Regional estimates of gross primary production applying the Process-Based Model 3D-CMCC-FEM vs. multiple Remote-Sensing datasets

Abstract

Process-based models (PBMs) offer the possibility to capture important spatial and temporal patterns of both carbon fluxes and stocks in forests, accounting for ecophysiological, climate, and geographical variability. For the first time, the stand scale process-based model 3D-CMCC-FEM is applied in a spatially explicit manner at 1 km spatial resolution in a Mediterranean region in southern Italy. We developed a methodology to initialize the model that comprehends the use of spatial information derived from the integration of the national forest inventory data, remote sensing data, and regional forest maps to characterize structural features of the main forest species. Gross primary production (GPP) fluxes are simulated over the period 2005-2019 and the predictive capability of the model in simulating the carbon fluxes is evaluated by means of independent multiple data sources based on remote sensing products. We show that the model is able to reproduce most of the spatial and seasonal variability and patterns of the observed productivity, even at the species-level. These new very promising results open the possibility of using the 3D-CMCC-FEM to investigate the forests' behavior under climate and environmental variability over large areas in the future, across the highly variable ecological and bio-geographical heterogeneity of the Mediterranean region.