Abstract
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
The spatial distribution of snow water equivalent (SWE) is affected by numerous processes at multiple scales including, but not limited to, orography, wind and avalanche redistribution, and ablation dynamics driven by the land-surface energy balance
Airborne Snow Observatory (ASO) produces a SWE dataset that combines the lidar-derived snow depth with modeled density, but we focus on snow depth depletion curves in this study to keep the analysis anchored by direct measurements
84% of depletion slopes fall within the 95% confidence interval of the mean cross-validated depletion slope
Summary
The spatial distribution of snow water equivalent (SWE) is affected by numerous processes at multiple scales including, but not limited to, orography, wind and avalanche redistribution, and ablation dynamics driven by the land-surface energy balance. The existing literature regarding depletion curves typically relates SWE and SCA using empirical relationships or probability distribution functions for modeling applications These cover a variety of spatial scales from pointbased derivations over hillslopes (Anderson ; Luce et al ; Luce & Tarboton ), physically based gridded models applied to the watershed scale (Clark et al ), and regional to global-scale land-surface models (Niu & Yang ; Swenson & Lawrence ; Driscoll et al ). Satellite-based methods of SWE estimation have limited utility resolving the CV of SWE and snow depth, given that the accuracy of SWE estimates is highly sensitive to SWE spatial variability (Vander Jagt et al )
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