Extracting Signals of Snowmelt From the Surface Energy Budget at a Rocky Mountain Eddy Covariance Site

Abstract

Snowmelt is a dominant hydrologic process in mountainous watersheds and western river systems, yet automatic logging of snowpack properties such as depth, density, and internal energy fluxes, especially during melt, is not done at many Rocky Mountain surface flux sites. In this study, we show that springtime variations in the surface energy budget imbalance can be used to estimate snowpack energy fluxes. Using observations from the US‐GLE AmeriFlux site, we demonstrate a data‐driven method to extract springtime energy budget anomalies and show that 83% of this anomaly is explained by the latent heat flux of melting snow when computed at a 6‐hour resolution. Additionally, we demonstrate that our model can explain this latent heat flux while preserving the statistical structure of the energy imbalance observed during the rest of the year. Our findings suggest that analysis of the surface energy budget, when tightly constrained, can reveal latent hydrologic processes, improving confidence in surface energy budget measurements and offering a low‐cost, data‐driven method for estimating snowmelt dynamics at existing flux sites without additional instrumentation.