Monitoring forest attributes, C-fluxes, and C-stocks using the process-based model 3D-CMCC-FEM at the National level

Process-based forest models (PBFMs) are valuable tools for investigating the effects of climate change and alternative forest management strategies. However, they can also be considered a tool for monitoring forest conditions over short to extended periods, when ancillary data are scarce and continuous measurements are time-consuming. This study aims to evaluate the PBFM named ‘3D-CMCC-FEM’ on its capacity to monitor Italian forests. We simulated 5135 plots, corresponding to ~83 % of the 6174 field plots included in the second Italian National Forest Inventory (NFI).. The model was used to predict the carbon, nitrogen, and water cycles, including structural variables, and validated against observations from the third NFI. We also compared gross primary productivity (GPP) with two well-known remote sensing-based (RS) datasets. Overall, the model showed good performance in reproducing aboveground stocks and structural variables, with r 2 values ranging from 0.65 for diameter to 0.49 for height, and RMSE% ranging from 32 % for diameter and height to 46 % for volume. We aggregated and validated the simulation at the NUT2 level against the estimate of the third NFI, obtaining higher accuracy than the plot-level validation. Compared to RS–data the modeled GPP showed higher variability, with an overall RMSE% of 43 % and 41 % against the MODIS and GOSIF datasets, respectively. The 3D-CMCC-FEM model has consistently demonstrated reliability across multiple data sources and spatial scales, establishing it as a robust tool for forest monitoring, being, capable of delivering insights at daily, monthly, and annual resolutions over broad and heterogeneous areas. This approach offers innovative and promising improvements in the continuity of forest data, supporting more informed decision-making in climate policy and environmental management. • A national-scale evaluation of the 3D-CMCC-FEM forest model was carried out. • The model effectively reproduces key forest structural and functional variables. • Performance was demonstrated at the plot level and across larger spatial scales. • Integrating process-based models and forest inventory allow better monitoring and carbon accounting.