News & Events

Grad student talk - Establishing transferable sub-pixel relationships for estimating snow depth from

Thursday, April 16, 2015, 4:30PM - 5:30PM

Speaker

Dominik Schneider

Location:

RL-1 room 269

Full title

Establishing transferable sub-pixel relationships for estimating snow depth from a remotely-sensed snow covered area and a DEM

Abstract

Snowmelt is the primary water source in the Western United States and mountainous regions globally. Forecasts of streamflow and water supply rely heavily on snow measurements from sparse observation networks that may not provide adequate information during abnormal climatic conditions. To this end, we have developed a method that is not expected to depend on repeated climatic conditions because it considers the snow holding capacity of the ground based on small-scale terrain roughness. Snow depth is estimated from remotely-sensed fractional snow covered area (fSCA) and a digital elevation model (DEM). A temporal analysis of fSCA near peak SWE derived from Landsat TM/ETM+ for 2000-2007 in Green Lakes Valley, Colorado (Jepsen et al., 2012) yields an r2=0.56 when relating the median fSCA and average basin snow depth. Spatial analysis of fSCA using a Light Detection and Ranging (LiDaR) dataset from 2010 from Green Lakes Valley, Colorado (Harpold et al., 2012) was used to relate snow depth, fSCA and the sub-fSCA-pixel terrain roughness. The pixel terrain variability is calculated as the standard deviation of slope. 30-meter pixels of snow depth are modeled by regression with 30 m fSCA pixels, categorized by terrain variability. Relative MAE ranged from 39%-58% of the measured snow depth, with higher errors in less rough terrain. Future analysis includes improving the quantification of potential wind redistribution and applying the proposed relationship to other research basins in California and abroad. The utility of these relationships is such that snow depth could be estimated above treeline for any set of climatic conditions and could have far-reaching implications for understanding snow distribution and water forecasting.