Improving a Snow Energy and Mass Balance Model with Observations of Albedo Decay and Recovery Following Colorado’s 2020 Cameron Peak Wildfire

Abstract

Wildfires have grown in both intensity and extent, with recent events increasingly affecting seasonal snow zones that accumulate deep, persistent snowpacks. Post-fire impacts—such as forest canopy loss and deposition of light-absorbing impurities (e.g., charred debris and soot)—increase net shortwave radiation to the snowpack by diminishing canopy shading and reducing snow albedo, resulting in accelerated snowmelt and earlier snow disappearance. While previous work has shown that snow albedo can gradually recover, the rate and magnitude of recovery remain poorly constrained in physically based snow models. This study analyzes observations of albedo decay and recovery following Colorado’s 2020 Cameron Peak Fire, the largest wildfire in state history. Station-measured albedo indicated that median ablation-season albedo increased by ~40% during the study period (0.38 in WY 2022 to 0.53 in WY 2025). Event-scale analysis showed faster and more variable albedo decay following snowfall at burned sites, with median decay rates up to ~2–3 times more negative than unburned sites. These observations inform updates to post-fire albedo decay parameterization in iSnobal, a snow energy and mass balance model. Compared to the base configuration, implementing a new exponential albedo decay method improved agreement with observed albedo distributions by 50–63% during water years 2021–2023 and shifted modeled snow disappearance 8–14 days closer to observations. iSnobal simulations demonstrated that canopy loss exerted a stronger and more persistent control on net shortwave energy absorption than post-fire albedo changes. Accounting for post-wildfire canopy loss and albedo changes in snow models is critical for predicting snowmelt timing and water yield in fire-affected regions.