Small scale spatial variability of snow density and depth over complex alpine terrain: Implications for estimating snow water equivalent

• Published in: Advances in water resources Vol. 55; pp. 40 - 52
• Main Authors: López-Moreno, J.I., Fassnacht, S.R., Heath, J.T., Musselman, K.N., Revuelto, J., Latron, J., Morán-Tejeda, E., Jonas, T.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.05.2013
• Subjects: Correlation; Density; Equivalence; Errors; Mathematical models; Pyrenees; Regression; Snow; Snow depth and density; Snow water equivalent (SWE); Spatial variability; Terrain; Spatial variability; Pyrenees; Snow depth and density; Snow water equivalent (SWE)
• ISSN: 0309-1708; 1872-9657
• DOI: 10.1016/j.advwatres.2012.08.010

► In this study we analyze the spatial variability of snow density at the local scale. ► We find that variability of snow density is much lower than variability of snow depth. ► We have not found a relation between snow density and snow depth. ► We have not found a clear relation between snow density and terrain characteristic. ► Errors in density computation cause an absolute error for SWE estimation ranging from 5% to 25%. This study analyzes spatial variability of snow depth and density from measurements made in February and April of 2010 and 2011 in three 1–2km2 areas within a valley of the central Spanish Pyrenees. Snow density was correlated with snow depth and different terrain characteristics. Regression models were used to predict the spatial variability of snow density, and to assess how the error in computed densities might influence estimates of snow water equivalent (SWE). The variability in snow depth was much greater than that of snow density. The average snow density was much greater in April than in February. The correlations between snow depth and density were generally statistically significant but typically not very high, and their magnitudes and signs were highly variable among sites and surveys. The correlation with other topographic variables showed the same variability in magnitude and sign, and consequently the resulting regression models were very inconsistent, and in general explained little of the variance. Antecedent climatic and snow conditions prior to each survey help highlight the main causes of the contrasting relation shown between snow depth, density and terrain. As a consequence of the moderate spatial variability of snow density relative to snow depth, the absolute error in the SWE estimated from computed densities using the regression models was generally less than 15%. The error was similar to that obtained by relating snow density measurements directly to adjacent snow depths.