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.     

Snow density variations: consequences for ground‐penetrating radar

Reliable hydrological forecasts of snowmelt runoff are of major importance for many areas. Ground‐penetrating radar (GPR) measurements are used to assess snowpack water equivalent for planning of hydropower production in northern Sweden. The travel time of the radar pulse through the snow cover is recorded and converted to snow water equivalent (SWE) using a constant snowpack mean density from the drainage basin studied. In this paper we improve the method to estimate SWE by introducing a depth‐dependent snowpack density. We used 6 years measurements of peak snow depth and snowpack mean density at 11 locations in the Swedish mountains. The original method systematically overestimates the SWE at shallow depths (+25% for 0·5 m) and underestimates the SWE at large depths (?35% for 2·0 m). A large improvement was obtained by introducing a depth–density relation based on average conditions for several years, whereas refining this by using separate relations for individual years yielded a smaller improvement. The SWE estimates were substantially improved for thick snow covers, reducing the average error from 162 ± 23 mm to 53 ± 10 mm for depth range 1·2–2·0 m. Consequently, the introduction of a depth‐dependent snow density yields substantial improvements of the accuracy in SWE values calculated from GPR data. Copyright © 2005 John Wiley & Sons, Ltd.     

Measuring snow ablation rates in alpine terrain with a mobile multioffset ground‐penetrating radar system

Ground‐penetrating radar (GPR) has become a promising technique in the field of snow hydrological research. It is commonly used to measure snow depth, density, and water equivalent over large distances or along gridded snow courses. Having built and tested a mobile lightweight set‐up, we demonstrate that GPR is capable of accurately measuring snow ablation rates in complex alpine terrain. Our set‐up was optimized for efficient measurements and consisted of a multioffset radar with four pairs of antennas mounted to a plastic sled, which was small enough to permit safe and convenient operations. Repeated measurements at intervals of 2 to 7 days were taken during the 2014/2015 winter season along 10 profiles of 50 to 200 m length within two valleys located in the eastern Swiss Alps. Resulting GPR‐based data of snow depth, density, and water equivalent, as well as their respective change over time, were in good agreement with concurrent manual measurements, in particular if accurate alignment between repeated overpasses could be achieved. Corresponding root‐mean‐square error (RMSE) values amounted to 4.2 cm for snow depth, 17 mm for snow water equivalent, and 22 kg/m³ for snow density, with similar RMSE values for corresponding differential data. With this performance, the presented radar set‐up has the potential to provide exciting new and extensive datasets to validate snowmelt models or to complement lidar‐based snow surveys.     

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.     

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.     

Investigating snowpack across scale in the northern Great Lakes–St. Lawrence forest region of Central Ontario,Canada

This study investigates scaling issues by evaluating snow processes and quantifying bias in snowpack properties across scale in a northern Great Lakes–St. Lawrence forest. Snow depth and density were measured along transects stratified by land cover over the 2015/2016 and 2016/2017 winters. Daily snow depth was measured using a time‐lapse (TL) camera at each transect. Semivariogram analysis of the transect data was conducted, and no autocorrelation was found, indicating little spatial structure along the transects. Pairwise differences in snow depth and snow water equivalent (SWE) between land covers were calculated and compared across scales. Differences in snowpack between forested sites at the TL points corresponded to differences in canopy cover, but this relationship was not evident at the transect scale, indicating a difference in observed process across scale. TL and transect estimates had substantial bias, but consistency in error was observed, which indicates that scaling coefficients may be derived to improve point scale estimates. TL and transect measurements were upscaled to estimate grid scale means. Upscaled estimates were compared and found to be consistent, indicating that appropriately stratified point scale measurements can be used to approximate a grid scale mean when transect data are not available. These findings are important in remote regions such as the study area, where frequent transect data may be difficult to obtain. TL, transect, and upscaled means were compared with modelled depth and SWE. Model comparisons with TL and transect data indicated that bias was dependent on land cover, measurement scale, and seasonality. Modelled means compared well with upscaled estimates, but model SWE was underestimated during spring melt. These findings highlight the importance of understanding the spatial representativeness of in situ measurements and the processes those measurements represent when validating gridded snow products or assimilating data into models.     

Variable effects of forest canopies on snow processes in a valley of the central Spanish Pyrenees

Snowpacks and forests have complex interactions throughout the large range of altitudes where they co-occur. However, there are no reliable data on the spatial and temporal interactions of forests with snowpacks, such as those that occur in nearby areas that have different environmental conditions and those that occur during different snow seasons. This study monitored the interactions of forests with snowpacks in four forest stands in a single valley of the central Spanish Pyrenees during three consecutive snow seasons (2015/2016, 2016/2017 and 2017/2018). Daily snow depth data from time-lapse cameras were compared with snow data from field surveys that were performed every 10–15 days. These data thus provided information on the spatial and temporal changes of snow–water equivalent (SWE). The results indicated that forest had the same general effects on snowpack in each forest stand and during each snow season. On average, forest cover reduced the duration of snowpack by 17 days, reduced the cumulative SWE of the snowpack by about 60% and increased the spatial heterogeneity of snowpack by 190%. Overall, forest cover reduced SWE total accumulation by 40% and the rate of SWE accumulation by 25%. The forest-mediated reduction of the accumulation rate, in combination with the occasional forest-mediated enhancement of melting rate, explained the reduced duration of snowpacks beneath forest canopies. However, the magnitude and timing of certain forest effects on snowpack had significant spatial and temporal variations. This variability must be considered when selecting the location of an experimental site in a mountainous area, because the study site should be representative of surrounding areas. The same considerations apply when selecting a time period for study.     

Modification and testing of a one‐dimensional energy and mass balance model for supraglacial snowpacks

A one‐dimensional energy and mass balance snow model (SNTHERM) has been modified for use with supraglacial snowpacks and applied to a point on Haut Glacier d'Arolla, Switzerland. It has been adapted to incorporate the underlying glacier ice and a site‐specific, empirically derived albedo routine. Model performance was tested against continuous measurements of snow depth and meltwater outflow from the base of the snowpack, and intermittent measurements of surface albedo and snowpack density profiles collected during the 1993 and 2000 melt seasons. Snow and ice ablation was simulated accurately. The timing of the daily pattern of meltwater outflow was well reproduced, although magnitudes were generally underestimated, possibly indicating preferential flow into the snowpack lysimeter. The model was used to assess the quantity of meltwater stored temporally within the unsaturated snowpack and meltwater percolation rates, which were found to be in agreement with dye tracer experiments undertaken on this glacier. As with other energy balance studies on alpine valley glaciers, the energy available for melt was dominated by net radiation (64%), with a sizable contribution from sensible heat flux (36%) and with a negligible latent heat flux overall, although there was more complex temporal variation on diurnal timescales. A basic sensitivity analysis indicated that melt rates were most sensitive to radiation, air temperature and snowpack density, indicating the need to accurately extrapolate/interpolate these variables when developing a spatially distributed framework for this model. Copyright © 2008 John Wiley & Sons, Ltd.     

Seasonal snowpack dynamics and runoff in a cool temperate forest: lysimeter experiment in Niigata,Japan

Seasonal snowpack dynamics are described through field measurements under contrasting canopy conditions for a mountainous catchment in the Japan Sea region. Microclimatic data, snow accumulation, albedo and lysimeter runoff are given through the complete winter season 2002–03 in (1) a mature cedar stand, (2) a larch stand, and (3) a regenerating cedar stand or opening. The accumulation and melt of seasonal snowpack strongly influences streamflow runoff during December to May, including winter baseflow, mid‐winter melt, rain on snow, and diurnal peaks driven by radiation melt in spring. Lysimeter runoff at all sites is characterized by constant ground melt of 0·8–1·0 mm day⁻¹. Rapid response to mid‐winter melt or rainfall shows that the snowpack remains in a ripe or near‐ripe condition throughout the snow‐cover season. Hourly and daily lysimeter discharge was greatest during rain on snow (e.g. 7 mm h⁻¹ and 53 mm day⁻¹ on 17 December) with the majority of runoff due to rainfall passing through the snowpack as opposed to snowmelt. For both rain‐on‐snow and radiation melt events lysimeter discharge was generally greatest at the open site, although there were exceptions such as during interception melt events. During radiation melt instantaneous discharge was up to 4·0 times greater in the opening compared with the mature cedar, and 48 h discharge was up to 2·5 times greater. Perhaps characteristic of maritime climates, forest interception melt is shown to be important in addition to sublimation in reducing snow accumulation beneath dense canopies. While sublimation represents a loss from the catchment water balance, interception melt percolates through the snowpack and contributes to soil moisture during the winter season. Strong differences in microclimate and snowpack albedo persisted between cedar, larch and open sites, and it is suggested further work is needed to account for this in hydrological simulation models. Copyright © 2005 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%.     

Snow Mass Quantification and Avalanche Victim Search by Ground Penetrating Radar

Ground penetrating radar (GPR) systems can be used in many applications of snow and ice research. The information from the GPR is used to identify and interpret layers, objects and different structures in the snow. A commercially available GPR system was further developed to work in the rough environment of snow and ice. The applied GPR is a 900 MHz system that easily reaches snow depths of up to 10 meters. The system was calibrated in the course of several manual snow depth measurements during each survey. The depth resolution depends on the snow type and is around ±0.1 m. The GPR system is carried alongside a line of interest and is triggered by an odometer wheel at regular adjustable steps. All equipment is mounted in a sledge and is pulled by a snowmobile over the snow surface. This setup allows for an efficient coverage of several kilometers of terrain profiles. The radar profiles give a real time two-dimensional impression of structures and objects and the interface between snow and the underlying ground. The actual radar profile is shown on a screen on the sledge allowing the immediate marking of objects and structures. During the past three years the instrument was successfully used for the study of snow distributions, for the detection of glacier crevasses under the snow cover, and for the search of avalanche victims in avalanche debris. The results show the capability of the instrument to detect persons and objects in the snow cover. In the future, this device may be a new tool for avalanche rescue operations. Today, the size and weight of the system prevents the accessing of very steep slopes and areas not accessible to snowmobiles. Further developments will decrease the size of the system and make it a valuable tool to quantify snow masses in avalanche release zones and run-out areas.     

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.     

Understanding hydrological processes in glacierized catchments: Evidence and implications of highly variable isotopic and electrical conductivity data

The spatial and temporal characterization of geochemical tracers over Alpine glacierized catchments is particularly difficult, but fundamental to quantify groundwater, glacier melt, and rain water contribution to stream runoff. In this study, we analysed the spatial and temporal variability of δ²H and electrical conductivity (EC) in various water sources during three ablation seasons in an 8.4‐km² glacierized catchment in the Italian Alps, in relation to snow cover and hydro‐meteorological conditions. Variations in the daily streamflow range due to melt‐induced runoff events were controlled by maximum daily air temperature and snow covered area in the catchment. Maximum daily streamflow decreased with increasing snow cover, and a threshold relation was found between maximum daily temperature and daily streamflow range. During melt‐induced runoff events, stream water EC decreased due to the contribution of glacier melt water to stream runoff. In this catchment, EC could be used to distinguish the contribution of subglacial flow (identified as an end member, enriched in EC) from glacier melt water to stream runoff, whereas spring water in the study area could not be considered as an end member. The isotopic composition of snow, glacier ice, and melt water was not significantly correlated with the sampling point elevation, and the spatial variability was more likely affected by postdepositional processes. The high spatial and temporal variability in the tracer signature of the end members (subglacial flow, rain water, glacier melt water, and residual winter snow), together with small daily variability in stream water δ²H dynamics, are problematic for the quantification of the contribution of the identified end members to stream runoff, and call for further research, possibly integrated with other natural or artificial tracers.     

Characteristics of subarctic snowcover / Les caractéristiques de la couverture de neige presque arctique

Abstract

The areal and temporal characteristics of the snowpack in a small subarctic drainage basin at Schefferville, Quebec, were analysed prior to and during the snowmelt in 1972 and 1973. The data showed that vegetation cover is of prime importance in determining the areal distribution of snowpack properties. The areal distribution of snow water equivalent could be characterized by a normal distribution in each of four vegetation cover types. It was found that the mean and standard deviation of snow water equivalent are closely related to vegetation cover. Also, mean snow water equivalent varies from year to year but standard deviation shows no significant variation. This suggests that mean accumulation is the result of annual snowfall amounts, while the variability is due to the effects of vegetation cover and accumulation processes. The data also showed that during the snowmelt, the variability of snowcover properties shows no significant change. Using the normal distributions of the peak accumulation snow water equivalents, and observed and calculated melt rates, the areal extent of snowcover was determined.     

II. Snow hydrology processes and remote sensing

In the last 20 years remote sensing research has led to significant progress in monitoring and measuring certain snow hydrology processes. Snow distribution in a drainage basin can be adequately assessed by visible sensors. Although there are still some interpretation problems, the NOAA-AVHRR sensor can provide frequent views of the areal snow cover in a basin, and snow cover maps are produced operationally by the National Weather Service on about 3000 drainage basins in North America. Measurement of snow accumulation or snow water equivalent with microwave remote sensing has great potential because of the capabilities for depth penetration, all-weather observation and night-time viewing. Several critical areas of research remain, namely, the acquisition of snow grain size information for input to microwave models and improvement in passive microwave resolution from space. Methods that combine both airborne gamma ray and visible satellite remote sensing of the snowpack with field measurements also hold promise for determining areal snow water equivalent. Some remote sensing techniques can also be used to detect different stages of snow metamorphism. Various aspects of snowpack ripening can be detected using microwave and thermal infra-red capabilities. The capabilities for measurement of snow albedo and surface temperature have direct application in both snow metamorphism and snowpack energy balance studies. The potentially most profitable research area here is the study of the bidirectional reflectance distribution function to improve snow albedo measurements. Most of the remote sensing capabilities in snow hydrology have been developed for improving snowmelt-run-off forecasting. Most applications have used the input of snow cover extent to deterministic models, both of the degree day and energy balance types. Snowmelt-run-off forecasts using satellite derived snow cover depletion curves and the models have been successfully made. As the extraction of additional snow cover characteristics becomes possible, remote sensing will have an even greater impact on snow hydrology. Important remote sensing capabilities will become available in the next 20 years through space platform observing systems that will improve our capability to observe the snowpack on an operational basis.     

SNOW COVER CHARACTERIZATION USING MULTIBAND FMCW RADARS

The use of radars to characterize the physical properties of a snow cover offers an attractive alternative to manual snow pit measurements. Radar techniques are non-invasive and have the potential to cover large areas of a snow-covered terrain. A promising radar technique for snow cover studies is the frequency modulated continuous wave (FMCW) radar. The use of a multiband radar approach for snow cover studies was investigated in order to fully exploit the capabilities of FMCW radars. FMCW radars operating at and near the C-, X- and K_a-bands were used to obtain radar profiles over a wide range of snow cover conditions. These frequency-dependent radar signatures were used to identify important snow cover features such as ice and depth hoar layers. Snow grain size information was also obtained from the frequency-dependent scattering losses that were observed in the snow cover. Several case studies of FMCW radar profiles are presented in order to demonstrate the advantages of a multiband radar approach for monitoring the spatial and temporal variability of snow cover properties and/or processes over an extended area.     

Analysis of snowpack dynamics during the spring melt season for a sub‐alpine site using point measurements and numerical modeling

Snowpack dynamics through October 2014–June 2017 were described for a forested, sub‐alpine field site in southeastern Wyoming. Point measurements of wetness and density were combined with numerical modeling and continuous time series of snow depth, snow temperature, and snowpack outflow to identify 5 major classes of distinct snowpack conditions. Class (i) is characterized by no snowpack outflow and variable average snowpack temperature and density. Class (ii) is characterized by short durations of liquid water in the upper snowpack, snowpack outflow values of 0.0008–0.005 cm hr^?1, an increase in snowpack temperature, and average snow density between 0.25–0.35 g cm^?3. Class (iii) is characterized by a partially saturated wetness profile, snowpack outflow values of 0.005–0.25 cm hr^?1, snowpack temperature near 0 °C, and average snow density between 0.25–0.40 g cm^?3. Class (iv) is characterized by strong diurnal snowpack outflow pattern with values as high as 0.75 cm hr^?1, stable snowpack temperature near 0 °C, and stable average snow density between 0.35–0.45 g cm^?3. Class (v) occurs intermittently between Classes (ii)–(iv) and displays low snowpack outflow values between 0.0008–0.04 cm hr^?1, a slight decrease in temperature relative to the preceding class, and similar densities to the preceding class. Numerical modeling of snowpack properties with SNOWPACK using both the Storage Threshold scheme and Richards' equation was used to quantify the effect of snowpack capillarity on predictions of snowpack outflow and other snowpack properties. Results indicate that both simulations are able to predict snow depth, snow temperature, and snow density reasonably well with little difference between the 2 water transport schemes. Richards' equation more accurately simulates the timing of snowpack outflow over the Storage Threshold scheme, especially early in the melt season and at diurnal timescales.     

Subgrid variability of snow water equivalent at operational snow stations in the western USA

The spatial distribution of snow water equivalent (SWE) is a key variable in many regional‐scale land surface models. Currently, the assimilation of point‐scale snow sensor data into these models is commonly performed without consideration of the spatial representativeness of the point data with respect to the model grid‐scale SWE. To improve the understanding of the relationship between point‐scale snow measurements and surrounding areas, we characterized the spatial distribution of snow depth and SWE within 1‐, 4‐ and 16‐km² grids surrounding 15 snow stations (snowpack telemetry and California snow sensors) in California, Colorado, Wyoming, Idaho and Oregon during the 2008 and 2009 snow seasons. More than 30 000 field observations of snowpack properties were used with binary regression tree models to relate SWE at the sensor site to the surrounding area SWE to evaluate the sensor representativeness of larger‐scale conditions. Unlike previous research, we did not find consistent high biases in snow sensor depth values as biases over all sites ranged from 74% overestimates to 77% underestimates. Of the 53 assessments, 27 surveys indicated snow station biases of less than 10% of the surrounding mean observed snow depth. Depth biases were largely dictated by the physiographic relationship between the snow sensor locations and the mean characteristics of the surrounding grid, in particular, elevation, solar radiation index and vegetation density. These scaling relationships may improve snow sensor data assimilation; an example application is illustrated for the National Operational Hydrologic Remote Sensing Center National Snow Analysis SWE product. The snow sensor bias information indicated that the assimilation of point data into the National Operational Hydrologic Remote Sensing Center model was often unnecessary and reduced model accuracy. Copyright © 2012 John Wiley & Sons, Ltd.     

Influence of melt‐freeze‐cycles on the radionuclide transport in homogeneous laboratory snowpack

Radionuclides released to the environment and deposited with or onto snow can be stored over long time periods if ambient temperature stays low, particularly in glaciated areas or high alpine sites. The radionuclides will be accumulated in the snowpack during the winter unless meltwater runoff at the snow base occurs. They will be released to surface waters within short time during snowmelt in spring. In two experiments under controlled melting conditions of snow in the laboratory, radionuclide migration and runoff during melt‐freeze‐cycles were examined. The distribution of Cs‐134 and Sr‐85 tracers in homogeneous snow columns and their fractionation and potential preferential elution in the first meltwater portions were determined. Transport was associated with the percolation of meltwater at ambient temperatures above 0 °C after the snowpack became ripe. Mean migration velocities in the pack were examined for both nuclides to about 0.5 cm hr^?1 after one diurnal melt‐freeze‐cycle at ambient temperatures of ?2 to 4 °C. Meltwater fluxes were calculated with a median of 1.68 cm hr^?1. Highly contaminated portions of meltwater with concentration factors between 5 and 10 against initial bulk concentrations in the snowpack were released as ionic pulse with the first meltwater. Neither for caesium nor strontium preferential elution was observed. After recurrent simulated day‐night‐cycles (?2 to 4 °C), 80% of both radionuclides was released with the first 20% of snowmelt within 4 days. 50% of Cs‐134 and Sr‐85 were already set free after 24 hr. Snowmelt contained highest specific activities when the melt rate was lowest during the freeze‐cycles due to concentration processes in remaining liquids, enhanced by the melt‐freeze‐cycling. This implies for natural snowpack after significant radionuclide releases, that long‐time accumulation of radionuclides in the snow during frost periods, followed by an onset of steady meltwater runoff at low melt rates, will cause the most pronounced removal of the contaminants from the snow cover. This scenario represents the worst case of impact on water quality and radiation exposure in aquatic environments.     

Spatio‐temporal controls of snowmelt and runoff generation during rain‐on‐snow events in a mid‐latitude mountain catchment

A network of 30 standalone snow monitoring stations was used to investigate the snow cover distribution, snowmelt dynamics, and runoff generation during two rain‐on‐snow (ROS) events in a 40 km² montane catchment in the Black Forest region of southwestern Germany. A multiple linear regression analysis using elevation, aspect, and land cover as predictors for the snow water equivalent (SWE) distribution within the catchment was applied on an hourly basis for two significant ROS flood events that occurred in December 2012. The available snowmelt water, liquid precipitation, as well as the total retention storage of the snow cover were considered in order to estimate the amount of water potentially available for the runoff generation. The study provides a spatially and temporally distributed picture of how the two observed ROS floods developed in the catchment. It became evident that the retention capacity of the snow cover is a crucial mechanism during ROS. It took several hours before water was released from the snowpack during the first ROS event, while retention storage was exceeded within 1 h from the start of the second event. Elevation was the most important terrain feature. South‐facing terrain contributed more water for runoff than north‐facing slopes, and only slightly more runoff was generated at open compared to forested areas. The results highlight the importance of snowmelt together with liquid precipitation for the generation of flood runoff during ROS and the large temporal and spatial variability of the relevant processes. Copyright © 2015 John Wiley & Sons, Ltd.