Snowpack variability across various spatio‐temporal resolutions

High‐resolution snow depth (SD) maps (1 × 1 m) obtained from terrestrial laser scanner measurements in a small catchment (0.55 km²) in the Pyrenees were used to assess small‐scale variability of the snowpack at the catchment and sub‐grid scales. The coefficients of variation are compared for various plot resolutions (5 × 5, 25 × 25, 49 × 49, and 99 × 99 m) and eight different days in two snow seasons (2011–2012 and 2012–2013). We also studied the relation between snow variability at the small scale and SD, topographic variables, small‐scale variability in topographic variables. The results showed that there was marked variability in SD, and it increased with increasing scales. Days of seasonal maximum snow accumulation showed the least small‐scale variability, but this increased sharply with the onset of melting. The coefficient of variation (CV) in snowpack depth showed statistically significant consistency amongst the various spatial resolutions studied, although it declined progressively with increasing difference between the grid sizes being compared. SD best explained the spatial distribution of sub‐grid variability. Topographic variables including slope, wind sheltering, sub‐grid variability in elevation, and potential incoming solar radiation were also significantly correlated with the CV of the snowpack, with the greatest correlation occurring at the 99 × 99 m resolution. At this resolution, stepwise multiple regression models explained more than 70% of the variance, whereas at the 25 × 25 m resolution they explained slightly more than 50%. The results highlight the importance of considering small‐scale variability of the SD for comprehensively representing the distribution of snowpack from available punctual information, and the potential for using SD and other predictors to design optimized surveys for acquiring distributed SD data. Copyright © 2014 John Wiley & Sons, Ltd.     

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.     

Watershed‐scale controls on snow accumulation in a small montane watershed,southwestern Alberta,Canada

Snow accumulation in mountain headwater basins is a major water source, particularly in semi‐arid environments such as southern Alberta where water resources are stressed and snowmelt supplies more than 80% of downstream runoff. Relationships between landscape predictor variables and snow water equivalent (SWE) were quantified by combining field and LiDar measurements with classification and regression tree analysis over two winter seasons (2010 and 2011) in a small, montane watershed. 2010 was a below average snow accumulation year, while 2011 was well above normal. In both the field and regression tree data, elevation was the dominant control on snow distribution in both years, although snow distribution was driven by melt processes in 2010 and accumulation processes in 2011. The importance of solar radiation and wind exposure was represented in the regression trees in both years. The regression trees also noted the lower importance of canopy closure, slope, and aspect, which was not observed in the field data. This technique could provide an additional method of forecasting annual water supply from melting snow. However, further research is required to address the lack of data collected above treeline, to provide a full‐basin estimate of SWE. Copyright © 2012 John Wiley & Sons, Ltd.     

Scaling properties and spatial organization of snow depth fields in sub‐alpine forest and alpine tundra

This paper reports on a study analysing the spatial distribution functions, the correlation structures, and the power spectral densities of high‐resolution LIDAR snow depths (～1 m) in two adjacent 500 m × 500 m areas in the Colorado Rocky Mountains, one a sub‐alpine forest the other an alpine tundra. It is shown how and why differences in the controlling physical processes induced by variations in vegetation cover and wind patterns lead to the observed differences in spatial organization between the snow depth fields of these environments. In the sub‐alpine forest area, the mean of snow depth increases with elevation, while its standard deviation remains uniform. In the tundra subarea, the mean of snow depth decreases with elevation, while its standard deviation varies over a wide range. The two‐dimensional correlations of snow depth in the forested area indicate little spatial memory and isotropic conditions, while in the tundra they indicate a marked directional bias that is consistent with the predominant wind directions and the location of topographic ridges and depressions. The power spectral densities exhibit a power law behaviour in two frequency intervals separated by a break located at a scale of around 12 m in the forested subarea, and 65 m in the tundra subarea. The spectral exponents obtained indicate that the snow depth fields are highly variable over scales larger than the scale break, while highly correlated below. Based on the observations and on synthetic snow depth fields generated with one‐ and two‐dimensional spectral techniques, it is shown that the scale at which the break occurs increases with the separation distance between snow depth maxima. In addition, the breaks in the forested area coincide with those of the corresponding vegetation height field, while in the tundra subarea they are displaced towards larger scales than those observed in the corresponding vegetation height field. Copyright © 2009 John Wiley & Sons, Ltd.     

Long‐term change of seasonal snow cover and its effects on river runoff in the Tarim River basin,northwestern China

Spatio‐temporal variation of snow depth in the Tarim River basin has been studied by the empirical orthogonal function (EOF) based on the data collected by special sensor microwave/imager (SSM/I) and scanning multichannel microwave radiometer (SMMR) during the period from 1979 to 2005. The long‐term trend of snow depth and runoff was presented using the Mann‐Kendall non‐parametric test, and the effects of the variations of snow depth and climatic factors on runoff were analysed and discussed by means of the regression analysis. The results suggested that the snow depth variation on the entire basin was characterised by four patterns: all consistency, north–south contrast, north‐middle‐south contrast and complex. The first pattern accounting 39·13% of the total variance was dominant. The entire basin was mainly affected by one large‐scale weather system. However, the spatial and temporal differences also existed among the different regions in the basin. The significant snow depth changes occurred mainly in the Aksu River basin with the below‐normal snow depth anomalies in the 1980s and the above‐normal snow depth anomalies in the 1990s. The long‐term trend of snow depth was significant in the northwestern, western and southern parts of the basin, whereas the long‐term trend of runoff was significant in the northwestern and northeastern parts. The regression analysis revealed that the runoff of the rivers replenished by snow melt water and rainfall was related primarily to the summer precipitation, followed by the summer temperature or the maximum snow depth in the cold season. Our results suggest that snow is not the principal factor that contributes to the runoff increase in headstreams, although there was a slow increase in snow depth. It is the climatic factors that are responsible for the steady and continuous water increase in the headstreams. Copyright © 2009 John Wiley & Sons, Ltd.     

Spatial and temporal patterns of snow accumulation and aerial ablation across the McMurdo Dry Valleys,Antarctica

Snow in the McMurdo Dry Valleys is a potential source of moisture for subnivian soils in a cold desert ecosystem. In a water‐limited environment, enhanced soil moisture is expected to provide more favourable conditions for subnivian soil communities. In addition, snow cover insulates the underlying soil from air temperature extremes. Quantifying the spatial and temporal patterns of seasonal snow accumulation and ablation is necessary to understand these dynamics. Repeat high‐resolution imagery acquired for the 2009–2010 austral summer was used to map the seasonal distribution of snow across Taylor and Wright valleys, Southern Victorialand, Antarctica. An edge detection algorithm was used to perform an object‐based classification of snow‐covered area. Coupled with topographic parameters obtained from a 30‐m digital elevation model, unique distribution patterns were characterized for five regions within the neighbouring valleys. Time lapses of snow distribution in each region provide insight into spatially variable aerial ablation rates (change in area of landscape covered by snow) across the region. A strong coastal to interior gradient of decreasing snow‐covered area was evident for both Taylor and Wright valleys. The surrounding regions of Lake Fryxell, Lake Hoare, Lake Bonney, Lake Brownworth, and Lake Vanda exhibited losses of snow‐covered area of 9.61 km² (?93%), 1.63 km² (?72%), 1.07 km² (?97%), 2.60 km² (?82%), and 0.25 km² (?96%), respectively, as measured from peak accumulation in October to mid‐January. Differences in aerial ablation rates within and across local regions suggest that both topographic variation and regional microclimates influence the ablation of seasonal snow cover. Copyright © 2012 John Wiley & Sons, Ltd.     

Snow model sensitivity analysis to understand spatial and temporal snow dynamics in a high‐elevation catchment

In this paper, we addressed a sensitivity analysis of the snow module of the GEOtop2.0 model at point and catchment scale in a small high‐elevation catchment in the Eastern Italian Alps (catchment size: 61 km²). Simulated snow depth and snow water equivalent at the point scale were compared with measured data at four locations from 2009 to 2013. At the catchment scale, simulated snow‐covered area (SCA) was compared with binary snow cover maps derived from moderate‐resolution imaging spectroradiometer (MODIS) and Landsat satellite imagery. Sensitivity analyses were used to assess the effect of different model parameterizations on model performance at both scales and the effect of different thresholds of simulated snow depth on the agreement with MODIS data. Our results at point scale indicated that modifying only the “snow correction factor” resulted in substantial improvements of the snow model and effectively compensated inaccurate winter precipitation by enhancing snow accumulation. SCA inaccuracies at catchment scale during accumulation and melt period were affected little by different snow depth thresholds when using calibrated winter precipitation from point scale. However, inaccuracies were strongly controlled by topographic characteristics and model parameterizations driving snow albedo (“snow ageing coefficient” and “extinction of snow albedo”) during accumulation and melt period. Although highest accuracies (overall accuracy = 1 in 86% of the catchment area) were observed during winter, lower accuracies (overall accuracy < 0.7) occurred during the early accumulation and melt period (in 29% and 23%, respectively), mostly present in areas with grassland and forest, slopes of 20–40°, areas exposed NW or areas with a topographic roughness index of ?0.25 to 0 m. These findings may give recommendations for defining more effective model parameterization strategies and guide future work, in which simulated and MODIS SCA may be combined to generate improved products for SCA monitoring in Alpine catchments.     

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.     

Snow accumulation distribution inferred from time‐lapse photography and simple modelling

The spatial and temporal distribution of snow accumulation is complex and significantly influences the hydrological characteristics of mountain catchments. Many snow redistribution processes, such as avalanching, slushflow or wind drift, are controlled by topography, but their modelling remains challenging. In situ measurements of snow accumulation are laborious and generally have a coarse spatial or temporal resolution. In this respect, time‐lapse photography shows itself as a powerful tool for collecting information at relatively low cost and without the need for direct field access. In this paper, the snow accumulation distribution of an Alpine catchment is inferred by adjusting a simple snow accumulation model combined with a temperature index melt model to match the modelled melt‐out pattern evolution to the pattern monitored during an ablation season through terrestrial oblique photography. The comparison of the resulting end‐of‐winter snow water equivalent distribution with direct measurements shows that the achieved accuracy is comparable with that obtained with an inverse distance interpolation of the point measurements. On average over the ablation season, the observed melt‐out pattern can be reproduced correctly in 93% of the area visible from the fixed camera. The relations between inferred snow accumulation distribution and topographic variables indicate large scatter. However, a significant correlation with local slope is found and terrain curvature is detected as a factor limiting the maximal snow accumulation. Copyright © 2010 John Wiley & Sons, Ltd.     

Evaluation of temporal consistency of snow depth drivers of a Rocky Mountain watershed in southern Alberta

Collecting spatially representative data over large areas is a challenge within snow monitoring frameworks. Identifying consistent trends in snow accumulation properties enables increased sampling efficiency by minimizing field collection time and/or remote sensing costs. Seasonal snowpack depth estimations during mid-winter and melt onset conditions were derived from airborne Lidar over the West Castle Watershed in the southern Canadian Rockies on three dates. Each dataset was divided into five sets of snow depth driver classes: elevation, aspect, topographic position index, canopy cover and slope. Datasets were quality controlled by eliminating snow depth values above the 99th percentile value, which had a negligible effect on average snow depths. Consistent trends were observed among driver classes with peak snow accumulation occurring within the treeline ecotone, north-facing aspects, open canopies, topographic depressions and areas with low slope angle. Although mid-winter class trends for each driver were similar and watershed-scale snow depth distributions were significantly correlated (0.76, p < .01), depth distributions within the same driver class of the three datasets were not correlated due to recent snowfall events, redistribution and settling processes. Trends in driver classes during late season melt onset were similar to mid-winter conditions but watershed scale distribution correlation results varied with seasonality (0.68 mid-winter 2014 and melt onset 2016; 0.65 mid-winter 2017 and melt onset 2016, p < .1). This is due to the differing stages of accumulation or ablation and the upward migration in the 0°C isotherm during spring, when snow depth can be declining in valley bottoms while still increasing at higher elevations. The observed consistency in depth driver controls can be used to guide future integrated snow monitoring frameworks.     

Random forests as a tool to understand the snow depth distribution and its evolution in mountain areas

The small scale distribution of the snowpack in mountain areas is highly heterogeneous, and is mainly controlled by the interactions between the atmosphere and local topography. However, the influence of different terrain features in controlling variations in the snow distribution depends on the characteristics of the study area. As this leads to uncertainties in high spatial resolution snowpack simulations, a deeper understanding of the role of terrain features on the small scale distribution of snow depth is required. This study applied random forest algorithms to investigate the temporal evolution of snow depth in complex alpine terrain using as predictors various topographical variables and in situ snow depth observations at a single location. The high spatial resolution (1 m x 1 m) snow depth distribution database used in training and evaluating the random forests was derived from terrestrial laser scanner (TLS) devices at three study sites, in the French Alps (2 sites) and the Spanish Pyrenees (1 site). The results show the major importance of two topographic variables, the topographic position index and the maximum upwind slope parameter. For these variables the search distances and directions depended on the characteristics of each site and the TLS acquisition date, but are consistent across sites and are tightly related to main wind directions. The weight of the different topographic variables on explaining snow distribution evolves while major snow accumulation events still take place and minor changes are observed after reaching the annual snow accumulation peak. Random forests have demonstrated good performance when predicting snow distribution for the sites included in the training set with R² values ranging from 0.82 to 0.94 and mean absolute errors always below 0.4 m. Oppositely, this algorithm failed when used to predict snow distribution for sites not included in the training set, with mean absolute errors above 0.8 m.     

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.     

Combining MODIS and AMSR‐E observations to improve MCD43A3 short‐time snow‐covered Albedo estimation

Land surface albedo plays an important role in the radiation budget and global climate models. NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) provide 16‐day albedo product with 500‐m resolution every 8 days (MCD43A3). Some in‐situ albedo measurements were used as the true surface albedo values to validate the MCD43A3 product. As the 16‐day MODIS albedo retrievals do not include snow observations when there is ephemeral snow on the ground surface in a 16‐day period, comparisons between MCD43A3 and 16 day averages of field data do not agree well. Another reason is that the MODIS cannot detect the snow when the area is covered by clouds. The Advanced Microwave Scanning Radiometer for EOS (AMSR‐E) data are not affected by weather conditions and are a good supplement for optical remote sensing in cloudy weather. When the surface is covered by ephemeral snow, the AMSR‐E data can be used as the additional information to retrieve the snow albedo. In this study, we developed an improved method by using the MODIS products and the AMSR‐E snow water equivalent (SWE) product to improve the MCD43A3 short‐time snow‐covered albedo estimation. The MODIS daily snow products MOD10A1 and MYD10A1 both provide snow and cloud information from observations. In our study region, we updated the MODIS daily snow product by combining MOD10A1 and MYD10A1. Then, the product was combined with the AMSR‐E SWE product to generate new daily snow‐cover and SWE products at a spatial resolution of 500 m. New SWE datasets were integrated into the Noah Land Surface Model snow model to calculate the albedo above a snow surface, and these values were then utilized to improve the MODIS 16‐day albedo product. After comparison of the results with in‐situ albedo measurements, we found that the new corrected 16‐day albedo can show the albedo changes during the short snowfall season. For example, from January 25 to March 14, 2007 at the BJ site, the albedo retrieved from snow‐free observations does not indicate the albedo changes affected by snow; the improved albedo conforms well to the in‐situ measurements. The correlation coefficient of the original MODIS albedo and the in‐situ albedo is 0.42 during the ephemeral snow season, but the correlation coefficient of the improved MODIS albedo and the in‐situ albedo is 0.64. It is concluded that the new method is capable of capturing the snow information from AMSR‐E SWE to improve the short‐time snow‐covered albedo estimation. Copyright © 2012 John Wiley & Sons, Ltd.     

Snow measurement by GPS interferometric reflectometry: an evaluation at Niwot Ridge,Colorado

Snow is a critical storage component in the hydrologic cycle, but current measurement networks are sparse. In addition, the heterogeneity of snow requires surveying larger areas to measure the areal average. We presented snow measurements using GPS interferometric reflectometry (GPS‐IR). GPS‐IR measures a large area (~100 m²), and existing GPS installations around the world have the potential to expand existing snow measurement networks. GPS‐IR uses a standard, geodetic GPS installation to measure the snow surface via the reflected component of the signal. We reported GPS‐IR snow depth measurements made at Niwot Ridge, Colorado, from October 2009 through June 2010. This site is in a topographic saddle at 3500 m elevation with a peak snow depth of 1.7 m near the GPS antenna. GPS‐IR measurements are compared with biweekly snow surveys, a continuously operating scanning laser system and an airborne light detection and ranging (LIDAR) measurement. The GPS‐IR measurement of peak snowpack (1.36–1.76 m) matches manual measurements (0.95–1.7 m) and the scanning laser (1.16 m). GPS‐IR has RMS error of 13 cm (bias = 10 cm) compared with the laser, although differences between the measurement locations make comparison imprecise. Over the melt season, when the snowpack is more homogenous, the difference between the GPS‐IR and the laser is reduced (RMS = 9 cm, bias = 6 cm). In other locations, the GPS and the LIDAR agree on which areas have more or less snow, but the GPS estimates more snow on the ground on tracks to the west (1.58 m) than the LIDAR (1.14 m). Copyright © 2011 John Wiley & Sons, Ltd.     

Understanding subgrid variability of snow depth at 1‐km scale using Lidar measurements

It is well known that snow plays an important role in land surface energy balance; however, modelling the subgrid variability of snow is still a challenge in large‐scale hydrological and land surface models. High‐resolution snow depth data and statistical methods can reveal some characteristics of the subgrid variability of snow depth, which can be useful in developing models for representing such subgrid variability. In this study, snow depth was measured by airborne Lidar at 0.5‐m resolution over two mountainous areas in south‐western Wyoming, Snowy Range and Laramie Range. To characterize subgrid snow depth spatial distribution, measured snow depth data of these two areas were meshed into 284 grids of 1‐km × 1‐km. Also, nine representative grids of 1‐km × 1‐km were selected for detailed analyses on the geostatistical structure and probability density function of snow depth. It was verified that land cover is one of the important factors controlling spatial variability of snow depth at the 1‐km scale. Probability density functions of snow depth tend to be Gaussian distributions in the forest areas. However, they are eventually skewed as non‐Gaussian distribution, largely due to the no‐snow areas effect, mainly caused by snow redistribution and snow melt. Our findings show the characteristics of subgrid variability of snow depth and clarify the potential factors that need to be considered in modelling subgrid variability of snow depth.     

Spatial interpolation of snow water equivalency using surface observations and remotely sensed images of snow‐covered area

As demand for water continues to escalate in the western Unites States, so does the need for accurate monitoring of the snowpack in mountainous areas. In this study, we describe a simple methodology for generating gridded‐estimates of snow water equivalency (SWE) using both surface observations of SWE and remotely sensed estimates of snow‐covered area (SCA). Multiple regression was used to quantify the relationship between physiographic variables (elevation, slope, aspect, clear‐sky solar radiation, etc.) and SWE as measured at a number of sites in a mountainous basin in south‐central Idaho (Big Wood River Basin). The elevation of the snowline, obtained from the SCA estimates, was used to constrain the predicted SWE values. The results from the analysis are encouraging and compare well to those found in previous studies, which often utilized more sophisticated spatial interpolation techniques. Cross‐validation results indicate that the spatial interpolation method produces accurate SWE estimates [mean R² = 0·82, mean mean absolute error (MAE) = 4·34 cm, mean root mean squared error (RMSE) = 5·29 cm]. The basin examined in this study is typical of many mid‐elevation mountainous basins throughout the western United States, in terms of the distribution of topographic variables, as well as the number and characteristics of sites at which the necessary ground data are available. Thus, there is high potential for this methodology to be successfully applied to other mountainous basins. Copyright © 2010 John Wiley & Sons, Ltd.     

Backward snow depth reconstruction at high spatial resolution based on time‐lapse photography

We report a methodology for reconstructing the daily snow depth distribution at high spatial resolution in a small Pyrenean catchment using time‐lapse photographs and snow depletion rates derived from an on‐site measuring meteorological station. The results were compared with the observed snow depth distribution, determined on a number of separate occasions using a terrestrial laser scanner (TLS). The time‐lapse photographs were projected onto a digital elevation model of the study site, and converted into snow presence/absence information. The melt‐out date (MOD; first occurrence of melt out after peak snow accumulation) was obtained from the projected photograph series. Commencing the backward reconstruction for each grid cell at the MOD, the method uses simulated snow depth depletion rates using a temperature index approach, which are extrapolated to the grid cells of the domain to arrive at the snow distribution of the previous day. Two variants of the reconstruction techniques were applied (1) using a spatially constant degree day factor (DDF) for calculating the daily expected snow depth depletion rate, and (2) allowing a spatially distributed DDF calculated from two consecutive TLS acquisitions compared to the snow depth depletion rate observed at the meteorological station. Validation revealed that both methods performed well (average R² = 0.68; standard RMSE = 0.58), with better results obtained from the spatially distributed approach. Nevertheless, the spatially corrected DDF reconstruction, which requires TLS data, suggests that the constant DDF approach is an efficient, and for most applications sufficiently accurate and easily reproducible method. The results highlight the usefulness of time‐lapse photography for not only determining the snow covered area, but also for estimating the spatial distribution of snow depth. Copyright © 2016 John Wiley & Sons, Ltd.     

Assessing the effects of post‐pine beetle forest litter on snow albedo

The effect of forest litter on snow surface albedo has been subject to limited study, mainly in the hardwood‐dominated forests of the northeastern United States. Given the recent pine beetle infestation in Western North America and associated increases in litter production, this study examines the effects of forest litter on snow surface albedo in the coniferous forests of south‐central British Columbia. Measured changes in canopy transmittance provide an indication of canopy loss or total litterfall over the winter of 2007–2008. Relationships between percent litter cover, an index of albedo, snow depth, and snow ablation during the 2008 melt season are compared between a mature, young, and clearcut coniferous stand. Results indicate a strong feedback effect between canopy loss and subsequent enhanced shortwave transmittance, and litter accumulation on the snow surface from that canopy loss. However, this relationship is confounded by other variables concurrently affecting albedo. While results suggest that a relatively small percent litter cover can have a significant effect on albedo and ablation, further research is underway to extract the litter signal from that of other factors affecting albedo, particularly snow depth. Copyright © 2010 John Wiley & Sons, Ltd.     

博斯腾湖流域积雪变化及影响因素

积雪是西北干旱地区河流的主要补给源,是绿洲的生命线.积雪的时空变化是全球变化的区域响应敏感因子之一,同时也是影响西北干旱地区地表水资源变化的主要因子之一.本研究利用MODIS雪盖产品、地表温度、SSM/I雪深、DEM等数据,通过GIS空间分析及地统计分析功能,系统分析了博斯腾湖流域雪盖、雪深的时空变化规律及其与影响因素之间的关系.研究表明,研究区雪深和雪盖多年月平均值从8月份到1月份达到最大值,到7月份降到最低值.但月最大雪深却出现在3月份.雪盖、雪深与地温相关系数分别达到-0.878、-0.853,与分布高程均值相关系数分别达到-0.626和-0.791.雪深最大值受海拔影响有明显的陡坎效应.从12月到8月份随着时间的推移雪的深度在降低,陡坎向高海拔方向移动.9-11月份雪深在加深,陡坎向低海拔方向移动.同一高程段雪深的变幅反应坡向对雪深的影响,变幅越宽坡向影响越大.并且变幅也有先从低海拔到高海拔移动,然后再回到低海拔的特点.本研究对了解该研究区积雪特性的研究有很大作用,可为在该地区开展融雪径流模拟等研究提供重要的参考信息.     

Are flat‐field snow depth measurements representative? A comparison of selected index sites with areal snow depth measurements at the small catchment scale

Single or a limited number of point observations, such as from index stations, are commonly assumed to be representative for the snow cover of larger areas in many applications. This study presents a systematic investigation of the relationship between point observations and areal mean snow depths ranging from the scale of tens of metres to entire catchments. We analyse aerial snow depth information from four mountain regions in the European Alps, one in the Spanish Pyrenees and one in the Canadian Rocky Mountains, obtained from airborne laser scanning surveys. This rich data set allowed to compare point values with snow cover statistics, reflecting the real snow depth distribution of the investigation areas. We present two contrasting approaches in order to assess the representativeness of typical flat‐field snow depth measurements. In the first approach, we define potential index stations based on topographic characteristics as commonly applied for snow cover monitoring stations. The point observations of these index stations are then compared with the mean values in their vicinities. We show that most of the index stations strongly overestimate the snow depth of the catchment and of their surrounding area at distances of several hundreds of metres. Results confirm the expectation that the larger the support area, the smaller the difference to the mean of the complete catchment. The second approach was to analyse topographic characteristics of all cells with snow depths that deviated less than 10% from the catchment mean. It appears that these representative cells are rather randomly distributed and cannot be identified a priori. In summary, our results show large potential biases of index stations with respect to snow distribution and therefore also snow water equivalent. Copyright © 2014 John Wiley & Sons, Ltd.