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In recent decades, droughts, deforestation and wildfires have become recurring phenomena that have heavily affected both human activities and natural ecosystems in Amazonia. The time needed for an ecosystem to recover from carbon losses is a crucial metric to evaluate disturbance impacts on forests. However, little is known about the impacts of these disturbances, alone and synergistically, on forest recovery time and the resulting spatiotemporal patterns at the regional scale. In this study, we combined the 3-PG forest growth model, remote sensing and field derived equations, to map the Amazonia-wide (3 km of spatial resolution) impact and recovery time of aboveground biomass (AGB) after drought, fire and a combination of logging and fire. Our results indicate that AGB decreases by 4%, 19% and 46% in forests affected by drought, fire and logging + fire, respectively, with an average AGB recovery time of 27 years for drought, 44 years for burned and 63 years for logged + burned areas and with maximum values reaching 184 years in areas of high fire intensity. Our findings provide two major insights in the spatial and temporal patterns of drought and wildfire in the Amazon: (1) the recovery time of the forests takes longer in the southeastern part of the basin, and, (2) as droughts and wildfires become more frequent&mdash, since the intervals between the disturbances are getting shorter than the rate of forest regeneration&mdash, the long lasting damage they cause potentially results in a permanent and increasing carbon losses from these fragile ecosystems.

Understanding the dynamics of organic carbon mineralization is fundamental in forecasting biosphere to atmosphere net carbon ecosystem exchange (NEE). With this perspective, we developed 3D-CMCC-PSM, a new version of the hybrid process based model 3D‐CMCC FEM where also heterotrophic respiration (Rh) is explicitly simulated. The aim was to quantify NEE as a forward problem, by subtracting ecosystem respiration (Reco) to gross primary productivity (GPP). To do so, we developed a simplification of the soil carbon dynamics routine proposed in the DNDC (DeNitrification-DeComposition) computer simulation model. The method calculates decomposition as a function of soil moisture, temperature, state of the organic compartments, and relative abundance of microbial pools. Given the pulse dynamics of soil respiration, we introduced modifications in some of the principal constitutive relations involved in phenology and littering sub-routines. We quantified the model structure-related uncertainty in NEE, by running our training simulations over 1000 random parameter-sets extracted from parameter distributions expected from literature. 3D-CMCC-PSM predictability was tested on independent time series for 6 Fluxnet sites. The model resulted in daily and monthly estimations highly consistent with the observed time series. It showed lower predictability in Mediterranean ecosystems, suggesting that it may need further improvements in addressing evapotranspiration and water dynamics.