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

Housen Chu, Xiangzhong Luo, Zutao Ouyang, W. Stephen Chan, Sigrid Dengel, Sébastien C. Biraud, Margaret S. Torn, Stefan Metzger, Jitendra Kumar, M. Altaf Arain, Tim J. Arkebauer, Dennis Baldocchi, Carl Bernacchi, Dave Billesbach, T. Andrew Black, Peter D. Blanken, Gil Bohrer, Rosvel Bracho, Shannon Brown, Nathaniel A. BrunsellJiquan Chen, Xingyuan Chen, Kenneth Clark, Ankur R. Desai, Tomer Duman, David Durden, Silvano Fares, Inke Forbrich, John A. Gamon, Christopher M. Gough, Timothy Griffis, Manuel Helbig, David Hollinger, Elyn Humphreys, Hiroki Ikawa, Hiroki Iwata, Yang Ju, John F. Knowles, Sara H. Knox, Hideki Kobayashi, Thomas Kolb, Beverly Law, Xuhui Lee, Marcy Litvak, Heping Liu, J. William Munger, Asko Noormets, Kim Novick, Steven F. Oberbauer, Walter Oechel, Patty Oikawa, Shirley A. Papuga, Elise Pendall, Prajaya Prajapati, John Prueger, William L. Quinton, Andrew D. Richardson, Eric S. Russell, Russell L. Scott, Gregory Starr, Ralf Staebler, Paul C. Stoy, Ellen Stuart-Haëntjens, Oliver Sonnentag, Ryan C. Sullivan, Andy Suyker, Masahito Ueyama, Rodrigo Vargas, Jeffrey D. Wood, Donatella Zona

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Article number108350
JournalAgricultural and Forest Meteorology
Volume301-302
DOIs
StatePublished - May 15 2021

Bibliographical note

Funding Information:
We thank the AmeriFlux site teams for sharing their data and metadata with the network. Funding for these flux sites is acknowledged in the site data DOI, shown in Table S1. This analysis was supported in part by funding provided to the AmeriFlux Management Project by the U.S. Department of Energy's Office of Science under Contract No. DE-AC02-05CH11231. All footprint climatologies, site-level representativeness indices, and monthly EVI and sensor location biases can be accessed via the Zenodo Data Repository (Datasets S1?S6, http://doi.org/10.5281/zenodo.4015350).

Publisher Copyright:
© 2021

Keywords

  • Flux footprint
  • Land cover
  • Landsat EVI
  • Model-data benchmarking
  • Sensor location bias
  • Spatial representativeness

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