Spatial and temporal variability in seasonal snow density.
Bormann, K., J.P. Evans and M.F. McCabe
Journal of Hydrology, 484, 63-73, doi:10.1016/j.jhydrol.2013.01.032, 2013.
Snow density is a fundamental physical property of snowpacks used in many aspects of snow
research. As an integral component in the remote sensing of snow water equivalent and
parameterisation of snow models, snow density may be used to describe many important features
of snowpack behaviour. The present study draws on a significant dataset of snow density and
climate observations from the United States, Australia and the former Soviet Union and uses
regression-based techniques to identify the dominant climatological drivers for snow densification
rates, characterise densification rate variability and estimate spring snow densities from more
readily available climate data. Total winter precipitation was shown to be the most prominent driver
of snow densification rates, with mean air temperature and melt-refreeze events also found to be
locally significant. Densification rate variance is very high at Australian sites, very low throughout
the former Soviet Union and between these extremes throughout much of the US. Spring snow
densities were estimated using a statistical model with climate variable inputs and best results
were achieved when snow types were treated differently. Given the importance of snow density
information in many snow-related research disciplines, this work has implications for current
methods of converting snow depths to snow water equivalent, the representation of snow
dynamics in snow models and remote sensing applications globally.