Characterizing Satellite-Based Passive Microwave Estimates of Snow Water Equivalent at Sub-Grid Resolution (Invited Presentation)

Snow water equivalent (SWE) is the amount of water contained within the snowpack if melted. The accurate assessment of this snow parameter is crucial in estimating spring runoff as it relates to water resource management, flood hazard mitigation, drought monitoring, and climate change impacts. Satellite-based passive microwave estimates of SWE offer the only operational platform for which a near real-time, global SWE product is available. In general, satellite-based passive microwave SWE estimates are possible due to the naturally emitted microwave signal from the soil being attenuated by the snowpack. This microwave energy is relatively small; therefore, the satellite-based products are often at very coarse resolution (tens of kilometers) in order to detect the signal. For hydrology applications, passive microwave estimates of SWE are particularly difficult to interpret when only a handful of pixels represent a single hydrologic basin. Moreover, passive microwave retrieval algorithms are subject to difficulties in both deep and shallow snow (depending on the microwave frequencies available on the satellite platform) as well as uncertainties due to forest fraction, snow microstructure, and snow wetness. Here, a spatially-distributed, snow-evolution modeling system (SnowModel) is used to simulate 14 years (water years 2000 through 2013) of snow properties for the Hubbard Brook Long Term Ecological Research site (New Hampshire, USA) at fine resolution (50 meters). These data are used to generate snow depth climatology over the satellite-based passive microwave pixels that encompass the Hubbard Brook watershed. This climatology is then processed in conjunction with the daily passive microwave estimate of SWE to appropriately distribute the satellite-based observation at coarse resolution to a sub-grid, finer resolution. Methodology and results of the technique are presented as well as identification of potential benefits of using this in snow hydrology applications.