Process‐Based Model Parameters for Post‐Fire Snow Hydrology: Looking Beyond Snow Albedo and Vegetation

Abstract Wildfires fundamentally alter snow accumulation and melt dynamics by modifying forest canopy structure, snow albedo, and snowpack physical properties. Despite the growing body of work on fire impacts on snow, few studies have used physically based models to simulate post‐fire changes, and none have examined the global parameter sensitivities that govern these dynamics. In this study, we apply the Structure for Unifying Multiple Modeling Alternatives in a high elevation Colorado watershed to identify and evaluate the model parameters that influence post‐fire snowpack evolution. We conduct a global sensitivity analysis, then sample sensitive parameters to identify behavioral parameter sets with respect to airborne LiDAR‐based snow depth in burned and unburned forests. Among behavioral parameter sets, we identify 4 parameters that differ significantly in burned versus unburned forests. These include the albedo decay rate (45% faster in burn than unburned forest), maximum canopy interception for rain (60% lower), minimum temperature for snow unloading (3.9°C warmer), and snow surface roughness (902% higher). Model simulations using behavioral parameter sets for burned and unburned conditions indicate 17% lower peak absolute cold content on average and 17% lower peak snow‐water equivalent (SWE) in burned conditions on average, with differences in depth, SWE, and cold content increasing throughout the snow season. These findings highlight the combined influence of canopy loss and accelerated albedo decay on snow energy balance and reveal additional post‐fire snow processes that are not typically represented in existing modeling frameworks. This study therefore provides a process‐level foundation for improving the simulated representation of post‐fire snow dynamics.