Representativeness of Eddy-Covariance flux footprints for areas surrounding AmeriFlux sites

Authors: CHU, HOUSEN - Lawrence Berkeley National Laboratory; LUO, XIANGZHONG - Lawrence Berkeley National Laboratory; OUYANG, ZUTAO - Stanford University; CHAN, W STEPHEN - Lawrence Berkeley National Laboratory; DENGEL, SIGRID - Lawrence Berkeley National Laboratory; BIRAUD, SEBASTIEN - Lawrence Berkeley National Laboratory; TORN, MARGARET - Lawrence Berkeley National Laboratory; METZGER, STEFAN - University Of Wisconsin; KUMAR, JITENDRA - Oak Ridge National Laboratory; ARAIN, M - McMaster University; ARKEBAUER, TIM - University Of Nebraska; BALDOCCHI, DENNIS - University Of California; Bernacchi, Carl; Knowles, John; Prueger, John

Submitted to: Agricultural and Forest Meteorology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/31/2021
Publication Date: 5/15/2021
Citation: Chu, H., Luo, X., Ouyang, Z., Chan, W., Dengel, S., Biraud, S.C., Torn, M.S., Metzger, S., Kumar, J., Arain, M.A., Arkebauer, T.J., Baldocchi, D., Bernacchi, C.J., Knowles, J.F., Prueger, J.H., et al. 2021. Representativeness of Eddy-Covariance flux footprints for areas surrounding AmeriFlux sites. Agricultural and Forest Meteorology. 301-302. Article 108350. https://doi.org/10.1016/j.agrformet.2021.108350.
DOI: https://doi.org/10.1016/j.agrformet.2021.108350

Interpretive Summary: Large datasets that focus on how ecosystems, including forests, croplands, grasslands, etc., function have been installed globally. These experiments measure the transfer of greenhouse gases, water, and energy between the ecosystems and the atmosphere and the techniques rely on measuring information that is contained in wind. These measurements provide the basis for how scientists understand ecosystem functioning at large scales and are used for building and testing ecosystem models and for testing "remote sensing" data from aircraft and satellites. This research addresses how well the ground-based datasets represent the grid cells (or areas of land measured by remote sensing or models). The results show that changing wind speeds and directions coupled with variation in land surface covering (for example a grid cell representing part of a forest and part of a crop) create mismatches between the satellite or modeling grid cell and the actual ground-based measurement. A recommendation from this research is that the ground-based measurements should include information that represents that actual area being measured to ensure that the mismatch errors are minimized or eliminated.

Technical Abstract: Large datasets of greenhouse gas and energy surface-atmosphere fluxes measured with the eddy-covariance technique (e.g., FLUXNET2015, AmeriFlux BASE) are widely used to benchmark models and remote-sensing products. This study addresses one of the major challenges facing model-data integration: To what spatial extent do flux measurements taken at individual eddy-covariance sites reflect model- or satellite-based grid cells? We evaluated flux footprints—the temporally dynamic source areas that contribute to measured fluxes—and the representativeness of these footprints for target areas (e.g., within 250–3000 m radii around flux towers) that are often used in flux-data synthesis and modeling studies. We examine the land-cover composition and vegetation characteristics, represented here by the Enhanced Vegetation Index (EVI), in the flux footprints and target areas across 214 AmeriFlux sites, and evaluate potential biases as a consequence of the footprint-to-target-area mismatch. Monthly 80% footprint climatologies varied across sites and through time ranging four orders of magnitude from 103 to 107 m2 due to the measurement heights, underlying vegetation- and ground-surface characteristics, wind directions, and turbulent state of the atmosphere. Few eddy-covariance sites are located in a truly homogeneous landscape. Thus, the common model-data integration approaches that use a fixed-extent target area across sites introduce biases on the order of 4%–20% for EVI and 6%–20% for the dominant land cover percentage. These biases were site-specific functions of measurement heights, target area extents, and land-surface characteristics. We advocate that flux datasets need to be used with footprint awareness, especially in research and applications that benchmark models and data products with explicit spatial information. We propose a simple representativeness index based on our evaluations that can be used as a guide to identify site-periods suitable for specific applications and to provide general guidance for data use.