Embedded Sensor Network Design for Spatial Snowcover

Scaling point observations of snow water equivalent (SWE) to model grid-element scales is particularly challenging given the considerable sub-grid variability in snow accumulation over complex terrain. In an effort to capture this sub-grid variability and provide spatially explicit ground-truth snow data an embedded snow sensor network was designed and installed in Yosemite National Park. Extensive snow surveys were used to guide the installation of the network and to relate the observations to more detailed spatial SWE fields. Four years of continuous spatial and temporal data from both Yosemite National Park indicate that accumulation and ablation rates can vary as much as 50% based on variability in topography and vegetation. These snow distribution patterns are especially apparent in the open forests where vegetation structure largely controls variability in snow distribution. Comparisons with historical snow course data shows that a single point measurement is a poor estimator of snow depth over a homogenous area, but 4 or more measurement points can reduce the uncertainty by 50%. Further analyses indicated that an optimal snow depth network consists of 7 to 10 snow depth sensors.