Résumé
Global radiation is a key climate input in process-based models (PBMs) for forests, as it determines photosynthesis, transpiration and the canopy energy balance. While radiation is highly variable at a fine spatial resolution in complex terrain due to shadowing effects, the data required for PBMs that are currently available over large extents are generally at a spatial resolution coarser than ∼9 km. Downscaling large-scale radiation data to the high resolution available from digital elevation models (DEMs) is therefore of potential importance to refine global radiation estimates and improve PBM estimations. In this study, we introduced a new downscaling model that aims to refine sub-daily global radiation data obtained from climate reanalysis data or projections at large scales to the resolution of a given DEM. First, downscaling involves splitting radiation into a direct and diffuse fraction. The influences of surrounding mountains' shade on direct radiation and the “bowl” (deep valley) effect (or sky-view factor) on diffuse radiation are then considered. The model was evaluated by comparing simulated and observed radiation at the Mont Ventoux study site (southeast of France) using the recent ERA5-Land hourly data available at a 9 km resolution as input and downscaled to different spatial resolutions (from 1 km to 30 m resolution) using a DEM. The downscaling algorithm improved the reliability of radiation at the study site, in particular at scales below 150 m. Finally, by using two different PBMs (CASTANEA, a PBM simulating tree growth, and SurEau, a plant hydraulic model simulating hydraulic failure risk), we showed that accounting for fine-resolution radiation can have a great impact on predictions of forest functions.