Towards Improved Representation of Forest Snow Processes at Coarse Model Resolutions: Lessons Learnt from Upscaling Hyper-Resolution Simulations

Abstract:

The spatiotemporal dynamics of snow under forest canopies is of interest for modelling applications that range from the scale of individual stands to global scales. At large scales and coarse model resolutions, high spatial variability of the processes shaping forest snow cover evolution constitutes a major modelling challenge. Variability of canopy-snow interactions is determined by heterogeneous canopy structure and can only be explicitly resolved with hyper-resolution models (<5m).

Here, we present the use of hyper-resolution simulations as intermediaries between experimental data and larger-scale simulations. To this end, we perform model upscaling experiments with the forest snow model FSM2. When run at 2-m resolution, FSM2 is shown to capture the spatial variability of forest snow dynamics with a high level of detail: Its accurate performance is verified at the level of individual energy balance components based on extensive, spatially distributed sub-canopy measurements of micrometeorological and snow variables, obtained with mobile multi-sensor platforms. Results from hyper-resolution simulations over a 100,000 m² domain are then compared to spatially lumped, coarse-resolution runs, where canopy structure descriptors are aggregated over 50m x 50m grid cells by applying different strategies. This approach allows us to explore the representation of forest snow processes at model resolutions coarser than the spatial scales at which these processes vary and interact.

Different upscaling strategies exhibited large discrepancies in simulated (1) distribution of snow water equivalent at peak of winter, and (2) timing of snow disappearance. Our results indicate that detailed canopy structure metrics, as included in hyper-resolution runs, are necessary to capture the spatial variability of forest snow processes even at coarser resolutions. They further demonstrate the relevance of accounting for unresolved sub-grid variability in snowmelt calculations even at relatively small spatial aggregation scales.