The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data

Abstract

The FLUXNET2015 dataset provides ecosystem-scale data on CO 2 , water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible.

Partitioning net carbon dioxide fluxes into photosynthesis and respiration using neural networks

“…FLUXNET2015 includes meteorological and EC measurements that were quality checked and processed with standard tools (Papale et al, 2006;Pastorello et al, 2020) Data used for training and validation of the neural network were taken from the "FULLSET" "TIER 1" collection. Among the sites available in this collection, we selected a subset of 36 study sites (listed in Table 1) on the basis of the data quality and data availability in order to ensure the best conditions for the partitioning methods comparison.…”

“…The variable used as target values in the NN C-part training was the half hourly NEE (µmol CO 2 m −2 s −1 ) measured with the EC technique. In particular, we used the NEE_CUT_USTAR50 variable (Pastorello et al, 2020). We used a comprehensive subset of micrometeorological vari- which is important in particular if WD changes systematically (e.g., nighttime vs. daytime, morning vs. afternoon) and if the surrounding land cover is heterogeneous.…”

Frost and drought: Effects of extreme weather events on stem carbon dynamics in a Mediterranean beech forest

“…For the period 1989-2014, FLUXNET2015 data release was used for half-hourly air temperature and precipitation (Pastorello et al, 2020;Reyer et al, 2020). For the study period (2015-2017), measured data were gap filled using downloaded data by the ERA5 database of the (Zhang et al, 2003).…”

Discerning the thermodynamic feasibility of the spontaneous coexistence of multiple functional vegetation groups

“…Three sites from the FLUXNET2015 dataset 31 are modeled: Santa Rita Mesquite (SRM) in Arizona, Willow Creek (WCR) in Wisconsin, and Tapajos National Forest (TAP) in Pará, Brazil (Fig. S2 ) 32 – 34 .…”