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

Authors

Housen Chu, Lawrence Berkeley National Laboratory
Xiangzhong Luo, Lawrence Berkeley National Laboratory
Zutao Ouyang, Stanford University
W. Stephen Chan, Lawrence Berkeley National Laboratory
Sigrid Dengel, Lawrence Berkeley National Laboratory
Sébastien C. Biraud, Lawrence Berkeley National Laboratory
Margaret S. Torn, Lawrence Berkeley National Laboratory
Stefan Metzger, National Ecological Observatory Network
Jitendra Kumar, ORNL Environmental Sciences Division
M. Altaf Arain, McMaster University
Tim J. Arkebauer, University of Nebraska–Lincoln
Dennis Baldocchi, Department of Environmental Science, Policy, and Management
Carl Bernacchi, USDA-ARS Global Change and Photosynthesis Research Unit
Dave Billesbach, University of Nebraska–Lincoln
T. Andrew Black, The University of British Columbia
Peter D. Blanken, University of Colorado Boulder
Gil Bohrer, The Ohio State University
Rosvel Bracho, University of Florida
Shannon Brown, University of Guelph
Nathaniel A. Brunsell, University of Kansas
Jiquan Chen, Michigan State University
Xingyuan Chen, Pacific Northwest National Laboratory
Kenneth Clark, USDA Forest Service
Ankur R. Desai, University of Wisconsin-Madison
Tomer Duman, The University of New Mexico
David Durden, National Ecological Observatory Network
Silvano Fares, Consiglio Nazionale delle Ricerche
Inke Forbrich, Ecosystems Center
John A. Gamon, University of Alberta
Christopher M. Gough, Virginia Commonwealth University
Timothy Griffis, University of Minnesota Twin Cities
Manuel Helbig, Dalhousie University
David Hollinger, USDA Forest Service
Elyn Humphreys, Carleton University
Hiroki Ikawa, National Agriculture and Food Research Organization, NARO
Hiroki Iwata, Shinshu University
Yang Ju, The Ohio State University
John F. Knowles, Southwest Watershed Research Center
Sara H. Knox, The University of British Columbia
Hideki Kobayashi, Japan Agency for Marine-Earth Science and Technology

Document Type

Article

Date of this Version

5-15-2021

Citation

Agricultural and Forest Meteorology 301-302 (2021) 108350

doi:10.1016/j.agrformet.2021.108350

Comments

U.S. government work

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 evaluate 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 vary 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 are 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 against 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.