Understanding the representativeness of FLUXNET for upscaling carbon flux from eddy covariance measurements
- 1Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
- 2Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
- 3Eastern Forest Environmental Threat Assessment Center, USDA Forest Service, Asheville, NC, USA
Abstract. Eddy covariance data from regional flux networks are direct in situ measurement of carbon, water, and energy fluxes and are of vital importance for understanding the spatio-temporal dynamics of the the global carbon cycle. FLUXNET links regional networks of eddy covariance sites across the globe to quantify the spatial and temporal variability of fluxes at regional to global scales and to detect emergent ecosystem properties. This study presents an assessment of the representativeness of FLUXNET based on the recently released FLUXNET2015 data set. We present a detailed high resolution analysis of the evolving representativeness of FLUXNET through time. Results provide quantitative insights into the extent that various biomes are sampled by the network of networks, the role of the spatial distribution of the sites on the network scale representativeness at any given time, and how that representativeness has changed through time due to changing operational status and data availability at sites in the network. To realize the full potential of FLUXNET observations for understanding emergent ecosystem properties at regional and global scales, we present an approach for upscaling eddy covariance measurements. Informed by the representativeness of observations at the flux sites in the network, the upscaled data reflects the spatio-temporal dynamics of the carbon cycle captured by the in situ measurements. This study presents a method for optimal use of the rich point measurements from FLUXNET to derive an understanding of upscaled carbon fluxes, which can be routinely updated as new data become available, and direct network expansion by identifying regions poorly sampled by the current network.
Data from this study are available at http://dx.doi.org/10.15486/NGT/1279968
Jitendra Kumar et al.
Jitendra Kumar et al.
Global 4 km resolution monthly gridded Gross Primary Productivity (GPP) data set derived from FLUXNET2015 https://doi.org/10.15486/NGT/1279968
Jitendra Kumar et al.
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