Analysis of topographic controls on depletion curves derived from airborne lidar snow depth data

Abstract

The annual consistency of spatial patterns of snow accumulation and melt suggests that the evolution of these patterns, known as depletion curves, is useful for estimating basin water content and runoff prediction. Theoretical snow cover depletion curves are used in models to parameterize fractional snow-covered area (fSCA) based on modeled estimates of snow accumulation and snowmelt. Directly measuring the spatio-temporal snow distribution, characterization of depletion curves, and understanding how they vary across mountainous landscapes was not possible until the recent U.S. National Aeronautics and Space Administration (NASA) Airborne Snow Observatory (ASO). Herein, for the first time, high-resolution spatio-temporal snow depth information from the ASO is used to derive observation-based snow cover depletion curves across physiographic gradients by estimating the slope of the fSCA–snow depth relationship (i.e. depletion slope). The depletion slope reveals important insights into snow processes as it is strongly related to snow depth variability (r2 = 0.58). Regression tree analysis between observed depletion slopes and physiography, particularly vegetation height and terrain roughness, displays clear nonlinear dynamics and explains 31% of the variance in depletion slope. This unique observation-based analysis of snow cover depletion curves has implications for energy and water flux calculations across many earth system models. HIGHLIGHTS Relationships between snow depth and fSCA (i.e. depletion slope) were robust over the 4 years of study.; Significant spatial variability in depletion slope is well correlated with snow depth variability.; Increased vegetation height and decreased terrain roughness were associated with more homogeneous snowpacks and lower depletion slopes.;

Keywords

• depletion curves
• lidar
• remote sensing
• snow depth
• snow distribution