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

Mizukami, Naoki; Perica, Sanja

doi:10.1175/2008jhm981.1

Cited by 80 publications

(94 citation statements)

References 27 publications

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“…We found low spatial variability in density that showed no significant relationship with elevation at our sites. This observation concurs with other studies of mountain snowpacks finding spatial consistency in the density of mountain snowpacks (Jonas et al, 2009;Mizukami and Perica, 2008).…”

Section: Ground Measurementssupporting

confidence: 93%

LiDAR measurement of seasonal snow accumulation along an elevation gradient in the southern Sierra Nevada, California

Kirchner

Bales

Molotch

et al. 2014

Hydrol. Earth Syst. Sci.

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Abstract. We present results from snow-on and snow-off airborne-scanning LiDAR measurements over a 53 km 2 area in the southern Sierra Nevada. We found that snow depth as a function of elevation increased approximately 15 cm per 100 m, until reaching an elevation of 3300 m, where depth sharply decreased at a rate of 48 cm per 100 m. Departures from the 15 cm per 100 m trend, based on 1 m elevation-band means of regression residuals, showed slightly less steep increases below 2050 m; steeper increases between 2050 and 3300 m; and less steep increases above 3300 m. Although the study area is partly forested, only measurements in open areas were used. Below approximately 2050 m elevation, ablation and rainfall are the primary causes of departure from the orographic trend. From 2050 to 3300 m, greater snow depths than predicted were found on the steeper terrain of the northwest and the less steep northeast-facing slopes, suggesting that ablation, aspect, slope and wind redistribution all play a role in local snow-depth variability. At elevations above 3300 m, orographic processes mask the effect of wind deposition when averaging over large areas. Also, terrain in this basin becomes less steep above 3300 m. This suggests a reduction in precipitation from upslope lifting and/or the exhaustion of precipitable water from ascending air masses. Our results suggest a cumulative precipitation lapse rate for the 2100-3300 m range of about 6 cm per 100 m elevation for the accumulation period of 3 December 2009 to 23 March 2010. This is a higher gradient than the widely used PRISM (Parameter-elevation Relationships on Independent Slopes Model) precipitation products, but similar to that from reconstruction of snowmelt amounts from satellite snow-cover data. Our findings provide a unique characterization of the consistent, steep average increase in precipitation with elevation in snow-dominated terrain, using high-resolution, highly accurate data and highlighs the importance of solar radiation, wind redistribution and mid-winter melt with regard to snow distribution.

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Section: Ground Measurementssupporting

confidence: 93%

LiDAR measurement of seasonal snow accumulation along an elevation gradient in the southern Sierra Nevada, California

Kirchner

Bales

Molotch

et al. 2014

Hydrol. Earth Syst. Sci.

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“…While SWE is the primary variable of interest for hydrologic applications, snow depth is a key variable for avalanche forecasting [20]. Because snow density is generally less variable in space than snow depth [67,68], knowledge of snow depth distribution can also potentially be converted to SWE via empirical regressions [69] or dynamic models [62]. In this context, portable, low-cost techniques, such as UAS and a MS, can fill an important gap between laborious, manual measurements and large-scale surveys at lower resolution using satellites or manned aircrafts.…”

Section: Discussionmentioning

confidence: 99%

Centimetric Accuracy in Snow Depth Using Unmanned Aerial System Photogrammetry and a MultiStation

et al. 2018

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Performing two independent surveys in 2016 and 2017 over a flat sample plot (6700 m 2 ), we compare snow-depth measurements from Unmanned-Aerial-System (UAS) photogrammetry and from a new high-resolution laser-scanning device (MultiStation) with manual probing, the standard technique used by operational services around the world. While previous comparisons already used laser scanners, we tested for the first time a MultiStation, which has a different measurement principle and is thus capable of millimetric accuracy. Both remote-sensing techniques measured point clouds with centimetric resolution, while we manually collected a relatively dense amount of manual data (135 pt in 2016 and 115 pt in 2017). UAS photogrammetry and the MultiStation showed repeatable, centimetric agreement in measuring the spatial distribution of seasonal, dense snowpack under optimal illumination and topographic conditions (maximum RMSE of 0.036 m between point clouds on snow). A large fraction of this difference could be due to simultaneous snowmelt, as the RMSE between UAS photogrammetry and the MultiStation on bare soil is equal to 0.02 m. The RMSE between UAS data and manual probing is in the order of 0.20-0.30 m, but decreases to 0.06-0.17 m when areas of potential outliers like vegetation or river beds are excluded. Compact and portable remote-sensing devices like UASs or a MultiStation can thus be successfully deployed during operational manual snow courses to capture spatial snapshots of snow-depth distribution with a repeatable, vertical centimetric accuracy.

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“…At the basin scale, an approach to reducing the sampling effort is to use snow depth as a surrogate for SWE by developing a model for snow density, as manual snow density measurements require more time and effort than snow depth measurements. Recent studies have attempted to characterize the spatiotemporal characteristics of snow density (e.g., Mizukami and Perica, 2008;Fassnacht et al, 2010), or to develop reliable methods for modeling snow density and thus estimating SWE from snow depth measurements (e.g., Jonas et al, 2009;Sturm et al, 2010).…”

Section: G a Sexstone And S R Fassnacht: What Drives Basin Scale mentioning

confidence: 99%

“…At operational sites, the seasonal variability of snow density is largely dictated by time of year, and interannual variability is typically low (Mizukami and Perica, 2008). However, previous spatial snow surveys have shown that snow density can exhibit inter-annual variability, particularly in continental regions (e.g., Balk and Elder, 2000;Jepsen et al, 2012).…”

Section: Snow Density Modelmentioning

confidence: 99%

“…Snow density tends to increase gradually throughout the snow season due to crystal metamorphism, settling, and compaction. Therefore, snow density tends to increase with the day of year (Mizukami and Perica, 2008) and with increasing snow depth (Pomeroy and Gray, 1995). Elevation has also been shown to influence snow density, with the effect often being dependent on the day of year (e.g., Jonas et al, 2009).…”

Section: Snow Density Modelmentioning

confidence: 99%

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

Sexstone

Fassnacht

2014

The Cryosphere

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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 km 2 ). 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 timeconsuming 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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Spatiotemporal Characteristics of Snowpack Density in the Mountainous Regions of the Western United States

Cited by 80 publications

References 27 publications

LiDAR measurement of seasonal snow accumulation along an elevation gradient in the southern Sierra Nevada, California

LiDAR measurement of seasonal snow accumulation along an elevation gradient in the southern Sierra Nevada, California

Centimetric Accuracy in Snow Depth Using Unmanned Aerial System Photogrammetry and a MultiStation

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

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