Increasing the physical representation of forest-snow processes in coarseresolution models: lessons learned from upscaling hyper-resolution simulations

Processes shaping forest snow cover evolution often vary at small spatial scales, which are not resolved by most model applications. Representing this variability at larger scales and coarser model resolutions constitutes a major challenge for model developers. In this study, we use a well‐validated hyper‐resolution forest snow model that explicitly resolves the spatial variability of canopy‐snow interactions at the meter scale to explore adequate representation of forest‐snow processes at coarser resolutions (50 m). For this purpose, we assess coarser‐resolution runs against spatially averaged results from corresponding hyper‐resolution simulations over a 150,000 m2 model domain. For the coarser‐resolution simulations, we tested alternative upscaling strategies. Our results reveal considerable discrepancies between strategies that utilize generalized canopy metrics versus strategies that apply a more detailed set of process‐specific canopy descriptors. Particularly, the inclusion of canopy descriptors that represent the various scales and perspectives relevant to the individual processes leads to accurate simulation of forest snow cover dynamics at coarse resolutions. Our results further demonstrate that a realistic representation of snow‐covered fraction in snowmelt calculations is important even for relatively small (∼50 m) grid cells. Ultimately, this work provides recommendations for modeling forest‐snow processes in large‐scale applications, which allow coarse resolution simulations to approximate spatially averaged results of corresponding hyper‐resolution simulations.