Improving net carbon flux estimates in Tropical South America by accounting for forest degradation and recovery in a Vegetation Photosynthesis and Respiration Model

The net carbon exchange between ecosystems and the atmosphere (Net Ecosystem Exchange, NEE) is determined by the balance between Gross Primary Production (GPP) and Ecosystem Respiration (R_eco). However, in tropical South America (TSA), the spatial variability of these factors and the factors that influence them are not well understood, especially in areas affected by forest degradation and secondary forest recovery. Here, we use a new implementation of the Vegetation Photosynthesis and Respiration Model (pyVPRM) to generate hourly, spatially explicit estimates of NEE, GPP, and Reco across TSA. The model is constrained by eddy-covariance measurements from 22 flux towers spanning Amazonian upland and lowland forests, Andean-influenced forests, tropical wetlands, and Orinoco savannas, combined with remotely sensed vegetation indices (EVI), meteorological forcing, and annually varying land-cover maps from MapBiomas. We extend the standard pyVPRM land-cover classification to explicitly represent forest disturbance and recovery states, distinguishing undisturbed, degraded, deforested, and regenerating forests. This allows us to quantify how forest degradation and regrowth alter the magnitude and spatial distribution of gross carbon fluxes compared with simulations that do not distinguish between degradation classes. We expect that resolving these disturbance states will reduce systematic biases in both GPP and R_eco over human-modified landscapes and improve the attribution of carbon sources and sinks across the TSA region beyond what is captured by climate forcing alone. By separating disturbance-driven from climate-driven flux variability, this framework provides a more realistic prior for regional atmospheric inverse modelling and a stronger basis for assessing the carbon consequences of tropical forest degradation and recovery.