Quantitative understanding of regional gross primary productivity (GPP) and net ecosystem exchanges (NEE) and their responses to environmental changes are critical to quantifying the feedbacks of ecosystems to the global climate system. Numerous studies have used the eddy flux data to upscale the eddy covariance derived carbon fluxes from stand scales to regional and global scales. However, few studies incorporated atmospheric carbon dioxide (CO2) concentrations into those extrapolations. Here, we consider the effect of atmospheric CO2 using an artificial neural network (ANN) approach to upscale the AmeriFlux tower of NEE and the derived GPP to the conterminous United States. Two ANN models incorporating remote sensing variables at an 8-day time step were developed. One included CO2 as an explanatory variable and the other did not. The models were first trained, validated using eddy flux data, and then extrapolated to the region at a 0.05o×0.05o (latitude×longitude) resolution from 2001 to 2006. We found that both models performed well in simulating site-level carbon fluxes. The spatially-averaged annual GPP with and without considering the atmospheric CO2 were 789 and 788gCm-2yr-1, respectively (for NEE, the values were -112 and -109gCm-2yr-1, respectively). Model predictions were comparable with previous published results and MODIS GPP products. However, the difference in GPP between the two models exhibited a great spatial and seasonal variability, with an annual difference of 200gCm-2yr-1. Further analysis suggested that air temperature played an important role in determining the atmospheric CO2 effects on carbon fluxes. In addition, the simulation that did not consider atmospheric CO2 failed to detect ecosystem responses to droughts in part of the US in 2006. The study suggests that the spatially and temporally varied atmospheric CO2 concentrations should be factored into carbon quantification when scaling eddy flux data to a region.
Bibliographical noteFunding Information:
This study is supported through projects funded to Q. Z. by the NASA Land-Use and Land-Cover Change program ( NASA-NNX09AI26G ), Department of Energy ( DE-FG02-08ER64599 ), the NSF Division of Information and Intelligent Systems ( NSF-1028291 ), and the NSF Carbon and Water in the Earth Program ( NSF-0630319 ). The supercomputing resource is provided by the Rosen Center for Advanced Computing at Purdue University. Special thanks to all scientists and supporting staffs at AmeriFlux sites. Finally, we acknowledged two anonymous reviewers for their comments. Gabriela Shirkey helped editing of the manuscript.
© 2016 Elsevier B.V.
- Artificial neural network
- Eddy flux tower
- Gross primary production
- Net ecosystem change