Improved Water Resources Modeling Using Satellite-Based Snow Observations to Estimate SWE and SCA

In the United States, a dedicated system of snow measurement stations and snowpack modeling products are available to estimate the snow covered area (SCA) and snow water equivalent (SWE) throughout the winter season. In other regions of the world that depend on snowmelt for water resources, snow data can be scarce, and these regions are vulnerable to drought or flood conditions. Even in the U.S., water resource management is hampered by limited snow observations in certain regions, as evident by the recent Midwest flooding events due in large part to the significant Plains snowpack. This study evaluates the use of satellite data to provide important snow information in water resource applications relative to modeled SNODAS spatially distributed estimates. Results show that the SNODAS product contains greater uncertainty in regions with limited observations or that experience wind redistribution of snow. Here we present a focused evaluation of HUC8 watersheds throughout the United States where AMSR-E and SSM/I satellites show the most promise compared to the SNODAS product, by HUC8 watersheds. These SWE observations are paired with a new relatively cloud-free SCA product based on 16 days of MODIS imagery that provide reliable indicators of ground conditions on any given day. Combined, these products can provide reliable snow data in under-sampled regions for use in water resource modeling. We found that lack of observations in the Central Plains region negatively impacts the modeling results both on magnitude and timing of SWE, and passive microwave data has potential for providing an accurate source of SWE data in this region.