What drives basin scale spatial variability of snow water equivalent during two extreme years?

Abstract. This study uses a combination of field measurements and Natural Resource Conservation Service (NRCS) operational snow data to understand the drivers of snow water equivalent (SWE) spatial variability at the basin scale. Historic snow course snowpack density observations were analyzed within a multiple linear regression snow density model to estimate SWE directly from snow depth measurements. Snow surveys were completed on or about 1 April 2011 and 2012 and combined with NRCS operational measurements to investigate the spatial variability of SWE. Bivariate relations and multiple linear regression models were developed to understand the relation of SWE with terrain and canopy variables (derived using a geographic information system (GIS)). Calculation of SWE directly from snow depth measurement using the snow density model has strong statistical performance and model validation suggests the model is transferable to independent data within the bounds of the original dataset. This pathway of estimating SWE directly from snow depth measurement is useful when evaluating snowpack properties at the basin scale, where many time consuming measurements of SWE are often not feasible. During both water year (WY) 2011 and 2012, elevation and location (UTM Easting and UTM Northing) were the most important model variables, suggesting that orographic precipitation and storm track patterns are likely consistent drivers of basin scale SWE variability. Terrain characteristics, such as slope, aspect, and curvature, were also shown to be important variables, but to a lesser extent at the scale of interest.

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What drives basin scale spatial variability of snowpack properties in northern Colorado?

The Cryosphere ◽

10.5194/tc-8-329-2014 ◽

2014 ◽

Vol 8 (2) ◽

pp. 329-344 ◽

Cited By ~ 24

Author(s):

G. A. Sexstone ◽

S. R. Fassnacht

Keyword(s):

Linear Regression ◽

Spatial Variability ◽

Multiple Linear Regression ◽

Snow Depth ◽

Density Model ◽

Snow Density ◽

Data Set ◽

Depth Measurement ◽

Basin Scale ◽

Snowpack Properties

Abstract. This study uses a combination of field measurements and Natural Resource Conservation Service (NRCS) operational snow data to understand the drivers of snow density and snow water equivalent (SWE) variability at the basin scale (100s to 1000s km2). Historic snow course snowpack density observations were analyzed within a multiple linear regression snow density model to estimate SWE directly from snow depth measurements. Snow surveys were completed on or about 1 April 2011 and 2012 and combined with NRCS operational measurements to investigate the spatial variability of SWE near peak snow accumulation. Bivariate relations and multiple linear regression models were developed to understand the relation of snow density and SWE with terrain variables (derived using a geographic information system (GIS)). Snow density variability was best explained by day of year, snow depth, UTM Easting, and elevation. Calculation of SWE directly from snow depth measurement using the snow density model has strong statistical performance, and model validation suggests the model is transferable to independent data within the bounds of the original data set. This pathway of estimating SWE directly from snow depth measurement is useful when evaluating snowpack properties at the basin scale, where many time-consuming measurements of SWE are often not feasible. A comparison with a previously developed snow density model shows that calibrating a snow density model to a specific basin can provide improvement of SWE estimation at this scale, and should be considered for future basin scale analyses. During both water year (WY) 2011 and 2012, elevation and location (UTM Easting and/or UTM Northing) were the most important SWE model variables, suggesting that orographic precipitation and storm track patterns are likely driving basin scale SWE variability. Terrain curvature was also shown to be an important variable, but to a lesser extent at the scale of interest.

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Improving the snow physics of WEB-DHM and its point evaluation at two SnowMIP alpine sites

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-7-3481-2010 ◽

2010 ◽

Vol 7 (3) ◽

pp. 3481-3519 ◽

Cited By ~ 1

Author(s):

M. Shrestha ◽

L. Wang ◽

T. Koike ◽

Y. Xue ◽

Y. Hirabayashi

Keyword(s):

Surface Temperature ◽

Liquid Water ◽

Snow Depth ◽

Hydrological Model ◽

Snow Water Equivalent ◽

Bias Error ◽

Snow Albedo ◽

Basin Scale ◽

Snow Water ◽

Snow Physics

Abstract. The snow physics of a distributed biosphere hydrological model, referred to as the Water and Energy Budget based Distributed Hydrological Model (WEB-DHM) is improved by incorporating the three-layer physically based energy balance snowmelt model of Simplified Simple Biosphere 3 (SSiB3) and the Biosphere-Atmosphere Transfer Scheme (BATS) albedo scheme. WEB-DHM with improved snow physics (WEB-DHM-S) can simulate the variability of snow density, snow depth and snow water equivalent, liquid water and ice content in each layer, prognostic snow albedo, diurnal variation in snow surface temperature, thermal heat due to conduction and liquid water retention. The performance of WEB-DHM-S is evaluated at two alpine sites of the Snow Model Intercomparison Project with different climate characteristics: Col de Porte in France and Weissfluhjoch in Switzerland. The simulation results of the snow depth, snow water equivalent, surface temperature, snow albedo and snowmelt runoff reveal that WEB-DHM-S is capable of simulating the internal snow process better than the original WEB-DHM, with the root mean square error and bias error being remarkably reduced. Although WEB-DHM-S is only evaluated at a point scale for the simulation of snow processes, this study provides a benchmark for the application of WEB-DHM-S in cold regions in the assessment of the basin-scale snow water equivalent and seasonal discharge simulation for water resources management.

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Improved Northern Hemisphere Snow Water Equivalent product from passive microwave remote sensing and in situ data

10.5194/egusphere-egu21-1325 ◽

2021 ◽

Author(s):

Colleen Mortimer ◽

Lawrence Mudryk ◽

Chris Derksen ◽

Kari Luojus ◽

Pinja Venalainen ◽

...

Keyword(s):

Spatial Resolution ◽

Snow Depth ◽

Snow Water Equivalent ◽

Microwave Remote Sensing ◽

Passive Microwave ◽

Snow Density ◽

Snow Season ◽

Snow Water ◽

Snow Mass

The European Space Agency Snow CCI+ project provides global homogenized long time series of daily snow extent and snow water equivalent (SWE). The Snow CCI SWE product is built on the Finish Meteorological Institute's GlobSnow algorithm, which combines passive microwave data with in situ snow depth information to estimate SWE. The CCI SWE product improves upon previous versions of GlobSnow through targeted changes to the spatial resolution, ancillary data, and snow density parameterization.Previous GlobSnow SWE products used a constant snow density of 0.24 kg m-3 to convert snow depth to SWE. The CCI SWE product applies spatially and temporally varying density fields, derived by krigging in situ snow density information from historical snow transects to correct biases in estimated SWE. Grid spacing was improved from 25 km to 12.5 km by applying an enhanced spatial resolution microwave brightness temperature dataset. We assess step-wise how each of these targeted changes acts to improve or worsen the product by evaluating with snow transect measurements and comparing hemispheric snow mass and trend differences.Together, when compared to GlobSnow v3, these changes improved RMSE by ~5 cm and correlation by ~0.1 against a suite of snow transect measurements from Canada, Finland, and Russia. Although the hemispheric snow mass anomalies of CCI SWE and GlobSnow v3 are similar, there are sizeable differences in the climatological SWE, most notably a one month delay in the timing of peak SWE and lower SWE during the accumulation season. These shifts were expected because the variable snow density is lower than the former fixed value of 0.24 kg m-3 early in the snow season, but then increases over the course of the snow season. We also examine intermediate products to determine the relative improvements attributable solely to the increased spatial resolution versus changes due to the snow density parameterizations. Such systematic evaluations are critical to directing future product development.

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Spatiotemporal Characteristics of Snowpack Density in the Mountainous Regions of the Western United States

Journal of Hydrometeorology ◽

10.1175/2008jhm981.1 ◽

2008 ◽

Vol 9 (6) ◽

pp. 1416-1426 ◽

Cited By ~ 45

Author(s):

Naoki Mizukami ◽

Sanja Perica

Keyword(s):

United States ◽

Snow Depth ◽

Snow Water Equivalent ◽

Winter Season ◽

Western United States ◽

Densification Rate ◽

Snow Density ◽

Mountainous Regions ◽

Spatiotemporal Characteristics ◽

Snow Water

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.

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Technical note: Snow Water Equivalence Estimation (SWEE) Algorithm from Snow Depth Time Series Using a Snow Density Model

10.5194/hess-2018-451 ◽

2018 ◽

Author(s):

Noriaki Ohara ◽

Siwei He ◽

Andrew D. Parsekian ◽

Thijs Kelleners

Keyword(s):

Time Series ◽

Snow Depth ◽

Temperature Sensors ◽

Technical Note ◽

Density Model ◽

Snow Density ◽

Maintenance Costs ◽

The Us ◽

Snow Water ◽

Weight Sensor

Abstract. Snow water equivalence (SWE) is typically computed from snow weight by the SNOTEL system in the US. However, a snow pillow, the main snow weight sensor used by SNOTEL, requires a large, open, flat area (at least 9 square meters) and substantial maintenance costs. This article presents the snow water equivalence estimation (SWEE) algorithm that estimates the SWE evolution merely from continuous snow depth and temperature measurements using common sensors. The key component is a depth-averaged snow density model that is available in the literature, but is underutilized. Here, we demonstrate that the snow density model can estimate mass exchanges (SWE changes due to snowfall, erosion, deposition, and snowmelt) as well as the SWE. The SWEE algorithm can potentially increase the number of snow monitoring locations because snow depth and temperature sensors are considerably more accessible and economical than snow weighing sensor.

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Supplementary material to "Technical note: Snow Water Equivalence Estimation (SWEE) Algorithm from Snow Depth Time Series Using a Snow Density Model"

10.5194/hess-2018-451-supplement ◽

2018 ◽

Author(s):

Noriaki Ohara ◽

Siwei He ◽

Andrew D. Parsekian ◽

Thijs Kelleners

Keyword(s):

Time Series ◽

Snow Depth ◽

Technical Note ◽

Density Model ◽

Snow Density ◽

Snow Water ◽

Supplementary Material

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Characteristics of avalanching: Kootenay Pass, British Columbia, Canada

Journal of Glaciology ◽

10.1017/s0022143000015975 ◽

1993 ◽

Vol 39 (132) ◽

pp. 316-322 ◽

Cited By ~ 20

Author(s):

D. M. McClung ◽

John Tweedy

Keyword(s):

British Columbia ◽

Wind Speed ◽

Correlation Analysis ◽

Snow Depth ◽

Snow Water Equivalent ◽

Snow Density ◽

Precipitation Total ◽

Snow Water ◽

Individual Variables

AbstractIndividual variables found to be significant from a correlation analysis are analyzed as a function of probability of avalanching for data from Kootenay Pass, British Columbia. The analysis is compared with a similar study for data from Alta, Utah, U.S.A. The results show that the variable significance is very similar for the two areas. Primary variables include: snowfall rate, weight of new snow, water equivalent of new precipitation, total storm snow and new snow depth. Secondary variables include wind speed and direction, and new-snow density.

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Weather SDM: estimating snow density with high precision using snow depth and local climate

Hydrology Research ◽

10.2166/nh.2015.059 ◽

2015 ◽

Vol 46 (4) ◽

pp. 494-506 ◽

Cited By ~ 3

Author(s):

Oddbjørn Bruland ◽

Åshild Færevåg ◽

Ingelin Steinsland ◽

Glen E. Liston ◽

Knut Sand

Keyword(s):

High Precision ◽

Snow Depth ◽

Prediction Error ◽

Snow Water Equivalent ◽

Snow Density ◽

Final Model ◽

Local Climate ◽

Density Measurements ◽

Snow Water ◽

Seasonal Weather

Snow density is an important measure in hydrology used to convert snow depth to the snow water equivalent (SWE). A model developed by Sturm, Tara and Liston predicts the snow density by using snow depth, the snow age and a snow class defined by the location. In this work this model is extended to include location and seasonal weather-specific variables. The model is named Weather Snow Density Model (Weather SDM). A Bayesian framework is chosen, and the model is fitted to and tested for 4,040 Norwegian snow depth and densities measurements between 1998 and 2011. The final model improved the snow density predictions for the Norwegian data compared to the model of Sturm by up to 50%. Further, the Weather SDM is extended to utilize local year-specific snow density observations (Weather&ObsDensity SDM). This reduced the prediction error an additional 16%, indicating a significant improvement when utilizing information provided by annual snow density measurements.

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