Fine-scale forest radiation dynamics from a lidar-informed canopy ray tracing model

Below-canopy downwelling shortwave radiation (SWR) influences snow ablation, soil microclimate, and carbon uptake, yet remains difficult to map in heterogeneous forests. To address this, we measured below-canopy SWR using a 122-m cable-borne mobile tram equipped with an upward-facing pyranometer along a transect spanning a subalpine forest-to-fen ecotone. Subsequently, airborne lidar-derived forest structure was used in a Lidar-to-Radiation canopy ray tracing model to simulate spatial patterns of SWR at 30-min timesteps across the full growing season (21 Mar. - 21 Sep. 2023). Simulated SWR demonstrated strong agreement with field measurements (Spearman ρ = 0.95). At 1-m horizontal resolution, the model captured tree- and gap-scale variability and growing-season contrasts. When applied to two neighboring 1 ha forest plots, simulated SWR had a monotonic, log-linear relation with sky-view factor (SVF) (R 2 = 0.96 and 0.78). SVF adequately explained the mean tendency but not the fine-scale or temporal variation of SWR. Within a single SVF class (±5% SVF interval), growing-season SWR spanned wide ranges (e.g., 46-166 W m -2 at 10% SVF and 311-398 W m -2 at 50% SVF), suggesting additional geometric controls such as relative crown position, height, and shape that were not captured by SVF. Thus, ecohydrological extremes—early snow disappearance, soil moisture deficits, or persistent shade—can coexist within a narrow SVF range. Results demonstrate the ability of a three-dimensional ray tracing model to capture sunflecks, canopy edge shadows, and terrain masking in a subalpine forest. Model errors likely reflect sensitivity to woody element representation, meter-scale uncertainty in lidar geolocation and sensor positioning, and model parameters. Coupled observation and model systems provide a transferable method to scale results from meteorological stations to transects and landscapes. Integrating such detailed measurements with process-based modeling is essential for understanding how canopy structure shapes energy availability, soil moisture and plant water availability, and land-atmosphere interactions.