TY - Generic T1 - Capturing Spatial Variability in the Influence of Topography and Vegetation on Snow Depth in the Tuolumne River Basin, CA T2 - 84th Annual Western Snow Conference Y1 - 2016 A1 - Ian W. Bolliger A1 - Noah P. Molotch A1 - Alexei Pozdnukhov A1 - Margaret S. Torn AB -

Understanding spatial nonstationarity in the influence of topography and vegetation on patterns of snow water equivalent (SWE) can improve distributed SWE models and guide investigations of physical processes that generate this variability. In this study, we seek to understand the nature of this nonstationarity and improve upon a basin-scale statistical snow depth model by allowing for spatial patterns within observed relationships. To accomplish these objectives, we apply geographically weighted regression (GWR) (Fotheringham, Brunsdon, and Charlton, 2002) to gridded, 3m-resolution, LiDAR-derived elevation, canopy height, land cover classification, and snow depth products from the Tuolomne River Basin in California. The data are obtained from the Airborne Snow Observatory (ASO) (“ASO | NASA Airborne Snow Observatory” 2015), with snow depth observations from early April flights in 2013, 2014, and 2015 and topographic/vegetation features from 2014. Preliminary results suggest that our approach leads to a ~10% decrease in error relative to a comparable global model and that the spatial patterns in local relationships between snow depth observations and topographic and vegetation features are persistent across seasons. Furthermore, they suggest that the scale of nonstationarity in these relationships may fall between 50-100m, but that there is also a wide range in the magnitude of spatial variation across features.

JF - 84th Annual Western Snow Conference CY - Seattle, Washington UR - /files/PDFs/2016Bolliger.pdf ER -