Routing of canopy drip in the snowpack below a spruce crown

Snow that is retained by a forest canopy may either sublimate or evaporate directly from the crown or drop as snow clumps or meltwater to the ground. Redistributed snow and meltwater affect the snow structure and prevent the formation of mechanically weak layers, which is the prerequisite for avalanche formation in forests. In this paper we describe the results of dye tracer experiments conducted in a subalpine forest near Davos, Switzerland. Before a snowfall event we stained snow‐free branches of a spruce with a dye tracer solution. After snowfall the coloured meltwater dripping from the branches down on to the snowpack stained the percolation pathways of the meltwater in the snowpack. Photographs of the snow profiles indicate that the meltwater seeped almost vertically through the isothermal snowpack to the soil surface not exceeding the projected crown edge. Meltwater of different events moves along different preferential flow channels in the snow suggesting that old channels are not non‐conducting and additional meltwater fronts create new channels. Copyright © 1999 John Wiley & Sons, Ltd.     

Spatial controls on the distribution and dynamics of a marginal snowpack in the Australian Alps

Seasonal snowpacks in marginal snow environments are typically warm and nearly isothermal, exhibiting high inter‐ and intra‐annual variability. Measurements of snow depth and snow water equivalent were made across a small subalpine catchment in the Australian Alps over two snow seasons in order to investigate the extent and implications of snowpack spatial variability in this marginal setting. The distribution and dynamics of the snowpack were found to be influenced by upwind terrain, vegetation, solar radiation, and slope. The role of upwind vegetation was quantified using a novel parameter based on gridded vegetation height. The elevation range of the catchment was relatively modest (185 m), and elevation impacted distribution but not dynamics. Two characteristic features of marginal snowpack behaviour are presented. Firstly, the evolution of the snowpack is described in terms of a relatively unstable accumulation state and a highly stable ablation state, as revealed by temporal variations in the mean and standard deviation of snow water equivalent. Secondly, the validity of partitioning the snow season into distinct accumulation and ablation phases is shown to be compromised in such a setting. Snow at the most marginal locations may undergo complete melt several times during a season and, even where snow cover is more persistent, ablation processes begin to have an effect on the distribution of the snowpack early in the season. Our results are consistent with previous research showing that individual point measurements are unable to fully represent the variability in the snowpack across a catchment, and we show that recognising and addressing this variability are particularly important for studies in marginal snow environments.     

Long‐term investigations of the snow cover in a subalpine semi‐forested catchment

To improve spring runoff forecasts from subalpine catchments, detailed spatial simulations of the snow cover in this landscape is obligatory. For more than 30 years, the Swiss Federal Research Institute WSL has been conducting extensive snow cover observations in the subalpine watershed Alptal (central Switzerland). This paper summarizes the conclusions from past snow studies in the Alptal valley and presents an analysis of 14 snow courses located at different exposures and altitudes, partly in open areas and partly in forest. The long‐term performance of a physically based numerical snow–vegetation–atmosphere model (COUP) was tested with these snow‐course measurements. One single parameter set with meteorological input variables corrected to the prevailing local conditions resulted in a convincing snow water equivalent (SWE) simulation at most sites and for various winters with a wide range of snow conditions. The snow interception approach used in this study was able to explain the forest effect on the SWE as observed on paired snow courses. Finally, we demonstrated for a meadow and a forest site that a successful simulation of the snowpack yields appropriate melt rates. Copyright © 2006 John Wiley & Sons, Ltd.     

Soil C and N response to changes in winter precipitation in a subalpine forest ecosystem,NW Italy

Among the potential effects of climate change on subalpine forest ecosystems during the winter season, the shift in snowline towards higher altitudes and the increase in frequency of rain events on the snowpack are of particular interest. Here, we present the results of a 2‐year field experiment conducted in a forest stand (Larix decidua) in NW Italy at 2020 m a.s.l. From 2009 to 2011, we monitored soil physical characteristics (temperature and moisture), and soil and soil solution chemistry, in particular carbon (C) and nitrogen (N) forms and their change in time, as affected by simulated late snowpack accumulation and rain on snow events. Late snowpack accumulation determined a stronger effect on soil thermal and moisture regimes than rain on snow events. Also soil chemistry was significantly affected by late snowfall simulation. Although microbial biomass C and N were not reduced by soil freezing, soil contents of the more labile dissolved organic carbon and inorganic N increased when the soil was affected by mild/hard freezing. Variations in the soil solution were shifted with respect to those observed in soil, with an increase in N‐NO₃^? concentrations occurring during spring and summer. This study highlights the potential N loss in subalpine soils under changing environmental conditions driven by a changing climate. Copyright © 2013 John Wiley & Sons, Ltd.     

Temporal observations of a seasonal snowpack using upward‐looking GPR

An increase of the spatial and temporal resolution of snowpack measurements in Alpine or Arctic regions will improve the predictability of flood and avalanche hazards and increase the spatial validity of snowpack simulation models. In the winter season 2009, we installed a ground‐penetrating radar (GPR) system beneath the snowpack to measure snowpack conditions above the antennas. In comparison with modulated frequency systems, GPR systems consist of a much simpler technology, are commercially available and therefore are cheaper. The radar observed the temporal alternation of the snow height over more than 2·5 months. The presented data showed that with moved antennas, it is possible to record the snow height with an uncertainty of less than 8% in comparison with the probed snow depth. Three persistent melt crusts, which formed at the snow surface and were buried by further new snow events, were used as reflecting tracers to follow the snow cover evolution and to determine the strain rates of underlaying layers between adjacent measurements. The height in two‐way travel time of each layer changed over time, which is a cumulative effect of settlement and variation of wave speed in response to densification and liquid water content. The infiltration of liquid water with depth during melt processes was clearly observed during one event. All recorded reflections appeared in concordance with the physical principles (e.g. in phase structure), and one can assume that distinct density steps above a certain threshold result in reflections in the radargram. The accuracy of the used impulse radar system in determining the snow water equivalent is in good agreement with previous studies, which used continuous wave radar systems. The results of this pilot study encourage further investigations with radar measurements using the described test arrangement on a daily basis for continuous destruction‐free monitoring of the snow cover. Copyright © 2010 John Wiley & Sons, Ltd.     

Validation of spatial variability of snowpack thickness and density obtained with GPR and TDR methods

We evaluate the reliability of the joint use of Ground Penetrating Radar (GPR) and Time Domain Reflectometry (TDR) to map dry snow depth, layering, and density where the snowpack thickness is highly irregular and the use of classical survey methods (i.e., hand probes and snow sampling) is unsustainable.We choose a test site characterised by irregular ground morphology, slope, and intense wind action (about 3000 m a.s.l., Western Alps, northern Italy) in dry snow conditions and with a snow-depth ranging from 0.3 m to 3 m over a few tens of metres over the course of a season.The combined use of TDR and high-frequency GPR (at a nominal frequency of 900 MHz) allows for rapid high-resolution imaging of the snowpack. While the GPR data show the interface between the snowpack and the ground, the snow layering, and the presence of snow crusts, the TDR survey allows the local calibration of wave speed based on GPR measurements and the estimation of layer densities. From January to April, there was a slight increase in the average wave speed from 0.22 to 0.24 m/ns from the accumulation zone to the eroded zone. The values are consistent with density values in the range of 350–450 kg/m³, with peaks of 600 kg/m³, as gravimetrically measured from samples from snow pits at different times. The conversion of the electromagnetic wave speed into density agrees with the core samples, with an estimated uncertainty of about 10%.     

Charred forests accelerate snow albedo decay: parameterizing the post‐fire radiative forcing on snow for three years following fire

As large, high‐severity forest fires increase and snowpacks become more vulnerable to climate change across the western USA, it is important to understand post‐fire disturbance impacts on snow hydrology. Here, we examine, quantify, parameterize, model, and assess the post‐fire radiative forcing effects on snow to improve hydrologic modelling of snow‐dominated watersheds having experienced severe forest fires. Following a 2011 high‐severity forest fire in the Oregon Cascades, we measured snow albedo, monitored snow, and micrometeorological conditions, sampled snow surface debris, and modelled snowpack energy and mass balance in adjacent burned forest (BF) and unburned forest sites. For three winters following the fire, charred debris in the BF reduced snow albedo, accelerated snow albedo decay, and increased snowmelt rates thereby advancing the date of snow disappearance compared with the unburned forest. We demonstrate a new parameterization of post‐fire snow albedo as a function of days‐since‐snowfall and net snowpack energy balance using an empirically based exponential decay function. Incorporating our new post‐fire snow albedo decay parameterization in a spatially distributed energy and mass balance snow model, we show significantly improved predictions of snow cover duration and spatial variability of snow water equivalent across the BF, particularly during the late snowmelt period. Field measurements, snow model results, and remote sensing data demonstrate that charred forests increase the radiative forcing to snow and advance the timing of snow disappearance for several years following fire. Copyright © 2016 John Wiley & Sons, Ltd.     

Modelling snowpack bulk density using snow depth,cumulative degree-days,and climatological predictor variables

Snowpack water equivalent (SWE) is a key variable for water resource management in snow-dominated catchments. While it is not feasible to quantify SWE at the catchment scale using either field surveys or remotely sensed data, technologies such as airborne LiDAR (light detection and ranging) support the mapping of snow depth at scales relevant to operational water management. To convert snow depth to water equivalent, models have been developed to predict SWE or snowpack density based on snow depth and additional predictor variables. This study builds upon previous models that relate snowpack density to snow depth by including additional predictor variables to account for (1) long-term climatologies that describe the prevailing conditions influencing regional snowpack properties, and (2) the effect of intra- and inter-year variability in meteorological conditions on densification through a cumulative degree-day index derived from North American Regional Reanalysis products. A non-linear model was fit to 114 506 snow survey measurements spanning 41 years from 1166 snow courses across western North America. Under spatial cross-validation, the predicted densities had a root-mean-square error of 47.1 kg m⁻³, a mean bias of −0.039 kg m⁻³, and a Nash-Sutcliffe Efficiency of 0.70. The model developed in this study had similar overall performance compared to a similar regression-based model reported in the literature, but had reduced seasonal biases. When applied to predict SWE from simulated depths with random errors consistent with those obtained from LiDAR or Structure-from-Motion, 50% of the SWE estimates for April and May fell within −45 to 49 mm of the observed SWE, representing prediction errors of −15% to 20%.     

EFFECT OF SNOW AND FIRN HYDROLOGY ON THE PHYSICAL AND CHEMICAL CHARACTERISTICS OF GLACIAL RUNOFF

Near-surface processes on glaciers, including water flow over bare ice and through seasonal snow and firn, have a significant effect on the speed, volume and chemistry of water flow through the glacier. The transient nature of the seasonal snow profoundly affects the water discharge and chemistry. Water flow through snow is fairly slow compared with flow over bare ice and a thinning snowpack on a glacier decreases the delay between peak meltwater input and peak stream discharge. Furthermore, early spring melt flushes the snowpack of solutes and by mid-summer the melt water flowing into the glacier is fairly clean by comparison. The firn, a relatively constant feature of glaciers, attenuates variations in water drainage into the glacier by temporarily storing water in saturated layer. Bare ice exerts opposite influences by accentuating variations in runoff by water flowing over the ice surface. The melt of firn and ice contributes relatively clean (solute-free) water to the glacier water system.     

Incorporating topographic variability into a simple regional snowmelt model

General circulation models (GCMs), or stand‐alone models that are forced by the output from GCMs, are increasingly being used to simulate the interactions between snow cover, snowmelt, climate and water resources. The variation in snowpack extent, and hence albedo, through time in a cell is likely to be substantial, especially in mid‐latitude mountainous regions. As a consequence, the energy budget simulation by a GCM relies on a realistic representation of snowpack extent. Similarly, from a water resource perspective, the spatial extent of the pack is key in predicting meltwater discharges into rivers. In this paper a simple computationally efficient regional snow model has been developed, which is based on a degree‐day approach and simulates the fraction of the model domain covered by snow, the spatially averaged melt rate and the mean snowpack depth. Computational efficiency is achieved through a novel spatial averaging procedure, which relies on the assumptions that precipitation and temperature scale linearly with elevation and that the distribution of elevations in the domain can be modelled by a continuous function. The resulting spatially averaged model is compared with both observations of the duration of snow cover throughout Austria and with results from a distributed model based on the same underlying assumptions but applied at a fine spatial resolution. The new spatially averaged model successfully simulated the seasonal snow duration observations and reproduced the daily dynamics of snow cover extent, the spatially averaged melt rate and mean pack depth simulated by the distributed model. It, therefore, offers a computationally efficient and easily applied alternative to the current crop of regional snow models. Copyright © 2004 John Wiley & Sons, Ltd.     

Relative influence upon microwave emissivity of fine-scale stratigraphy,internal scattering,and dielectric properties

Summary The microwave emissivity of relatively low-loss media such as snow, ice, frozen ground, and lunar soil is strongly influenced by fine-scale layering and by internal scattering. Radiometric data, however, are commonly interpreted using a model of emission from a homogeneous, dielectric halfspace whose emissivity derives exclusively from dielectric properties. Conclusions based upon these simple interpretations can be erroneous. Examples are presented showing that the emission from fresh or hardpacked snow over either frozen or moist soil is governed dominantly by the size distribution of ice grains in the snowpack. Similarly, the thickness of seasonally frozen soil and the concentration of rock clasts in lunar soil noticeably affect, respectively, the emissivities of northern latitude soils in winter and of the lunar regolith. Petrophysical data accumulated in support of the geophysical interpretation of microwave data must include measurements of not only dielectric properties, but also of geometric factors such as finescale layering and size distributions of grains, inclusions, and voids.     

Response of snow processes to climate change: spatial variability in a small basin in the Spanish Pyrenees

In this study, the Cold Regions Hydrological Modelling platform was used to create an alpine snow model including wind redistribution of snow and energy balance snowmelt to simulate the snowpack over the period 1996–2009 in a small (33 ha) snow‐dominated basin in the Spanish Pyrenees. The basin was divided into three hydrological response units (HRUs), based on contrasting physiographic and aerodynamic characteristics. A sensitivity analysis was conducted to calculate the snow water equivalent regime for various combinations of temperature and precipitation that differed from observed conditions. The results show that there was large inter‐annual variability in the snowpack in this region of the Pyrenees because of its marked sensitivity to climatic conditions. Although the basin is small and quite homogeneous, snowpack seasonality and inter‐annual evolution of the snowpack varied in each HRU. Snow accumulation change in relation to temperature change was approximately 20% for every 1 °C, and the duration of the snowpack was reduced by 20–30 days per °C. Melting rates decreased with increased temperature, and wind redistribution of snow was higher with decreased temperature. The magnitude and sign of changes in precipitation may markedly affect the response of the snowpack to changes in temperature. There was a non‐linear response of snow to individual and combined changes in temperature and precipitation, with respect to both the magnitude and sign of the change. This was a consequence of the complex interactions among climate, topography and blowing snow in the study basin. Copyright © 2012 John Wiley & Sons, Ltd.     

Quantifying snow water equivalent using terrestrial ground penetrating radar and unmanned aerial vehicle photogrammetry

This study demonstrates the potential value of a combined unmanned aerial vehicle (UAV) Photogrammetry and ground penetrating radar (GPR) approach to map snow water equivalent (SWE) over large scales. SWE estimation requires two different physical parameters (snow depth and density), which are currently difficult to measure with the spatial and temporal resolution desired for basin-wide studies. UAV photogrammetry can provide very high-resolution spatially continuous snow depths (SD) at the basin scale, but does not measure snow densities. GPR allows nondestructive quantitative snow investigation if the radar velocity is known. Using photogrammetric snow depths and GPR two-way travel times (TWT) of reflections at the snow-ground interface, radar velocities in snowpack can be determined. Snow density (RSN) is then estimated from the radar propagation velocity (which is related to electrical permittivity of snow) via empirical formulas. A Phantom-4 Pro UAV and a MALA GX450 HDR model GPR mounted on a ski mobile were used to determine snow parameters. A snow-free digital surface model (DSM) was obtained from the photogrammetric survey conducted in September 2017. Then, another survey in synchronization with a GPR survey was conducted in February 2019 whilst the snowpack was approximately at its maximum thickness. Spatially continuous snow depths were calculated by subtracting the snow-free DSM from the snow-covered DSM. Radar velocities in the snowpack along GPR survey lines were computed by using UAV-based snow depths and GPR reflections to obtain snow densities and SWEs. The root mean square error of the obtained SWEs (384 mm average) is 63 mm, indicating good agreement with independent SWE observations and the error lies within acceptable uncertainty limits.     

Prediction of temperature variation within a snowpack in open areas and under different canopy covers

Snow temperature is a major component of many physical processes in a snowpack. The temperature and the change in temperature across a layer have a dominant effect on physical properties of snow grains as well as its hardness, strength, and failure resistance. In this study, temperature and snow cover thickness were measured during the snow season of 2007–2008 in 11 elevation classes and in three different sampling locations, one in an open area and two under different forest canopy covers for each class along Kartalkaya road, Bolu. Each sampling site was visited 44 times to collect data including snow depth, snow surface temperature, ground temperature, and temperature within snowpack at 20‐cm intervals. Seven different models are developed to determine snowpack temperature variations under forest canopy covers and in an open area with different leaf area index values. All models were performed using a multilayer perceptron (MP) method for the Bolu–Kartalkaya area, Turkey. MP approach constitutes a standard form of neural network modeling and can modify two‐layer linear perceptron methods using three and more layers. The ability of MP is to handle complex nonlinear interactions, which ease the natural process of modeling. This method can overcome complex computations using neuron networks, and they can easily nonlinearly link input and output variables. The predictive errors are determined on the basis of mean absolute error and mean square error criteria. The Nash–Sutcliffe sufficiency score showing compliance between observed and predicted values is also calculated. According to the mean absolute error, the mean square error, and the Nash–Sutcliffe sufficiency score criteria, the predictive errors are within reasonable error intervals, justifying the use of the developed MP models for engineering applications. Copyright © 2012 John Wiley & Sons, Ltd.     

The nutrient cycle through snow and ice, a review

This paper reviews the merging of the nutrient cycle with the water cycle in the seasonal alpine snow cover, emphasizing physical processes at the snowpack and snow grain scale. Nutrients are incorporated into snowflakes growing in the atmosphere, they are part of the dry deposition from the atmosphere to the snowpack and they reach the snow as plant litter. The physical processes of the accumulation of nutrients and their redistribution in and on the snow grains and in the pore space of the snow matrix are described.¶The first flush of meltwater that reaches the soil carries a solution of nutrients and acids in a concentration several times higher than bulk values, an effect that increases with the age of the snow and the number of melt/freeze cycles and is more pronounced for sulfate than for chloride. Species that are attached to insoluble particles will be concentrated near the snow surface and will display peak concentrations in the final fraction of meltwater.     

PUBLICATIONS DE L'A. I. H. S.

ABSTRACT

First of all models of the various transfer processes that occur in the snow cover are discussed in relation to its internal structure. When these processes are unsteady they can be described by the various forms of the equation of diffusion. However, the fact that the snowpack has a stratified structure complicates the application of this type of equation. To overcome this problem, in a particular case, a model is proposed for the flow of melt water in the presence of ice layers.     

A modelling framework to simulate field‐scale nitrate response and transport during snowmelt: The WINTRA model

Modelling nutrient transport during snowmelt in cold regions remains a major scientific challenge. A key limitation of existing nutrient models for application in cold regions is the inadequate representation of snowmelt, including hydrological and biogeochemical processes. This brief period can account for more than 80% of the total annual surface runoff in the Canadian Prairies and Northern Canada and processes such as atmospheric deposition, overwinter redistribution of snow, ion exclusion from snow crystals, frozen soils, and snow‐covered area depletion during melt influence the distribution and release of snow and soil nutrients, thus affecting the timing and magnitude of snowmelt runoff nutrient concentrations. Research in cold regions suggests that nitrate (NO₃) runoff at the field‐scale can be divided into 5 phases during snowmelt. In the first phase, water and ions originating from ion‐rich snow layers travel and diffuse through the snowpack. This process causes ion concentrations in runoff to gradually increase. The second phase occurs when this snow ion meltwater front has reached the bottom of the snowpack and forms runoff to the edge‐of‐the‐field. During the third and fourth phases, the main source of NO₃ transitions from the snowpack to the soil. Finally, the fifth and last phase occurs when the snow has completely melted, and the thawing soil becomes the main source of NO₃ to the stream. In this research, a process‐based model was developed to simulate hourly export based on this 5‐phase approach. Results from an application in the Red River Basin of southern Manitoba, Canada, shows that the model can adequately capture the dynamics and rapid changes of NO₃ concentrations during this period at relevant temporal resolutions. This is a significant achievement to advance the current nutrient modelling paradigm in cold climates, which is generally limited to satisfactory results at monthly or annual resolutions. The approach can inform catchment‐scale nutrient models to improve simulation of this critical snowmelt period.     

Assessing the impact of distributed snow water equivalent calibration and assimilation of Copernicus snow water equivalent on modelled snow and streamflow performance

Accuracy of the Copernicus snow water equivalent (SWE) product and the impact of SWE calibration and assimilation on modelled SWE and streamflow was evaluated. Daily snowpack measurements were made at 12 locations from 2016 to 2019 across a 4104 km² mixed-forest basin in the Great Lakes region of central Ontario, Canada. Sub-basin daily SWE calculated from these sites, observed discharge, and lake levels were used to calibrate a hydrologic model developed using the Raven modelling framework. Copernicus SWE was bias corrected during the melt period using mean bias subtraction and was compared to daily basin average SWE calculated from the measured data. Bias corrected Copernicus SWE was assimilated into the models using a range of parameters and the parameterizations from the model calibration. The bias corrected Copernicus product agreed well with measured data and provided a good estimate of mean basin SWE demonstrating that the product shows promise for hydrology applications within the study region. Calibration to spatially distributed SWE substantially improved the basin scale SWE estimate while only slightly degrading the flow simulation demonstrating the value of including SWE in a multi-objective calibration formulation. The particle filter experiments yielded the best SWE estimation but moderately degraded the flow simulation. The particle filter experiments constrained by the calibrated snow parameters produced similar results to the experiments using the upper and lower bounds indicating that, in this study, model calibration prior to assimilation was not valuable. The calibrated models exhibited varying levels of skill in estimating SWE but demonstrated similar streamflow performance. This indicates that basin outlet streamflow can be accurately estimated using a model with a poor representation of distributed SWE. This may be sufficient for applications where estimating flow is the primary water management objective. However, in applications where understanding the physical processes of snow accumulation, melt and streamflow generation are important, such as assessing the impact of climate change on water resources, accurate representations of SWE are required and can be improved via multi-objective calibration or data assimilation, as demonstrated in this study.     

Preferential exchange rate effect of isotopic fractionation in a melting snowpack

Stable isotope exchange processes between solid and liquid phases of a natural melting snowpack are investigated in detail by separating the liquid water from snow grains at different depths of the snowpack and collecting the bottom discharge using a lysimeter. In the melting–freezing mass exchange process between the two phases, the theoretical slope of the δD? δ¹⁸O line for newly refrozen ice is calculated to be nearly that of pore water. However, based on observations of the isotopic evolution and snow grain coarsening of the snowpack, it is demonstrated that the slope of the δD? δ¹⁸O line for newly refrozen ice is equal to that of the original ice. This is proved to be due to preferential water flow in the snowpack, which leads to relatively more deuterium and less oxygen‐18 in the mobile water than the immobile water because of the kinetic effect. Higher mass exchange rate in the mobile water region results in excess deuterium in the bulk refrozen ice, compared with the fractionation of uniform fractionation factors and exchange rate. This effect, which is termed the ‘preferential exchange rate effect of isotopic fractionation’, is shown to be larger in the lower part than the upper part of the snowpack. Copyright © 2008 John Wiley & Sons, Ltd.