Estimating Snow Water Equivalent Using Snow Depth Data and Climate Classes

Abstract

In many practical applications snow depth is known, but snow water equivalent (SWE) is needed as well. Measuring SWE takes ;20 times as long as measuring depth, which in part is why depth measurements outnumber SWE measurements worldwide. Here a method of estimating snow bulk density is presented and then used to convert snow depth to SWE. The method is grounded in the fact that depth varies over a range that is many times greater than that of bulk density. Consequently, estimates derived from measured depths and modeled densities generally fall close to measured values of SWE. Knowledge of snow climate classes is used to improve the accuracy of the estimation procedure. A statistical model based on a Bayesian analysis of a set of 25 688 depth-density-SWE data collected in the United States, Canada, and Switzerland takes snow depth, day of the year, and the climate class of snow at a selected location from which it produces a local bulk density estimate. When converted to SWE and tested against two continental-scale datasets, 90% of the computed SWE values fell within 68 cm of the measured values, with most estimates falling much closer.

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

“…3b), which suggests that additional variables should be included to describe the variability of snow density. Snowpack relations shown here are similar to those found in previous studies (e.g., Jonas et al, 2009;Sturm et al, 2010). The mean snow density from the snow course data set is 287 kg m −3 with a standard deviation of 64.8 kg m −3 .…”

“…Jonas et al (2009) developed a set of regression equations to model snow density using snow depth, day of year, elevation, and region for the Swiss Alps, while Sturm et al (2010) employed a statistical method based on Bayesian analysis for the United States, Canada, and Switzerland using snow depth, day of year, and climate class. These previous studies and our research show that snow density is a conservative variable that varies spatially much less than snow depth and SWE, however the previous studies used spatial domains…”

“…We have applied the snow density alpine model developed by Sturm et al (2010) to our data set and directly compared the results to those of our snow density model, given their model was developed for global applications and data from the western US were used within model development. We did not, however, test the model developed by Jonas et al (2009), as it was developed specifically for Switzerland and requires regional and elevational parameters that are specific to this area, and was not developed for use in other areas.…”

“…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).…”

“…Since the range of variability of snow density is more conservative than snow depth and SWE (e.g., Logan, 1973;Fassnacht et al, 2010), estimating density from depth has been shown to provide a reasonable pathway for estimating SWE from a snow depth measurement. Based on the approaches presented by Jonas et al (2009) and Sturm et al (2010) we have analyzed historic operational snow density data for the Cache la Poudre Basin to develop and evaluate a snow density model. We acknowledge that the operational snow density data evaluated are not representative of potential terrain and canopy controls on the variability of snow density at the basin scale.…”