Enhancing environmental models with a new downscaling method for global radiation in complex terrain

Abstract. Global radiation is a key climate input in forest process-based models (PBM) as it determines photosynthesis, transpiration and the canopy energy balance. While radiation is highly variable at fine spatial resolution in complex terrain due to shadowing effects, data required for PBM currently available over large extents are generally at spatial resolution coarser than ~9 km. Downscaling radiation from large-scale to high resolution available from digital elevation models is therefore of potential importance to refine global radiation estimates and improve PBM estimations. In this study, we introduce a new downscaling model that aims to refine sub-daily global radiation data obtained from climate reanalysis or projection at large scales to the resolution of a given digital elevation model. First, downscaling involves splitting radiation into direct and diffuse fraction. Then, the influence of surrounding mountains' shade on direct radiation and the “bowl” (deep valley) effect on diffuse radiation is considered. The model was evaluated by comparing simulated and observed radiation at the Mont Ventoux mountain study site (southeast of France) using the recent ERA5-Land hourly data available at 9 km resolution as input and downscaled at different spatial resolution (from 1 km to 30 m resolution) using a digital elevation model. The downscaling algorithm improved the reliability of radiation at the study site in particular at scales below 150 m. Finally, by using two different process based models (Castanea, a process-based model 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 and climatic risks.