A Regression-Based Approach For Blending Remotely Sensed And In-Situ Snow Water Equivalent Estimates In The Colorado River Basin
Schneider, Dominik 1 ; Molotch, Noah 2
1 Institute of Arctic and Alpine Research
2 Institute of Arctic and Alpine Research
Snowmelt is the primary source of water supply in many parts of the world so it is important to understand the spatial and inter-year variability of snow accumulation and ablation. Several studies have analyzed the effect of physiographic variables on snow distribution so as to improve the basin-wide interpolations of point measurements. Concurrently, efforts exist to estimate snow water equivalent (SWE) distribution via hind-cast energy balance modeling (i.e. reconstruction) without the need for in-situ measurements. We developed a method that merges these two approaches by treating hind-cast energy balance snow distribution estimates as independent variables used to interpolate in-situ measurements. In this regard, we used a multiple linear regression to model SWE distribution based on physiography and reconstructed SWE estimates (independent variables) and observed SNOTEL SWE (dependent variable). Through this approach we were able to improve the explained variability of the model when including both reconstructed SWE and physiography as independent variables. For the years 2001 to 2010, the r-squared value improved an average of 0.23 for April 1st SWE predictions. R-squared values are statistically significant (>0.05) for all years for the months Mar-Jun and the increase in R-squared ranged from 0.04 to 0.59 (mean = 0.25). These preliminary results support the hypothesis that past patterns of snow cover depletion used in the reconstruction estimates may be useful for estimating the spatial distribution of SWE in real time.
Fassnacht, S. R., Dressler, K. A., & Bales, R. C. (2003). Snow water equivalent interpolation for the Colorado River Basin from snow telemetry (SNOTEL) data. Water Resour. Res., 39(8), 1208. doi:10.1029/2002WR001512
Molotch, N. P. (2009). Reconstructing snow water equivalent in the Rio Grande headwaters using remotely sensed snow cover data and a spatially distributed snowmelt model. Hydrological Processes, 23(7), 1076–1089. doi:10.1002/hyp.7206