Simulating snow properties and Ku-band backscatter across the forest-tundra ecotone

Sophisticated snowpack models are required to provide accurate estimation of snowpack properties across the forest-tundra ecotone where in situ measurements are rare. As snowpack properties strongly influence radar scattering signals, accurate simulation is crucial for the success of spaceborne radar missions to retrieve snow water equivalent (SWE). In this study, we evaluate the ability of default and Arctic Crocus embedded within the Soil, Vegetation and Snow version 2 (SVS2-Crocus) land surface model to simulate snowpack properties (e.g. depth, density, SWE, specific surface area) across a 40-km transect of the Northwest Territories, Canada, using two winter seasons (2021-22 & 2022-23) of in situ measurements. An ensemble of simulated snowpack properties (120 members from default and Arctic SVS2-Crocus) were used in the Snow Microwave Radiative Transfer (SMRT) model to simulate Ku-band (13.5 GHz) backscatter. SMRT backscatter using multi-layer SVS2-Crocus snowpack simulations were compared to backscatter using a simplified 3-layer radar-equivalent snowpack. Results highlight that Arctic SVS2-Crocus wind-induced compaction modifications were spatially transferable across the forest-tundra ecotone and lead to an improvement in the simulation of surface snow density at all sites, reducing the RMSE of surface density by an average of 29%. The parameterisation of below-canopy wind speed limits the ability of SVS2-Crocus to increase surface density to match measurements, despite the inclusion of Arctic modifications and should be revised for sparse (e.g. canopy densities < 15 %) canopy environments. Basal vegetation modifications were less effective in simulating low-density basal snow layers at all sites (default RMSE: 67 kg m^-3; Arctic RMSE: 69 kg m^-3) but were necessary to simulate a physically representative Arctic density profile. SVS2-Crocus underestimated snow specific surface area (SSA) leading to high errors in the simulation of snow backscatter (default RMSE: 3.5 dB; Arctic RMSE: 5.3 dB). RMSE of backscatter was reduced by implementing a minimum SSA value (8.7 m² kg^-1; default RMSE: 1.4 dB; Arctic RMSE: 1.3 dB) or by scaling the scattering effects of the snowpack (polydispersity: 0.63; default RMSE: 1.6 dB; Arctic RMSE: 2.6 dB). Utilising a radar-equivalent snowpack was effective in retaining the scattering behaviour of the multi-layer snowpack (RMSE < 1 dB) providing a means to monitor SWE with reduced computational complexity.