Abstract
Abstract. Airborne light detection and ranging (lidar) measurements carried out in the southern Sierra Nevada in 2010 in the snow-free and peak-snow-accumulation periods were analyzed for topographic and vegetation effects on snow accumulation. Point-cloud data were processed from four primarily mixed-conifer forest sites covering the main snow-accumulation zone, with a total surveyed area of over 106 km2. The percentage of pixels with at least one snow-depth measurement was observed to increase from 65–90 to 99 % as the sampling resolution of the lidar point cloud was increased from 1 to 5 m. However, a coarser resolution risks undersampling the under-canopy snow relative to snow in open areas and was estimated to result in at least a 10 cm overestimate of snow depth over the main snow-accumulation region between 2000 and 3000 m, where 28 % of the area had no measurements. Analysis of the 1 m gridded data showed consistent patterns across the four sites, dominated by orographic effects on precipitation. Elevation explained 43 % of snow-depth variability, with slope, aspect and canopy penetration fraction explaining another 14 % over the elevation range of 1500–3300 m. The relative importance of the four variables varied with elevation and canopy cover, but all were statistically significant over the area studied. The difference between mean snow depth in open versus under-canopy areas increased with elevation in the rain–snow transition zone (1500–1800 m) and was about 35 ± 10 cm above 1800 m. Lidar has the potential to transform estimation of snow depth across mountain basins, and including local canopy effects is both feasible and important for accurate assessments.
Highlights
In the western United States, ecosystem processes and water supplies for agricultural and urban users depend on the mountain snowpack as the primary source of late-spring and early-summer streamflow (Bales et al, 2006)
Using lidar data from four headwater areas in the southern Sierra Nevada, we addressed the following three questions
The rasterized lidar data show that the percentage of pixels with at least one ground return, and a snow-depth measurement, increases from 65–90 to 99 % as the sampling resolution increases from 1 to 5 m. This coarser resolution may mask undersampling of under-canopy snow relative to snow in open areas
Summary
In the western United States, ecosystem processes and water supplies for agricultural and urban users depend on the mountain snowpack as the primary source of late-spring and early-summer streamflow (Bales et al, 2006). California’s multi-billion-dollar agricultural economy as well as multi-trillion-dollar urban economy depend on these predictions (California Department of Water Resources, 2013). Both topographic and vegetation factors are important in influencing the snowpack conditions, as they closely interact with meteorological conditions to affect precipitation and snow distribution in the mountains (McMillen, 1988; Raupach, 1991; Wigmosta et al, 1994). In most forested regions, snow distribution is highly sensitive to vegetation structure (Anderson, 1963; Revuelto et al, 2015; Musselman et al, 2008), and canopy interception, sublimation and unloading result in less accumulation of snow beneath the forest canopies in comparison with canopy gaps (Berris and Harr, 1987; Golding and Swanson, 1986; Mahat and Tarboton, 2013; Sturm, 1992)
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