Predicting seasonal fluxes of water and energy across the Amazon
Forests regulate the water cycle, and store and absorb carbon dioxide from the atmosphere. However, their success to drive evaporative cooling and to do photosynthesis depends on how efficiently forests access and use light and water during the year. We used measurements from flux towers installed in Amazonia, and compared observations with predictions from four computer-based forest simulators (models). Models are useful for predicting how forests will cope with climate change, but need to be tested first. We found that models think the Amazon dry-up too often and too quickly, and that they reflect more light when compared to observations.
Our knowledge about forest ecology, climate, structure, etc., is used by models to describe different ecosystems. Moreover, models are representations of how forests work — how much trees transpire and grow, leaves reflect light, soil stores water, etc. Models are tested by comparing their output to current observations as they will be used to forecast the future of tropical forests (e.g. we can infer tree growth if rainfall is reduced). This work describes measurements (e.g. temperature, tropical forest evaporation and transpiration) at different sites across Amazonia and points to processes that need to be improved on the tested models.
We used data collected in four eddy flux towers across the Amazon to quantify the seasonal cycle of sensible heat flux, evapotranspiration, emission of thermal infrared radiation, and the optical properties of the forest canopies. We simulated the seasonal cycle of the same quantities using four terrestrial biosphere models that are often used to predict the future of the Amazon (namely IBIS, ED2, JULES, and CLM3.5). We compared the model predictions with the tower measurements and identified that most models predict a strong seasonality of the Bowen ratio (ratio between sensible and latent heat flux), and overall low water use efficiency. Consequently, models predicted that Amazon forests experience more frequent water stress than what has been observed. Likewise, the models predicted that the forest canopies would reflect more light than observed.
We identified possible explanations for such differences. First, models do not represent when leaves shed or replace leaves, which may bias the canopy reflectance. Likewise, models seem to exaggerate the canopy interception of rainfall, which reduces the soil available water. Finally, the incorrect estimates of water stress also led to discrepancies between predicted and observed outgoing longwave radiation. Our findings can be used as references for future model development.
Figure. A 65-m eddy covariance flux tower in the Amazon Forest (Tapajos, Brazil) where we measure meteorology and exchanges of water, energy, and carbon between the forest and the atmosphere, and can help us to understand how tropical forests (Jaru and Tapajos shown here) will respond to climate change. Image courtesy of Natalia Restrepo-Coupe.
Contact (BER): Daniel Stover, SC-23.1, Daniel.Stover@science.doe.gov (301-903-0289)
Science Contact: Natalia Restrepo-Coupe, Saleska Lab, Ecology and Evolutionary Biology, University of Arizona, email@example.com
This research was supported as part of the Next Generation Ecosystem Experiments-Tropics, funded by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research. This research was also funded by the Gordon and Betty Moore Foundation “Simulations from the Interactions Between Climate, Forests, and Land Use in the Amazon Basin: Modeling and Mitigating Large-Scale Savannization” project, and the National Aeronautics and Space Administration (NASA) through the NASA LBA-DMIP project.
Restrepo-Coupe, et al., “Understanding water and energy fluxes in the Amazonia: Lessons from an observation-model intercomparison” Global Change Biology 27, 1802–1819 (2021) [DOI: 10.1111/gcb.15555]