Schneider, Dominik听1听;听Molotch, Noah P.听2
1听INSTAAR, University of Colorado Boulder
2听INSTAAR, University of Colorado Boulder
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 variability. Snow depth is estimated from remotely-sensed fractional snow covered area (fsca) and a digital elevation model (dem). The sub-pixel terrain variability was calculated with metrics such as the Terrain Roughness Index, roughness, a wind redistribution parameter and the coefficient of variation of elevation (elev-cv). In preliminary investigations, a Light Detection and Ranging (LiDaR) dataset from 2010 from Green Lakes Valley, Colorado, USA (Harpold et al. 2012) was used to relate snow depth, fsca and the sub-pixel terrain variability. Snow depth and fsca were aggregated from 1 meter to 30 meters from the LiDaR snow depth product while the terrain variability metric was calculated for the 900 1-meter elevation pixels inside each aforementioned 30 meter pixel. Single linear regressions using the gamma distribution and the inverse link function, in which the square root of snow depth is the dependent variable and fsca is the independent variable, explains 38% of the variability with a mean absolute error (mae) of 0.36 m. The coefficient of variation of elevation is the most informative terrain variable. The goodness of fit increases to an average of 55% with mae of 0.23 m as the terrain variability (elev-cv) is binned. Further analysis of the scales at which these relationships are applicable and the viability with off-the-shelf dem and fsca products is needed. 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.