We are seeking to develop a SWE monitoring technique that can leverage both point scale measurements and spatially explicit patterns of SWE from remote sensing in near real-time. Current estimates of SWE distribution are frequently interpolated from point measurements based on physiographics with a observations of SCA occasionally used to constrain modeled values. Statistical models relating physiography and SNOTEL SWE only explain up to ~15% of the observed variability and thus these techniques provide limited credibility for water resource applications. Recent improvements in SWE estimates have been obtained using SWE reconstruction models whereby satellite data of SCA are coupled with fully distributed energy balance modeling to reconstruct peak snow mass. The first goal of this project is to combine a statistical interpolation model with remote-sensing based spatially distributed reconstructed SWE to augment resources available to water managers. The second goal of this project is to incorporate explicitly modeled patterns of SWE and use it as a spatial distribution field for winter precipitation in a streamflow modeling exercise. The intention is to examine the sensitivity and potential improvement in simulated streamflow timing and volume due to an improved representation of the physiographic distribution of SWE.
See more, including some preliminary results, on the CSDMS project page.