Soil moisture response to snowmelt timing in mixed‐conifer subalpine forests

Western US forest ecosystems and downstream water supplies are reliant on seasonal snowmelt. Complex feedbacks govern forest–snow interactions in which forests influence the distribution of snow and the timing of snowmelt but are also sensitive to snow water availability. Notwithstanding, few studies have investigated the influence of forest structure on snow distribution, snowmelt and soil moisture response. Using a multi‐year record from co‐located observations of snow depth and soil moisture, we evaluated the influence of forest‐canopy position on snow accumulation and snow depth depletion, and associated controls on the timing of soil moisture response at Boulder Creek, Colorado, Jemez River Basin, New Mexico, and the Wolverton Basin, California. Forest‐canopy controls on snow accumulation led to 12–42 cm greater peak snow depths in open versus under‐canopy positions. Differences in accumulation and melt across sites resulted in earlier snow disappearance in open positions at Jemez and earlier snow disappearance in under‐canopy positions at Boulder and Wolverton sites. Irrespective of net snow accumulation, we found that peak annual soil moisture was nearly synchronous with the date of snow disappearance at all sites with an average deviation of 12, 3 and 22 days at Jemez, Boulder and Wolverton sites, respectively. Interestingly, sites in the Sierra Nevada showed peak soil moisture prior to snow disappearance at both our intensive study site and the nearby snow telemetry stations. Our results imply that the duration of soil water stress may increase as regional warming or forest disturbance lead to earlier snow disappearance and soil moisture recession in subalpine forests. Copyright © 2014 John Wiley & Sons, Ltd.     

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.     

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.     

Evaluating the functionality and streamflow impacts of explicitly modelling forest–snow interactions and canopy gaps in a distributed hydrologic model

Many plot‐scale studies have shown that snow‐cover dynamics in forest gaps are distinctly different from those in open and continuously forested areas, and forest gaps have the potential to alter the magnitude and timing of snowmelt. However, the watershed‐level impacts of canopy gap treatment on streamflows are largely unknown. Here, we present the first research that explicitly assesses the impact of canopy gaps on seasonal streamflows and particularly late‐season low flows at the watershed scale. To explicitly model forest–snow interactions in canopy gaps, we made major enhancements to a widely used distributed hydrologic model, distributed hydrology soil vegetation model, with a canopy gap component that represents physical processes of snowpack evolution in the forest gap separately from the surrounding forest on the subgrid scale (within a grid typically 10–150 m). The model predicted snow water equivalent using the enhanced distributed hydrology soil vegetation model showed good agreement (R² > 0.9) with subhourly snow water equivalent measurements collected from open, forested, and canopy gap sites in Idaho, USA. Compared with the original model that does not account for interactions between gaps and surrounding forest, the enhanced model predicted notably later melt in small‐ to medium‐size canopy gaps (the ratio of gap radius (r) to canopy height (h) ≤ 1.2), and snow melt rates exhibited great sensitivity to changing gap size in medium‐size gaps (0.5 ≤ r/h ≤ 1.2). We demonstrated the watershed‐scale implications of canopy gaps on streamflow in the snow‐dominated Chiwawa watershed, WA, USA. With 24% of the watershed drainage area (about 446 km²) converted to gaps of 60 m diameter, the mean annual 7‐day low flow was increased by 19.4% (i.e., 0.37 m³/s), and the mean monthly 7‐day low flows were increased by 13.5% (i.e., 0.26 m³/s) to 40% (i.e., 1.76 m³/s) from late summer through fall. Lastly, in practical implementation of canopy gaps with the same total gap areas, a greater number of distributed small gaps can have greater potential for longer snow retention than a smaller number of large gaps.     

Modelling forest snow processes with a new version of WaSiM

ABSTRACT

We present a new model extension for the Water balance Simulation Model, WaSiM, which features (i) snow interception and (ii) modified meteorological conditions under coniferous forest canopies, complementing recently developed model extensions for particular mountain hydrological processes. Two study areas in Austria and Germany are considered in this study. To supplement and constrain the modelling experiments with on-site observations, a network of terrestrial time-lapse cameras was set up in one of these catchments. The spatiotemporal patterns of snow depth inside the forest and at the adjacent open field sites were recorded along with snow interception dynamics. Comparison of observed and modelled snow cover and canopy interception indicates that the new version of WaSiM reliably reconstructs the variability of snow accumulation for both the forest and the open field. The Nash-Sutcliffe efficiency computed for selected runoff events in spring increases from ?0.68 to 0.71 and 0.21 to 0.87, respectively.     

Assimilating snow observations to snow interception process simulations

Snow interception is a crucial hydrological process in cold regions needleleaf forests, but is rarely measured directly. Indirect estimates of snow interception can be made by measuring the difference in the increase in snow accumulation between the forest floor and a nearby clearing over the course of a storm. Pairs of automatic weather stations with acoustic snow depth sensors provide an opportunity to estimate this, if snow density can be estimated reliably. Three approaches for estimating fresh snow density were investigated: weighted post-storm density increments from the physically based Snobal model, fresh snow density estimated empirically from air temperature (Hedstrom, N. R., et al. [1998]. Hydrological Processes, 12, 1611–1625), and fresh snow density estimated empirically from air temperature and wind speed (Jordan, R. E., et al. [1999]. Journal of Geophysical Research, 104, 7785–7806). Automated snow depth observations from adjacent forest and clearing sites and estimated snow densities were used to determine snowstorm snow interception in a subalpine forest in the Canadian Rockies, Alberta, Canada. Then the estimated snow interception and measured interception information from a weighed, suspended tree and a time-lapse camera were assimilated into a model, which was created using the Cold Regions Hydrological Modelling platform (CRHM), using Ensemble Kalman Filter or a simple rule-based direct insertion method. Interception determined using density estimates from the Hedstrom-Pomeroy fresh snow density equation agreed best with observations. Assimilating snow interception information from automatic snow depth measurements improved modelled snow interception timing by 7% and magnitude by 13%, compared to an open loop simulation driven by a numerical weather model; its accuracy was close to that simulated using locally observed meteorological data. Assimilation of tree-measured snow interception improved the snow interception simulation timing and magnitude by 18 and 19%, respectively. Time-lapse camera snow interception information assimilation improved the snow interception simulation timing by 32% and magnitude by 7%. The benefits of assimilation were greatly influenced by assimilation frequency and quality of the forcing data.     

Evaluation of spatial variability in snow water equivalent for a high mountain catchment

Multivariate statistical analysis was used to explore relationships between catchment topography and spatial variability in snow accumulation and melt processes in a small headwater catchment in the Spanish Pyrenees. Manual surveys of snow depth and density provided information on the spatial distribution of snow water equivalent (SWE) and its depletion over the course of the 1997 and 1998 melt seasons. A number of indices expressing the topographic control on snow processes were extracted from a detailed digital elevation model of the catchment. Bivariate screening was used to assess the relative importance of these topographic indices in controlling snow accumulation at the start of the melt season, average melt rates and the timing of snow disappearance. This suggested that topographic controls on the redistribution of snow by wind are the most important influence on snow distribution at the start of the melt season. Furthermore, it appeared that spatial patterns of snow disappearance were largely determined by the distribution of snow water equivalent (SWE) at the start of the melt season, rather than by spatial variability in melt rates during the melt season. Binary regression tree models relating snow depth and disappearance date to terrain indices were then constructed. These explained 70–80% of the variance in the observed data. As well as providing insights into the influence of topography on snow processes, it is suggested that the techniques presented herein could be used in the parameterization of distributed snowmelt models, or in the design of efficient stratified snow surveys. Copyright © 2003 John Wiley & Sons, Ltd.     

Snow accumulation and ablation response to changes in forest structure and snow surface albedo after attack by mountain pine beetle

This study quantified changes in snow accumulation and ablation with forest defoliation in a young pine stand attacked by mountain pine beetle, a mature mixed species stand, and a clearcut in south‐central British Columbia. From 2006 to 2012, as trees in the pine stand turned from green to grey, average canopy transmittance increased from 27% to 49%. In the mixed stand, transmittance remained constant at 19%. In 2009, the year of greatest needle loss, average snow surface litter cover in the pine stand was 29% (range 4 – 61%), compared to ≤9% in other years and over double that in the mixed stand. By 2012, litter accumulation in the now‐grey pine stand was only a sixth of that in the mixed stand. Inter‐annual variability in the weather had the greatest effect on snow accumulation and ablation, with the greatest differences between both forested stands and the clearcut occurring in 2010, the year of lowest SWE. Differences in snow accumulation between the pine and mixed stand increased in 2010 as a result of decreased snow interception in the young stand after needlefall. Average ablation rates in the attacked stand were most different from the mixed stand in 2009 and 2012, the years with the largest and smallest over‐winter needle loss, respectively. This study shows that grey, non‐pine, and understory trees moderate snow response to changes in the main canopy. It also highlights the complex interrelationships between ecohydrological processes key to assessing watershed response to forest cover loss in snow dominated hydrologic regimes. Copyright © 2012 John Wiley & Sons, Ltd.     

Influence of canopy density on snow distribution in a temperate mountain range

We analyse spatial variability and different evolution patterns of snowpack in a mixed beech–fir stand in the central Pyrenees. Snow depth and density were surveyed weekly along six transects of contrasting forest cover during a complete accumulation and melting season; we also surveyed a sector unaffected by canopy cover. Forest density was measured using the sky view factor (SVF) obtained from digital hemispherical photographs. During periods of snow accumulation and melting, noticeable differences in snow depth and density were found between the open site and those areas covered by forest canopy. Principal component analysis provided valuable information in explaining these observations. The results indicate a high variability in snow accumulation within forest areas related to differences in canopy density. Maximum snow water equivalent (SWE) was reduced by more than 50% beneath dense canopies compared with clearings, and this difference increased during the melting period. We also found significant temporal variations: when melting began in sectors with low SVF, most of the snow had already thawed in areas with high SVF. However, specific conditions occasionally produced a different response of SWE to forest cover, with lower melting rates observed beneath dense canopies. The high values of correlation coefficients for SWE and SVF (r > 0·9) indicate the reliability of predicting the spatial distribution of SWE in forests when only a moderate number of observations are available. Digital hemispherical photographs provide an appropriate tool for this type of analysis, especially for zenith angles in the range 35–55 . Copyright © 2007 John Wiley & Sons, Ltd.     

Representation of canopy snow interception,unloading and melt in a parsimonious snowmelt model

Recent improvements in the Utah Energy Balance (UEB) snowmelt model are focused on snow–vegetation–atmosphere interactions to understand how different types of vegetation affect snow processes in the mountains of Western USA. This work presents field work carried out in the Rocky Mountains of Northern Utah to evaluate new UEB model algorithms that represent the processes of canopy snow interception, sublimation, mass unloading and melt. Four years' continuous field observations showed generally smaller accumulations of snow beneath the forest canopies in comparison with open (sage and grass) areas, a difference that is attributed to interception and subsequent sublimation and redistribution of intercepted snow by wind, much of it into surrounding open areas. Accumulations beneath the denser forest (conifer) canopies were found to be less than the accumulation beneath the less dense forest (deciduous) canopies. The model was able to represent the accumulation of snow water equivalent in the open and beneath the deciduous forest quite well but without accounting for redistribution tended to overestimate the snow water equivalent beneath the conifer forest. Evidence of redistribution of the intercepted snow from the dense forest (i.e. conifer forest) to the adjacent area was inferred from observations. Including a simple representation of redistribution in the model gave satisfactory prediction of snow water equivalent beneath the coniferous forest. The simulated values of interception, sublimation and unloading were also compared with previous studies and found in agreement. Copyright © 2013 John Wiley & Sons, Ltd.     

Snow process monitoring in montane forests with time‐lapse photography

A camera network with hourly resolution was used to monitor the complex snow processes in montane forest environments. We developed a semi‐automatic procedure to interpret snow depths from the digital images, which exhibited high consistency with manual measurements and station‐based recordings. To extract snow interception dynamics, six binary classification methods were compared. The MaxEntropy classifier demonstrated better performance than the other methods under conditions of varying illumination and was therefore selected as the method used for quantifying snow in tree canopies. Snow accumulation and ablation on the ground, as well as snow loading and unloading in the forest canopies, were investigated using snow parameters derived from the time‐lapse photography monitoring. The influences of meteorologic conditions, forest cover, and elevation on the snow processes were also considered. Time‐lapse photography proved to be an effective and low‐cost approach for collecting useful information on snow processes and facilitating the set‐up of hydrological models.     

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.     

Dynamics of snow ablation in a small Alpine catchment observed by repeated terrestrial laser scans

The spatio‐temporal distribution of snow in a catchment during ablation reflects changes in the total amount of snow water equivalent and is thus a key parameter for the estimation of melt water run‐off. This study explores possible rules behind the spatial variability of snow depth during the ablation season in a small Alpine catchment with complex topography. The snow depth observations are based on more than 160 000 terrestrial laser scanner data points with a spatial resolution of 1 m, which were obtained from 11 scanning campaigns of two consecutive ablation seasons. The analysis suggests that for estimating cumulative snow melt dynamics from the catchment investigated, assessing the initial snow distribution prior to the melt season is more important than addressing spatial differences in the melt behaviour. Snow volume and snow‐covered area could be predicted well using a conceptual melt model assuming spatially uniform melt rates. However, accurate results were only obtained if the model was initialized with a pre‐melt snow distribution that reflected measured mean and standard deviation. Using stratified melt rates on the other hand did not improve the model results. At least for sites with similar meteorological and topographical conditions, the model approach presented here comprises an efficient way to estimate snow depletion dynamics, especially if persistent snow accumulation pattern between years facilitate the characterization of the initial snow distribution prior to the melt. Copyright © 2011 John Wiley & Sons, Ltd.     

Variations in snowmelt energy and energy balance characteristics with larch forest density on Mt Iwate,Japan: observations and energy balance analyses

The variation in snowmelt energy and energy components were evaluated with respect to forest density. Surface snowmelt rates, surface evaporation from snow cover and meteorological elements were measured in the open and under sparse (411 trees/ha) and dense (1433 trees/ha) larch canopies. The surface snowmelt rate decreased as the forest density increased. Based on the observations and energy balance analyses, we concluded the following. (1) Albedo decreased while the bulk coefficient for latent heat increased with forest density. (2) The duration of snowmelt increased with forest density because the energy for nocturnal cooling of the snow cover decreased. (3) When comparing the open and forested sites, the changes in snowmelt energy with forest density were caused by sensible heat flux. However, the contribution of net radiation was highest in the forested sites. Therefore, the effects of forest cover on the snowmelt energy were different when comparing both the open and forested sites and the sparse and densely forested sites. (4) The ratio of net radiation to snowmelt energy increased with forest density; although both snowmelt energy and net radiation decreased with increased forest density, the snowmelt energy decreased more rapidly. Sensible heat also decreased as forest density increased. Both albedo and downward long‐wave radiation influenced net radiation. Copyright © 1999 John Wiley & Sons, Ltd.     

Measuring snow accumulation and ablation dynamics during rain-on-snow events: innovative measurement techniques

Rain-on-snow (ROS) is the primary generator of peak flow events in mountainous coastal regions of North America. Uncertainty remains as to the role of forest canopy interception leading up to and during ROS events. Much of this uncertainty can be attributed to a lack of suitable techniques to collect data during ROS, due in part to the dynamic nature of climatic conditions, particularly related to snow accumulation and melt. We supplemented a meteorological network with non-weighing snow melt lysimeters, suspended spring scales to measure snow throughfall and an automated time lapse photography network to monitor state of precipitation (rain vs. snow), snow accumulation/ablation, canopy interception and unloading of snow from the canopy. Image analysis software allowed for the extraction of data from images. Rapid loading and unloading of snow from the canopy, closely linked to changes in temperature, was observed using this approach. We were also able to continuously monitor throughfall snow water equivalent using low cost suspended spring scales. This experimental design allowed us to capture information previously unavailable without direct observation. Copyright © 2008 John Wiley & Sons, Ltd.     

Influences of forest on MODIS snow cover mapping and snow variations in the Amur River basin in Northeast Asia during 2000–2014

The study applies the improved cloud‐free moderate resolution imaging spectral radiometer daily snow cover product (MODMYD_MC) to investigate the snow cover variations from snow hydrologic year (HY) HY2000 to HY2013 in the Amur River basin (ARB), Northeast Asia. The fractions of forest cover were 38%, 63%, and 47% in 2009 in China (the southern ARB), Russia (the northern ARB), and ARB, respectively. Validation results show that MODMYD_MC has a snow agreement of 88% against in situ snow depth (SD) observations (SD ≥ 4 cm). The agreement is about 10% lower at the forested stations than at the nonforested stations. Snow cover durations (SCDs) from MODMYD_MC are 20 days shorter than ground observations (SD ≥ 1 cm) at the forested stations, whereas they are just 8 days shorter than ground observations (SD ≥ 1 cm) at the nonforested stations. Annual mean SCDs from MODMYD_MC in the forested areas are 21 days shorter than those in the nearby farmland in the Sanjiang Plain. This indicates forest has a complex influence on the snow accumulation and melting processes and even on optical satellite snow cover mapping. Meanwhile, SCD and mean snow cover are negatively correlated with air temperature in ARB, especially in the snow melting season, when mean air temperature in March and April can explain 86% and 74% of the mean snow cover variations in China ARB and Russia ARB, respectively. From 1961 to 2015, the annual mean air temperature presented an increased trend by 0.33 °C/decade in both China ARB and Russia ARB, whereas it had a decrease trend from HY2000 to HY2013. The decrease of air temperature led to an increase of snow cover, which is different from the global decrease trend of snow cover variations. SCD and snow cover had larger increase rates in China ARB than in Russia ARB, and they were larger in the forested areas than in the nearby farmland in the Sanjiang Plain.     

Inter-annual variability in snow cover depletion patterns and atmospheric circulation indices in the Upper Irtysh basin,Central Asia

The Irtysh River is the main water resource of Eastern Kazakhstan and its upper basin is severely affected by spring floods each year, primarily as a result of snowmelt. Knowledge of the large-scale processes that influence the timing of these snow-induced floods is currently lacking, but critical for the management of water resources in the area. In this study, we evaluated the variability in winter–spring snow cover in five major sub-basins of the Upper Irtysh basin between 2000 and 2017 as a possible explanatory factor of spring flood events, assessing the time of peak snow cover depletion rate and snow cover disappearance from the moderate-resolution imaging spectroradiometer (MODIS) MOD10A2 data set. We found that on average, peak snow cover retreat occurs between 22 March and 14 April depending on the basin, with large interannual variations but no clear trend over the MODIS period, while our comparative analysis of longer-term snow cover extent from the National Oceanic and Atmospheric Administration Climate Data Record data set suggests a shift to earlier snow cover disappearance since the 1970s. In contrast, the annual peak snow cover depletion rate displays a weak increasing trend over the study period and exceeded 5,900 km²/day in 2017. The timing of snow disappearance in spring shows significant correlations of up to 0.82 for the largest basin with winter indices of the Arctic Oscillation (AO) over the region. The primary driver is the impact of the large-scale pressure anomalies upon the mean spring (MAM) air temperatures and resultant timing of snow cover disappearance, particularly at elevations 500–2,000 m above sea level. This suggests a lagged effect of this atmospheric circulation pattern in spring snow cover retreat. The winter AO index could therefore be incorporated into long-term runoff forecasts for the Irtysh. Our approach is easily transferable to other similar catchments and could support flood management strategies in Kazakhstan and other countries.     

The effects of forest litter on snow energy budget in the Tianshan Mountains,China

Forest litter exerts an impact on the energy budget of snow surfaces, which lie beneath forest canopies. In this study, we measured shortwave and longwave radiation levels, as well as quantities of Asian spruce (Picea schrenkinan ) forest litter, over 3 snow study plots that representing an open environment, 20% forest canopy openness (20% FCO), and 80% forest canopy openness (80% FCO). The fractional litter coverage (lc ) was obtained through the binarization of digital photographs of forest litter. The effects of forest litter on snow surface albedo (α ), snow surface temperature (T _s ), upward shortwave and longwave radiation (K _↑ and L _↑), and sensible heat flux (H ) were then analyzed. According to our results, the energy budget over snow surface influenced by forest litter principally due to forest litter forcing α decrease and T _s increase. The effects of forest litter on the energy budget increased with time and lc . We found that forest litter exerted the most significant impact on K _↑ and L _↑ at daytime during the latter stages of the snowmelt period. The influence of forest litter on H was more apparent on windy days. The presence of forest litter increased gains in shortwave radiation and losses in longwave radiation and decreased gains in H . Compared to the simulated energy (K _↑ + L _↑ + H ) over a snow surface without litter, the calculated energy decreased by ?13.4 W/m² and increased by 9.0 W/m², respectively, at the 20% FCO and 80% FCO sites during the latter stages of the snowmelt period. Overall, forest litter facilitated snow surface energy gains at the 80% FCO site and impeded them at the 20% FCO site during the latter stages of the snowmelt period.     

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.     

Exploration of remotely sensed forest structure and ultrasonic range sensor metrics to improve empirical snow models

Current methods to estimate snow accumulation and ablation at the plot and watershed levels can be improved as new technologies offer alternative approaches to more accurately monitor snow dynamics and their drivers. Here we conduct a meta‐analysis of snow and vegetation data collected in British Columbia to explore the relationships between a wide range of forest structure variables – obtained from Light Detection and Ranging (LiDAR), hemispherical photography (HP) and Landsat Thematic Mapper – and several indicators of snow accumulation and ablation estimated from manual snow surveys and ultrasonic range sensors. By merging and standardizing all the ground plot information available in the study area, we demonstrate how LiDAR‐derived forest cover above 0.5 m was the variable explaining the highest percentage of absolute peak snow water equivalent (SWE) (33%), while HP‐derived leaf area index and gap fraction (45° angle of view) were the best potential predictors of snow ablation rate (explaining 57% of variance). This study reveals how continuous SWE data from ultrasonic sensors are fundamental to obtain statistically significant relationships between snow indicators and structural metrics by increasing mean r² by 20% when compared to manual surveys. The relationships between vegetation and spectral indices from Landsat and snow indicators, not explored before, were almost as high as those shown by LiDAR or HP and thus point towards a new line of research with important practical implications. While the use of different data sources from two snow seasons prevented us from developing models with predictive capacity, a large sample size helped to identify outliers that weakened the relationships and suggest improvements for future research. A concise overview of the limitations of this and previous studies is provided along with propositions to consistently improve experimental designs to take advantage of remote sensing technologies, and better represent spatial and temporal variations of snow. Copyright © 2013 John Wiley & Sons, Ltd.