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.     

Intercomparison of measurements of bulk snow density and water equivalent of snow cover with snow core samplers: Instrumental bias and variability induced by observers

Manually collected snow data are often considered as ground truth for many applications such as climatological or hydrological studies. However, there are many sources of uncertainty that are not quantified in detail. For the determination of water equivalent of snow cover (SWE), different snow core samplers and scales are used, but they are all based on the same measurement principle. We conducted two field campaigns with 9 samplers commonly used in observational measurements and research in Europe and northern America to better quantify uncertainties when measuring depth, density and SWE with core samplers. During the first campaign, as a first approach to distinguish snow variability measured at the plot and at the point scale, repeated measurements were taken along two 20 m long snow pits. The results revealed a much higher variability of SWE at the plot scale (resulting from both natural variability and instrumental bias) compared to repeated measurements at the same spot (resulting mostly from error induced by observers or very small scale variability of snow depth). The exceptionally homogeneous snowpack found in the second campaign permitted to almost neglect the natural variability of the snowpack properties and focus on the separation between instrumental bias and error induced by observers. Reported uncertainties refer to a shallow, homogeneous tundra-taiga snowpack less than 1 m deep (loose, mostly recrystallised snow and no wind impact). Under such measurement conditions, the uncertainty in bulk snow density estimation is about 5% for an individual instrument and is close to 10% among different instruments. Results confirmed that instrumental bias exceeded both the natural variability and the error induced by observers, even in the case when observers were not familiar with a given snow core sampler.     

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

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

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

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

Snow density variations: consequences for ground‐penetrating radar

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

Improvement of snow depth retrieval for FY3B-MWRI in China

The primary objective of this work is to develop an operational snow depth retrieval algorithm for the FengYun3B Microwave Radiation Imager(FY3B-MWRI)in China.Based on 7-year(2002–2009)observations of brightness temperature by the Advanced Microwave Scanning Radiometer-EOS(AMSR-E)and snow depth from Chinese meteorological stations,we develop a semi-empirical snow depth retrieval algorithm.When its land cover fraction is larger than 85%,we regard a pixel as pure at the satellite passive microwave remote-sensing scale.A 1-km resolution land use/land cover(LULC)map from the Data Center for Resources and Environmental Sciences,Chinese Academy of Sciences,is used to determine fractions of four main land cover types(grass,farmland,bare soil,and forest).Land cover sensitivity snow depth retrieval algorithms are initially developed using AMSR-E brightness temperature data.Each grid-cell snow depth was estimated as the sum of snow depths from each land cover algorithm weighted by percentages of land cover types within each grid cell.Through evaluation of this algorithm using station measurements from 2006,the root mean square error(RMSE)of snow depth retrieval is about 5.6 cm.In forest regions,snow depth is underestimated relative to ground observation,because stem volume and canopy closure are ignored in current algorithms.In addition,comparison between snow cover derived from AMSR-E and FY3B-MWRI with Moderate-resolution Imaging Spectroradiometer(MODIS)snow cover products(MYD10C1)in January 2010 showed that algorithm accuracy in snow cover monitoring can reach 84%.Finally,we compared snow water equivalence(SWE)derived using FY3B-MWRI with AMSR-E SWE products in the Northern Hemisphere.The results show that AMSR-E overestimated SWE in China,which agrees with other validations.     

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.     

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.     

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.     

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.     

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

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

Determination of snow water equivalent over the eastern part of Turkey using passive microwave data

Information on regional snow water equivalent (SWE) is required for the management of water generated from snowmelt. Modeling of SWE in the mountainous regions of eastern Turkey, one of the major headwaters of Euphrates–Tigris basin, has significant importance in forecasting snowmelt discharge, especially for optimum water usage. An assimilation process to produce daily SWE maps is developed based on Helsinki University of Technology (HUT) model and AMSR‐E passive microwave data. The characteristics of the HUT emission model are analyzed in depth and discussed with respect to the extinction coefficient function. A new extinction coefficient function for the HUT model is proposed to suit models for snow over mountainous areas. Performance of the modified model is checked against the original, other modified cases and ground truth data covering the 2003–2007 winter periods. A new approach to calculate grain size and density is integrated inside the developed data assimilation process. An extensive validation was successfully performed by means of snow data measured at ground stations during the 2008–2010 winter periods. The root mean square error of the data set for snow depth and SWE between January and March of the 2008–2010 periods compared with the respective AMSR‐E footprints indicated that errors for estimated snow depth and predicted SWE values were 16.92 cm and 40.91 mm, respectively, for the 3‐year period. Validation results were less satisfactory for SWE less than 75.0 mm and greater than 150.0 mm. An underestimation for SWE greater than 150 mm could not be resolved owing to the microwave signal saturation that is observed for dense snowpack. Copyright © 2012 John Wiley & Sons, Ltd.     

EFFECTS OF FOREST ON THE SNOW PARAMETERS DERIVED FROM MICROWAVE MEASUREMENTS DURING THE BOREAS WINTER FIELD CAMPAIGN

Passive microwave data have been used to infer the areal snow water equivalent (SWE) with some success. However, the accuracy of these retrieved SWE values have not been well determined for heterogeneous vegetated regions. The Boreal Ecosystem–Atmosphere Study (BOREAS) Winter Field Campaign (WFC), which took place in February 1994, provided the opportunity to study in detail the effects of boreal forests on snow parameter retrievals. Preliminary results reconfirmed the relationship between microwave brightness temperature and snow water equivalent. The pronounced effect of forest cover on SWE retrieval was studied. A modified vegetation mixing algorithm is proposed to account for the forest cover. The relationship between the microwave signature and observed snowpack parameters matches results from this model.     

Estimating the distribution of snow water equivalent and snow extent beneath cloud cover in the Salt–Verde River basin,Arizona

The temporal and spatial continuity of spatially distributed estimates of snow‐covered area (SCA) are limited by the availability of cloud‐free satellite imagery; this also affects spatial estimates of snow water equivalent (SWE), as SCA can be used to define the extent of snow telemetry (SNOTEL) point SWE interpolation. In order to extend the continuity of these estimates in time and space to areas beneath the cloud cover, gridded temperature data were used to define the spatial domain of SWE interpolation in the Salt–Verde watershed of Arizona. Gridded positive accumulated degree‐days (ADD) and binary SCA (derived from the Advanced Very High Resolution Radiometer (AVHRR)) were used to define a threshold ADD to define the area of snow cover. The optimized threshold ADD increased during snow accumulation periods, reaching a peak at maximum snow extent. The threshold then decreased dramatically during the first time period after peak snow extent owing to the low amount of energy required to melt the thin snow cover at lower elevations. The area having snow cover at this later time was then used to define the area for which SWE interpolation was done. The area simulated to have snow was compared with observed SCA from AVHRR to assess the simulated snow map accuracy. During periods without precipitation, the average commission and omission errors of the optimal technique were 7% and 11% respectively, with a map accuracy of 82%. Average map accuracy decreased to 75% during storm periods, with commission and omission errors equal to 11% and 12% respectively. The analysis shows that temperature data can be used to help estimate the snow extent beneath clouds and therefore improve the spatial and temporal continuity of SCA and SWE products. Copyright © 2004 John Wiley & Sons, Ltd.     

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

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

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.     

Impact of land-use changes on snow in a forested region with heavy snowfall in Hokkaido,Japan

Abstract

We simulated snow processes in a forested region with heavy snowfall in Japan, and evaluated both the regional-scale snow distribution and the potential impact of land-use changes on the snow cover and water balances over the entire domain. SnowModel reproduced the snow processes at open and forested sites, which were confirmed by snow water equivalent (SWE) measurements at two intensive observation sites and snow depth measurements at the Automated Meteorological Data Acquisition System sites. SnowModel also reproduced the observed snow distribution (from the MODIS snow cover data) over the simulation domain during thaw. The observed SWE was less at the forested site than at the open site. The SnowModel simulations showed that this difference was caused mainly by differences in sublimation. The type of land use changed the maximum SWE, onset and duration of snowmelt, and the daily snowmelt rate due to canopy snow interception.

Citation Suzuki, K., Kodama, Y., Nakai, T., Liston, G. E., Yamamoto, K., Ohata, T., Ishii, Y., Sumida, A., Hara, T. & Ohta, T. (2011) Impact of land-use changes in a forested region with heavy snowfall in Hokkaido, Japan. Hydrol. Sci. J. 56(3), 443–467.     

A theory of pressure sensor performance in snow

A theory of pressure sensor response in snow is derived and used to examine the sources of measurement errors in snow water equivalent (SWE) pressure sensors. Measurement errors in SWE are caused by differences in the compressibility of the pressure sensor and the adjacent snow layer, which produces a shear stress along the perimeter of the sensor. When the temperature at the base of the snow cover equals 0 °C, differences in the snowmelt rate between the snow–SWE sensor interface and the adjacent snow–soil interface may also produce a shear stress along the sensor's perimeter. This shear stress perturbs the pressure field over the sensor, producing SWE measurement errors. Snow creep acts to reduce shear stresses along the SWE sensor's perimeter at a rate that is inversely proportional to the snow viscosity. For sustained periods of differential snowmelt, a difference in the mass of snow over the sensor compared with the surrounding soil will develop, producing additional permanent errors in SWE measurements. The theory indicates that SWE pressure sensor performance can be improved by designing a sensor with a high Young's modulus (low compressibility), low aspect ratio, large diameter and thermal properties that match those of the surrounding soil. Simulations of SWE pressure sensor errors using the theory are in close agreement with observed errors and may provide a means to correct historical SWE measurements for use in hydrological hindcast or climate studies. Published in 2003 by John Wiley & Sons, Ltd.     

Evaluation of snow water equivalent datasets over the Saint‐Maurice river basin region of southern Québec

A 10‐km gridded snow water equivalent (SWE) dataset is developed over the Saint‐Maurice River basin region in southern Québec from kriging of observed snow survey data for evaluation of SWE products. The gridded SWE dataset covers 1980–2014 and is based on manual gravimetric snow surveys carried out on February 1, March 1, March 15, April 1, and April 15 of each snow season, which captures the annual maximum SWE (SWEM) with a mean interpolation error of ±19%. The dataset is used to evaluate SWEM from a range of sources including satellite retrievals, reanalyses, Canadian regional climate models, and the Canadian Meteorological Centre operational snow depth analysis. We also evaluate a number of solid precipitation datasets to determine their contribution to systematic errors in estimated SWEM. None of the evaluated datasets is able to provide estimates of SWEM that are within operational requirements of ±15% error, and insufficient solid precipitation is determined to be one of the main reasons. The Climate System Forecast Reanalysis is the only dataset where snowfall is sufficiently large to generate SWEM values comparable to observations. Inconsistencies in precipitation are also found to have a strong impact on year‐to‐year variability in SWEM dataset performance and spread. Version 3.6.1 of the Canadian Land Surface Scheme land surface scheme driven with ERA‐Interim output downscaled by Version 5.0.1 of the Canadian Regional Climate Model was the best physically based model at explaining the observed spatial and temporal variability in SWEM (root‐mean‐square error [RMSE] = 33%) and has potential for lower error with adjusted precipitation. Operational snow products relying on the real‐time snow depth observing network performed poorly due to a lack of real‐time data and the strong local scale variability of point snow depth observations. The results underscore the need for more effort to be invested in improving solid precipitation estimates for use in snow hydrology applications.     

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.