Simulating drought impacts on energy balance in an Amazonian rainforest

Abstract:

The studies of the interaction between vegetation and climate change in the Amazon Basin indicate that up to half of the region’s forests may be displaced by savanna vegetation by the end of the century. Additional analyses suggest that complex interactions among land use, fire-frequency, and episodic drought are driving an even more rapid process of the forest impoverishment and displacement referred here as “savannization”. But it is not clear whether surface/ecosystem models are suitable to analyze extreme events like a drought. A long-term simulation of throughfall exclusion experiments has provided unique insights into the energy dynamics of Amazonian rainforests during drought conditions. In this study, we evaluate how well six surface/ecosystem models quantify the energy dynamics from two Amazonian throughfall exclusion experiments. All models were run for the Tapajós and Caxiuanã sites with one control plot using normal precipitation (i.e. do not impose a drought) and then the drought manipulation was imposed for several drought treatments (10 to 90% rainfall exclusion). The sap flow, net radiation (R_n), sensible (H), latent (LE) and ground (G) heat flux are used to analyze if the models are able to capture the dynamics of water stress and what the implications for the energy dynamics are. With respect to the model validation, when we compare the sap flow observed and transpiration simulated, models are more accurate to simulate control plots than drought treatments (50% rainfall exclusion). The results show that the models overestimate the sap flow data during the drought conditions, but they were able to capture the changes in the main energy balance components for different drought treatments. The R_n and LE decreased and H increased with more intensity of drought. The models sensitivity analysis indicate that models are more sensitive to drought when rainfall is excluded for more than 60% and when this reduction occurs during the dry season.