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 water equivalent estimation in the Mallero basin using snow gauge data and MODIS images and fieldwork validation

Snow accumulation and ablation rule the temporal dynamics of water availability in mountain areas and cold regions. In these environments, the evaluation of the snow water amount is a key issue. The spatial distribution of snow water equivalent (SWE) over a mountain basin at the end of the snow accumulation season is estimated using a minimal statistical model (SWE‐SEM). This uses systematic observations such as ground measurements collected at snow gauges and snow‐covered area (SCA) data retrieved by remote sensors, here MODIS. Firstly, SWE‐SEM calculates local SWE estimates at snow gauges, then the spatial distribution of SWE over a certain area using an interpolation method; linear regressions of the first two order moments of SWE with altitude. The interpolation has been made by both confining and unconfining the spatial domain by SCA. SWE‐SEM is applied to the Mallero basin (northern Italy) for calculating the snow water equivalent at the end of the winter season for 6 years (2001–2007). For 2007, SWE‐SEM estimates are validated through fieldwork measurements collected during an ‘ad hoc’ campaign on March 31, 2007. Snow‐surveyed measurements are used to check SCA, snow density and SWE estimates. Copyright © 2009 John Wiley & Sons, Ltd.     

Runoff sensitivity to snow depletion curve representation within a continental scale hydrologic model

The spatial variability of snow water equivalent (SWE) can exert a strong influence on the timing and magnitude of snowmelt delivery to a watershed. Therefore, the representation of sub-grid or sub-watershed snow variability in hydrologic models is important for accurately simulating snowmelt dynamics and runoff response. The U.S. Geological Survey National Hydrologic Model infrastructure with the precipitation-runoff modelling system (NHM-PRMS) represents the sub-grid variability of SWE with snow depletion curves (SDCs), which relate snow-covered area to watershed-mean SWE during the snowmelt period. The main objective of this research was to evaluate the sensitivity of simulated runoff to SDC representation within the NHM-PRMS across the continental United States (CONUS). SDCs for the model experiment were derived assuming a range of SWE coefficient of variation values and a lognormal probability distribution function. The NHM-PRMS was simulated at a daily time step for each SDC over a 14-year period. Results highlight that increasing the sub-grid snow variability (by changing the SDC) resulted in a consistently slower snowmelt rate and longer snowmelt duration when averaged across the hydrologic response unit scale. Simulated runoff was also found to be sensitive to SDC representation, as decreases in simulated snowmelt rate by 1 mm day⁻¹ resulted in decreases in runoff ratio by 1.8% on average in snow-dominated regions of the CONUS. Simulated decreases in runoff associated with slower snowmelt rates were approximately inversely proportional to increases in simulated evapotranspiration. High snow persistence and peak SWE:annual precipitation combined with a water-limited dryness index was associated with the greatest runoff sensitivity to changing snowmelt. Results from this study highlight the importance of carefully parameterizing SDCs for hydrologic modelling. Furthermore, improving model representation of snowmelt input variability and its relation to runoff generation processes is shown to be an important consideration for future modelling applications.     

Snow and frost: implications for spatiotemporal infiltration patterns – a review

Vast regions of the northern hemisphere are exposed to snowfall and seasonal frost. This has large effects on spatiotemporal distribution of infiltration and groundwater recharge processes as well as on the fate of pollutants. Therefore, snow and frost need to be central inherent elements of risk assessment and management schemes. However, snow and frost are often neglected or treated summarily or in a simplistic way by groundwater modellers. Snow deposition is uneven, and the snow is likely to sublimate, be redistributed and partly melt during the winter influencing the mass and spatial distribution of snow storage available for infiltration, the presence of ice layers within and under the snowpack and, therefore, also the spatial distribution of depths and permeability of the soil frost. In steep terrain, snowmelt may travel downhill tens of metres in hours along snow layers. The permeability of frozen soil is mainly influenced by soil type, its water and organic matter content, and the timing of the first snow in relation to the timing of sub‐zero temperatures. The aim with this paper is to review the literature on snow and frost processes, modelling approaches with the purpose to visualize and emphasize the need to include these processes when modelling, managing and predicting groundwater recharge for areas exposed to seasonal snow and frost. Copyright © 2015 John Wiley & Sons, Ltd.     

Observations of late winter Canadian tundra snow cover properties

Tundra snow cover is important to monitor as it influences local, regional, and global‐scale surface water balance, energy fluxes, as well as ecosystem and permafrost dynamics. Observations are already showing a decrease in spring snow cover duration at high latitudes, but the impact of changing winter season temperature and precipitation on variables such as snow water equivalent (SWE) is less clear. A multi‐year project was initiated in 2004 with the objective to quantify tundra snow cover properties over multiple years at a scale appropriate for comparison with satellite passive microwave remote sensing data and regional climate and hydrological models. Data collected over seven late winter field campaigns (2004 to 2010) show the patterns of snow depth and SWE are strongly influenced by terrain characteristics. Despite the spatial heterogeneity of snow cover, several inter‐annual consistencies were identified. A regional average density of 0.293 g/cm³ was derived and shown to have little difference with individual site densities when deriving SWE from snow depth measurements. The inter‐annual patterns of SWE show that despite variability in meteorological forcing, there were many consistent ratios between the SWE on flat tundra and the SWE on lakes, plateaus, and slopes. A summary of representative inter‐annual snow stratigraphy from different terrain categories is also presented. © 2013 Her Majesty the Queen in Right of Canada. Hydrological Processes. © 2013 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.     

A comparison between two statistical and a physically-based model in snow water equivalent mapping

Snow water equivalent (SWE) estimates at the end of the winter season have been compared for the 2002–2006 period in a 200 km² mountainous area in Switzerland, using three different models. The first model, ALPINE3D, is a physically based process-oriented model, which solves the snowpack energy and mass balance equations. The other two models, SWE-SEM and HS-SWE, are statistical algorithms interpolating snow data on a grid. While SWE-SEM interpolates local estimates of SWE, HS-SWE converts interpolated snow depth maps into maps of SWE using a regionally-calibrated conversion model. We discuss similarities and differences among the models’ results, both in terms of total volume, and spatial distribution of SWE. The comparison shows a general good agreement of the results of the three models, with a mean difference in the total volumes between the two statistical models of ∼8%, and between the physical model and the statistical ones of ∼−3% to −10%.     

Small scale spatial variability of snow density and depth over complex alpine terrain: Implications for estimating snow water equivalent

This study analyzes spatial variability of snow depth and density from measurements made in February and April of 2010 and 2011 in three 1–2 km² areas within a valley of the central Spanish Pyrenees. Snow density was correlated with snow depth and different terrain characteristics. Regression models were used to predict the spatial variability of snow density, and to assess how the error in computed densities might influence estimates of snow water equivalent (SWE).The variability in snow depth was much greater than that of snow density. The average snow density was much greater in April than in February. The correlations between snow depth and density were generally statistically significant but typically not very high, and their magnitudes and signs were highly variable among sites and surveys. The correlation with other topographic variables showed the same variability in magnitude and sign, and consequently the resulting regression models were very inconsistent, and in general explained little of the variance. Antecedent climatic and snow conditions prior to each survey help highlight the main causes of the contrasting relation shown between snow depth, density and terrain. As a consequence of the moderate spatial variability of snow density relative to snow depth, the absolute error in the SWE estimated from computed densities using the regression models was generally less than 15%. The error was similar to that obtained by relating snow density measurements directly to adjacent snow depths.     

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.     

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.     

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.     

Snowmelt runoff and soil moisture dynamics on steep subalpine hillslopes

Snowmelt water supplies streamflow and growing season soil moisture in mountain regions, yet pathways of snowmelt water and their effects on moisture patterns are still largely unknown. This study examined how flow processes during snowmelt runoff affected spatial patterns of soil moisture on two steep sub‐alpine hillslope transects in Rocky Mountain National Park, CO, USA. The transects have northeast‐facing and east‐facing aspects, and both extend from high‐elevation bedrock outcrops down to streams in valley bottoms. Spatial patterns of both snow depth and near‐surface soil moisture were surveyed along these transects in the snowmelt and summer seasons of 2008–2010. To link these patterns to flow processes, soil moisture was measured continuously on both transects and compared with the timing of discharge in nearby streams. Results indicate that both slopes generated shallow lateral subsurface flow during snowmelt through near‐surface soil, colluvium and bedrock fractures. On the northeast‐facing transect, this shallow subsurface flow emerged through mid‐slope seepage zones, in some cases producing saturation overland flow, whereas the east‐facing slope had no seepage zones or overland flow. At the hillslope scale, earlier snowmelt timing on the east‐facing slope led to drier average soil moisture conditions than on the northeast‐facing slope, but within hillslopes, snow patterns had little relation to soil moisture patterns except in areas with persistent snow drifts. Results suggest that lateral flow and exfiltration processes are key controls on soil moisture spatial patterns in this steep sub‐alpine location. Copyright © 2014 John Wiley & Sons, Ltd.     

Information content of snow hydrological signatures based on streamflow,precipitation and air temperature

Hydrological signatures that represent snow processes are valuable to gain insights into snow accumulation and snow melt dynamics. We investigated five snow signatures. Considering inter-annual average of each calendar day, two slopes derived from the relation between streamflow and air temperature for different periods and streamflow peak maxima are used as signatures. In addition, two different approaches are used to compute inter-annual average and yearly snow storage estimates. We evaluated the ability of these signatures to characterize average (a) snow melt dynamics and (b) snow storage. They were applied in 10 Critical Zone Observatory catchments of the Southern Sierra mountains (USA) characterized by a Mediterranean climate. The relevance and information content of the signatures are evaluated using snow depth and snow water equivalent measurements as well as inter-catchment differences in elevation. The slopes quantifying the relations between streamflow and air temperature and the date of streamflow peak were found to characterize snow melt dynamics in terms of snow melt rates and snow melt affected areas. Streamflow peak dates were linked to the period of highest snow melt rates. Snow storage could be estimated both on average, considering all years, and for each year. Snow accumulation dynamics could not be characterized due to the lack of streamflow response during the snow accumulation period. The signatures were found potentially valuable to gain insights into catchment scale snow processes. In particular, when comparing catchments or observed and simulated data, they could provide insights into differences in terms of (a) snow melt rate and/or snow melt affected area over the snow melt season and (b) average or yearly snow storage. Requiring only widely available data, these hydrological signatures can be valuable for snow processes characterization, catchment comparison/classification or model development, calibration or evaluation.     

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.     

Improvement of distributed snowmelt energy balance modeling with MODIS‐based NDSI‐derived fractional snow‐covered area data

Describing the spatial variability of heterogeneous snowpacks at a watershed or mountain‐front scale is important for improvements in large‐scale snowmelt modelling. Snowmelt depletion curves, which relate fractional decreases in snow‐covered area (SCA) against normalized decreases in snow water equivalent (SWE), are a common approach to scale‐up snowmelt models. Unfortunately, the kinds of ground‐based observations that are used to develop depletion curves are expensive to gather and impractical for large areas. We describe an approach incorporating remotely sensed fractional SCA (FSCA) data with coinciding daily snowmelt SWE outputs during ablation to quantify the shape of a depletion curve. We joined melt estimates from the Utah Energy Balance Snow Accumulation and Melt Model (UEB) with FSCA data calculated from a normalized difference snow index snow algorithm using NASA's moderate resolution imaging spectroradiometer (MODIS) visible (0·545–0·565 µm) and shortwave infrared (1·628–1·652 µm) reflectance data. We tested the approach at three 500 m² study sites, one in central Idaho and the other two on the North Slope in the Alaskan arctic. The UEB‐MODIS‐derived depletion curves were evaluated against depletion curves derived from ground‐based snow surveys. Comparisons showed strong agreement between the independent estimates. Copyright © 2010 John Wiley & Sons, Ltd.     

Deriving snow‐cover depletion curves for different spatial scales from remote sensing and snow telemetry data

During the melting of a snowpack, snow water equivalent (SWE) can be correlated to snow‐covered area (SCA) once snow‐free areas appear, which is when SCA begins to decrease below 100%. This amount of SWE is called the threshold SWE. Daily SWE data from snow telemetry stations were related to SCA derived from moderate‐resolution imaging spectroradiometer images to produce snow‐cover depletion curves. The snow depletion curves were created for an 80 000 km² domain across southern Wyoming and northern Colorado encompassing 54 snow telemetry stations. Eight yearly snow depletion curves were compared, and it is shown that the slope of each is a function of the amount of snow received. Snow‐cover depletion curves were also derived for all the individual stations, for which the threshold SWE could be estimated from peak SWE and the topography around each station. A station's peak SWE was much more important than the main topographic variables that included location, elevation, slope, and modelled clear sky solar radiation. The threshold SWE mostly illustrated inter‐annual consistency. Copyright © 2015 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.     

Global Snow Mass Measurements and the Effect of Stratigraphic Detail on Inversion of Microwave Brightness Temperatures

Snow provides large seasonal storage of freshwater, and information about the distribution of snow mass as snow water equivalent (SWE) is important for hydrological planning and detecting climate change impacts. Large regional disagreements remain between estimates from reanalyses, remote sensing and modelling. Assimilating passive microwave information improves SWE estimates in many regions, but the assimilation must account for how microwave scattering depends on snow stratigraphy. Physical snow models can estimate snow stratigraphy, but users must consider the computational expense of model complexity versus acceptable errors. Using data from the National Aeronautics and Space Administration Cold Land Processes Experiment and the Helsinki University of Technology microwave emission model of layered snowpacks, it is shown that simulations of the brightness temperature difference between 19 and 37 GHz vertically polarised microwaves are consistent with advanced microwave scanning radiometer-earth observing system and special sensor microwave imager retrievals once known stratigraphic information is used. Simulated brightness temperature differences for an individual snow profile depend on the provided stratigraphic detail. Relative to a profile defined at the 10-cm resolution of density and temperature measurements, the error introduced by simplification to a single layer of average properties increases approximately linearly with snow mass. If this brightness temperature error is converted into SWE using a traditional retrieval method, then it is equivalent to ±13 mm SWE (7 % of total) at a depth of 100 cm. This error is reduced to ±5.6 mm SWE (3 % of total) for a two-layer model.     

Evaluating the potential of a blended passive microwave‐interactive multi‐sensor product for improved mapping of snow cover and estimations of snow water equivalent

The retrieval of Snow Water Equivalent (SWE) from remote sensing satellites continues to be a very challenging problem. In this paper, we evaluate the accuracy of a new SWE product derived from the blending of a passive microwave SWE product based on the Advanced Microwave Sounding Unit (AMSU) with a multi‐sensor snow cover extent product based on the Interactive Multi‐sensor Snow and Ice Mapping System (IMS). The microwave measurements have the ability to penetrate the snow pack, and thus, the retrieval of SWE is best accomplished using the AMSU. On the other hand, the IMS maps snow cover more reliably due to the use of multiple satellite and ground observations. The evolution of global snow cover from the blended, the AMSU and the IMS products was examined during the 2006 snow season. Despite the overall good inter‐product agreement, it was shown that the retrievals of snow cover extent in the blended product are improved when using IMS, with implications for improved microwave retrievals of SWE. In a separate investigation, the skill of the microwave SWE product was also examined for its ability to correctly estimate SWE globally and regionally. Qualitative evaluation of global SWE retrievals suggested dependence on land surface temperature: the lower the temperature, the higher the SWE retrieved. This temperature bias was attributed in part to temperature effects on those snow properties that impact microwave response. Therefore, algorithm modifications are needed with more dynamical adjustments to account for changing snow cover. Quantitative evaluation over Slovakia in central Europe, for a limited period in 2006, showed reasonably good performance for SWE less than 100 mm. Sensitivity to deeper snow decreased significantly. Copyright © 2007 John Wiley & Sons, Ltd.