UC Merced

The design of sensor networks for measuring the mean and spatial distribution of snow depth at the scale of 1-16 km2 was evaluated by deploying an embedded-sensor network consisting of ultrasonic snow depth sensors to capture the variable physiographic features around an operational snow course in Yosemite National Park in the Sierra Nevada of California. Manual snow surveys were also carried out during accumulation and ablation periods. Four years of continuous data from the embedded-sensor network showed that snow depths during both accumulation and ablation periods can vary as much as 50% based on variability in topography and vegetation across a 0.4 ha study area. Spatial snow surveys showed that such a sensor network can be deployed so as to capture both the variability and mean for accumulation and ablation periods across a 1 km2 area surrounding the sensor network, with a broader network required to extend this to 4 and 16 km2 areas. In forested areas, higher canopy densities, greater than 60% closure, were associated with the lowest snow depths. Analysis of historical snow course records from 14 sites in Yosemite, including the 10 spatial measurements made during each monthly snow course survey, showed snow depths across the 300 m snow course transects to be relatively uniform, with 68% of all monthly values having standard deviations no more than 10% of the mean. Although existing snow courses do little to help define the spatial patterns of snow distribution at the 1-16 km2 scales, it is feasible to extend the representativeness of current operational networks by deploying low-cost embeddedsensor networks nearby. Such networks should be strategically located to also capture elevational differences in snow accumulation and melt, as well as local-scale variability in canopy cover and aspect Copyright 2010 by the American Geophysical Union.