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Forests strongly modify the accumulation, metamorphism and melting of snow in midlatitude and high-latitude regions. Recently, snow routines in hydrological and land surface models were improved to incorporate more accurate representations of forest snow processes, but model intercomparison projects have identified deficiencies, partly due to incomplete knowledge of the processes controlling snow cover in forests. The Snow Under Forest (SnoUF) project was initiated to enhance knowledge of the complex interactions between snow and vegetation. Two field campaigns, during the winters 2016–2017 and 2017–2018, were conducted in a coniferous forest bordering the snow study at Col de Porte (1325 m a.s.l., French Alps) to document the snow accumulation and ablation processes. This paper presents the field site, the instrumentation and the collection and postprocessing methods. The observations include distributed forest characteristics (tree inventory, lidar measurements of forest structure, subcanopy hemispherical photographs), meteorology (automatic weather station and an array of radiometers), snow cover and depth (snow pole transect and laser scan) and snow interception by the canopy during precipitation events. The weather station installed under dense canopy during the first campaign has been maintained since then and has provided continuous measurements throughout the year since 2018. Data are publicly available from the repository of the Observatoire des Sciences de l'Univers de Grenoble (OSUG) data center at 10.17178/SNOUF.2022 (Sicart et al., 2022). [ABSTRACT FROM AUTHOR]

The amount of water contained in a snowpack, known as snow water equivalent (SWE), is used to anticipate the amount of snowmelt that could supply hydroelectric power plants, fill water reservoirs, or sometimes cause flooding. This work introduces a wireless, non-destructive method for monitoring the SWE of a dry snowpack. The system is based on an array of low-cost passive radiofrequency identification (RFID) tags, placed under the snow and read at 865–868 MHz by a reader located above the snow. The SWE was deduced from the phase delay of the tag's backscattered response, which increases with the amount of snow traversed by the radiofrequency wave. Measurements taken in the laboratory, during snowfall events and over 4.5 months at the Col de Porte test field, were consistent with reference measurements of cosmic rays, precipitation and snow pits. SWE accuracy was ±18 kg m -2 throughout the season (averaged over three tags) and ±3 kg m -2 during dry snowfall events (averaged over data from two antennas and four or five tags). The overall uncertainty compared to snow weighing was ±10% for snow density in the range 61–390 kg m -3. The main limitations observed were measurement bias caused by wet snow (biased data were discarded) and the need for phase unwrapping. The method has a number of advantages: it allows for continuous measurement (1 min sampling rate in dry snow), it can provide complementary measurement of tag temperature, it does not require the reception of external data, and it opens the way towards spatialized measurements. The results presented also demonstrate that RFID propagation-based sensing can remotely monitor the permittivity of a low-loss dielectric material with scientific-level accuracy. [ABSTRACT FROM AUTHOR]

This work shows that passive radio-frequency identification (RFID) tags can be used as low-cost contactless sensors, to measure the variations in snow water equivalent (SWE) of a snowpack. RFID tags are produced massively to remotely identify industrial goods, hence are available commercially off-the-shelf at very low-cost. The introduced measurement system consists of a vertical profile of RFID tags installed before the first snowfall, interrogated continuously by a 865–868 MHz reader that remains above the snowpack. The system deduces the SWE variations from the increase of phase delay induced by the new layers of fresh snow which slows the propagation of the waves. The method is tested both in a controlled laboratory environment, and outdoors on the French national reference center of Col de Porte, to cross-check the results against a solid reference dataset (cosmic rays, precipitation weighting, temperature monitoring, and snow pit surveys). The technical challenges solved concern multipathing interferences, snowmelt acceleration during reheats, measurement discontinuity, and wet snow influence. This non-contact and non-destructive RFID technique can estimate the SWE of dry snow, with the accuracy of ±3−30 kg/m2 depending on the number of tags and antennas. In addition, the system can monitor the snow temperature with 1 °C accuracy and spatialization, using dedicated sensors embedded in the tags. [ABSTRACT FROM AUTHOR]

To understand snow structure and snowmelt timing, information about flows of liquid water within the snowpack is essential. Models can make predictions using explicit representations of physical processes, or through parameterization, but it is difficult to verify simulations. In situ observations generally measure bulk quantities. Where internal snowpack measurements are made, they tend to be destructive and unsuitable for continuous monitoring. Here, we present a novel method for in situ monitoring of water flow in seasonal snow using the electrical self-potential (SP) geophysical method. A prototype geophysical array was installed at Col de Porte (France) in October 2018. Snow hydrological and meteorological observations were also collected. Results for two periods of hydrological interest during winter 2018–19 (a marked period of diurnal melting and refreezing, and a rain-on-snow event) show that the electrical SP method is sensitive to internal water flow. Water flow was detected by SP signals before it was measured in conventional snowmelt lysimeters at the base of the snowpack. This initial feasibility study shows the utility of the SP method as a non-destructive snow sensor. Future development should include combining SP measurements with a high-resolution snow physics model to improve prediction of melt timing. [ABSTRACT FROM AUTHOR]

The 30-year simulations of seasonal snow cover in 22 physically based models driven with bias-corrected meteorological reanalyses are examined at four sites with long records of snow observations. Annual snow cover durations differ widely between models, but interannual variations are strongly correlated because of the common driving data. No significant trends are observed in starting dates for seasonal snow cover, but there are significant trends towards snow cover ending earlier at two of the sites in observations and most of the models. A simplified model with just two parameters controlling solar radiation and sensible heat contributions to snowmelt spans the ranges of snow cover durations and trends. This model predicts that sites where snow persists beyond annual peaks in solar radiation and air temperature will experience rapid decreases in snow cover duration with warming as snow begins to melt earlier and at times of year with more energy available for melting. [ABSTRACT FROM AUTHOR]

Snow interception by the forest canopy controls the spatial heterogeneity of subcanopy snow accumulation leading to significant differences between forested and nonforested areas at a variety of scales. Snow intercepted by the forest canopy can also drastically change the surface albedo. As such, accurately modeling snow interception is of importance for various model applications such as hydrological, weather, and climate predictions. Due to difficulties in the direct measurements of snow interception, previous empirical snow interception models were developed at just the point scale. The lack of spatially extensive data sets has hindered the validation of snow interception models in different snow climates, forest types, and at various spatial scales and has reduced the accurate representation of snow interception in coarse-scale models. We present two novel empirical models for the spatial mean and one for the standard deviation of snow interception derived from an extensive snow interception data set collected in an evergreen coniferous forest in the Swiss Alps. Besides open-site snowfall, subgrid model input parameters include the standard deviation of the DSM (digital surface model) and/or the sky view factor, both of which can be easily precomputed. Validation of both models was performed with snow interception data sets acquired in geographically different locations under disparate weather conditions. Snow interception data sets from the Rocky Mountains, US, and the French Alps compared well to the modeled snow interception with a normalized root mean square error (NRMSE) for the spatial mean of ≤10 % for both models and NRMSE of the standard deviation of ≤13 %. Compared to a previous model for the spatial mean interception of snow water equivalent, the presented models show improved model performances. Our results indicate that the proposed snow interception models can be applied in coarse land surface model grid cells provided that a sufficiently fine-scale DSM is available to derive subgrid forest parameters. [ABSTRACT FROM AUTHOR]

Snow interception by forest canopy drives the spatial heterogeneity of subcanopy snow accumulation leading to significant differences between forested and non-forested areas at a variety of scales. Snow intercepted by forest canopy can also drastically change the surface albedo. As such, accuratelly modelling snow interception is of importance for various model applications such as hydrological, weather and climate predictions. Due to difficulties in direct measurements of snow interception, previous empirical snow interception models were developed at just the point scale. The lack of spatially extensive data sets has hindered validation of snow interception models in different snow climates, forest types and at various spatial scales and has reduced accurate representation of snow interception in coarse-scale models. We present two novel models for the spatial mean and one for the standard deviation of snow interception derived from an extensive snow interception data set collected in a spruce forest in the Swiss Alps. Besides open area snow fall, subgrid model input parameters include the standard deviation of the DSM (digital surface models) and the sky view factor, both of which can be easily pre-computed. Validation of both models was performed with snow interception data sets acquired in geographically different locations under disparate weather conditions. Snow interception data sets from the Rocky Mountains, U.S. and the French Alps compared well to modelled snow interception with a NRMSE for the spatial mean of lower equal ≤ 10% and NRMSE of the standard deviation of lower equal ≤ 13%. Our results suggest that the proposed snow interception models can be applied in coarse land surface model grid cells provided that a sufficiently fine-scale DSM of the forest is available to derive subgrid forest parameters. [ABSTRACT FROM AUTHOR]

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: 10.17178/CRYOBSCLIM.CDP.2018. [ABSTRACT FROM AUTHOR]

In this paper, we introduce and provide access to a daily (1960-2017) and hourly (1993-2017) dataset of snow and meteorological data measured at the Col de Porte site, 1325 m a.s.l, Charteuse, 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 April 2015. A detailed study of the uncertainties originating from both measurements 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. The estimated root mean squared deviation, that can be mainly attributed to spatial variability, is ± 10 cm for snow depth, ± 25 kg m-2 for snow water 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 reduction of the mean snow depth (Dec. 1st to April 30th of 39 cm from 1960-1990 to 1990-2017 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) datacenter: https://doi.org/10.17178/CRYOBSCLIM.CDP.2018. [ABSTRACT FROM AUTHOR]

This article investigates the climatic response of a series of indicators for characterizing annual snow conditions and corresponding meteorological drivers at 1500m altitude in the Chartreuse mountain range in the Northern French Alps. Past and future changes were computed based on reanalysis and observations from 1958 to 2016, and using CMIP5-EURO-CORDEX GCM-RCM pairs spanning historical (1950-2005) and RCP2.6 (4), RCP4.5 and RCP8.5 (13 each) future scenarios (2006-2100). The adjusted climate model runs were used to drive the multiphysics ensemble configuration of the detailed snowpack model Crocus. Uncertainty arising from physical modeling of snow accounts for 20% typically, although the multiphysics is likely to have a much smaller impact on trends. Ensembles of climate projections are rather similar until the middle of the 21st century, and all show a continuation of the ongoing reduction in average snow conditions, and sustained interannual variability. The impact of the RCPs becomes significant for the second half of the 21st century, with overall stable conditions with RCP2.6, and continued degradation of snow conditions for RCP4.5 and 8.5, the latter leading to more frequent ephemeral snow conditions. Changes in local meteorological and snow conditions show significant correlation with global temperature changes. Global temperature levels 1.5 and 2 °C above preindustrial levels correspond to a 25 and 32%reduction, respectively, of winter mean snow depth with respect to the reference period 1986-2005. Larger reduction rates are expected for global temperature levels exceeding 2 °C. The method can address other geographical areas and sectorial indicators, in the field of water resources, mountain tourism or natural hazards. [ABSTRACT FROM AUTHOR]

Light-absorbing impurities (LAIs) decrease snow albedo, increasing the amount of solar energy absorbed by the snowpack. Its most intuitive and direct impact is to accelerate snowmelt. Enhanced energy absorption in snow also modifies snow metamorphism, which can indirectly drive further variations of snow albedo in the near-infrared part of the solar spectrum because of the evolution of the nearsurface snow microstructure. New capabilities have been implemented in the detailed snowpack model SURFEX/ISBACrocus (referred to as Crocus) to account for impurities' deposition and evolution within the snowpack and their direct and indirect impacts. Once deposited, the model computes impurities' mass evolution until snow melts out, accounting for scavenging by meltwater. Taking advantage of the recent inclusion of the spectral radiative transfer model TARTES (Two-stream Analytical Radiative TransfEr in Snow model) in Crocus, the model explicitly represents the radiative impacts of light-absorbing impurities in snow. The model was evaluated at the Col de Porte experimental site (French Alps) during the 2013-2014 snow season against in situ standard snow measurements and spectral albedo measurements. In situ meteorological measurements were used to drive the snowpack model, except for aerosol deposition fluxes. Black carbon (BC) and dust deposition fluxes used to drive the model were extracted from simulations of the atmospheric model ALADIN-Climate. The model simulates snowpack evolution reasonably, providing similar performances to our reference Crocus version in terms of snow depth, snow water equivalent (SWE), near-surface specific surface area (SSA) and shortwave albedo. Since the reference empirical albedo scheme was calibrated at the Col de Porte, improvements were not expected to be significant in this study. We show that the deposition fluxes from the ALADIN-Climate model provide a reasonable estimate of the amount of lightabsorbing impurities deposited on the snowpack except for extreme deposition events which are greatly underestimated. For this particular season, the simulated melt-out date advances by 6 to 9 days due to the presence of light-absorbing impurities. The model makes it possible to apportion the relative importance of direct and indirect impacts of lightabsorbing impurities on energy absorption in snow. For the snow season considered, the direct impact in the visible part of the solar spectrum accounts for 85% of the total impact, while the indirect impact related to accelerated snow metamorphism decreasing near-surface specific surface area and thus decreasing near-infrared albedo accounts for 15%of the total impact. Our model results demonstrate that these relative proportions vary with time during the season, with potentially significant impacts for snowmelt and avalanche prediction. [ABSTRACT FROM AUTHOR]

Three debris-free glaciers with strongly differing annual glaciological glacier-wide mass balances (MBs) are monitored in the Everest region (central Himalaya, Nepal). The mass budget of Mera Glacier (5.1 km2 in 2012), located in the southern part of this region, was balanced during 2007–15, whereas Pokalde (0.1 km2 in 2011) and West Changri Nup glaciers (0.9 km2 in 2013), ~30 km further north, have been losing mass rapidly with annual glacier-wide MBs of −0.69 ± 0.28 m w.e. a−1 (2009–15) and −1.24 ± 0.27 m w.e. a−1 (2010–15), respectively. An analysis of high-elevation meteorological variables reveals that these glaciers are sensitive to precipitation, and to occasional severe cyclonic storms originating from the Bay of Bengal. We observe a negative horizontal gradient of annual precipitation in south-to-north direction across the range (≤−21 mm km−1, i.e. −2% km−1). This contrasted mass-balance pattern over rather short distances is related (i) to the low maximum elevation of Pokalde and West Changri Nup glaciers, resulting in years where their accumulation area ratio is reduced to zero and (ii) to a steeper vertical gradient of MB for glaciers located in the inner arid part of the range. [ABSTRACT FROM PUBLISHER]

Snow spectral albedo in the visible/near-infrared range has been continuously measured during a winter season at Col de Porte alpine site (French Alps; 45.30° N, 5.77° E; 1325 m a.s.l.). The evolution of such alpine snow-pack is complex due to intensive precipitation, rapid melt events and Saharan dust deposition outbreaks. This study highlights that the resulting intricate variations of spectral albedo can be successfully explained by variations of the following snow surface variables: specific surface area (SSA) of snow, effective light-absorbing impurities content, presence of liquid water and slope. The methodology developed in this study disentangles the effect of these variables on snow spectral albedo. The presence of liquid water at the snow surface results in a spectral shift of the albedo from which melt events can be identified with an occurrence of false detection rate lower than 3.5 %. Snow SSA mostly impacts spectral albedo in the near-infrared range. Impurity deposition mostly impacts the albedo in the visible range but this impact is very dependent on snow SSA and surface slope. Our work thus demonstrates that the SSA estimation from spectral albedo is affected by large uncertainties for a tilted snow surface and medium to high impurity contents and that the estimation of impurity content is also affected by large uncertainties, especially for low values below 50 ng g-1 black carbon equivalent. The proposed methodology opens routes for retrieval of SSA, impurity content, melt events and surface slope from spectral albedo. However, an exhaustive accuracy assessment of the snow black properties retrieval would require more independent in situ measurements and is beyond the scope of the present study. This time series of snow spectral albedo nevertheless already provides a new insight into our understanding of the evolution of snow surface properties. [ABSTRACT FROM AUTHOR]

The article discusses a study which introduces the design, ability, and performance of the Crocus-DEB model to explore relationships between meteorological conditions, model parameters, and the critical debris thickness. It says that the model was developed as an adaptation of Crocus, the detailed snowpack model, to stimulate the mass balance and energy of debris-covered glaciers. It adds that the model was evaluated using field data from the debris-covered glaciers in Changri Nup in Nepal, Himalaya. Furthermore, the application of empirical methods is discussed.

Abstract: There are many models that attempt to predict physical processes in snow on the ground for a range of applications, and evaluations of these models show that they have a wide range of behaviours. A review of snow models, however, shows that many of them draw on a relatively small number of process parameterizations combined in different configurations and using different parameter values. A single model that combines existing parameterizations of differing complexity in many different configurations to generate large ensembles of simulations is presented here. The model is driven and evaluated with data from four winters at an alpine site in France. Consideration of errors in simulations of snow mass, snow depth, albedo and surface temperature show that there is no “best” model, but there is a group of model configurations that give consistently good results, another group that give consistently poor results, and many configurations that give good results in some cases and poor results in others. There is no clear link between model complexity and performance, but the most consistent results come from configurations that have prognostic representations of snow density and albedo and that take some account of storage and refreezing of liquid water within the snow. [Copyright &y& Elsevier]