News & Events

Grad student talk - Estimating the distribution of snow with remotely sensed fractional snow covered

Thursday, December 01, 2016, 12:30PM - 1:30PM


Dominik Schneider



Dominik will be doing a run-through of his dissertation defense, covering his work over the past 5 years on leveraging fractional snow covered area data to estimate snow distribution patterns.


Estimating the distribution of snow with remotely sensed fractional snow covered area


Snowmelt from snow makes up a large portion of the streamflow in the mountainous western United States. The distribution of snow water equivalent (SWE) can affect the magnitude and timing of the spring and summer runoff represented in the hydrograph. Hence, efforts to improve our understanding of the spatial distribution of SWE are vital for good management of our water resources. SWE is traditionally monitored by measuring stations spread across the western United States, but these stations have been shown to poorly represent the unsampled areas. Remote sensing from satellites has existed since the 1960s but is still unable to measure SWE at scales relevant for water resources. This research uses spatio-temporal datasets to promote the use of historical observations of fractional snow covered area (fSCA) to improve estimates of SWE. First, I show that retrospective models of historical SWE distributions from observed fSCA depletion patterns augment existing ground observations of SWE to improve real-time estimates of SWE in unsampled locations. Second, I show that remotely sensed observations of fSCA improve the temporal transferability of the relationship between topography and SWE. Third, a high resolution spatio-temporal dataset is used to evaluate the topographic controls on the relationship between fSCA and snow depth inherent in depletion curves. Each of these chapters leverages fSCA as an important component and together imply that fSCA has historically been an underutilized observation. Observations of fSCA are available globally for about three decades but necessitate spatially explicit observations of snow depth or SWE to make the most this long record. Emerging technologies, such as Light Detection and Ranging (LiDAR), that provide high resolution spatio-temporal observations of the snowpack and other environmental variables, should continue to be exploited to provide insights regarding the physical processes controlling snow accumulation and ablation dynamics, and more generally our water resources. Future adaptions to climate change rely on improving our understanding of the controlling processes and our ability to monitor them at the relevant scales.