment similar to the study site provided with long term data sets. This study leaves a firm foundation for the potential application of a highly accurate distributed energy balance model in the entire Karakoram and Himalaya region to understand the melt dynamics of such a rugged terrain glacier rich mountains.

Climate, snow, and soil moisture data set for the Tuolumne and Merced river watersheds, California, USA

Abstract. We present hourly climate data to force land surface process models and assessments over the Merced and Tuolumne watersheds in the Sierra Nevada, California, for the water year 2010–2014 period. Climate data (38 stations) include temperature and humidity (23), precipitation (13), solar radiation (8), and wind speed and direction (8), spanning an elevation range of 333 to 2987 m. Each data set contains raw data as obtained from the source (Level 0), data that are serially continuous with noise and nonphysical points removed (Level 1), and, where possible, data that are gap filled using linear interpolation or regression with a nearby station record (Level 2). All stations chosen for this data set were known or documented to be regularly maintained and components checked and calibrated during the period. Additional time-series data included are available snow water equivalent records from automated stations (8) and manual snow courses (22), as well as distributed snow depth and co-located soil moisture measurements (2–6) from four locations spanning the rain–snow transition zone in the center of the domain. Spatial data layers pertinent to snowpack modeling in this data set are basin polygons and 100 m resolution rasters of elevation, vegetation type, forest canopy cover, tree height, transmissivity, and extinction coefficient. All data are available from online data repositories (https://doi.org/10.6071/M3FH3D).

57 years (1960–2017) of snow and meteorological observations from a mid-altitude mountain site (Col de Porte, France, 1325 m of altitude)

Abstract. In this paper, we introduce and provide access to daily (1960–2017) and hourly (1993–2017) datasets of snow and meteorological data measured at the Col de Porte site, 1325 m a.s.l., Chartreuse, France. Site metadata and ancillary measurements such as soil properties and masks of the incident solar radiation are also provided. Weekly snow profiles are made available from September 1993 to March 2018. A detailed study of the uncertainties originating from both measurement errors and spatial variability within the measurement site is provided for several variables. We show that the estimates of the ratio of diffuse-to-total shortwave broadband irradiance is affected by an uncertainty of ±0.21 (no unit). The estimated root mean square deviation, which mainly represents spatial variability, is ±10 cm for snow depth, ±25 kg m−2 for the water equivalent of snow cover (SWE), and ±1 K for soil temperature (±0.4 K during the snow season). The daily dataset can be used to quantify the effect of climate change at this site, with a decrease of the mean snow depth (1 December to 30 April) of 39 cm from the 1960–1990 period to the 1990–2017 period (40 % of the mean snow depth for 1960–1990) and an increase in temperature of +0.90 K for the same periods. Finally, we show that the daily and hourly datasets are useful and appropriate for driving and evaluating a snowpack model over such a long period. The data are placed on the repository of the Observatoire des Sciences de l'Univers de Grenoble (OSUG) data centre: https://doi.org/10.17178/CRYOBSCLIM.CDP.2018.

Evaluation of Sub-Kilometric Numerical Simulations of C-Band Radar Backscatter over the French Alps against Sentinel-1 Observations

This study compares numerical simulations and observations of C-band radar backscatter in a wide region (2300 km 2 ) in the Northern French Alps. Numerical simulations were performed using a model chain composed of the SAFRAN meteorological reanalysis, the Crocus snowpack model and the radiative transfer model Microwave Emission Model for Layered Snowpacks (MEMLS3&amp;a), operating at a spatial resolution of 250-m. The simulations, without any bias correction, were evaluated against 141 Sentinel-1 synthetic aperture radar observation scenes with a resolution of 20 m over three snow seasons from October 2014 to June 2017. Results show that there is good agreement between observations and simulations under snow-free or dry snow conditions, consistent with the fact that dry snow is almost transparent at C-band. Under wet snow conditions, although the changes in time and space are well correlated, there is a significant deviation, up to 5 dB, between observations and simulations. The reasons for these discrepancies were explored, including a sensitivity analysis on the impact of the liquid water percolation scheme in Crocus. This study demonstrates the feasibility of performing end-to-end simulations of radar backscatter over extended geographical region. This makes it possible to envision data assimilation of radar data into snowpack models in the future, pending that deviations are mitigated, either through bias corrections or improved physical modeling of both snow properties and corresponding radar backscatter.

Assimilation of Sentinel-2 Data into a Snowpack Model in the High Atlas of Morocco

The snow melt from the High Atlas is a critical water resource in Morocco. In spite of its importance, monitoring the spatio-temporal evolution of key snow cover properties like the snow water equivalent remains challenging due to the lack of in situ measurements at high elevation. Since 2015, the Sentinel-2 mission provides high spatial resolution images with a 5 day revisit time, which offers new opportunities to characterize snow cover distribution in mountain regions. Here we present a new data assimilation scheme to estimate the state of the snowpack without in situ data. The model was forced using MERRA-2 data and a particle filter was developed to dynamically reduce the biases in temperature and precipitation using Sentinel-2 observations of the snow cover area. The assimilation scheme was implemented using SnowModel, a distributed energy-balance snowpack model and tested in a pilot catchment in the High Atlas. The study period covers 2015–2016 snow season which corresponds to the first operational year of Sentinel-2A, therefore the full revisit capacity was not yet achieved. Yet, we show that the data assimilation led to a better agreement with independent observations of the snow height at an automatic weather station and the snow cover extent from MODIS. The performance of the data assimilation scheme should benefit from the continuous improvements of MERRA-2 reanalysis and the full revisit capacity of Sentinel-2.

Cold-to-warm flow regime transition in snow avalanches

Abstract. Large avalanches usually encounter different snow conditions along their track. When they release as slab avalanches comprising cold snow, they can subsequently develop into powder snow avalanches entraining snow as they move down the mountain. Typically, this entrained snow will be cold (T‾&lt;-1 ∘C) at high elevations near the surface, but warm (T‾&gt;-1 ∘C) at lower elevations or deeper in the snowpack. The intake of warm snow is believed to be of major importance to increase the temperature of the snow composition in the avalanche and eventually cause a flow regime transition. Measurements of flow regime transitions are performed at the Vallée de la Sionne avalanche test site in Switzerland using two different radar systems. The data are then combined with snow temperatures calculated with the snow cover model SNOWPACK. We define transitions as complete when the deposit at runout is characterized only by warm snow or as partial if there is a warm flow regime, but the farthest deposit is characterized by cold snow. We introduce a transition index Ft, based on the runout of cold and warm flow regimes, as a measure to quantify the transition type. Finally, we parameterize the snow cover temperature along the avalanche track by the altitude Hs, which represents the point where the average temperature of the uppermost 0.5 m changes from cold to warm. We find that Ft is related to the snow cover properties, i.e. approximately proportional to Hs. Thus, the flow regime in the runout area and the type of transition can be predicted by knowing the snow cover temperature distribution. We find that, if Hs is more than 500 m above the valley floor for the path geometry of Vallée de la Sionne, entrainment of warm surface snow leads to a complete flow regime transition and the runout area is reached by only warm flow regimes. Such knowledge is of great importance since the impact pressure and the effectiveness of protection measures are greatly dependent on the flow regime.

Observation and modelling of snow at a polygonal tundra permafrost site: spatial variability and thermal implications

Abstract. The shortage of information on snow properties in high latitudes places a major limitation on permafrost and more generally climate modelling. A dedicated field program was therefore carried out to investigate snow properties and their spatial variability at a polygonal tundra permafrost site. Notably, snow samples were analysed for surface-normal thermal conductivity (Keff−z) based on X-ray microtomography. Also, the detailed snow model SNOWPACK was adapted to these Arctic conditions to enable relevant simulations of the ground thermal regime. Finally, the sensitivity of soil temperatures to snow spatial variability was analysed. Within a typical tundra snowpack composed of depth hoar overlain by wind slabs, depth hoar samples were found more conductive (Keff-z=0.22±0.05 W m−1 K−1) than in most previously published studies, which could be explained by their high density and microstructural anisotropy. Spatial variations in the thermal properties of the snowpack were well explained by the microtopography and ground surface conditions of the polygonal tundra, which control depth hoar growth and snow accumulation. Our adaptations to SNOWPACK, phenomenologically taking into account the effects of wind compaction, basal vegetation, and water vapour flux, yielded realistic density and Keff−z profiles that greatly improved simulations of the ground thermal regime. Also, a density- and anisotropy-based parameterization for Keff−z lead to further slight improvements. Soil temperatures were found to be particularly sensitive to snow conditions during the early winter and polar night, highlighting the need for improved snow characterization and modelling over this period.

Assimilation of passive microwave AMSR-2 satellite observations in a snowpack evolution model over northeastern Canada

Abstract. Over northeastern Canada, the amount of water stored in a snowpack, estimated by its snow water equivalent (SWE) amount, is a key variable for hydrological applications. The limited number of weather stations driving snowpack models over large and remote northern areas generates great uncertainty in SWE evolution. A data assimilation (DA) scheme was developed to improve SWE estimates by updating meteorological forcing data and snowpack states with passive microwave (PMW) satellite observations and without using any surface-based data. In this DA experiment, a particle filter with a Sequential Importance Resampling algorithm (SIR) was applied and an inflation technique of the observation error matrix was developed to avoid ensemble degeneracy. Advanced Microwave Scanning Radiometer 2 (AMSR-2) brightness temperature (TB) observations were assimilated into a chain of models composed of the Crocus multilayer snowpack model and radiative transfer models. The microwave snow emission model (Dense Media Radiative Transfer – Multi-Layer model, DMRT-ML), the vegetation transmissivity model (ω-τopt), and atmospheric and soil radiative transfer models were calibrated to simulate the contributions from the snowpack, the vegetation, and the soil, respectively, at the top of the atmosphere. DA experiments were performed for 12 stations where daily continuous SWE measurements were acquired over 4 winters (2012–2016). Best SWE estimates are obtained with the assimilation of the TBs at 11, 19, and 37 GHz in vertical polarizations. The overall SWE bias is reduced by 68 % compared to the original SWE simulations, from 23.7 kg m−2 without assimilation to 7.5 kg m−2 with the assimilation of the three frequencies. The overall SWE relative percentage of error (RPE) is 14.1 % (19 % without assimilation) for sites with a fraction of forest cover below 75 %, which is in the range of accuracy needed for hydrological applications. This research opens the way for global applications to improve SWE estimates over large and remote areas, even when vegetation contributions are up to 50 % of the PMW signal.

Challenges in Modeling Turbulent Heat Fluxes to Snowpacks in Forest Clearings

Abstract Forest clearings are common features of evergreen forests and produce snowpack accumulation and melt differing from that in adjacent forests and open terrain. This study has investigated the challenges in specifying the turbulent fluxes of sensible and latent heat to snowpacks in forest clearings. The snowpack in two forest clearings in the Canadian Rockies was simulated using a one-dimensional (1D) snowpack model. A trade-off was found between optimizing against measured snow surface temperature or snowmelt when choosing how to specify the turbulent fluxes. Schemes using the Monin–Obukhov similarity theory tended to produce negatively biased surface temperature, while schemes that enhanced turbulent fluxes, to reduce the surface temperature bias, resulted in too much melt. Uncertainty estimates from Monte Carlo experiments showed that no realistic parameter set could successfully remove biases in both surface temperature and melt. A simple scheme that excludes atmospheric stability correction was required to successfully simulate surface temperature under low wind speed conditions. Nonturbulent advective fluxes and/or nonlocal sources of turbulence are thought to account for the maintenance of heat exchange in low-wind conditions. The simulation of snowmelt was improved by allowing enhanced latent heat fluxes during low-wind conditions. Caution is warranted when snowpack models are optimized on surface temperature, as model tuning may compensate for deficiencies in conceptual and numerical models of radiative, conductive, and turbulent heat exchange at the snow surface and within the snowpack. Such model tuning could have large impacts on the melt rate and timing of the snow-free transition in simulations of forest clearings within hydrological and meteorological models.

Spatio‐temporal aspects of snowpack runoff formation during rain on snow

AbstractRain on snow (ROS) is a complex phenomenon leading to repeated flooding in many regions with a seasonal snow cover. The potential to generate floods during ROS depends not only on the magnitude of rainfall but also on the areal extent of the antecedent snow cover and the spatio‐temporal interaction between meteorologic and snowpack properties. The complex interaction of these factors makes it difficult to accurately predict the effect of snow cover on runoff formation for an upcoming ROS event. In this study, the detailed physics‐based snow cover model SNOWPACK was used to assess the influence of snow cover properties on converting rain input to available snowpack runoff during 191 ROS events for 58 catchments in the Swiss Alps. Conditions identified by the simulations that led to excessive snowpack runoff were a large snow‐covered fraction, spatially homogeneous snowpack properties, prolonged rainfall events, and a strong rise in air temperature over the course of the event. These factors entail a higher probability of snowpack runoff occurring synchronously within the catchment, which in turn favours higher overall runoff rates. The findings suggest that during autumn and late spring, flooding due to ROS is more likely to happen, whereas during winter a coincidence of the above conditions in the study area is quite rare. For example, an autumn event which occurred in October 2011 resulted from a combination of spatially homogeneous snowpack conditions following a recent snowfall and high, but not exceptional rainfall, and led to major flooding. The results of this study provide key factors to assess in advance of an incoming ROS event and emphasize the importance of detailed snow monitoring for flood forecasting in snow‐affected watersheds.

Variations of the Snow Water Equivalent in the Ourika Catchment (Morocco) over 2000–2018 Using Downscaled MERRA-2 Data

The Ourika River is an important tributary of the Tensift River in the water-stressed region of Marrakesh (Morocco). The Ourika river flow is dominated by the snow melt contribution from the High Atlas mountains. Despite its importance in terms of water resources, the snow water equivalent (SWE) is poorly monitored in the Ourika catchment. Here, we used MERRA-2 data to run a distributed energy-balance snowpack model (SnowModel) over 2000–2018. MERRA-2 data were downscaled to 250-m spatial resolution using a digital elevation model. The model outputs were compared to in situ measurements of snow depth, precipitation, river flow and remote sensing observations of the snow cover area from MODIS. The results indicate that the model provides an overall acceptable representation of the snow cover dynamics given the coarse resolution of the MERRA-2 forcing. Then, we used the model output to analyze the spatio-temporal variations of the SWE in the Ourika catchment for the first time. We suggest that MERRA-2 data, which are routinely available with a delay of a few weeks, can provide valuable information to monitor the snow resource in high mountain areas without in situ measurements.

Coupled Snow Cover and Avalanche Dynamics Simulations to Evaluate Wet Snow Avalanche Activity

We present physics‐based snowpack simulations for four snow seasons with detailed wet snow avalanche activity records. The distributed, spatially explicit simulations using the Alpine3D and SNOWPACK model show that the simulated snowpack in the release areas of documented wet snow avalanches often exhibits its first wetting of the season on the release day. This first wetting is accompanied in the simulations by liquid water accumulating on capillary barriers, often formed by depth hoar layers. The strongest water accumulations and largest increases in percolation depth are found on the day of avalanche release. For individual avalanche paths, however, this only holds in 25%–30% of the cases. Assuming that the depth of the strongest water accumulation corresponds to the avalanche fracture depth, the avalanche dynamics model RAMMS‐Extended was run using simulated snowpack properties as initial conditions in the release area and boundary conditions along the avalanche path. On average, the simulated affected area by the avalanche and runout distance for the release day are statistically significant in closer agreement with the observations than two days before the release. This does not hold for the simulations of 1 day before and 1 and 2 days after the release. This suggests that fracture depths and the temporal evolution of percolation depths are adequately simulated within a ±1‐day period. The results show a large potential for distributed snow cover and avalanche dynamics simulations to assess wet snow avalanche hazards, although predictions for individual avalanche paths remain challenging.

Targeting high robustness in snowpack modeling for Nordic hydrological applications in limited data conditions

Most hydrological models simulate snowmelt using a degree day or simplified energy balance method, which usually requires a calibration of snow-related parameters using discharge data. Despite its apparent efficiency, this method leads to empirical relations which are not proven to remain valid in a changing climate. The direct application of robust physically-based snow models in hydrological modeling is difficult due to the high number of not easily available input variables this model type requires. The objective of this study is to test the robustness of a physically-based snowpack model that requires only a limited number of common meteorological parameters. The MASiN model computes the energy and mass balance of multiple layers of the snowpack using hourly air temperature, relative humidity and wind speeds, as well as daily precipitations. MASiN was tested at 23 sites across Canada and Sweden, using a unique set of parameters fixed at a single site. At each site, the snow depth simulated by MASiN was compared against measurements. Robustness was challenged by comparing MASiN’s performance to that of three other models on three different criteria. MASiN showed the highest robustness among the tested models. With a unique set of parameters, it showed better results than the three reference models when used in similar conditions and matched their performances when reference models were calibrated at each site. The results prove non-data intensive physically based models to be promising tools for hydrological and other snow cover-related studies.

Multi-Criteria Evaluation of Snowpack Simulations in Complex Alpine Terrain Using Satellite and In Situ Observations

This work presents an extensive evaluation of the Crocus snowpack model over a rugged and highly glacierized mountain catchment (Arve valley, Western Alps, France) from 1989 to 2015. The simulations were compared and evaluated using in-situ point snow depth measurements, in-situ seasonal and annual glacier surface mass balance, snow covered area evolution based on optical satellite imagery at 250 m resolution (MODIS sensor), and the annual equilibrium-line altitude of glaciers, derived from satellite images (Landsat, SPOT, and ASTER). The snowpack simulations were obtained using the Crocus snowpack model driven by the same, originally semi-distributed, meteorological forcing (SAFRAN) reanalysis using the native semi-distributed configuration, but also a fully distributed configuration. The semi-distributed approach addresses land surface simulations for discrete topographic classes characterized by elevation range, aspect, and slope. The distributed approach operates on a 250-m grid, enabling inclusion of terrain shadowing effects, based on the same original meteorological dataset. Despite the fact that the two simulations use the same snowpack model, being potentially subjected to same potential deviation from the parametrization of certain physical processes, the results showed that both approaches accurately reproduced the snowpack distribution over the study period. Slightly (although statistically significantly) better results were obtained by using the distributed approach. The evaluation of the snow cover area with MODIS sensor has shown, on average, a reduction of the Root Mean Squared Error (RMSE) from 15.2% with the semi-distributed approach to 12.6% with the distributed one. Similarly, surface glacier mass balance RMSE decreased from 1.475 m of water equivalent (W.E.) for the semi-distributed simulation to 1.375 m W.E. for the distribution. The improvement, observed with a much higher computational time, does not justify the recommendation of this approach for all applications; however, for simulations that require a precise representation of snowpack distribution, the distributed approach is suggested.