Estimation of Snowpack Snow Water Equivalent from Remotely Sensed Data

In a time when erratic snowpack and pervasive flooding endangers global communities and livelihoods, estimation of the hydrological implications of snowpack, particularly Snow Water Equivalent (SWE), has major implications for the prediction of flooding intensity and timing, and for water system management. Current approaches to SWE estimation using LIDAR and snow core sampling are costly, labor intensive, and the results are time delayed, limiting their utility as predictive management tools. 

In collaboration with leading snow scientists in the UC system (primarily Dr. Manuela Girotto and her lab) DSE is exploring new methods of SWE estimation from remotely sensed data which, with successful validation in California, could then be applied in other global regions that lack the same available resources for snowpack monitoring.