Snowpack variability across various spatio-temporal resolutions
Version of Record online: 11 JUN 2014
Copyright © 2014 John Wiley & Sons, Ltd.
Volume 29, Issue 6, pages 1213–1224, 15 March 2015
How to Cite
2015), Snowpack variability across various spatio-temporal resolutions, Hydrol. Process., 29, 1213–1224, doi: 10.1002/hyp10245, , , , , , and (
- Issue online: 2 MAR 2015
- Version of Record online: 11 JUN 2014
- Accepted manuscript online: 15 MAY 2014 08:41PM EST
- Manuscript Accepted: 11 MAY 2014
- Manuscript Received: 16 FEB 2014
- snow variability;
- sub-grid resolution;
- terrestrial laser scanner;
High-resolution snow depth (SD) maps (1 × 1 m) obtained from terrestrial laser scanner measurements in a small catchment (0.55 km2) in the Pyrenees were used to assess small-scale variability of the snowpack at the catchment and sub-grid scales. The coefficients of variation are compared for various plot resolutions (5 × 5, 25 × 25, 49 × 49, and 99 × 99 m) and eight different days in two snow seasons (2011–2012 and 2012–2013). We also studied the relation between snow variability at the small scale and SD, topographic variables, small-scale variability in topographic variables. The results showed that there was marked variability in SD, and it increased with increasing scales. Days of seasonal maximum snow accumulation showed the least small-scale variability, but this increased sharply with the onset of melting. The coefficient of variation (CV) in snowpack depth showed statistically significant consistency amongst the various spatial resolutions studied, although it declined progressively with increasing difference between the grid sizes being compared. SD best explained the spatial distribution of sub-grid variability. Topographic variables including slope, wind sheltering, sub-grid variability in elevation, and potential incoming solar radiation were also significantly correlated with the CV of the snowpack, with the greatest correlation occurring at the 99 × 99 m resolution. At this resolution, stepwise multiple regression models explained more than 70% of the variance, whereas at the 25 × 25 m resolution they explained slightly more than 50%. The results highlight the importance of considering small-scale variability of the SD for comprehensively representing the distribution of snowpack from available punctual information, and the potential for using SD and other predictors to design optimized surveys for acquiring distributed SD data. Copyright © 2014 John Wiley & Sons, Ltd.