ForEdgeClim v1.0: a 3D process-based microclimate model incorporating vertical and lateral radiative and thermal fluxes to simulate forest edge-to-core transitions

Abstract. Forest microclimates play a fundamental role in regulating biodiversity, ecosystem functioning, and forest resilience to climate change. However, most existing microclimate models focus on vertical processes and neglect lateral energy exchanges, limiting their ability to represent forest edge effects. Due to ongoing forest fragmentation, such lateral fluxes play an essential role in forest microclimate and associated ecological processes, particularly given that up to 20 % of global forest cover lies within 100 m of a forest edge. Here, we introduce ForEdgeClim, a new process-based microclimate model implemented as a publicly available open-source R package that is able to simulate air and surface temperature at high spatial resolution along the forest edge-to-core continuum (here demonstrated at 1 m resolution). By explicitly leveraging high-resolution 3D forest structural data (e.g., derived from terrestrial laser scanning), the model represents a substantial advance over existing approaches that rely on simplified or spatially aggregated canopy descriptions. Building on this detailed structural representation, ForEdgeClim couples meteorological forcing with a physically based energy balance framework – including shortwave and longwave radiation, sensible and latent heat fluxes, and soil heat exchange – to simulate three-dimensional microclimate temperature patterns through a voxel-based radiative–thermal framework that explicitly represents vertical and lateral radiative and thermal exchanges, while representing wind-driven processes implicitly. Radiative transfer is represented using a two-stream approximation in both vertical and lateral directions, whereas the full energy balance is iteratively solved within a 3D voxel grid to account for coupled radiative and heat flux exchanges. A Sobol sensitivity analysis indicates that heat-transfer processes dominate local air temperature dynamics (≥67 % of the total model output variance), whereas radiative transport plays a stronger role in controlling surface temperature and spatial temperature heterogeneity. These insights informed a targeted calibration of key model parameters. Model performance was evaluated using high-frequency in situ temperature measurements, with forest structural information derived from terrestrial laser scanning data, collected along a forest edge-to-core transect in a temperate forest in Belgium. Validation shows that ForEdgeClim successfully reproduces observed edge-to-core temperature gradients and fine-scale spatial variability in air temperature (R2≥0.87, RMSE≤2.01 °C). By combining high-resolution structural information with a physically grounded yet computationally efficient framework, ForEdgeClim bridges the gap between simplified empirical microclimate models and computationally intensive ray-tracing approaches, which typically lack a full energy balance formulation. The model thus provides a versatile platform for microclimate research, ranging from biodiversity and habitat modelling to studies of forest-climate interactions under a changing environment, especially where edge effects play a key role in fragmented landscapes.