High-Resolution Seasonal Snow Cover Data Improve Climate and Hydrology Models
Because of the influence of seasonal snow cover on climate, weather, and water balance, it is a crucial quantity for climate and hydrology models. Furthermore, daily maps are necessary for hydrologic and climate models due to the dynamic nature of snow cover, which changes at a slower timescale than atmospheric phenomena but faster than other surface covers. The availability of daily global observations of this parameter was inconceivable prior to the satellite era. Nowadays, the global MODIS snow-cover product (Hall et al. 2002) is produced daily and as an 8-day composite at 500-m spatial resolution. For global climate models, daily snow cover is produced at 0.05° latitude-longitude grid cells (about 5.5 km in the north-south direction) along with monthly global composites. The composites are necessary because cloud cover and viewing geometry affect the daily images (Figure 6.2).
incoming solar radiation becomes greater as the solar elevation increases and the days get longer.
In the context of hydrologic models, this albedo decay has spatial variability. Molotch et al. (2004) examined snow ablation from a grid-based distributed snowmelt model, using field data from extensive snow surveys during the melt season to initialize the model with a spatial distribution of snow-water equivalent and then to test the model with subsequent surveys. Remotely sensed albedo typically differed by 20 percent from albedo estimated using a common snow age-based empirical relation applied uniformly across the domain. Snowpack models are just beginning to incorporate albedo evolution, based on the movement of water molecules in the snow to reduce the surface area of the grains in comparison to their volume (Flanner and Zender 2006).
A recent development in mapping snow cover and its albedo is “subpixel” analysis. Snow-covered area in mountainous terrain usually varies at a spatial scale finer than that of the ground instantaneous field of view of the remote sensing instrument. This spatial heterogeneity poses a “mixed-pixel” problem because the sensor may measure radiance reflected from snow, rock, soil, and vegetation. To use the snow characteristics in hydrologic models, snow must be mapped at subpixel resolution in order to accurately