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.
Highlights
Global and regional networks of eddy-covariance towers such as those within the FLUXNET and the AmeriFlux provide the largest syn thesized in situ datasets of energy, water, carbon, and momentum fluxes between Earth’s surface and the atmosphere
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 that are often used in flux-data synthesis and modeling studies
If focusing on the spatial aspect, the representativeness includes: 1) the network-to-region representative ness (Hargrove et al, 2003), i.e., to what extent do flux measurements taken at a relatively sparse network of tower locations reflect the aggregated flux conditions in a regional or global domain? 2) the point-to-area representativeness (Schmid, 1997), i.e., to what extent do flux measurements taken at a point location reflect the aggre gated conditions over an area that is represented by a model- or satellite-based grid cell? The point-to-area representativeness is of pri mary interest in the present manuscript
Summary
Global and regional networks of eddy-covariance towers such as those within the FLUXNET and the AmeriFlux provide the largest syn thesized in situ datasets of energy, water, carbon, and momentum fluxes between Earth’s surface and the atmosphere. While benchmarking models against flux data helps identify key model shortcomings and guide their development, the value of comparisons is greatest when the data are used to understand which processes matter at which spatial and temporal scales. This so-called "space-time representativeness issue" remains one of the major chal lenges facing model-data benchmarking (Durden et al, 2020; Hoffman et al, 2017). If focusing on the spatial aspect, the representativeness includes: 1) the network-to-region representative ness (Hargrove et al, 2003), i.e., to what extent do flux measurements taken at a relatively sparse network of tower locations reflect the aggregated flux conditions in a regional or global domain? Our primary focus is on eval uating flux data’s representativeness and realizing that similar issues exist in models and other datasets (e.g., satellite data)
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