Spatiotemporal Characteristics of Snowpack Density in the Mountainous Regions of the Western United States
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Abstract Snow density is calculated as a ratio of snow water equivalent to snow depth. Until the late 1990s, there were no continuous simultaneous measurements of snow water equivalent and snow depth covering large areas. Because of that, spatiotemporal characteristics of snowpack density could not be well described. Since then, the Natural Resources Conservation Service (NRCS) has been collecting both types of data daily throughout the winter season at snowpack telemetry (SNOTEL) sites located in the mountainous areas of the western United States. This new dataset provided an opportunity to examine the spatiotemporal characteristics of snowpack density. The analysis of approximately seven years of data showed that at a given location and throughout the winter season, year-to-year snowpack density changes are significantly smaller than corresponding snow depth and snow water equivalent changes. As a result, reliable climatological estimates of snow density could be obtained from relatively short records. Snow density magnitudes and densification rates (i.e., rates at which snow densities change in time) were found to be location dependent. During early and midwinter, the densification rate is correlated with density. Starting in early or mid-March, however, snowpack density increases by approximately 2.0 kg m−3 day−1 regardless of location. Cluster analysis was used to obtain qualitative information on spatial patterns of snowpack density and densification rates. Four clusters were identified, each with a distinct density magnitude and densification rate. The most significant physiographic factor that discriminates between clusters was proximity to a large water body. Within individual mountain ranges, snowpack density characteristics were primarily dependent on elevation.Keywords:
Snowpack
Water equivalent
Maximum density
Abstract Snow initialization is crucial for weather and seasonal prediction, but the National Centers for Environmental Prediction (NCEP) operational models have been found to produce too little snow water equivalent, partly because they assume a constant and unrealistically low snow density for the snowpack. One possible solution is to use the snow density formulation from the Noah land model used in NCEP operational forecast models. While this solution is better than the constant density assumption, the seasonal evolution of snow density in Noah is still found to be unrealistic, through the evaluation of both the offline Noah model output and the Noah snow density formulation itself. A physically based snow density parameterization is then developed, which performs considerably better than the Noah parameterization based on the measurements from the SNOTEL network over the western United States and Alaska. It also performs better than the snow density schemes used in three other models. This parameterization could be easily implemented in NCEP operational snow initialization. With the consideration of up to 10 snow layers, this parameterization can also be applied to multilayer snowpack initiation or to estimate snow water equivalent from in situ and airborne snow depth measurements.
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Initialization
Water equivalent
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For numerous climate studies, snowpack density is used to determine snow water equivalent from snow depth (or the reverse) or to determine snow surface albedo through the characterization of aging snow covers. In addition, high spring snowpack water content (and thus density) can act as a catalyst for wet avalanches. Surprisingly, there are few empirical studies that focus on spring snowpack density. In this study, spring snowpack densities in the western United States are statistically related to four variables that characterize the antecedent winter conditions: (1) mean air temperature for days without snowfall, (2) the fraction of precipitation falling as snow, (3) total precipitation, and (4) mean snowfall density. Areal composite regression analysis for the western United States indicates a highly significant (p = 0.005) positive relationship between winter precipitation total and April 1 snowpack density. This relationship weakens in lower elevation regions and coastal regions where warmer winter temperatures are conducive to more frequent rain events and melt events which affect snowpack density and ablate snow cover. These empirical results are supported by a simple snowpack model. The significant positive relationship between precipitation and density is likely due to increased densification rates through gravitational compaction from the presence of greater snow water equivalent resulting from more snowfall.
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Snowmelt
Albedo (alchemy)
Snow line
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An accurate assessment of snow depth and snow density is essential to determine that amount of water stored in the snowpack, i.e., snow water equivalent (SWE). The measurement of snow density is much more difficult and time consuming than snow depth. The variability in snow density is evaluated for a 5.4-km stretch of the Rio Esera headwaters in the Spanish Pyrenees Mountains. The traditional snow tube method is compared to the more labour-intensive but accu- rate snow pit method. The former method measures snow depth and extracts a snow core that is weighed. The latter method uses a wedge cutter to extract a 1-L snow sample to estimate density at 10-cm intervals through the snowpack. The variability in snowpack density is not systematic and can only be explained at lower elevation when the snowpack is known to be melting, as identify by an isothermal snowpack at zero degrees Celsius. This occurred during a mid-January survey. A late-April survey showed that these lower elevation sites were still more dense.
Snowpack
Water equivalent
Snow field
Snow line
Elevation (ballistics)
Meltwater
Snowmelt
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Snowmelt
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Abstract Snow density is calculated as a ratio of snow water equivalent to snow depth. Until the late 1990s, there were no continuous simultaneous measurements of snow water equivalent and snow depth covering large areas. Because of that, spatiotemporal characteristics of snowpack density could not be well described. Since then, the Natural Resources Conservation Service (NRCS) has been collecting both types of data daily throughout the winter season at snowpack telemetry (SNOTEL) sites located in the mountainous areas of the western United States. This new dataset provided an opportunity to examine the spatiotemporal characteristics of snowpack density. The analysis of approximately seven years of data showed that at a given location and throughout the winter season, year-to-year snowpack density changes are significantly smaller than corresponding snow depth and snow water equivalent changes. As a result, reliable climatological estimates of snow density could be obtained from relatively short records. Snow density magnitudes and densification rates (i.e., rates at which snow densities change in time) were found to be location dependent. During early and midwinter, the densification rate is correlated with density. Starting in early or mid-March, however, snowpack density increases by approximately 2.0 kg m−3 day−1 regardless of location. Cluster analysis was used to obtain qualitative information on spatial patterns of snowpack density and densification rates. Four clusters were identified, each with a distinct density magnitude and densification rate. The most significant physiographic factor that discriminates between clusters was proximity to a large water body. Within individual mountain ranges, snowpack density characteristics were primarily dependent on elevation.
Snowpack
Water equivalent
Maximum density
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Abstract Gravimetric and dielectric permittivity measurement systems (DMS) are applied to measure snow density, but few studies have addressed differences between the two measurement systems under complex snowpack conditions. A field experiment was conducted to measure the snow density using the two measurement systems in stratigraphical layers of different densities, liquid water content (LWC), hardness, and shear strength, and the performance of the two measurement systems was analyzed and compared. The results showed that the snow density from the DMS tended to underestimate by 9% in the dry snowpack and overestimate by 3% in the wet snowpack, expressed as the percentage of the mean density from the gravimetric measurement system (GMS). Compared with the GMS, the DMS has relatively low precision and accuracy in the dry snowpack and similar precision and accuracy in the wet snowpack. The accuracy and precision of the two measurement systems increased with the increase of hardness and shear strength of snow in the dry snowpack, but the accuracy and precision measured of the DMSs increased with the decrease of hardness and shear strength of snow in wet snowpack. The results will help field operators to choose a more reasonable measurement system based on snowpack characteristics to get reliable density data and optimize field measurements.
Snowpack
Gravimetric analysis
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An accurate assessment of snow depth and snow density is essential to determine that amount of water stored in the snowpack, i.e., snow water equivalent (SWE). The measurement of snow density is much more difficult and time consuming than snow depth. The variability in snow density is evaluated for a 5.4-km stretch of the Rio Esera headwaters in the Spanish Pyrenees Mountains. The traditional snow tube method is compared to the more labour-intensive but accurate snow pit method. The former method measures snow depth and extracts a snow core that is weighed. The latter method uses a wedge cutter to extract a 1-L snow sample to estimate density at 10-cm intervals through the snowpack. The variability in snowpack density is not systematic and can only be explained at lower elevation when the snowpack is known to be melting, as identify by an isothermal snowpack at zero degrees Celsius. This occurred during a mid-January survey. A late-April survey showed that these lower elevation sites were still more dense.
Snowpack
Water equivalent
Snow field
Elevation (ballistics)
Snow line
Meltwater
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Citations (35)