Introduction: More than 20% of the Brazilian Amazon has been deforested, and a similar, yet uncertain fraction of the remaining forests have been degraded through selective logging, fires, and fragmentation. Changes in disturbance regime associated with forest degradation cause reductions in carbon stocks, shifts in forest structure and composition, and potentially alter the forest resilience to climate extremes. While critical for understanding processes associated with degradation in detail, field plots are limited to small sampling size and challenges accessing hotspots of degradation. Earth System Models rarely account for the diversity of forest structures that exist within biomes.
Objectives: We seek to quantify how forest degradation is affecting the water and carbon cycles, as well as the role of degradation on the forest’s sensitivity to droughts, by combining field measurements, remote sensing, and modeling.
Methods: We integrate forest inventory plots and airborne lidar surveys across the Amazon to provide realistic forest structure conditions at regional scale to the Ecosystem Demography Model (ED2). ED2 is a terrestrial biosphere model that represents the dynamics of vertically structured and horizontally heterogenous forest canopies, and accounts for the variability in forest structure within each grid cell while solving the water, energy and carbon cycles. We carried out a series of 1°×1° simulations for the Brazilian Amazon initialized with forest structure obtained from airborne lidar surveys and driven with meteorological drivers based on WFDE5 reanalyses (1981–2018).
Results: According to the simulations, severe forest degradation (biomass loss > 50%) can reduce evapotranspiration and gross primary productivity by about 35%. Likewise, regional simulations across the Brazilian Amazon indicated that forests near the Brazilian arc of deforestation have high potential of carbon accumulation, but show stronger negative responses of gross primary productivity and evapotranspiration to hot drought conditions than intact forests. Furthermore, simulations under a scenario of expansion of deforestation and degradation suggest that even forests in wetter parts of the Amazon could become more sensitive to droughts if degraded.
Implications: These results suggest that the susceptibility of the Amazon rainforest to drought effects are amplified by prior forest disturbance, indicating that reducing or reversing forest degradation could ameliorate effects of climate variability and climate change.
Amazon; forest degradation; data-model integration; climate extremes; remote sensing