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

  • Euro-Mediterranean Center on Climate Change (CMCC) Foundation

Abstract and Figures

The FLUXNET2015 dataset provides ecosystem-scale data on CO2, 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.
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SCIENTIFIC Data | (2020) 7:225 |
et al.#
For over three decades, the eddy covariance technique1 has been used to measure land-atmosphere exchanges
of greenhouse gases and energy at sites around the world to study and determine the function and trajectories
of both ecosystems and the climate system. e technique allows nondestructive measurement of these uxes at
a high temporal resolution and ecosystem level, making it a unique tool. Based on high frequency (10–20 Hz)
measurements of vertical wind velocity and a scalar (CO2, H2O, temperature, etc.), it provides estimates of the
net exchange of the scalar over a source footprint area that extends up to hundreds of meters around the point
of measurement. Soon aer the rst consistent measurement sites were operational, regional networks of sites
were formed in Europe2 and the US3,4, followed by similar initiatives in other continents57. Networks enabled the
use of eddy covariance data beyond a single site or ecosystem for cross-site comparisons and regional-to-global
studies815. ese regional networks have evolved into long-term research infrastructures or monitoring activities,
such as ICOS, AmeriFlux, NEON, AsiaFlux, ChinaFLUX, and TERN-OzFlux.
FLUXNET was created as a global network of networks1618, a joint eort among regional networks to har-
monize and increase standardization of the data being collected. It made possible the creation of global eddy
covariance datasets. e rst gap-lled, global FLUXNET dataset, which included derived, partitioned uxes
like photosynthesis and respiration, was the Marconi dataset19 in 2000, with 97 site-years of data, followed by the
2007 Lauile dataset20 with 965 site-years of data, and nally in 2015 the FLUXNET2015 dataset18,21 (hereaer
FLUXNET2015) with 1532 site-years of data. Two main factors limited the numbers of sites and years included
in each dataset: data policy and data quality. Willingness to share data under the selected data policy is a major
reason why FLUXNET2015 likely only includes between 10–20% of existing sites globally–the total number of
existing sites is still unknown. en there is the evolution of processing pipelines and quality controls, leading to
new issues being identied in the data that, if not solved in time, led to leaving out that data. e Lauile dataset
had a more restrictive policy and, in a few cases, previously undiscovered data issues, leading to fewer sites being
included in FLUXNET2015.
#A full list of authors and their aliations appears at the end of the paper.
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SCIENTIFIC Data | (2020) 7:225 |
FLUXNET2015 includes for the rst time sites with records over two decades long (Fig.1). e dataset was
created through collaborations among many regional networks, with data preparation eorts happening at site,
regional network, and global network levels. e global coordination of data preparation activities and data pro-
cessing was done by a team from the AmeriFlux Management Project (AMP), the European Ecosystem Fluxes
Database, and the ICOS Ecosystem ematic Centre (ICOS-ETC). is team was responsible for the coding
eorts, quality checks, and execution of the data processing pipeline. ese combined eorts led to a dataset that
is standardized with respect to the (1) data products themselves, (2) data distribution formatting, and (3) data
quality across sites. e wide application of these datasets in global synthesis and modeling activities highlights
their value. At the same time, however, heterogeneity in the data–caused mainly by dierences in data collection,
ux calculations, and data curation before submission–highlights the need for estimates of data uncertainty and
uniform evaluation of data quality.
e data processing pipeline uses well-established and published methods, with new code implemented for
this release as well as code adapted from implementations by the community. e main products in this pipeline
are: (1) thorough data quality control checks; (2) calculation of a range of friction velocity thresholds to lter low
turbulence periods, allowing an estimate of the uncertainty from this ltering along with the random uncertainty;
(3) gap-lling of meteorological and ux measurements, including the use of a downscaled reanalysis data prod-
uct to ll long gaps in meteorological variables; (4) partitioning of CO2 uxes into respiration and photosynthesis
(gross primary productivity) components using three distinct methods; and (5) calculation of a correction factor
for energy uxes estimating the deviation from energy balance closure for the site. Two features of this pipeline
are the ranges of friction velocity thresholds, and the multiple methods for partitioning CO2 uxes. Both features
support a more thorough evaluation of the uncertainty introduced by the processing steps themselves. Our imple-
mentation of this pipeline is available as an open-source code package called ONEFlux (Open Network-Enabled
Flux processing pipeline)22. e goal of this paper is to describe FLUXNET2015 and additional products, present
the details about this processing pipeline, and document the methods used to generate the dataset. Doing so will
provide the community of FLUXNET end-users with the technical and practical knowledge necessary to harness
the full potential of the FLUXNET data, including data from the FLUXNET2015 release, and data submitted to
the network since.
e data contributed by site teams for inclusion in FLUXNET2015 encompassed uxes, meteorological, envi-
ronmental, and soil time series at half-hourly or hourly resolutions. Contributed data underwent a uniform
data quality control process, with issues addressed in consultation with site teams. Data were then processed
using the pipeline (Fig.2) described in this section. e resulting data products were distributed through the
FLUXNET-Fluxdata web portal23, where the usage of the dataset is tracked through a registration of all the
Fig. 1 Map of 206 tower sites included in this paper from the 212 sites in the February 2020 release of the
FLUXNET2015 dataset. e size of the circle indicates the length of the data record. e color of the circles
represents the ecosystem type based on the International Geosphere–Biosphere Programme (IGBP) denition.
When overlapping, locations are oset slightly to improve readability. Numbers in parentheses indicate
the number of sites in each IGBP group. e inset shows the distribution of data record lengths. See also
Supplementary Fig.SM4 for continental scale maps of Australia, Europe, and North America.
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SCIENTIFIC Data | (2020) 7:225 |
requests and details about the user and the data use plan. is information is crucial to better understand the user
needs and the impact of the dataset.
 e rst level of processing was completed by the site teams, including the calculation of
half-hourly or hourly turbulent uxes from high-frequency wind and concentration measurements, the aver-
aging of meteorological variables sampled at shorter temporal resolutions, and the site team’s own quality con-
trol procedures. e contributed uxes were required to be submitted separately as turbulent and storage uxes
(components to be added for total ux), and not gap-lled or ltered for low turbulence conditions–see Aubinet
et al.24 for details. Control checks were implemented to ensure that this was the case, and to detect additional
inconsistencies that site teams were asked to address, for example coherence of the timestamps, consistency across
correlated variables, etc25. Starting from these data, we applied the same set of methods for all the remaining pro-
cessing steps (from ltering to gap-lling and partitioning), increasing uniformity, and allowing quantication
of the uncertainty introduced by the processing. e harmonization of data–especially of data quality–was a high
priority while creating the dataset, and therefore extensive interaction with the site teams was necessary. Data
were contributed through a regional network, and the formats provided by the networks were converted into a
standard input for processing. is FLUXNET dataset led to the creation of a new cross-network specication for
standard site data and metadata submission formats, now adopted by dierent regional networks.
 e data processing pipeline (Fig.2) is divided into four main pro-
cessing blocks. e rst one is the data quality assurance and quality control (QA/QC) activities our team applied
to data from all sites. is part was done with a combination of automated procedures and manual checks for all
the variables in the contributed data. e next three blocks were all part of an automated pipeline that was exe-
cuted separately for each site: the Energy & Water Fluxes processing block encompasses sensible and latent heat
variables; the Carbon Fluxes processing block handles the variables for CO2 uxes like net ecosystem exchange
(NEE); and, the Meteorological Variables processing block deals with all the meteorological measurements that
are also used in the processing of uxes and other products. At each processing step, a set of automated pre- and
post-conditions are enforced, making sure the inputs and outputs of each step are within the expected behavior.
e nal step involved merging all the products generated at previous steps, and adding daily through yearly
temporal aggregations of most of the variables in the dataset and related quality ags. At this step, automated
checks were performed on all the variables to ensure consistency, and the nal les with all the contributed and
derived data products to be distributed are created. Supplementary Fig.SM2 shows the general steps involved in
the processing in the sequence organized in the code, as available in the ONEFlux package22. All the steps have
been implemented in the shared code except the Sundown partitioning (see Implementation Approach section
for details).
 e adoption of multiple methods for the same step (e.g., two methods for USTAR
threshold calculation, or two or three methods for CO2 ux partitioning, see below in this section for details)
is motivated by the existence of dierent methods in the literature, using dierent assumptions and potentially
Fig. 2 e logic of the data processing steps for FLUXNET2015 (details about the dierent steps and meaning
of abbreviations in the text).
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SCIENTIFIC Data | (2020) 7:225 |
having diverging results26,27. On the one hand, this lack of uniformity can represent a problem for synthesis stud-
ies. On the other hand, adopting a single method could lead to biases and underestimation of the uncertainty in
the methodology. e approach taken here, which simultaneously adopts multiple methods, allows the creation
of an ensemble, helping assess uncertainty, and also the suitability of individual methods to a site’s conditions.
 Prior to the processing that generated the derived data
products (hereaer called post-processing), the data for each site went through QA/QC checks following Pastorello
et al.25. All variables included in the dataset underwent checks, and critical variables underwent further scrutiny.
ese additional checks targeted variables critical to the processing, e.g., ux variables, meteorological variables
used in gap-lling, and variables used by uncertainty estimation procedures. e processing did not proceed for
sites with pending issues in critical variables.
Critical metadata variables for the post-processing:
• e site FluxID in the form CC-SSS (two character country code, three character site identier within coun-
try) – e.g., US-Ha1
• e latitude and longitude for the site in the WGS 84 decimal format with at least four decimal points resolu-
tion – e.g., 42.5378/72.1715
• Time zone of the site (time series, if time zone changed; timestamps are all local standard time, no daylight
savings) – e.g., UTC-5
• Height of the gas analyzer – e.g., 30.0 m
Critical data variables for the post-processing, averaged or integrated over 30 or 60 minutes (*required):
• *CO2 (µmolCO2 mol1): Carbon Dioxide (CO2) mole fraction in moist air
• *FC (µmolCO2 m2 s1): Carbon Dioxide (CO2) turbulent ux (without storage component)
• *SC (µmolCO2 m2 s1): Carbon Dioxide (CO2) storage ux measured with a vertical prole system, optional
if tower shorter than 3 m
• *H (W m2): sensible heat turbulent ux, without storage correction
• *LE (W m2): latent heat turbulent ux, without storage correction
• *WS (m s1): horizontal wind speed
• *USTAR (m s1): friction velocity
• *TA (deg C): air temperature
• *RH (%): relative humidity (range 0–100%)
• *PA (kPa): atmospheric pressure
• G (W m2): ground heat ux, not mandatory, but needed for the energy balance closure calculations
• NETRAD (W m2): net radiation, not mandatory, but needed for the energy balance closure calculations
• *SW_IN (W m2): incoming shortwave radiation
• SW_IN_POT (W m2): potential incoming shortwave radiation (top of atmosphere theoretical maximum
radiation), calculated based on the site coordinates22
• PPFD_IN (µmolPhotons m2 s1): incoming photosynthetic photon ux density
• P (mm): precipitation total of each 30 or 60 minute period
• LW_IN (W m2): incoming (down-welling) longwave radiation
• SWC (%): soil water content (volumetric), range 0–100%
• TS (deg C): soil temperature
File format standardization. To process hundreds of sites, we needed consistent le formats that supported the
input data and metadata. is led to multi-network agreements and creation of formats for data and metadata
contribution to the regional networks28,29. ese formats have now been adopted by networks in Europe and the
Americas and by some instrument manufacturers, and are under consideration by other regional networks. In
addition, automated extraction and conversion tools for direct format translation were implemented to work with
data in older formats.
Data QA/QC steps. Data quality was checked before the processing started. If issues were identied that could
not be resolved by the network-level data team, the site team was asked to suggest a course of action or send a
new version of the data addressing the quality issue identied. e main data QA/QC steps were: single-variable
checks, multi-variable checks, specialized checks, and automatic checks. Single-variable checks look at patterns
in the time series of one variable at a time, for long- and short-term trends and other issues. Multi-variable checks
look at the relationships among correlated variables (e.g., dierent radiation variables) to identify periods with
disagreements. Specialized checks test for common issues in EC and meteorological data, like timestamp shis
or sensor deterioration. During this phase, a time series of top-of-the-atmosphere potential radiation (SW_IN_
POT) is also computed, using latitude/longitude coordinates and time22. ese three types of checks are detailed
in Pastorello et al.25. e automated checks apply variable-specic despiking routines adapted from Papale et al.30
and apply a set of range controls per variable. is last step creates a series of ags that were discussed with the site
managers for corrections and resubmissions and then used to lter the data in subsequent steps.
 e main processing applied to meteorological data was gap-lling by two inde-
pendent methods: Marginal Distribution Sampling31 (MDS) and ERA-Interim32. Data gap-lled by MDS (applied
to all variables that are gap-filled) are identified by the _F_MDS suffix. Data gap-filled using ERA-Interim
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SCIENTIFIC Data | (2020) 7:225 |
downscaling (six variables that are available in the reanalysis dataset) have a _F_ERA sux. e nal gap-lled
time series for variables combines both of these methods (indicated by an unqualied _F sux), following a
data-quality-based selection approach (see below). For SW_IN, in case of gaps or in case the variable was not
measured, we performed a calculation from PPFD_IN when available, calculating the conversion factor from the
periods of overlap of the two measurements (and assuming a factor 0.48 J (µmol photon)1 when the sensors did
not run in parallel at the site).
MDS. e MDS method, introduced in Reichstein et al.31, is applied to all variables that may be gap-lled. It
works by seeking meteorological conditions physically and temporally similar to the ones for the missing data
point(s). e restrictions on the size of the time window and which variables must be available are incrementally
relaxed until a suitable set of records is found to ll the gap in the target variable. All values of the target variable
satisfying the current set of conditions are averaged to generate the ll value. e method was applied as described
in the original implementation31, using SW_IN, TA and VPD as drivers. e basic three scenarios for the time
when the target variable is missing are: (i) all three drivers are available; (ii) only SW_IN is available; and (iii) all
three drivers are missing. Based on the available co-located variables, a search for similar conditions is started,
keeping the searching window as small as possible to avoid changes in other slow-changing drivers (phenology,
water availability, etc.). e more variables missing and the larger the time window, the lower the condence indi-
cated by the _F_MDS_QC ag. e values for this ag are (0–3): _F_MDS_QC = 0 (measured); _F_MDS_QC = 1
(lled with high condence); _F_MDS_QC = 2 (lled with medium condence); _F_MDS_QC = 3 (lled with
low condence). For details on the implementation, see the original paper31 and the ONEFlux source code22.
ERA-Interim. is method is based on the ERA-Interim (ERA-I) Reanalysis global atmospheric product33,34
created by the European Centre for MediumRange Weather Forecasts (ECMWF)–ERA stands for ECMWF
Re-Analysis. Applied to the subset of variables that are also available in the ERA-Interim product, the method
involves a spatial and temporal downscaling process using the measured variable at the site. e ERA-I variables
that were used are: air temperature at 2 m (t2m, K), incoming shortwave solar radiation at the surface (Sw, W m2),
dew point temperature at 2 m (dt2m, K), wind speed horizontal components at 10 m (u10 and v10, m s1), total
precipitation (Pr, m of water per time step), and incoming longwave solar radiation at the surface (Lw, W m2).
e gap-lling procedure harmonizes units, identies periods that are long enough to allow a linear relationship
to be built, a simple debiasing of the linear relationship, evaluation of the diurnal cycle in the subset of variables,
and other evaluations of the results to identify potential missing or incorrect information (e.g., coordinate or tem-
poral mismatches). e linear relationships are built taking into account instantaneous and averaged variables,
and then applied to the whole ERA-I record, generating the spatially (coordinate-based) and temporally (diurnal
cycle-based) downscaled version of each variable. e method was applied as in the original implementation;
additional details can be found in Vuichard and Papale32.
Final gap-lled product. Measured or high quality gap-lled records using MDS (_F_MDS_QC < 2) are used
in the nal gap-lled products (_F suxed variables, without _MDS or _ERA). If the variable has a low quality
gap-ll ag (2 or 3), the ERA-I product is used instead. e nal quality ag (_F_QC) is 0 for measured, 1 for
high quality ll using MDS, and 2 for data gap-lled with the ERA-I downscaled product. A gap-lled version
of CO2 concentration is also generated (CO2_F_MDS) using the MDS method as described above, including the
corresponding quality ag.
 The main data products associated with energy and water fluxes are the
gap-lled versions of the data and the estimation of a version ensuring the energy balance closure and estimating its
uncertainty–for a description of the issue see Stoy et al.35 Turbulent energy uxes (sensible and latent heat, H and LE,
respectively) are gap-lled using the MDS method31 described above. From LE, it is possible to calculate the water
ux (evapotranspiration) using the latent heat of vaporization. An energy balance corrected version of LE and H is
also created, a data product oen needed when data are used in model parameterization and validation for which the
closure of the energy balance is prescribed. ere is no general agreement on the reasons and approaches to correct
the imbalance in the energy budget within EC measurements. In this product, the methodology used to calculate
the energy balance corrected uxes is based on the assumption that the Bowen ratio is correct36. Fluxes are corrected
by multiplying the original, gap-lled LE and H data by an energy balance closure correction factor (EBC_CF, in the
dataset). e correction factor is calculated starting from the half-hours where all the variables needed to estimate
energy balance closure are available (measured NETRAD and G, and measured or good-quality gap-lled H and
LE). e correction factor for each single half-hour is calculated as in Eq. (1), but is not applied directly.
First, to avoid transient conditions, the calculated EBC_CF time series is ltered by removing values outside
of 1.5 times its own interquartile range. en, the correction factor used in the calculations is obtained using one
of three methods, applied hierarchically (see also diagram in Supplementary Fig.SM3):
• EBC_CF Method 1: For each half-hour, a sliding window of ±15 days (31 days total) is used to select
half-hours between time periods 22:00–02:30 and 10:00–14:30 (local standard time). ese time-of-day
restrictions aim at removing sunrise and sunset time periods, when changes in ecosystem heat storage (not
measured) are more signicant, preventing energy balance closures. For all half-hours meeting these criteria,
the corresponding EBC_CFs are selected and used to calculate the corrected values of H and LE for the half-
hour processed (center of the sliding window), generating a pool of values for each of these two variables.
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SCIENTIFIC Data | (2020) 7:225 |
From each of these two pools, the 25th, 50th (median), and 75th percentiles are extracted for their corre-
sponding variables, generating the values for H_CORR25, H_CORR, H_CORR75 and LE_CORR25, LE_
CORR, LE_CORR75. If fewer than ve EBC_CF values are present in the sliding window, Method 2 is used
for the half-hour. (Note on temporal aggregations: for DD the sliding window size is ±7 days and the EBC_CF
are calculated from the daily average values of G, NETRAD, H and LE. For WW, MM, and YY the EBC_CFs are
calculated from corresponding average uxes of the period analysed, but no percentiles are computed. For WW,
MM, and YY, Method 1 fails if less than 50% of half-hours within the window have measured values for all four
component variables.)
• EBC_CF Method 2: For the current half-hour, EBC_CF is calculated as the average of the EBC_CF val-
ues used to calculate the H_CORR and LE_CORR with Method 1 within a sliding window of ±5 days and
±1 hour of the time-of-day of the current timestamp. H_CORR and LE_CORR are calculated and the cor-
responding _CORR25 and _CORR75 percentiles are not generated. If no EBC_CF is available, Method 3 is
used for the current half-hour. (Note on temporal aggregations: diering sliding windows are: DD: ±2 weeks,
WW: ±2 weeks, MM: ±1 month, and YY: ±1 year.)
• EBC_CF Method 3: An approach like Method 2 is applied but using a sliding window of ±5 days for the same
half-hour in the previous and next years, with the current EBC_CF being calculated from the average of the
EBC_CF values used to calculate the H_CORR and LE_CORR. H_CORR and LE_CORR are calculated and
the corresponding _CORR25 and _CORR75 percentiles are not generated. In case this method also cannot
be applied due to missing values, the energy balance closure corrected uxes are not calculated. (Note on tem-
poral aggregations: diering sliding windows are: DD: ±2 weeks, WW: ±2 weeks, MM: ±1 month, and YY: ±2
H and LE Random Uncertainty. e random uncertainty for H and LE is also estimated at half-hourly resolu-
tion, based on the method introduced by Hollinger & Richardson37 and then aggregated at the other temporal
resolutions. e random uncertainty (indicated by the sux _RANDUNC) in the measurements is estimated
using one of two methods, applied hierarchically:
• H-LE-RANDUNC Method 1 (direct standard deviation method): For a sliding window of ±5 days and
±1 hour of the time-of-day of the current timestamp, the random uncertainty is calculated as the standard
deviation of the measured uxes. e similarity in the meteorological conditions evaluated as in the MDS
gap-lling method31 and a minimum of ve measured values must be present; otherwise, method 2 is used.
• H-LE-RANDUNC Method 2 (median standard deviation method): For the same sliding window of ±5
days and ±1 hour of the time-of-day of the current timestamp, random uncertainty is calculated as the
median of the random uncertainty (calculated with H-LE-RANDUNC Method 1) of similar uxes, i.e.,
within the range of ±20% and not less than 10 W m–2.
e joint uncertainty for H and LE is computed from the combination of the uncertainty from the energy
balance closure correction factor and random uncertainty.
__ __75_ 25
1349 (2)
ese variables are identied by the _JOINTUNC sux and are computed for H as in Eq. (2), and similarly for
LE. (Note on temporal aggregations: joint uncertainties for H and LE are recomputed at HH and DD resolutions
separately, and not generated for WW, MM, and YY resolutions.)
 e processing steps applied to CO2 uxes were: calculation of net ecosystem exchange (NEE)
from CO2 turbulent and storage uxes, applying a spike detection algorithm, ltering for low turbulence con-
ditions using multiple friction velocity (USTAR) thresholds, gap-lling of all NEE time series generated by an
ensemble of USTAR thresholds, estimation of random uncertainty, and partitioning of NEE into its ecosystem
respiration (RECO) and gross primary production (GPP) components.
Calculation of NEE. CO2 storage uxes (SC) express the change of CO2 concentration below the measurement
level of the eddy covariance system within the half-hour. NEE was calculated as the sum of the CO2 turbulent
uxes (FC) and SC. Both FC and SC are part of the required data contributed by site teams. SC is usually esti-
mated using a prole system38. If SC was not provided or missing, two cases were implemented: for measurement
heights lower than 3 m and short canopies, the SC term was considered to be 0; for taller towers/canopies, a dis-
crete estimation based on the top measurement of CO2 concentration was used to compute SC39.
Despiking of NEE. Although the processing of high frequency data into half-hourly uxes usually includes steps
to remove spikes from instantaneous measurements, spikes can also occur in the half-hourly data. e method
described in Papale et al.30, based on the median absolute deviation (MAD) with z = 5.5, was applied to lter NEE
for residual spikes that were removed.
USTAR threshold estimation and ltering. Filtering for low turbulence conditions is necessary when there is not
enough turbulence, causing the ecosystem ux to be transported by advective ows and missed by both the eddy
covariance system and the storage prole, resulting in underestimated uxes. Despite dierent approaches having
been tested to measure and quantify horizontal and vertical advection40, the most oen used method to avoid the
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SCIENTIFIC Data | (2020) 7:225 |
underestimation of uxes is removing the data points potentially aected by strong advection1. ese points are
identied using the friction velocity (USTAR) as an indicator of turbulence strength, dening a threshold value
under which NEE measurements are discarded and replaced by gap-lled estimates.
is USTAR threshold is linked to the canopy structure, measurement height, wind regimes, and other factors
specic to an individual site. It is estimated using nighttime NEE measurements (only ecosystem respiration),
based on the dependency between USTAR and NEE at similar temperatures and periods of the year (main driv-
ers of ecosystem respiration). Under these conditions, NEE is assumed (and expected) to be independent from
USTAR, which is not a driver of respiration. However, in most sites below a certain USTAR threshold value, NEE
is found to increase with USTAR; this USTAR value is selected as the threshold to dene conditions with reduced
risk of ux underestimation. Dierent methods have been proposed to estimate the USTAR thresholds and the
related uncertainty as to how the approach works at a specic site1.
CP and MP USTAR threshold methods. Two methods to calculate USTAR thresholds were used:
change-point-detection (CP) proposed by Barr et al.41 and a modied version of the moving-point-transition
(MP) described originally by Reichstein et al.31 and Papale et al.30. Both methods are similar in terms of data
selection, preparation and grouping and aim to estimate the USTAR threshold value. Measurements collected
when USTAR is below the threshold are removed. e dierence between these methods is in how this threshold
value is estimated. For both methods, the nighttime data of a full year are divided in four three-month periods
(seasons) and 7 temperature classes (of equal size in terms of number of observations). For each season/temper-
ature group the data are divided into 20 USTAR classes (also with equal number of observations) and the average
NEE for each USTAR class is computed. e calculation of the threshold uses each of the methods (see below for
details on their dierences). For each season, the median value of the 7 temperature classes is calculated and a
nal threshold is dened by selecting the maximum of the 4 seasonal values.
e CP method uses two linear regressions between NEE and USTAR, the second with an imposed zero slope.
e change point is dened as where the two lines cross, i.e., constraining the shape of the NEE-USTAR depend-
ency. e method is extensively used to detect temporal discontinuities in climatic data. Details can be found in
Barr et al.41.
For the MP method30,31, the mean NEE value in each of the 20 USTAR classes is compared to the mean NEE
measured in the 10 higher USTAR classes. e threshold selected is the USTAR class in which the average night-
time NEE reaches more than 99% of the average NEE at the higher USTAR classes. An improvement of the MP
method was implemented here for robustness over noisy data, by adding a second step to the original MP imple-
mentation: when a threshold is selected, it was tested to ensure it was also valid for the following USTAR class. In
other words, assuming that Eq. (3) holds, where x is one of the 20 USTAR classes and
is the
average NEE for that USTAR class.
_()099 (_ (1),
_(2),, _(10))
>. ×+
+... +
e USTAR value associated to the xth-class was selected as threshold only if Eq. (4) also holds, to conrm
that the plateau where NEE is USTAR-independent was reached. If not, the search for the plateau and threshold
continued toward higher USTAR values.
+… +
_(1) 099(_(2),
_(3),, _(11))
Bootstrapping USTAR threshold estimation. For each of the two methods, a bootstrapping technique was used.
e full dataset (year of measurement) was re-sampled 100 times with the possibility to select the same data point
multiple times (i.e., with replacement), creating 100 versions of the dataset. e threshold values were calculated
for each of them, obtaining 100 threshold values per method (CP and MP) and year, for a total of 200 USTAR
threshold estimates for each year. is process and next steps are illustrated in Fig.3. ese 200 threshold values
represent the uncertainty in the threshold estimation that could also impact the uncertainty of NEE. It is worth
noting that there is not always a direct relationship between the threshold and NEE uncertainties. It is possible,
for instance, that a small variability in the thresholds has a strong eect on NEE or, conversely, with NEE almost
insensitive to the threshold value. is is related to the site characteristics (USTAR variability) and to the level of
diculty in lling the gaps created by the ltering.
ere are cases where not enough data are present to calculate a USTAR threshold (for both the CP and MP
methods) or where it is not possible to identify a clear change point (CP method only). is leads to the uncer-
tainty being underestimated (fewer or no USTAR threshold values available). is should be considered as a gen-
eral indication of diculties in the application of the USTAR ltering for the specic sites or years. Sites and years
where these conditions occurred are reported in the SUCCESS_RUN variable in the AUXNEE product (values 1:
threshold found, 0: failed/no threshold found).
Variable and constant variants of the USTAR threshold methods. To calculate the uncertainty in NEE due to the
uncertainty in the selected USTAR threshold, all the threshold values obtained with the two methods and the
bootstrapping were pooled together, from which 40 representative values were extracted: from the percentile 1.25
of the series to the percentile 98.75, with a step of 2.5, i.e., [1.25:2.5:98.75]. When long time series (multi-years)
are processed, it is possible to extract the 40 representative thresholds for each of the years. e threshold is
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SCIENTIFIC Data | (2020) 7:225 |
a function of slow-changing dynamics (height of canopy, height of measurement, roughness), but a threshold
changing every year could introduce false interannual variability. On the other hand, a constant threshold across
all the years would not represent changes in the ecosystem structure and EC system setup. For this reason, two
approaches were implemented:
• Variable USTAR reshold (VUT): e thresholds found for each year and the years immediately before
and aer (if available) have been pooled together, and from their joint population, the nal 40 thresholds
extracted. With that, the USTAR thresholds vary from year to year; however, they are still inuenced by
neighboring years. is is identied in FLUXNET2015 variables by the “_VUT” sux;
• Constant USTAR reshold (CUT): Across years, all the thresholds found have been pooled together and
the nal 40 thresholds extracted from this dataset. With that, all years were ltered with the same USTAR
threshold. is is identied in FLUXNET2015 variables by the “_CUT” sux.
If the dataset includes up to two years of data, the two methods give the same result, and only the _VUT is
For both the VUT and CUT approaches, 40 NEE datasets have been created, ltering the original NEE time
series using 40 dierent USTAR values estimated as explained above. e values of the thresholds are reported
in the AUXNEE product le. ese 40 NEE versions have been used as the basis for all the derived variables pro-
vided. An example of the variability of the two methods (CP and MP) is shown in Fig.4, contrasting the distribu-
tion of the bootstrapped results for each method, showing comparable values for some years and divergent values
for other years (of the same site). is highlights the importance of applying both methods in this ensemble-like
Filtering NEE based on USTAR thresholds. e USTAR thresholds are applied to daytime and nighttime data,
removing NEE values collected when USTAR is below the threshold and removing also the rst half-hour with
high turbulence aer a period of low turbulence to avoid false emission pulses due to CO2 accumulated under
the canopy and not detected by the storage system (in particular, when a prole is not available at the site). e
USTAR ltering is not applied to H and LE, because it has not been proved that when there are CO2 advective
uxes, these also impact energy uxes, specically due to the fact that when advection is in general large (night-
time), energy uxes are small. Figure5 shows the range of thresholds found (interquartile ranges) across sites in
FLUXNET2015. While some sites had low thresholds and low variability in the USTAR thresholds, others show
large ranges of values in some more extreme cases (indicating diculties in estimating the “real” threshold).
Fig. 3 To identify and remove data collected under low turbulence conditions, under which advective uxes
could lead to an underestimation of uxes, ltering based on the USTAR threshold was used. In order to
estimate the uncertainty in the USTAR threshold calculation, a bootstrapping approach was implemented,
with a selection of values representative of the distribution included in the nal data products. From the (up
to) 200 thresholds from the combined bootstrapping of the two methods, 40 percentiles are extracted. All the
subsequent steps of the pipeline are applied to all 40 versions. For each of the nal output products (e.g., NEE, as
illustrated here), seven percentiles representative of the distribution are included.
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SCIENTIFIC Data | (2020) 7:225 |
Gap-lling of NEE. Existing gaps from instrument or power failures are further increased aer QC and USTAR
ltering. e time series with gaps need to be lled, especially before aggregated values can be calculated (from
daily to annual). Moat et al.26 compared dierent gap-lling methods for CO2 concluding that most of the
methods currently available perform suciently well with respect to the general uncertainty associated with the
measurements. e method implemented here is the Marginal Distribution Sampling (MDS) method already
described in the meteorological products.
Selection of reference NEE variables. Aer ltering NEE using the 40 USTAR thresholds and gap-lling, 40
complete (gap-free) NEE time series were available for each site. For each half hour, it is possible to use the 40
values to estimate the NEE uncertainty resulting from the USTAR threshold estimation (reported as percentiles
Fig. 4 Example of the distribution of USTAR thresholds calculated for each year using the MP30 method in
blue and CP41 method in green for the US-UMB site (dark green where they overlap). All these thresholds were
pulled together to extract the CUT nal 40 thresholds, while for the VUT thresholds, each year was pulled with
the two immediately before and aer (e.g., 2005 + 2006 + 2007 to extract the 40 thresholds to be used to lter
2006). Note that the level of agreement between methods and between subsequent years is variable, justifying
the approach that propagates this variability into uncertainty in NEE.
Fig. 5 Ranked USTAR thresholds based on median threshold and error bars showing 25th to 75th percentiles of
the 40 thresholds calculated with the Constant USTAR reshold (CUT) method – only computed for sites with
3 or more years, so only 177 sites out of the 206 are shown. Colors show dierent ecosystem classes based on the
site’s IGBP.
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SCIENTIFIC Data | (2020) 7:225 |
of the NEE distribution, identied by the “_XX” numeric sux) and the average value (identied in the dataset
by the “_MEAN” sux). Since the average value has a smoothing eect on the time series, an additional reference
value of NEE was selected and identied in FLUXNET2015 variables by the “_REF” sux, in an attempt to iden-
tify which of the 40 NEE realizations was the most representative of the ensemble. e “_REF” NEE was selected
among the 40 dierent NEE instances in this way: (1) the Nash–Sutclie model eciency coecient42 was calcu-
lated between each NEE instance and the remaining 39; (2) the reference NEE has been selected as the one with
the highest model eciency coecients sum, i.e., the most similar to the other 39. Note that determining the
reference NEE is done independently for variables using VUT and CUT USTAR thresholds, as well as for each
temporal aggregation. erefore, the version selected as REF could be dierent for dierent temporal resolutions.
For instance, NEE_VUT_REF at half-hourly resolution might have been generated using a dierent USTAR
threshold than NEE_VUT_REF at daily resolution. Information on which threshold values were used for each
version and temporal aggregation can be found in the auxiliary products for NEE processing (AUXNEE). In addi-
tion to the reference NEE, the NEE instance obtained by ltering the data with the median value of the USTAR
thresholds distribution is also included. is NEE is identied in FLUXNET2015 variables by the “_USTAR50”
sux (for both CP and MP methods, and both VUT and CUT approaches) and is stable across temporal aggre-
gation resolutions. Individual percentiles of the USTAR thresholds distribution are reported in the AUXNEE le
(40 instances for CUT and 40 per year for VUT).
Random uncertainty for NEE. In addition to the uncertainty estimates based on multiple thresholds for
USTAR ltering, the random uncertainty for NEE is also estimated based on the method used by Hollinger &
Richardson37. Variables expressing random uncertainty are identied by the sux _RANDUNC. One of two
methods are used to estimate random uncertainty, applied hierarchically:
• NEE-RANDUNC Method 1 (direct standard deviation method): For a sliding window of ±7 days and
±1 hour of the time-of-day of the current timestamp, the random uncertainty is calculated as the standard
deviation of the measured uxes. e similarity in the meteorological conditions evaluated as in the MDS
gap-lling method31 and a minimum of ve measured values must be present; otherwise, method 2 is used.
• NEE-RANDUNC Method 2 (median standard deviation method): For a sliding window of ±5 days and
±1 hour of the time-of-day of the current timestamp, random uncertainty is calculated as the median of the
random uncertainty (calculated with NEE-RANDUNC Method 1) of similar uxes, i.e., within the range of
±20% and not less than 2 µmolCO2 m–2 s–1. (Note on temporal aggregations: diering sliding windows are:
WW: ±2 weeks, MM: ±1 month, and YY: ±2 years.)
e joint uncertainty for NEE is computed from the combination of the uncertainty from multiple USTAR
thresholds and random uncertainty. ese variables identied by the _JOINTUNC sux and are computed for
NEE ltered using the VUT method as in Eq. (5), and similarly for NEE ltered with the CUT method.
______ __84 __16
e 16th and 84th percentiles are used because they are equivalent to ±1 Standard Deviation in case of a
normal distribution. (Note on temporal aggregations: joint uncertainties for NEE are recomputed at all temporal
CO2 ux partitioning in GPP and RECO. Partitioning CO2 uxes from NEE into estimates of its two main com-
ponents, Gross Primary Production (GPP) and Ecosystem Respiration (RECO), was done by parameterizations
of models using measured data. All sites were partitioned with the nighttime uxes method31 (_NT suxes) and
the daytime uxes method43 (_DT suxes), while a third method, sundown reference respiration44 (_SR suxes),
was applied to all sites meeting the method’s requirements (e.g., high quality storage measurement).
e nighttime method uses nighttime data to parameterize a respiration-temperature model that is then
applied to the whole dataset to estimate RECO. GPP is then calculated as the dierence between RECO and
NEE. e parameterization uses short windows of time (14 days) to account for the dynamic of other important
respiration drivers such as water, substrate availability, and phenology (see Reichstein et al.31 for details on the
implementation and ONEFlux22 for the code).
e daytime method uses daytime and nighttime data to parameterize a model with one component based on a
light-response curve and vapor pressure decit for GPP, and a second component using a respiration-temperature
relationship similar to the nighttime method. In this case, NEE becomes a function of both GPP and RECO, both
of which are estimated by the model. Similarly to the nighttime method, the parameterization is done for short
windows (8 days) to take into consideration other slower-changing factors (see Lasslop et al.43 for details on the
implementation and ONEFlux22 for the code).
For forest sites where a CO2 concentration prole for storage uxes was available, an additional RECO esti-
mate was calculated using the method from van Gorsel et al.44, with variables identied by the _SR sux. In this
method, the parameterization of a respiration-temperature model is based solely on data acquired just aer sun-
down, aiming at excluding the measurements potentially aected by advection and also assuming that in the rst
hour of the evening the advective transport is not yet established.
The sundown partitioning method requires that the NEE is not filtered for low turbulence conditions
(USTAR), and for this reason it was applied only to the original time series. e nighttime and daytime methods
instead require NEE ltered for low turbulence conditions. For this reason they were applied to all the 40 NEE
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SCIENTIFIC Data | (2020) 7:225 |
versions resulting from the 40 USTAR thresholds, obtaining 40 versions of GPP and RECO for each of the two
partitioning methods, propagating the uncertainty from NEE to GPP and RECO. is has been done for both the
CUT and VUT ltering methods.
Similarly to NEE, the 40 GPP and RECO estimates (for each method and for CUT and VUT) have been used
to calculate the percentiles of their distribution for each timestep (describing their uncertainty due to the NEE
uncertainty). e average value (_MEAN) and the reference value use the same model eciency approach used
for NEE for each temporal aggregation. Similarly, NEE ltered with the median USTAR value (_USTAR50) has
been partitioned into GPP and RECO. Information on the threshold values used for all versions of GPP and
RECO (_NT and _DT, _VUT and _CUT, HH to YY resolutions) are in the auxiliary les for NEE processing
(AUXNEE). Variables for reference GPP and RECO are also identied by a _REF sux. e two methods for the
partitioning (three for the cases in which the sundown method is applied) are not merged in any way, because
their dierence is informative with the respect to the uncertainty of the methods, as in the case of a model com-
parison exercise.
 To increase the traceability of changes between versions of datasets and
reduce uncertainty stemming from choices made at implementation time, we favored using original code imple-
mentations or thoroughly validated re-implementations of original codes. us, our code organization strings
together loosely coupled components which implement each step, with clear-cut interfaces between steps. is
modular approach eases the maintenance and change eorts for any individual step, but adds complexity to
evaluating changes for the entire pipeline. Dierent programming languages (Python, C, MATLAB and IDL, plus
PV-WAVE for FLUXNET2015) were used to implement the dierent steps, all connected using a controller code
that makes appropriate calls in the correct order. e ONEFlux22 code collection replaced the PV-WAVE code
with a re-implementation in Python, and also collates most of these steps into a cohesive pipeline (see also the
Code Availability section). e IDL code, which applies the sundown partitioning method44, is not yet currently
implemented in ONEFlux, because some additional testing and development are needed to make it robust and
more suitable for general application. Implementation details of individual steps are discussed next, with refer-
ences to the outputs each step identied by an execution sequential number and the step name–e.g., 01_qc_visual
contains the results of the rst processing step, the visual check step. Each of these steps correspond to a code
module. Supplementary Fig.SM2 shows the steps and their inter-dependencies.
Steps implemented in python. e main controller code for ONEFlux is implemented in Python. Besides being
the glue code that executes each step, pre- and post-checks are also executed before and aer each step. ese
checks guarantee that the input data meet the minimum requirements to run the step, that the minimum expected
outputs were generated by the execution of the step, and that any errors or exception conditions were handled
correctly. Information about execution is recorded in a log for the entire pipeline, along with logs for individual
steps. Besides the controller code, two of the three ux partitioning steps were re-implemented in Python (the
nighttime and daytime methods, 10_nee_partition_nt and 11_nee_partition_dt), together with other specic
steps such as data preparation for the uncertainty estimates (12_ure_input), and the creation and checking of
nal products (99_uxnet2015). e original ux partitioning implementation in PV-WAVE was used for the
Lauile2007 and FLUXNET2015 datasets. Also, the tool for the downscaling of the ERA-I meteorological data
is implemented in Python and runs on a server connected to the ERA data.
Steps implemented in C. Several steps are implemented in the C programming language, allowing better control
over execution performance of these steps. ese steps include:
automated QA/QC agging (02_qc_auto), USTAR threshold estimation using the MP method (04_ustar_
mp), the ltering and gap-lling of meteorological data, including the merging with the ERA-I downscaled data
(07_meteo_proc), the ltering and gap-lling of CO2 uxes (08_nee_proc), the ltering, gap-lling, and energy
corrections of energy uxes (09_energy_proc), and the computation of uncertainty products (12_ure). e source
codes and the compiled executables are provided for steps implemented in C, as well as build procedures in make/
Makele format.
Steps implemented in MATLAB. e estimation of USTAR thresholds using the CP method (05_ustar_cp) is the
only step implemented in MATLAB. It is distributed both as source code and compiled code to be used with the
MATLAB Runtime Environment, such that it does not require a license purchase.
e FLUXNET2015 portion presented in this paper contains 1496 site-years of data from 206 sites45250, charac-
terizing ecosystem-level carbon and energy uxes in diverse ecosystems across the globe (Fig.1, Supplementary
Fig.SM1251,252), spanning from the early 1990s to 2014, with 69 sites having decade-long records. e dataset
covers the distribution of ecosystem uxes as reported in the recent meta-analyses253,254 (Fig.6).
e dataset is distributed in les separated by sites, by temporal aggregation resolutions (e.g., hourly, weekly),
and by data products (e.g., FULLSET with all the variables and SUBSET designed for less experienced users). All
data les for a site are available for download as a single ZIP le archive with site-specic DOI. e le-naming
conventions details these options for each le (Table1). Site metadata are also available as a single le containing
metadata for all sites, detailed later in this section and Supplementary TableSM8. Note that DOIs are assigned at
the site level, one DOI per site for all of that site’s products. A DOI was not assigned to the whole FLUXNET2015
dataset, since this would make citation and assigning credit imprecise and hard to track.
e FLUXNET2015 dataset provides data at ve temporal resolutions. Site teams contribute either half-hourly
(HH) or hourly (HR) datasets, depending on the integration/aggregation time decided by the site managers and
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SCIENTIFIC Data | (2020) 7:225 |
function of the characteristics of the turbulence. References to half-hourly in this paper also apply to hourly
data, unless explicitly stated otherwise. Half-hourly data are the basis of all the processing done for this dataset
and are the nest grained temporal resolution provided. Coarser aggregations are generated uniformly from
half-hourly data within the data processing pipeline. e other standard temporal aggregations are: daily (DD),
weekly (WW), monthly (MM), and yearly (YY).
e complete output from the data processing pipeline includes over 200 variables–among which are meas-
ured and derived data, quality ags, uncertainty quantication variables, and results from intermediate data pro-
cessing steps. e variable names follow the naming conventions of <BASENAME>_<QUALIFIER>, where
BASENAME describes the physical quantities (e.g., TA, NEE, Table2) and QUALIFIER describes the informa-
tion of processing methods (e.g., VUT, CUT), uncertainties (e.g., RANDUNC), and quality ags (e.g., QC) (see
Supplementary TableSM1).
To serve the users with an easier-to-use data product, we created two variants with dierent selections of
variables for data distribution: the FULLSET with all the results and variables; and the SUBSET, designed to help
non-expert users, with a reduced set of variables that should t most needs.
• FULLSET: variables generated by the processing such as uncertainty quantication variables, all variants of
the data products, all quality information ags, and many variables generated by intermediate processing
steps to allow in-depth understanding of individual processing steps and their eect in the nal data prod-
ucts. A summary of the main variable basenames is in Table2, while a full list of variables is provided in
Supplementary TableSM1. Key features of the FULLSET version are:
Fig. 6 Distribution of the yearly (a) net ecosystem exchange (NEE), (b) gross primary production (GPP),
and (c) ecosystem respiration (RECO) in FLUXNET2015. Only data with QC ag (NEE_VUT_REF_QC)
higher than 0.5 are shown here. e values are reference NEE, GPP, and RECO based on the Variable USTAR
reshold (VUT) and selected reference for model eciency (REF). GPP and RECO are based on the nighttime
partitioning (NT) method. e grey histogram (bin width 100 gC m2 y1) shows the ux distribution in 1224
of the available site-years; negative GPP and RECO values are kept to preserve distributions, see Data processing
methods section for details. Black lines show the distribution curves based on published data253,254. e boxplots
show the ux distribution (i.e., 25th, 50th, and 75th percentiles) for vegetation types dened and color-coded
according to IGBP (International Geosphere–Biosphere Programme) denitions. Circles represent data points
beyond the 1.5-times interquartile range (25th to 75th percentile) plus the 75th percentile or minus 25th
percentile (whisker). Numbers in parentheses indicate the number of site-years used in each IGBP group. e
NO-Blv site from the snow/ice IGBP group is not shown in the boxplots.
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SCIENTIFIC Data | (2020) 7:225 |
• Meteorological variables filled with multiple gap-filling methods (e.g., MDS, ERA) are provided
• NEE versions ltered with two dierent methods of extracting the USTAR thresholds (i.e., CUT, VUT)
are provided. Multiple percentiles and reference NEE are also provided.
• GPP and RECO partitioned from NEE ltered with VUT and CUT methods, using both daytime and
nighttime partitioning methods (i.e., NT, DT). Multiple percentiles and reference GPP and RECO are
• LE and H gap-lled, adjusted and non-adjusted for energy balance closure, are both provided.
• Random, methodological, and joint uncertainties for NEE, GPP, RECO, LE, and H are provided.
• SUBSET: Includes a subset of the data product. e selection of the variables for this data product was done
based on the expected usage for most users and to help less experienced users. Although the number of
variables used is reduced, they are still accompanied by a set of quality ags and uncertainty quantication
variables essential to correctly interpret the data. Key features of the SUBSET version are:
• Only the consolidated gap-lled meteorological variables are provided.
• Only the REF version of NEE ltered with the VUT method is provided. Selected percentiles and ref-
erence NEE are also provided.
• GPP and RECO (only REF versions) partitioned from NEE ltered with only the VUT method, using
both daytime and nighttime partitioning methods, are provided. Selected percentiles and reference
GPP and RECO are also provided.
File Name Conventions
Field Denition Possible options
SITE_ID FLUXNET site ID in the format CC-SSS (CC is
two-letter country code, SSS is three-character
site-level identier)
DATA_PRODUCT Grouping of variables from release included in le.
SUBSET: Core set of variables with quality and uncertainty
information needed for general uses of the data
FULLSET: All variables, including all quality and uncertainty
information, and key variables from intermediate processing
AUXMETEO: Auxiliary variables related to meteorological
AUXNEE: Auxiliary variables related to NEE, RECO, and
GPP processing
ERAI: Full record (1989–2014) of ERA-Interim downscaled
meteorological variables for the site
RESOLUTION Temporal resolution of data product
HH: Half-Hourly time steps
HR: Hourly time steps
DD: Daily time steps
WW: Weekly time steps
MM: Monthly time steps
YY: Yearly time steps
LAST_YEAR First and last years of eddy covariance ux data
Version string in integer. SITE_VERSION
indicates the version of the original dataset for the
site used; CODE_VERSION indicates the version
of the code of the data processing pipeline used to
process the dataset for the site
EXT File extension csv: Comma-separated values in a text le (ASCII)
zip: Archive le with all temporal resolutions for the same
site and data product
Examples of le names and structures:
- FLX_US-Ha1_FLUXNET2015_FULLSET_HH_1992-2012_1-3.csv
- FLX_US-Ha1_FLUXNET2015_FULLSET_DD_1992-2012_1-3.csv
- FLX_US-Ha1_FLUXNET2015_FULLSET_WW_1992-2012_1-3.csv
- FLX_US-Ha1_FLUXNET2015_FULLSET_MM_1992-2012_1-3.csv
- FLX_US-Ha1_FLUXNET2015_FULLSET_YY_1992-2012_1-3.csv
- FLX_US-Ha1_FLUXNET2015_ERAI_HH_1992-2012_1-3.csv
- FLX_US-Ha1_FLUXNET2015_ERAI_DD_1992-2012_1-3.csv
- FLX_US-Ha1_FLUXNET2015_ERAI_WW_1992-2012_1-3.csv
- FLX_US-Ha1_FLUXNET2015_ERAI_MM_1992-2012_1-3.csv
- FLX_US-Ha1_FLUXNET2015_ERAI_YY_1992-2012_1-3.csv
- FLX_US-Ha1_FLUXNET2015_AUXMETEO_1992-2012_1-3.csv
- FLX_US-Ha1_FLUXNET2015_AUXNEE_1992-2012_1-3.csv
Tab le 1. e template of le naming conventions, including the eld, eld denition, and the possible options.
Examples of le names from a zipped le of a single site are provided.
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SCIENTIFIC Data | (2020) 7:225 |
• LE and H gap-lled, adjusted and non-adjusted for energy balance closure, are both provided.
• Random and methodological uncertainties for NEE, GPP, RECO, LE, and H are provided.
e variable proposed in the SUBSET product is NEE_VUT_REF since it maintains the temporal variability
(as opposed to the MEAN NEE), it is representative of the ensemble, and the VUT method is sensitive to possible
changes of the canopy (density and height) and site setup, which can have an impact on the turbulence and conse-
quently on the USTAR threshold. e RECO and GPP products in SUBSET are calculated from the correspond-
ing NEE variables ltered with the VUT method, generating RECO_NT_VUT_REF and RECO_DT_VUT_REF
for RECO, and GPP_NT_VUT_REF and GPP_DT_VUT_REF for GPP. It is important to use both daytime (DT)
and nighttime (NT) variables, and consider their dierence as uncertainty.
Auxiliary data products provide extra information on specic parameters of the data processing pipeline. e
groups of products are:
• AUXMETEO: Auxiliary data product containing information about the downscaling of meteorological var-
iables using the ERA-Interim reanalysis data product (TA, PA, VPD, WS, P, SW_IN, and LW_IN). Variables
in these les relate to the linear regression and error/correlation estimates for each data variable used in the
Basename Description
Units by Resolution
TA Air temperature deg C
SW_IN_POT Shortwave radiation,
incoming, potential (top of
atmosphere) W m2
SW_IN Shortwave radiation W m2
LW_IN Longwave radiation,
incoming W m2
VPD Vapor Pressure saturation
Decit hPa
PA Atmospheric pressure kPa
PPrecipitation mm mm d1mm y1
WS Wind speed m s1
WD Wind direction Decimal degrees n/a
RH Relative humidity % n/a
USTAR Friction velocity m s1
NETRAD Net radiation W m2
PPFD_IN Photosynthetic photon ux
density, incoming µmolPhoton m2 s1
PPFD_DIF Photosynthetic photon ux
density, diuse incoming µmolPhoton m2 s1
PPFD_OUT Photosynthetic photon ux
density, outgoing µmolPhoton m2 s1
SW_DIF Shortwave radiation, diuse
incoming W m2
SW_OUT Shortwave radiation,
outgoing W m2
LW_OUT Longwave radiation,
outgoing W m2
CO2CO2 mole fraction µmolCO2 mol1
TS Soil temperature deg C
SWC Soil water content %
GSoil heat ux W m2
LE Latent heat ux W m2
HSensible heat ux W m2
NEE Net Ecosystem Exchange µmolCO2 m2 s1gC m2 d1gC m2
RECO Ecosystem Respiration µmolCO2 m2 s1gC m2 d1gC m2
GPP Gross Primary Production µmolCO2 m2 s1gC m2 d1gC m2
Tab le 2. List of the variable basenames, descriptions, available resolutions and units. Separate units are listed if
dierent units are used in dierent temporal aggregation resolutions. n/a indicates a variable is not provided at
the specied resolution.
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SCIENTIFIC Data | (2020) 7:225 |
• Parameters:
• ERA_SLOPE: the slope of linear regression
• ERA_INTERCEPT: intercept point of linear regression
• ERA_RMSE: root mean square error between site data and downscaled data
• ERA_CORRELATION: correlation coecient of linear t
• AUXNEE: Auxiliary data product with variables resulting from the processing of NEE (mainly related to
USTAR ltering) and generation of RECO and GPP. Variables in this product include success/failure of exe-
cution of USTAR ltering methods, USTAR thresholds applied to dierent versions of variables, and percen-
tile/threshold pairs with best model eciency results.
• Variables:
• USTAR_MP_METHOD: Moving Point Test USTAR threshold method run
• USTAR_CP_METHOD: Change Point Detection USTAR threshold method run
• NEE_USTAR50_[UT]: 50th percentile of USTAR thresholds obtained from bootstrapping and
sed to generate NEE_USTAR50_[UT] (with UT either CUT or VUT)
• NEE_[UT]_REF: USTAR threshold used to calculate the reference NEE, using model eciency
approach (with UT either CUT or VUT)
• [PROD]_[ALG]_[UT]_REF: USTAR threshold used to lter the NEE that was used to produce
the reference product PROD (RECO or GPP), selected using model eciency approach, using
algorithm ALG (NT, DT) (with UT either CUT or VUT)
• Parameters:
• SUCCESS_RUN: 1 if a run of a method (USTAR_MP_METHOD or USTAR_CP_METHOD)
was successful, 0 otherwise
• USTAR_PERCENTILE: percentile of USTAR thresholds from bootstrapping at USTAR ltering
• USTAR_THRESHOLD: USTAR threshold value corresponding to USTAR_PERCENTILE
• [RR]_USTAR_PERCENTILE: percentile of USTAR thresholds from bootstrapping at USTAR
ltering step at resolution RR (HH, DD, WW, MM, YY)
• [RR]_USTAR_THRESHOLD: USTAR threshold value corresponding to USTAR_PERCEN-
TILE at resolution RR (HH, DD, WW, MM, YY)
• ERAI: Auxiliary data product containing full record (1989–2014) of downscaled meteorological variables
using the ERA-Interim reanalysis data product, including TA, PA, VPD, WS, P, SW_IN, and LW_IN.
The FLUXNET2015 metadata are included in a single file (FLX_AA-Flx_BIF_[RESOLUTION]_
[YYYYMMDD].xlsx) for all sites for each data product resolution (see Table1 for resolution options). e meta-
data follow the Biological, Ancillary, Disturbance, and Metadata (BADM255,256) standards and are provided in
the BADM Interchange Format29 (BIF). Table3 illustrates the type of metadata included with selected metadata
variables (See full lists and descriptions of the metadata in Supplementary TablesSM2–SM7). Height and instru-
ment models for the ux variables, as well as soil temperature and moisture depths, are reported in the Variable
Information metadata.
Eddy covariance measurements oer a direct method to estimate trace gas or energy exchanges between sur-
face and atmosphere at an ecosystem scale (approximately up to 1 km around the measurement point). is
makes eddy covariance dicult to compare with other methods. Nonetheless, eddy covariance data have been
extensively used in numerous scientic papers and studies that indirectly validate their reliability and usefulness.
Hundreds of articles have been published based on eddy covariance measurements; examples of multi-site studies
using FLUXNET2015 data include Jung et al.15, Tramontana et al.257, and Keenan et al.258.
Eddy covariance data were evaluated with respect to other methods such as inventory and chambers by
Campioli et al.259, who showed that “EC [eddy covariance] biases are not apparent across sites, suggesting the
eectiveness of standard post-processing procedures. Our results increase condence in EC . e approach
of Campioli et al.259 requires sites that have several additional (and rare) pieces of information; therefore, it is not
generally applicable, particularly not across the sites used in this study. However, the eddy covariance site teams
co-authoring this paper have compared and technically validated their measurements with respect to knowledge
of their site. Unavoidably, measurement and processing uncertainties exist, and can be large for certain sites and
ecosystem conditions. However, in general, ux values provided in this dataset are consistent with expectations,
and eddy covariance remains one of the more reliable techniques for assessing land-air exchanges at ecosystem
Detailed documentation on how to use and interpret FLUXNET2015 is available online at https://uxnet.ux-uxnet2015-dataset/. Here, we present some of the main points to guide the usage of the data.
 When standardized procedures are applied across
dierent sites, the possible dierences owing to data treatment are avoided or minimized; this is one of the main
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SCIENTIFIC Data | (2020) 7:225 |
goals of FLUXNET2015 and ONEFlux. However, there is also the risk and possibility that the standard methods
don’t work properly or as expected at specic sites and under certain conditions. is is particularly true for the
CO2 ux partitioning, which as with all models is based on assumptions that could not always be not valid. For
this reason, it could be necessary to contact the site PIs that are listed in Supplementary TableSM9.
 ere are quality-control ag variables in the dataset to help users lter and interpret
variables, especially for gap-lled and process knowledge-based variables. ese ags are described in the variable
documentation (Supplementary TableSM1). It is highly recommended that one carefully considers the QC ags
when using the data.
 For most ux variables, there are reference val-
ues and percentile versions of the variables to help understand some of the uncertainty in the record. For NEE,
RECO, and GPP, the percentiles are generated from the bootstrapping of the USTAR threshold estimation step,
i.e., they characterize the variability from a range of values obtained as USTAR thresholds. In addition, three
dierent reference values are provided (“_MEAN”, “_USTAR50” and “_REF”) in order to cover dierent user
needs. In general the “_REF” version should be the most representative, particularly if related to the percentiles.
It is, however, important to clearly refer to which NEE version is used in order to ensure reproducibility. For the
energy balance corrected H and LE variables, the percentiles indicate the variability due to the uncertainty in the
correction factor applied. Similarly to NEE, there are gap-lled and energy balance corrected versions of H and
LE variables; therefore, it is also important to clearly refer to which version is used. e SUBSET version of the
dataset includes a reduced number of variables, selected for non-expert users. We encourage users to carefully
evaluate their requirements and options in the dataset, and if needed to contact regional networks, site teams, or
even co-authors of this article for help and recommendations. For more detail, see the Methods section above.
 All data products are provided at multiple temporal resolutions
where feasible. e nest resolution is either hourly or half-hourly (indicated by the lename tags HR and HH,
respectively). ese data are then aggregated into daily (DD), 7-day weekly (WW), monthly (MM), and yearly
(YY) resolutions, with appropriate aggregations for each variable, such as averaging for TA and summation for P.
 Timestamps in the data and metadata les use the format YYYYMMDDHHMM, truncated at
the appropriate resolution (e.g., YYYYMMDD for a date or YYYYMM for a month). Two formats of time associ-
ated with a record are used: (1) single timestamp, where the meaning of the timestamp is unambiguous, and, (2)
a pair of timestamps, indicating the start and end of the period the timestamps represent.
 To allow more direct site comparability, all time variables are reported in local standard time
(i.e., without daylight saving time). e time zone information with respect to UTC time is reported in the site
 e oating point numbers are maintained at their original resolution throughout
processing steps, using double precision for the majority of cases, and are truncated at up to nine decimal places
in the distributed les for numbers between 0.0 and 1.0, and at up to ve decimal places for larger numbers.
Metadata Type Selected Metadata Variables
Site General Info
(25 variables)
SITE_ID: Unique site identier (CC-sss, where CC is the country code)
SITE_NAME: Site name
SITE_DESC: Site description
LOCATION_LAT: Latitude of site
LOCATION_LONG: Longitude of site
FLUX_MEASUREMENTS_VARIABLE: Flux variables measured at the site
IGBP: Vegetation type based on International Geosphere-Biosphere Programme classication
UTC_OFFSET: Oset from UTC of site data
TEAM_MEMBER_NAME: Team member name
MAT, MAP: Mean annual temperature and precipitation
TOWER_TYPE, TOWER_POWER: Type of tower and power type
(12 variables)
DOI: Digital Object Identier (DOI) for the ux-met data product
DOI_CONTRIBUTOR_NAME: Name of contributor to the development of data (and associated info)
DOI_ORGANIZATION: Organization contributing to the data
Reference publications
(4 variables)
REFERENCE_PAPER: Reference for understanding the site
REFERENCE_DOI: DOI of the reference
REFERENCE_USAGE: Suggested usage of the reference
Canopy Height
(2 variables)
HEIGHTC: Canopy height. In a forest ecosystem, canopy height is the distribution of overstory trees
that see light at the top of the canopy.
Note: e reported value is representative of the mean of such a distribution.
HEIGHTC_DATE: Date of canopy height observation
Variable Information
(5 variables)
Note: Variable Information groups are
only reported for variables w ith data.
VAR_INFO_VARNAME: Variable name
VAR_INFO_UNIT: Variable unit
VAR_INFO_DATE: Start date for reported variable information
VAR_INFO_HEIGHT: Height/depth of observation (meters)
VAR_INFO_MODEL: Model(s) used to collect observation.
Tab le 3. Metadata types and selected variables. See SupplementaryTablesSM2–SM7 for a full list of metadata
with descriptions. Variables collected from or generated for all sites are in bold.
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SCIENTIFIC Data | (2020) 7:225 |
 e order of columns is not always the same in dierent les (e.g., dierent sites). User
data-processing routines should use the variable label (which is always consistent) and not the order of occur-
rence of that variable in the le. Timestamps are the only exception and will always be the rst variable(s)/
column(s) of the data le. is applies to text le data representations (i.e., CSV formatted).
 Missing data values are indicated with 9999, without decimal points, independent of the
cause of the missing value.
 A list of known issues and limitations relevant to the dataset is maintained online: http://
 e original FLUXNET2015 release was in December 2015,
followed by incremental releases in July 2016 and November 2016, and, nally, a release in February 2020 with
xes and additional metadata as described in this paper. More information on the releases can be found in the
online change log: http://uxnet. A newer release replaces all previous ones, and only
the newest release is available for direct download. Access to previous versions can be obtained upon request.
 Scientists and sta responsible for the creation of the dataset oer
support to data users and can be reached at uxdata-support@
 ere is strong interest and engagement in order to ensure the availability
of new data (new sites and new years), keeping the open policy and the high quality data that we tried to reach
with this work. We expect that the processing pipeline and QA/QC procedures will continue evolving, in support
of new products. However, the amount of both technical and coordination work, along with diculty securing
long-term international funding, hamper creation of new versions of the dataset. ere are ongoing discussions
among regional networks and FLUXNET on this coordination, but currently there is no plan for a follow-up
version of FLUXNET2015.
e ONEFlux collection of codes used to create data intercomparable with FLUXNET2015 has been packaged
to be executed as a complete pipeline and is available in both source-code and executable forms under a 3-clause
BSD license on GitHub: The complete environment to run this
pipeline requires a GCC compatible C compiler (or capability to run pre-compiled Windows, Linux, and/or Mac
executables), a MATLAB Runtime Environment, and a Python interpreter with a few numeric and scientic
packages installed. All of these can be obtained at no cost.
Received: 5 March 2020; Accepted: 20 May 2020;
Published: 9 July 2020
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41. Barr, A. G. et al. Use of change-point detection for friction–velocity threshold evaluation in eddy-covariance studies. Agr. Forest
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42. Nash, J. E. & Sutclie, J. V. iver ow forecasting through conceptual models part I — A discussion of principles. J. Hydrol. 10,
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Forest Meteorol. 149, 1919–1930 (2009).
45. Garcia, A. et al. FLUXNET2015 A-SLu San Luis. FLUXNET; Instituto Nacional de Tecnología Agropecuaria (INTA) https://doi.
org/10.18140/x/1440191 (2016).
46. Posse, G., Lewczu, N., ichter, . & Cristiano, P. FLUXNET2015 A-Vir Virasoro. FLUXNET; Instituto Nacional de Tecnología
Agropecuariax/1440192 (2016).
47. Wohlfahrt, G., Hammerle, A. & Hörtnagl, L. FLUXNET2015 AT-Neu Neusti. FLUXNET; University of Innsbruc https://doi.
org/10.18140/x/1440121 (2016).
48. Beringer, J. & Hutley, L. FLUXNET2015 AU-Ade Adelaide iver. FLUXNET; Monash University; Charles Darwin University https://x/1440193 (2016).
49. Cleverly, J. & Eamus, D. FLUXNET2015 AU-ASM Alice Springs. FLUXNET; University of Technology Sydney https://doi.
org/10.18140/x/1440194 (2016).
50. Meyer, W. et al. FLUXNET2015 AU-Cpr Calperum. FLUXNET; University of Adelaidex/1440195 (2016).
51. Pendall, E. & Griebel, A. FLUXNET2015 AU-Cum Cumberland Plains. FLUXNET; Western Sydney University https://doi.
org/10.18140/x/1440196 (2016).
52. Beringer, J. & Hutley, L. FLUXNET2015 AU-DaP Daly iver Savanna. FLUXNET; Monash University; Charles Darwin Universityx/1440123 (2016).
53. Beringer, J. & Hutley, P. FLUXNET2015 AU-DaS Daly iver Cleared. FLUXNET; University of Western Australia; Charles Darwin
University; Monash Universityx/1440122 (2016).
54. Beringer, J. & Hutley, L. FLUXNET2015 AU-Dry Dry iver. FLUXNET; Monash University; University of Western Australia; Charles
Darwin Universityx/1440197 (2016).
55. Schroder, I., Zegelin, S., Palu, T. & Feitz, A. FLUXNET2015 AU-Emr Emerald. FLUXNET; CSIO; Geoscience Australia https://doi.
org/10.18140/x/1440198 (2016).
56. Beringer, J. & Hutley, L. FLUXNET2015 AU-Fog Fogg Dam. FLUXNET; Monash University; Charles Darwin University https://doi.
org/10.18140/x/1440124 (2016).
57. Macfarlane, C. et al. FLUXNET2015 AU-Gin Gingin. FLUXNET; Edith Cowan University (Centre for Ecosystem Management)x/1440199 (2016).
58. Macfarlane, C., Prober, S. & Wiehl, G. FLUXNET2015 AU-GWW Great Western Woodlands, Western Australia, Australia.
FLUXNET; CSIOx/1440200 (2016).
59. Beringer, J. & Hutley, L. FLUXNET2015 AU-How Howard Springs. FLUXNET; Charles Darwin University; University of Western
Australia; Monash Universityx/1440125 (2016).
60. Ewenz, C., Stevens, . & Grigson, G. FLUXNET2015 AU-Lox Loxton. FLUXNET; South Australian esearch and Development
Institute (SADI)x/1440247 (2016).
61. Beringer, J. & Hutley, L. FLUXNET2015 AU-DF ed Dirt Melon Farm, Northern Territory. FLUXNET; Monash University;
Charles Darwin Universityx/1440201 (2016).
62. Beringer, J. et al. FLUXNET2015 AU-ig iggs Cree. FLUXNET; Monash Universityx/1440202 (2016).
63. Liddell, M. FLUXNET2015 AU-ob obson Cree, Queensland, Australia. FLUXNET; James Coo University https://doi.
org/10.18140/x/1440203 (2016).
64. Beringer, J. & Hutley, L. FLUXNET2015 AU-Stp Sturt Plains. FLUXNET; University of Western Australia; Charles Darwin
University; Monash Universityx/1440204 (2016).
65. Cleverly, J. & Eamus, D. FLUXNET2015 AU-TTE Ti Tree East. FLUXNET; University of Technology Sydney https://doi.
org/10.18140/x/1440205 (2016).
Content courtesy of Springer Nature, terms of use apply. Rights reserved
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66. Woodgate, W., Van Gorsel, E. & Leuning, . FLUXNET2015 AU-Tum Tumbarumba. FLUXNET; CSIO
x/1440126 (2016).
67. Beringer, J., Hutley, L., McGuire, D. & U, P. FLUXNET2015 AU-Wac Wallaby Cree. FLUXNET; Monash University; University of
California Davis; Charles Darwin University; University of Alasa Fairbans; University of Melbourne,
x/1440127 (2016).
68. Beringer, J. et al. FLUXNET2015 AU-Whr Whroo. FLUXNET; Monash Universityx/1440206 (2016).
69. Arndt, S., Hino-Najera, N. & Griebel, A. FLUXNET2015 AU-Wom Wombat. FLUXNET; University of Melbourne, School of
Ecosystem and Forest Sciencesx/1440207 (2016).
70. Beringer, J. & Waler, J. FLUXNET2015 AU-Ync Jaxa. FLUXNET; University of Western Australia; Monash University https://doi.
org/10.18140/x/1440208 (2016).
71. Neirync, J. et al. FLUXNET2015 BE-Bra Brasschaat. FLUXNET; University of Antwerpx/1440128
72. De Ligne, A. et al. FLUXNET2015 BE-Lon Lonzee. FLUXNET; University of Liege - Gembloux Agro-Bio Tech https://doi.
org/10.18140/x/1440129 (2016).
73. De Ligne, A. et al. FLUXNET2015 BE-Vie Vielsalm. FLUXNET; University of Liege - Gembloux Agro-Bio Tech; University catholic
of Louvain-la-Neuve,x/1440130 (2016).
74. Salesa, S. FLUXNET2015 B-Sa1 Santarem-m67-Primary Forest. FLUXNET; University of Arizona
x/1440032 (2016).
75. Goulden, M. FLUXNET2015 B-Sa3 Santarem-m83-Logged Forest. FLUXNET; University of California - Irvine https://doi.
org/10.18140/x/1440033 (2016).
76. McCaughey, H. FLUXNET2015 CA-Gro Ontario - Groundhog iver, Boreal Mixedwood Forest. FLUXNET; Queen’s Universityx/1440034 (2016).
77. Amiro, B. FLUXNET2015 CA-Man Manitoba - Northern Old Blac Spruce (former BOEAS Northern Study Area). FLUXNET;
University of Manitobax/1440035 (2016).
78. Goulden, M. FLUXNET2015 CA-NS1 UCI-1850 burn site. FLUXNET; University of California - Irvine
x/1440036 (2016).
79. Goulden, M. FLUXNET2015 CA-NS2 UCI-1930 burn site. FLUXNET; University of California - Irvine
x/1440037 (2016).
80. Goulden, M. FLUXNET2015 CA-NS3 UCI-1964 burn site. FLUXNET; University of California - Irvine
x/1440038 (2016).
81. Goulden, M. FLUXNET2015 CA-NS4 UCI-1964 burn site wet. FLUXNET; University of California - Irvine https://doi.
org/10.18140/x/1440039 (2016).
82. Goulden, M. FLUXNET2015 CA-NS5 UCI-1981 burn site. FLUXNET; University of California - Irvine
x/1440040 (2016).
83. Goulden, M. FLUXNET2015 CA-NS6 UCI-1989 burn site. FLUXNET; University of California - Irvine
x/1440041 (2016).
84. Goulden, M. FLUXNET2015 CA-NS7 UCI-1998 burn site. FLUXNET; University of California - Irvine
x/1440042 (2016).
85. Blac, T. FLUXNET2015 CA-Oas Sasatchewan - Western Boreal, Mature Aspen. FLUXNET; e University of British Columbiax/1440043 (2016).
86. Blac, T. FLUXNET2015 CA-Obs Sasatchewan - Western Boreal, Mature Blac Spruce. FLUXNET; e University of British
Columbiax/1440044 (2016).
87. Margolis, H. FLUXNET2015 CA-Qfo Quebec - Eastern Boreal, Mature Blac Spruce. FLUXNET; Université Laval https://doi.
org/10.18140/x/1440045 (2016).
88. Amiro, B. FLUXNET2015 CA-SF1 Sasatchewan - Western Boreal, forest burned in 1977. FLUXNET; University of Manitobax/1440046 (2016).
89. Amiro, B. FLUXNET2015 CA-SF2 Sasatchewan - Western Boreal, forest burned in 1989. FLUXNET; University of Manitobax/1440047 (2016).
90. Amiro, B. FLUXNET2015 CA-SF3 Sasatchewan - Western Boreal, forest burned in 1998. FLUXNET; University of Manitoba;
Canadian Forest Servicex/1440048 (2016).
91. Arain, M. FLUXNET2015 CA-TP1 Ontario - Turey Point 2002 Plantation White Pine. FLUXNET; McMaster University https://x/1440050 (2016).
92. Arain, M. FLUXNET2015 CA-TP2 Ontario - Turey Point 1989 Plantation White Pine. FLUXNET; McMaster University https://x/1440051 (2016).
93. Arain, M. FLUXNET2015 CA-TP3 Ontario - Turey Point 1974 Plantation White Pine. FLUXNET; McMaster University https://x/1440052 (2016).
94. Arain, M. FLUXNET2015 CA-TP4 Ontario - Turey Point 1939 Plantation White Pine. FLUXNET; McMaster University https://x/1440053 (2016).
95. Arain, M. FLUXNET2015 CA-TPD Ontario - Turey Point Mature Deciduous. FLUXNET; McMaster University https://doi.
org/10.18140/x/1440112 (2016).
96. Nouvellon, Y. FLUXNET2015 CG-Tch Tchizalamou. FLUXNET; Centre de coopération internationale en recherche agronomique
pour le développementx/1440142 (2016).
97. Hörtnagl, L. et al. FLUXNET2015 CH-Cha Chamau. FLUXNET; ETH Zurichx/1440131 (2016).
98. Hörtnagl, L. et al. FLUXNET2015 CH-Dav Davos. FLUXNET; ETH Zurichx/1440132 (2016).
99. Hörtnagl, L. et al. FLUXNET2015 CH-Fru Früebüel. FLUXNET; ETH Zurichx/1440133 (2016).
100. Hörtnagl, L. et al. FLUXNET2015 CH-Lae Laegern. FLUXNET; ETH Zurichx/1440134 (2016).
101. Ammann, C. FLUXNET2015 CH-Oe1 Oensingen grassland. FLUXNET; Agroscope Zuerichx/1440135
102. Hörtnagl, L. et al. FLUXNET2015 CH-Oe2 Oensingen crop. FLUXNET; ETH Zurichx/1440136 (2016).
103. Zhang, J. & Han, S. FLUXNET2015 CN-Cha Changbaishan. FLUXNET; IAE Chinese Academy of Sciences
x/1440137 (2016).
104. Dong, G. FLUXNET2015 CN-Cng Changling. FLUXNET; Shanxi Universityx/1440209 (2016).
105. Shi, P., Zhang, X. & He, Y. FLUXNET2015 CN-Dan Dangxiong. FLUXNET; IGSN Chinese Academy of Sciences https://doi.
org/10.18140/x/1440138 (2016).
106. Zhou, G. & Yan, J. FLUXNET2015 CN-Din Dinghushan. FLUXNET; SCIB Chinese Academy of Sciences
x/1440139 (2016).
107. Chen, S. FLUXNET2015 CN-Du2 Duolun_grassland (D01). FLUXNET; Institute of Botany, Chinese Academy of Sciences https://x/1440140 (2016).
108. Shao, C. FLUXNET2015 CN-Du3 Duolun Degraded Meadow. FLUXNETx/1440210 (2016).
109. Li, Y. FLUXNET2015 CN-Ha2 Haibei Shrubland. FLUXNET; NWIPB Chinese Academy of Sciences
x/1440211 (2016).
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SCIENTIFIC Data | (2020) 7:225 |
110. Tang, Y., ato, T. & Du, M. FLUXNET2015 CN-HaM Haibei Alpine Tibet site. FLUXNET; National Institute for Environmental
Studiesx/1440190 (2016).
111. Wang, H. & Fu, X. FLUXNET2015 CN-Qia Qianyanzhou. FLUXNET; IGSN Chinese Academy of Sciences https://doi.
org/10.18140/x/1440141 (2016).
112. Shao, C. FLUXNET2015 CN-Sw2 Siziwang Grazed (SZWG). FLUXNETx/1440212 (2016).
113. Sigut, L. et al. FLUXNET2015 CZ-B1 Bily riz forest. FLUXNET; Global Change esearch Institute CAS
x/1440143 (2016).
114. Sigut, L. et al. FLUXNET2015 CZ-B2 Bily riz grassland. FLUXNET; Global Change esearch Institute CAS https://doi.
org/10.18140/x/1440144 (2016).
115. Duše, J., Janouš, D. & Pavela, M. FLUXNET2015 CZ-wet Trebon (CZECHWET). FLUXNET; Global Change esearch Institute
CASx/1440145 (2016).
116. Bernhofer, C. et al. FLUXNET2015 DE-Am Anlam. FLUXNET; TU Dresdenx/1440213 (2016).
117. Brümmer, C., Lucas-Moat, A., Herbst, M. & olle, O. FLUXNET2015 DE-Geb Gebesee. FLUXNET; ünen Institute of Climate-
Smart Agriculture, Braunschweigx/1440146 (2016).
118. Bernhofer, C. et al. FLUXNET2015 DE-Gri Grillenburg. FLUXNET; TU Dresdenx/1440147 (2016).
119. nohl, A. et al. FLUXNET2015 DE-Hai Hainich. FLUXNET; University of Goettingen, Bioclimatology
x/1440148 (2016).
120. Bernhofer, C. et al. FLUXNET2015 DE-li lingenberg. FLUXNET; TU Dresdenx/1440149 (2016).
121. Lindauer, M. et al. FLUXNET2015 DE-Lb Lacenberg. FLUXNET; arlsruhe Institute of Technology, IM-IFU https://doi.
org/10.18140/x/1440214 (2016).
122. nohl, A. et al. FLUXNET2015 DE-Lnf Leinefelde. FLUXNET; University of Goettingen, Bioclimatology
x/1440150 (2016).
123. Bernhofer, C. et al. FLUXNET2015 DE-Obe Oberbärenburg. FLUXNET; TU Dresdenx/1440151 (2016).
124. Schmidt, M. & Graf, A. FLUXNET2015 DE-u ollesbroich. FLUXNET; IBG-3 Agrosphäre, esearch Centre Jülich GmbH https://x/1440215 (2016).
125. Schmidt, M. & Graf, A. FLUXNET2015 DE-uS Selhausen Juelich. FLUXNET; IBG-3 Agrosphäre, esearch Centre Jülich GmbHx/1440216 (2016).
126. Schneider, . & Schmidt, M. FLUXNET2015 DE-Seh Selhausen. FLUXNET; University of Cologne
x/1440217 (2016).
127. latt, J., Schmid, H., Mauder, M. & Steinbrecher, . FLUXNET2015 DE-SfN Schechenlz Nord. FLUXNET; arlsruhe Institute of
Technology, IM-IFUx/1440219 (2016).
128. Bernhofer, C. et al. FLUXNET2015 DE-Spw Spreewald. FLUXNET; TU Dresdenx/1440220 (2016).
129. Bernhofer, C. et al. FLUXNET2015 DE-a arandt. FLUXNET; TU Dresdenx/1440152 (2016).
130. Sachs, T., Wille, C., Larmanou, E. & Franz, D. FLUXNET2015 DE-Zr Zarneow. FLUXNET; GFZ German esearch Centre for
Geosciencesx/1440221 (2016).
131. Pilegaard, . & Ibrom, A. FLUXNET2015 D-Eng Enghave. FLUXNET; Technical University of Denmar (DTU) https://doi.
org/10.18140/x/1440153 (2016).
132. Olesen, J. FLUXNET2015 D-Fou Foulum. FLUXNET; Danish Institute of Agricultural Sciences
x/1440154 (2016).
133. Ibrom, A. & Pilegaard, . FLUXNET2015 D-Sor Soroe. FLUXNET; Technical University of Denmar (DTU) https://doi.
org/10.18140/x/1440155 (2016).
134. Poveda, F. et al. FLUXNET2015 ES-Amo Amoladeras. FLUXNET; Estación Experimental de Zona Áridas (EEZA, CSIC) https://doi.
org/10.18140/x/1440156 (2016).
135. everter, B., Perez-Cañete, E. & owalsi, A. FLUXNET2015 ES-LgS Laguna Seca. FLUXNET; Universidad de Granada https://doi.
org/10.18140/x/1440225 (2016).
136. Cañete, E. et al. FLUXNET2015 ES-LJu Llano de los Juanes. FLUXNET; University of Granadax/1440157
137. everter, B., Perez-Cañete, E. & owalsi, A. FLUXNET2015 ES-Ln2 Lanjaron-Salvage logging. FLUXNET; Universidad de
Granadax/1440226 (2016).
138. Mammarella, I. et al. FLUXNET2015 FI-Hyy Hyytiala. FLUXNET; University of Helsinix/1440158 (2016).
139. Lohila, A. et al. FLUXNET2015 FI-Jo Joioinen. FLUXNET; Finnish Meteorological Institutex/1440159
140. Lohila, A. et al. FLUXNET2015 FI-Let Lettosuo. FLUXNET; Finnish Meteorological Institutex/1440227
141. Aurela, M. et al. FLUXNET2015 FI-Lom Lompolojana. FLUXNET; Finnish Meteorological Institute
x/1440228 (2016).
142. Aurela, M. et al. FLUXNET2015 FI-Sod Sodanyla. FLUXNET; Finnish Meteorological Institute
x/1440160 (2016).
143. Berveiller, D. et al. FLUXNET2015 F-Fon Fontainebleau-Barbeau. FLUXNET; CNSx/1440161
144. Buysse, P. et al. FLUXNET2015 F-Gri Grignon. FLUXNET; French National Institute for Agricultural esearch https://doi.
org/10.18140/x/1440162 (2016).
145. Berbigier, P. & Loustau, D. FLUXNET2015 F-LBr Le Bray. FLUXNET; INA - UM ISPAx/1440163
146. Ourcival, J. FLUXNET2015 F-Pue Puechabon. FLUXNET; CNSx/1440164 (2016).
147. Bonal, D. & Burban, B. FLUXNET2015 GF-Guy Guyaux (French Guiana). FLUXNET; INAx/1440165
148. Valentini, . et al. FLUXNET2015 GH-An Anasa. FLUXNET; Euro Mediterranean Center for Climate Change - Viterbo;
University of Tuscia - Vietrbo,x/1440229 (2016).
149. Hansen, B. FLUXNET2015 GL-NuF Nuu Fen. FLUXNET; University of Copenhagen; University of Aarhus; Asiaq - Greenland
Surveyx/1440222 (2016).
150. Lund, M., Jacowicz-orczyńsi, M. & Abermann, J. FLUXNET2015 GL-ZaF Zacenberg Fen. FLUXNET; Aarhus Universityx/1440223 (2016).
151. Lund, M., Jacowicz-orczyńsi, M. & Abermann, J. FLUXNET2015 GL-ZaH Zacenberg Heath. FLUXNET; Aarhus Universityx/1440224 (2016).
152. Magliulo, V. et al. FLUXNET2015 IT-BCi Borgo Cio. FLUXNET; CN ISAFOMx/1440166 (2016).
153. Sabbatini, S., Arriga, N. & Papale, D. FLUXNET2015 IT-CA1 Castel d’Asso1. FLUXNET; University of Tuscia - Vietrbo https://doi.
org/10.18140/x/1440230 (2016).
154. Sabbatini, S., Arriga, N., Gioli, B. & Papale, D. FLUXNET2015 IT-CA2 Castel dAsso2. FLUXNET; CN IBIMET; University of
Tuscia - Vietrbox/1440231 (2016).
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SCIENTIFIC Data | (2020) 7:225 |
155. Sabbatini, S., Arriga, N., Matteucci, G. & Papale, D. FLUXNET2015 IT-CA3 Castel d’Asso 3. FLUXNET; University of Tuscia -
Vietrbo; CN IBAFx/1440232 (2016).
156. Matteucci, G. FLUXNET2015 IT-Col Collelongo. FLUXNET; Istituto di Ecologia e Idrologia Forestale CN
x/1440167 (2016).
157. Fares, S., Savi, F. & Conte, A. FLUXNET2015 IT-Cp2 Castelporziano 2. FLUXNET; Council for Agricultural esearch and Economicsx/1440233 (2016).
158. Valentini, . et al. FLUXNET2015 IT-Cpz Castelporziano. FLUXNET; University of Tuscia - Vietrbo
x/1440168 (2016).
159. Gruening, C., Goded, I., Cescatti, A. & Poorsa, O. FLUXNET2015 IT-Isp Ispra ABC-IS. FLUXNET; European Commission - Joint
esearch C entrex/1440234 (2016).
160. Cescatti, A., Marcolla, B., Zorer, . & Gianelle, D. FLUXNET2015 IT-La2 Lavarone2. FLUXNET; Centro di Ecologia Alpina https://x/1440235 (2016).
161. Gianelle, D., Zampedri, ., Cavagna, M. & Sottocornola, M. FLUXNET2015 IT-Lav Lavarone. FLUXNET; Edmund Mach
Foundationx/1440169 (2016).
162. Gianelle, D., Cavagna, M., Zampedri, . & Marcolla, B. FLUXNET2015 IT-MBo Monte Bondone. FLUXNET; Edmund Mach
Foundationx/1440170 (2016).
163. Spano, D. et al. FLUXNET2015 IT-Noe Arca di Noe - Le Prigionette. FLUXNET; University of Sassari; CN-Ibimet Sassari https://x/1440171 (2016).
164. Manca, G. & Goded, I. FLUXNET2015 IT-PT1 Parco Ticino forest. FLUXNET; European Commission - DG Joint esearch Centrex/1440172 (2016).
165. Montagnani, L. & Minerbi, S. FLUXNET2015 IT-en enon. FLUXNET; Autonomous Province of Bolzano, Forest Services https://x/1440173 (2016).
166. Valentini, . et al. FLUXNET2015 IT-o1 occarespampani 1. FLUXNET; University of Tuscia - Vietrbo
x/1440174 (2016).
167. Papale, D. et al. FLUXNET2015 IT-o2 occarespampani 2. FLUXNET; University of Tuscia - Vietrbo
x/1440175 (2016).
168. Gruening, C., Goded, I., Cescatti, A. & Poorsa, O. FLUXNET2015 IT-S2 San ossore 2. FLUXNET; European Commission -
Joint esearch Centrex/1440236 (2016).
169. Gruening, C. et al. FLUXNET2015 IT-So San ossore. FLUXNET; European Commission - Joint esearch Centre https://doi.
org/10.18140/x/1440176 (2016).
170. Cremonese, E., Galvagno, M., Di Cella, U. & Migliavacca, M. FLUXNET2015 IT-Tor Torgnon. FLUXNET; Environmental
Protection Agency of Aosta Valleyx/1440237 (2016).
171. otani, A. FLUXNET2015 JP-MBF Moshiri Birch Forest Site. FLUXNET; Nagoya Universityx/1440238
172. otani, A. FLUXNET2015 JP-SMF Seto Mixed Forest Site. FLUXNET; Nagoya Universityx/1440239 (2016).
173. osugi, Y. & Taanashi, S. FLUXNET2015 MY-PSO Pasoh Forest eserve (PSO). FLUXNET; FIM(Forest esearch Institute of
Malaysia); yoto Universityx/1440240 (2016).
174. Dolman, H. et al. FLUXNET2015 NL-Hor Horstermeer. FLUXNET; Vrije Universiteit Amsterdam
x/1440177 (2016).
175. Moors, E. & Elbers, J. FLUXNET2015 NL-Loo Loobos. FLUXNET; ALTEA/Wageningen Environmental esearch https://doi.
org/10.18140/x/1440178 (2016).
176. Wolf, S., Eugster, W. & Buchmann, N. FLUXNET2015 PA-SPn Sardinilla Plantation. FLUXNET; ETH Zurich https://doi.
org/10.18140/x/1440180 (2016).
177. Wolf, S., Eugster, W. & Buchmann, N. FLUXNET2015 PA-SPs Sardinilla-Pasture. FLUXNET; ETH Zurich
x/1440179 (2016).
178. Merbold, L., ebmann, C. & Corradi, C. FLUXNET2015 U-Che Chersi. FLUXNET; Max-Planc Institute for Biogeochemistryx/1440181 (2016).
179. Dolman, H. et al. FLUXNET2015 U-Co Chourdah. FLUXNET; Vrije Universiteit Amsterdam
x/1440182 (2016).
180. Varlagin, A., urbatova, J. & Vygodsaya, N. FLUXNET2015 U-Fyo Fyodorovsoye. FLUXNET; A.N. Severtsov Institute of
Ecology and Evolutionx/1440183 (2016).
181. Belelli, L., Papale, D. & Valentini, . FLUXNET2015 U-Ha1 Haasia steppe. FLUXNET; University of Tuscia - Vietrbo https://doi.
org/10.18140/x/1440184 (2016).
182. Ardö, J., El Tahir, B. & Elhidir, H. FLUXNET2015 SD-Dem Demoeya. FLUXNET; LUND UNIVESITY https://doi.
org/10.18140/x/1440186 (2016).
183. Christensen, T. FLUXNET2015 SJ-Adv Adventdalen. FLUXNET; NATEO; Lund Universityx/1440241
184. Boie, J. et al. FLUXNET2015 SJ-Blv Bayelva, Spitsbergen. FLUXNET; University of Oslo, Department of Geosciences, 0316 OSLO,
Norway; Universität Bayreuth, Department of Earth Sciences, 95440 Bayreuth, Germany; Alfred Wegener Institute, Helmholtz Centre
for Polar and Marine esearch, Periglacial esearch Unit, 14473 Potsdam, Germany,x/1440242 (2016).
185. Tagesson, T., Ardö, J. & Fensholt, . FLUXNET2015 SN-Dhr Dahra. FLUXNET; Lund University
x/1440246 (2016).
186. Billesbach, D., Bradford, J. & Torn, M. FLUXNET2015 US-A1 AM USDA UNL OSU Woodward Switchgrass 1. FLUXNET;
Lawerence Bereley National Lab; U.S. Department of Agriculture; University of Nebrasax/1440103
187. Billesbach, D., Bradford, J. & Torn, M. FLUXNET2015 US-A2 AM USDA UNL OSU Woodward Switchgrass 2. FLUXNET;
Lawrence Bereley National Lab; U.S. Department of Agriculture; University of Nebrasax/1440104
188. Torn, M. FLUXNET2015 US-Ab AM Southern Great Plains burn site- Lamont. FLUXNET; Lawrence Bereley National
Laboratoryx/1440064 (2016).
189. Torn, M. FLUXNET2015 US-Ac AM Southern Great Plains control site- Lamont. FLUXNET; Lawrence Bereley National
Laboratoryx/1440065 (2016).
190. Biraud, S., Fischer, M., Chan, S. & Torn, M. FLUXNET2015 US-AM AM Southern Great Plains site- Lamont. FLUXNET;
Lawrence Bereley National Laboratoryx/1440066 (2016).
191. Zona, D. & Oechel, W. FLUXNET2015 US-Atq Atqasu. FLUXNET; San Diego State University
x/1440067 (2016).
192. Goldstein, A. FLUXNET2015 US-Blo Blodgett Forest. FLUXNET; University of California, Bereley
x/1440068 (2016).
193. Bowling, D. FLUXNET2015 US-Cop Corral Pocet. FLUXNET; University of Utahx/1440100 (2016).
194. Chen, J. & Chu, H. FLUXNET2015 US-CT Curtice Walter-Berger cropland. FLUXNET; University of Toledo/Michigan State
Universityx/1440117 (2016).
Content courtesy of Springer Nature, terms of use apply. Rights reserved
SCIENTIFIC Data | (2020) 7:225 |
195. Massman, B. FLUXNET2015 US-GBT GLEES Broolyn Tower. FLUXNET; USDA Forest Service
x/1440118 (2016).
196. Massman, B. FLUXNET2015 US-GLE GLEES. FLUXNET; USDA Forest Servicex/1440069 (2016).
197. Meyers, T. FLUXNET2015 US-Goo Goodwin Cree. FLUXNET; NOAA/ALx/1440070 (2016).
198. Munger, J. FLUXNET2015 US-Ha1 Harvard Forest EMS Tower (HF1). FLUXNET; Harvard University
x/1440071 (2016).
199. Matamala, . FLUXNET2015 US-IB2 Fermi National Accelerator Laboratory- Batavia (Prairie site). FLUXNET; Argonne National
Laboratoryx/1440072 (2016).
200. Zona, D. & Oechel, W. FLUXNET2015 US-Ivo Ivotu. FLUXNET; San Diego State Universityx/1440073
201. Drae, B. & Hinle, . FLUXNET2015 US-S1 ennedy Space Center (slash pine). FLUXNET; Smithsonian Environmental
esearch Center; University of Central Floridax/1440074 (2016).
202. Drae, B. & Hinle, . FLUXNET2015 US-S2 ennedy Space Center (scrub oa). FLUXNET; Smithsonian Environmental
esearch Center; University of Central Floridax/1440075 (2016).
203. Fares, S. FLUXNET2015 US-Lin Lindcove Orange Orchard. FLUXNET; Entecrax/1440107 (2016).
204. Desai, A. FLUXNET2015 US-Los Lost Cree. FLUXNET; University of Wisconsinx/1440076 (2016).
205. Meyers, T. FLUXNET2015 US-LWW Little Washita Watershed. FLUXNET; NOAA/ALx/1440077 (2016).
206. Law, B. FLUXNET2015 US-Me1 Metolius - Eyerly burn. FLUXNET; Oregon State Universityx/1440078 (2016).
207. Law, B. FLUXNET2015 US-Me2 Metolius mature ponderosa pine. FLUXNET; Oregon State University
x/1440079 (2016).
208. Law, B. FLUXNET2015 US-Me3 Metolius-second young aged pine. FLUXNET; Oregon State University
x/1440080 (2016).
209. Law, B. FLUXNET2015 US-Me4 Metolius-old aged ponderosa pine. FLUXNET; Oregon State University
x/1440081 (2016).
210. Law, B. FLUXNET2015 US-Me5 Metolius-rst young aged pine. FLUXNET; Oregon State University
x/1440082 (2016).
211. Law, B. FLUXNET2015 US-Me6 Metolius Young Pine Burn. FLUXNET; Oregon State University
x/1440099 (2016).
212. Novic, . & Phillips, . FLUXNET2015 US-MMS Morgan Monroe State Forest. FLUXNET; Indiana University https://doi.
org/10.18140/x/1440083 (2016).
213. Sturtevant, C. et al. FLUXNET2015 US-Myb Mayberry Wetland. FLUXNET; University of California, Bereley https://doi.
org/10.18140/x/1440105 (2016).
214. Suyer, A. FLUXNET2015 US-Ne1 Mead - irrigated continuous maize site. FLUXNET; University of Nebrasa - Lincoln https://doi.
org/10.18140/x/1440084 (2016).
215. Suyer, A. FLUXNET2015 US-Ne2 Mead - irrigated maize-soybean rotation site. FLUXNET; University of Nebrasa - Lincolnx/1440085 (2016).
216. Suyer, A. FLUXNET2015 US-Ne3 Mead - rainfed maize-soybean rotation site. FLUXNET; University of Nebrasa - Lincoln https://x/1440086 (2016).
217. Blanen, P. et al. FLUXNET2015 US-N1 Niwot idge Forest (LTE NWT1). FLUXNET; University of Colorado https://doi.
org/10.18140/x/1440087 (2016).
218. Chen, J., Chu, H. & Noormets, A. FLUXNET2015 US-Oho Oa Openings. FLUXNET; University of Toledo/Michigan State
Universityx/1440088 (2016).
219. Bohrer, G. FLUXNET2015 US-Ov Olentangy iver Wetland esearch Par. FLUXNET; e Ohio State University https://doi.
org/10.18140/x/1440102 (2016).
220. Desai, A. FLUXNET2015 US-PFa Par Falls/WLEF. FLUXNET; University of Wisconsinx/1440089
221. obayashi, H. & Suzui, . FLUXNET2015 US-Prr Poer Flat esearch ange Blac Spruce Forest. FLUXNET; Japan Agency for
Marine-Earth Science and Technologyx/1440113 (2016).
222. urc, S. FLUXNET2015 US-SC Santa ita Creosote. FLUXNET; University of Arizonax/1440098
223. Scott, . FLUXNET2015 US-SG Santa ita Grassland. FLUXNET; United States Department of Agriculture https://doi.
org/10.18140/x/1440114 (2016).
224. Scott, . FLUXNET2015 US-SM Santa ita Mesquite. FLUXNET; United States Department of Agriculture https://doi.
org/10.18140/x/1440090 (2016).
225. Ewers, B. & Pendall, E. FLUXNET2015 US-Sta Saratoga. FLUXNET; University of Wyomingx/1440115
226. Desai, A. FLUXNET2015 US-Syv Sylvania Wilderness Area. FLUXNET; University of Wisconsin
x/1440091 (2016).
227. Baldo cchi, D. & Ma, S. FLUXNET2015 US-Ton Tonzi anch. FLUXNET; University of California, Bereley
x/1440092 (2016).
228. Szutu, D., Baldocchi, D., Eichelmann, E. & nox, S. FLUXNET2015 US-Tw1 Twitchell Wetland West Pond. FLUXNET; University
of California, Bereleyx/1440108 (2016).
229. Baldocchi, D. FLUXNET2015 US-Tw2 Twitchell Corn. FLUXNET; University of California, Bereley
x/1440109 (2016).
230. Szutu, D. & Baldocchi, D. FLUXNET2015 US-Tw3 Twitchell Alfalfa. FLUXNET; University of California, Bereley https://doi.
org/10.18140/x/1440110 (2016).
231. Sanchez, C. et al. FLUXNET2015 US-Tw4 Twitchell East End Wetland. FLUXNET; University of California, Bereley https://doi.
org/10.18140/x/1440111 (2016).
232. Baldocchi, D. FLUXNET2015 US-Twt Twitchell Island. FLUXNET; University of California, Bereley
x/1440106 (2016).
233. Gough, C., Bohrer, G. & Curtis, P. FLUXNET2015 US-UMB Univ. of Mich. Biological Station. FLUXNET; Ohio State University;
Virginia Commonwealth Universityx/1440093 (2016).
234. Gough, C., Bohrer, G. & Curtis, P. FLUXNET2015 US-UMd UMBS Disturbance. FLUXNET; Ohio State University; Virginia
Commonwealth Universityx/1440101 (2016).
235. Baldocchi, D., Ma, S. & Xu, L. FLUXNET2015 US-Var Vaira anch- Ione. FLUXNET; University of California, Bereley https://doi.
org/10.18140/x/1440094 (2016).
236. Desai, A. FLUXNET2015 US-WCr Willow Cree. FLUXNET; University of Wisconsinx/1440095 (2016).
237. Scott, . FLUXNET2015 US-Whs Walnut Gulch Lucy Hills Shrub. FLUXNET; United States Department of Agriculture https://doi.
org/10.18140/x/1440097 (2016).
238. Chen, J. FLUXNET2015 US-Wi0 Young red pine (YP). FLUXNET; Michigan State Universityx/1440055
Content courtesy of Springer Nature, terms of use apply. Rights reserved
SCIENTIFIC Data | (2020) 7:225 |
239. Chen, J. FLUXNET2015 US-Wi1 Intermediate hardwood (IHW). FLUXNET; Michigan State University
x/1440054 (2016).
240. Chen, J. FLUXNET2015 US-Wi2 Intermediate red pine (IP). FLUXNET; Michigan State University
x/1440056 (2016).
241. Chen, J. FLUXNET2015 US-Wi3 Mature hardwood (MHW). FLUXNET; Michigan State University
x/1440057 (2016).
242. Chen, J. FLUXNET2015 US-Wi4 Mature red pine (MP). FLUXNET; Michigan State University
x/1440058 (2016).
243. Chen, J. FLUXNET2015 US-Wi5 Mixed young jac pine (MYJP). FLUXNET; Michigan State University
x/1440059 (2016).
244. Chen, J. FLUXNET2015 US-Wi6 Pine barrens #1 (PB1). FLUXNET; Michigan State Universityx/1440060
245. Chen, J. FLUXNET2015 US-Wi7 ed pine clearcut (PCC). FLUXNET; Michigan State University
x/1440061 (2016).
246. Chen, J. FLUXNET2015 US-Wi8 Young hardwood clearcut (YHW). FLUXNET; Michigan State University
x/1440062 (2016).
247. Chen, J. FLUXNET2015 US-Wi9 Young Jac pine (YJP). FLUXNET; Michigan State University
x/1440063 (2016).
248. Scott, . FLUXNET2015 US-Wg Walnut Gulch endall Grasslands. FLUXNET; United States Department of Agriculture https://x/1440096 (2016).
249. Chen, J. & Chu, H. FLUXNET2015 US-WPT Winous Point North Marsh. FLUXNET; University of Toledo/Michigan State Universityx/1440116 (2016).
250. utsch, W., Merbold, L. & olle, O. FLUXNET2015 ZM-Mon Mongu. FLUXNET; Max-Planc Institute for Biogeochemistry https://x/1440189 (2016).
251. Harris, I., Jones, P. D., Osborn, T. J. & Lister, D. H. Updated high-resolution grids of monthly climatic observations - the CU
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252. Channan, S., Collins, . & Emanuel, W. Global mosaics of the standard MODIS land cover type data. University of Maryland and
the Pacic Northwest National Laboratory, College Par, Maryland, USA (2014).
253. Baldocchi, D., Chu, H. & eichstein, M. Inter-annual variability of net and gross ecosystem carbon uxes: A review. Agr. Forest
Meteorol. 249, 520–533 (2018).
254. Baldocchi, D. D. How eddy covariance ux measurements have contributed to our understanding of Global Change Biology. Glob.
Change Biol. 26, 242–260 (2019).
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ematic Centre, (2017).
257. Tramontana, G. et al. Predicting carbon dioxide and energy uxes across global FLUXNET sites with regression algorithms.
Biogeosciences 13, 4291–4313 (2016).
258. eenan, T. F. et al. Widespread inhibition of daytime ecosystem respiration. Nat. Ecol. Evol. 3, 407–415 (2019).
259. Campioli, M. et al. Evaluating the convergence between eddy-covariance and biometric methods for assessing carbon budgets of
forests. Nat. Commun. 7, 13717 (2016).
We thank the many people who helped with generating high quality data through the years at all sites, and the
funding sources to the sites and networks, for the data collection, curation, and sharing that make this dataset
possible. e eddy covariance data were acquired and shared by the following networks: AmeriFlux, AfriFlux,
AsiaFlux, CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, ChinaFlux, Fluxnet-Canada, GreenGrass, ICOS,
KoFlux, LBA, NECC, OzFlux-TERN, TCOS-Siberia, Swiss FluxNet and USCCC. e ERA-Interim reanalysis data
were provided by ECMWF and processed by Laboratoire des sciences du climat et de lenvironnement (LSCE).
e FLUXNET eddy covariance data processing and harmonization was carried out by the European Fluxes
Database Cluster and the AmeriFlux Management Project (with support by European Union H2020 projects
and U.S. Department of Energy Oce of Science, respectively), with contributions from the Carbon Dioxide
Information Analysis Center, ICOS Ecosystem ematic Centre, and OzFlux, ChinaFlux, and AsiaFlux oces.
Dario Papale, Gilberto Pastorello, Margaret Torn, and Deb Agarwal conceived of and organized the FLUXNET2015
dataset. Eleonora Canfora, You-Wei Cheah, Danielle Christianson, Housen Chu, Dario Papale, Gilberto Pastorello,
Diego Polidori, Cristina Poindexter, and Carlo Trotta were responsible for quality checking, post-processing, and
creating data products. You-Wei Cheah, Danielle Christianson, Abdelrahman Elbashandy, Marty Humphrey,
Gilberto Pastorello, Diego Polidori, Markus Reichstein, Alessio Ribeca, Carlo Trotta, Catharine van Ingen and
Nicolas Vuichard were responsible for code and soware preparation and implementation (processing pipeline and
data distribution platform). Eleonora Canfora, You-Wei Cheah, Jiquan Chen, Danielle Christianson, Housen Chu,
Peter Isaac, Dario Papale and Leiming Zhang managed the ux data and metadata collections. Gilberto Pastorello,
Dario Papale, Deb Agarwal, Sebastien Biraud, You-Wei Cheah, Danielle Christianson, Housen Chu, and Margaret
Torn conceived of the paper and prepared the rst dra that was reviewed and commented on by all the coauthors.
All the coauthors collected, processed and contributed the data, also participated in the quality assessment and
correction of errors. e link between sites and coauthors is provided in Supplementary Table SM9.
e authors declare no competing interests.
Supplementary information is available for this paper at
Correspondence and requests for materials should be addressed to G.P. or D.P.
Content courtesy of Springer Nature, terms of use apply. Rights reserved
SCIENTIFIC Data | (2020) 7:225 |
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is is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may
apply 2020, corrected publication 2021
Gilberto Pastorello
1 ✉ , Carlo Trotta
2, Eleonora Canfora2,3, Housen Chu
Danielle Christianson
1, You-Wei Cheah1, Cristina Poindexter5, Jiquan Chen
Abdelrahman Elbashandy1, Marty Humphrey7, Peter Isaac8, Diego Polidori2,3, Markus Reichstein9,
Alessio Ribeca2,3, Catharine van Ingen1, Nicolas Vuichard10, Leiming Zhang11, Brian Amiro
Christof Ammann
13, M. Altaf Arain
14, Jonas Ardö
15, Timothy Arkebauer16, Stefan K. Arndt
Nicola Arriga18,19, Marc Aubinet20, Mika Aurela
21, Dennis Baldocchi22, Alan Barr23,24,
Eric Beamesderfer
14, Luca Belelli Marchesini
25,26, Onil Bergeron27, Jason Beringer
Christian Bernhofer
29, Daniel Berveiller
30, Dave Billesbach31, Thomas Andrew Black32,
Peter D. Blanken
33, Gil Bohrer
34, Julia Boike
35,36, Paul V. Bolstad37, Damien Bonal
Jean-Marc Bonnefond39, David R. Bowling
40, Rosvel Bracho41, Jason Brodeur
Christian Brümmer
43, Nina Buchmann
44, Benoit Burban45, Sean P. Burns
Pauline Buysse
47, Peter Cale48, Mauro Cavagna25, Pierre Cellier47, Shiping Chen49, Isaac Chini25,
Torben R. Christensen
50, James Cleverly
51,52, Alessio Collalti
2,53, Claudia Consalvo2,54,
Bruce D. Cook55, David Cook
56, Carole Coursolle
57,58, Edoardo Cremonese
Peter S. Curtis60, Ettore D’Andrea
53, Humberto da Rocha
61, Xiaoqin Dai11, Kenneth J. Davis62,
Bruno De Cinti63, Agnes de Grandcourt64, Anne De Ligne20, Raimundo C. De Oliveira
Nicolas Delpierre
30, Ankur R. Desai
66, Carlos Marcelo Di Bella67, Paul di Tommasi53,
Han Dolman
68, Francisco Domingo
69, Gang Dong70, Sabina Dore
71, Pierpaolo Duce
Eric Dufrêne30, Allison Dunn
73, Jiří Dušek74, Derek Eamus
51, Uwe Eichelmann29,
Hatim Abdalla M. ElKhidir75, Werner Eugster
44, Cacilia M. Ewenz
76, Brent Ewers77,
Daniela Famulari
53, Silvano Fares78,79, Iris Feigenwinter44, Andrew Feitz
80, Rasmus Fensholt
Gianluca Filippa
59, Marc Fischer
82, John Frank
83, Marta Galvagno59, Mana Gharun44,
Damiano Gianelle
25, Bert Gielen18, Beniamino Gioli
84, Anatoly Gitelson85, Ignacio Goded19,
Mathias Goeckede
86, Allen H. Goldstein
22, Christopher M. Gough87, Michael L. Goulden
Alexander Graf
89, Anne Griebel
17, Carsten Gruening19, Thomas Grünwald
Albin Hammerle
90, Shijie Han91,92, Xingguo Han49, Birger Ulf Hansen81, Chad Hanson93,
Juha Hatakka21, Yongtao He11,94, Markus Hehn29, Bernard Heinesch20, Nina Hinko-Najera
Lukas Hörtnagl
44, Lindsay Hutley
96, Andreas Ibrom
97, Hiroki Ikawa
98, Marcin Jackowicz-
Korczynski15,50, Dalibor Janouš74, Wilma Jans99, Rachhpal Jassal
32, Shicheng Jiang100,
Tomomichi Kato
101,102, Myroslava Khomik14,103, Janina Klatt104, Alexander Knohl
Sara Knox
107, Hideki Kobayashi108, Georgia Koerber
109, Olaf Kolle
86, Yoshiko Kosugi110,
Ayumi Kotani
111, Andrew Kowalski112, Bart Kruijt
113, Julia Kurbatova
114, Werner L. Kutsch
Hyojung Kwon93, Samuli Launiainen116, Tuomas Laurila21, Bev Law
93, Ray Leuning197,
Yingnian Li117, Michael Liddell118, Jean-Marc Limousin119, Marryanna Lion
120, Adam J. Liska31,
Annalea Lohila
21,121, Ana López-Ballesteros
122, Efrén López-Blanco
50, Benjamin Loubet47,
Denis Loustau
39, Antje Lucas-Moat43,123, Johannes Lüers124,125, Siyan Ma22,
Craig Macfarlane126, Vincenzo Magliulo
53, Regine Maier
44, Ivan Mammarella
Giovanni Manca19, Barbara Marcolla25, Hank A. Margolis58, Serena Marras3,127, William Massman83,
Mikhail Mastepanov
50,128, Roser Matamala56, Jaclyn Hatala Matthes
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Francesco Mazzenga130, Harry McCaughey131, Ian McHugh17, Andrew M. S. McMillan
Lutz Merbold
133, Wayne Meyer
109, Tilden Meyers134, Scott D. Miller
135, Stefano Minerbi
Uta Moderow
29, Russell K. Monson137, Leonardo Montagnani
136,138, Caitlin E. Moore
Eddy Moors
140,141, Virginie Moreaux39,142, Christine Moureaux20, J. William Munger
Taro Nakai
145,146, Johan Neirynck147, Zoran Nesic
32, Giacomo Nicolini2,3, Asko Noormets
Matthew Northwood149, Marcelo Nosetto150,151, Yann Nouvellon
64,152, Kimberly Novick153,
Walter Oechel
154,155, Jørgen Eivind Olesen
156,157, Jean-Marc Ourcival119, Shirley A. Papuga
Frans-Jan Parmentier
15,159, Eugenie Paul-Limoges
160, Marian Pavelka
74, Matthias Peichl
Elise Pendall
162, Richard P. Phillips
163, Kim Pilegaard97, Norbert Pirk15,164, Gabriela Posse
Thomas Powell
4, Heiko Prasse29, Suzanne M. Prober164, Serge Rambal
119, Üllar Rannik121,
Naama Raz-Yaseef4, Corinna Rebmann166, David Reed
167, Victor Resco de Dios
Natalia Restrepo-Coupe
137, Borja R. Reverter169, Marilyn Roland
18, Simone Sabbatini2,
Torsten Sachs
170, Scott R. Saleska137, Enrique P. Sánchez-Cañete
112,171, Zulia M. Sanchez-
172, Hans Peter Schmid104, Marius Schmidt
89, Karl Schneider
173, Frederik Schrader
Ivan Schroder
174, Russell L. Scott175, Pavel Sedlák
74,176, Penélope Serrano-Ortíz171,177,
Changliang Shao178, Peili Shi11, Ivan Shironya
114, Lukas Siebicke105, Ladislav Šigut
Richard Silberstein
28,179, Costantino Sirca3,127, Donatella Spano3,127, Rainer Steinbrecher104,
Robert M. Stevens180, Cove Sturtevant
181, Andy Suyker85, Torbern Tagesson15,81,
Satoru Takanashi
182, Yanhong Tang183, Nigel Tapper
184, Jonathan Thom185,
Michele Tomassucci2,186, Juha-Pekka Tuovinen
21, Shawn Urbanski187, Riccardo Valentini
Michiel van der Molen
188, Eva van Gorsel189, Ko van Huissteden
68, Andrej Varlagin
Joseph Verfaillie
22, Timo Vesala121, Caroline Vincke190, Domenico Vitale2,3,
Natalia Vygodskaya114, Jerey P. Walker
191, Elizabeth Walter-Shea
85, Huimin Wang11,
Robin Weber22, Sebastian Westermann165, Christian Wille
170, Steven Wofsy143,144,
Georg Wohlfahrt
90, Sebastian Wolf
44, William Woodgate
192,193, Yuelin Li194,
Roberto Zampedri25, Junhui Zhang92, Guoyi Zhou
195, Donatella Zona
154,196, Deb Agarwal1,
Sebastien Biraud
4, Margaret Torn4 & Dario Papale
2,3 ✉
1Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA. 2DIBAF,
University of Tuscia, Viterbo, 01100, Italy. 3Euro-Mediterranean Centre on Climate Change Foundation (CMCC),
Lecce, 73100, Italy. 4Climate & Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA,
94720, USA. 5Department of Civil Engineering, California State University, Sacramento, CA, 95819, USA.
6Department of Geography, Environment, and Spatial Sciences, Michigan State University, East Lansing, MI, 48823,
USA. 7Department of Computer Science, University of Virginia, Charlottesville, VA, 22904, USA. 8TERN Ecosystem
Processes, Menzies Creek, VIC3159, Australia. 9Max Planck Institute for Biogeochemistry, Jena, 07701, Germany.
10Laboratoire des Sciences du Climat et de l’Environnement (LSCE), CEA CNRS, UVSQ UPSACLAY, Gif sur Yvette,
91190, France. 11Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences
and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China. 12Department of Soil
Science, University of Manitoba, Winnipeg, MB, R3T2N2, Canada. 13Department of Agroecology and Environment,
Agroscope Research Institute, Zürich, 8046, Switzerland. 14School of Geography and Earth Sciences, McMaster
University, L8S4K1, Hamilton, ON, Canada. 15Department of Physical Geography and Ecosystem Science, Lund
University, Lund, 22362, Sweden. 16Department of Agronomy and Horticulture, University of Nebraska-Lincoln,
Lincoln, NE, 68583, USA. 17School of Ecosystem and Forest Sciences, The University of Melbourne, Richmond,
VIC3121, Australia. 18Department of Biology, Research Group PLECO, University of Antwerp, Antwerp, 2610,
Belgium. 19Joint Research Centre, European Commission, Ispra, 21027, Italy. 20TERRA Teaching and Research Center,
University of Liege, Gembloux, B-5030, Belgium. 21Finnish Meteorological Institute, Helsinki, 00560, Finland.
22ESPM, University of California Berkeley, Berkeley, CA, 94720, USA. 23Global Institute for Water Security, University
of Saskatchewan, Saskatoon, SK, S7N3H5, Canada. 24Climate Research Division, Environment and Climate Change
Canada, Saskatoon, SK, S7N3H5, Canada. 25Department of Sustainable Agro-ecosystems and Bioresources,
Research and Innovation Centre, Fondazione Edmund Mach, San Michele All’adige, 38010, Italy. 26Department of
Landscape Design and Sustainable Ecosystems, Agrarian-Technological Institute, RUDN University, Moscow,
117198, Russia. 27Direction du marché du carbone, Ministère du Développement durable de l’Environnement et de
la Lutte contre les changements climatiques, Québec, QC, G1R5V7, Canada. 28School of Agriculture and
Environment, University of Western Australia, Crawley, 6009, Australia. 29Institute of Hydrology and Meteorology,
Technische Universität Dresden, Tharandt, 01737, Germany. 30Université Paris-Saclay, CNRS, AgroParisTech,
Ecologie Systématique et Evolution, Orsay, 91405, France. 31Biological Systems Engineering, University of Nebraska-
Lincoln, Lincoln, NE, 68583, USA. 32Faculty of Land and Food Systems, University of British Columbia, Vancouver,
BC, V6T1Z4, Canada. 33Department of Geography, University of Colorado, Boulder, CO, 80309, USA. 34Department
of Civil, Environmental & Geodetic Engineering, Ohio State University, Columbus, OH, 43210, USA. 35Alfred Wegener
Institute Helmholtz Centre for Polar and Marine Research, Potsdam, 14482, Germany. 36Geography Department,
Humboldt-Universität zu Berlin, Berlin, Germany. 37Forest Resources, University of Minnesota, St Paul, MN, 55108,
USA. 38Université de Lorraine, AgroParisTech, INRAE, UMR Silva, Nancy, 54000, France. 39ISPA, Bordeaux Sciences
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Agro, INRAE, Villenave d’Ornon, 33140, France. 40School of Biological Sciences, University of Utah, Salt Lake City,
UT, 84112, USA. 41School of Forest Resources and Conservation, University of Florida, Gainesville, FL, 32611, USA.
42McMaster University Library, McMaster University, Hamilton, ON, L8S4L6, Canada. 43Thünen Institute of Climate-
Smart Agriculture, Federal Research Institute of Rural Areas, Forestry and Fisheries, Braunschweig, 38116, Germany.
44Department of Environmental Systems Science, ETH Zurich, Zurich, 8092, Switzerland. 45INRAE UMR ECOFOG,
Kourou, 97387, French Guiana. 46Mesoscale and Microscale Meteorology Laboratory, National Center for
Atmospheric Research, Boulder, CO, 80301, USA. 47Université Paris-Saclay, INRAE, AgroParisTech, UMR ECOSYS,
Thiverval-Grignon, 78850, France. 48Australian Landscape Trust, Renmark, SA5341, Australia. 49State Key Laboratory
of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China.
50Department of Bioscience, Arctic Research Center, Aarhus University, Roskilde, 4000, Denmark. 51School of Life
Sciences, University of Technology Sydney, Sydney, 2007, Australia. 52Terrestrial Ecosystem Research Network
TERN, University of Technology, Sydney, 2007, Australia. 53Institute for Agricultural and Forestry Systems in the
Mediterranean, National Research Council of Italy, Ercolano, 80056, Italy. 54Research Institute on Terrestrial
Ecosystems, National Research Council of Italy, Porano, 05010, Italy. 55Biospheric Sciences Laboratory, NASA
Goddard Space Flight Center, Greenbelt, MD, 20771, USA. 56Environmental Science Division, Argonne National
Laboratory, Lemont, IL, 60439, USA. 57Canadian Forest Service, Natural Resources Canada, Québec, QC, G1V4C7,
Canada. 58Centre d’étude de la forêt, Faculté de foresterie, de géographie et de géomatique, Université Laval,
Québec, QC, G1V0A6, Canada. 59Climate Change Unit, Environmental Protection Agency of Aosta Valley, Saint
Christophe, 11020, Italy. 60Department of Evolution, Ecology, and Organismal Biology, Ohio State University,
Columbus, OH, 43210, USA. 61Instituto de Astronomia, Geofísica e Ciências Atmosféricas, Universidade de São
Paulo, São Paulo, SP, 01000-000, Brazil. 62Department of Meteorology and Atmospheric Science, The Pennsylvania
State University, University Park, PA, 16802, USA. 63Institute of Research on Terrestrial Ecosystems, National
Research Council of Italy, Montelibretti, 00010, Italy. 64UMR Eco&Sols, CIRAD, Montpellier, 34060, France.
65Pedology, Embrapa Amazonia Oriental, Belém, PA, 68020640, Brazil. 66Atmospheric and Oceanic Sciences,
University of Wisconsin-Madison, Madison, WI, 53706, USA. 67Departamento de Métodos Cuantitativos y Sistemas
de Información, Facultad de Agronomía, UBA, Buenos Aires, 1417, Argentina. 68Department of Earth Sciences, Vrije
Universiteit Amsterdam, Amsterdam, 1081 HV, The Netherlands. 69Desertication and Geoecology Department,
Experimental Station of Arid Zones, CSIC, Almería, 04120, Spain. 70School of Life Science, Shanxi University, Taiyuan,
030006, China. 71HydroFocus, Davis, CA, 95618, USA. 72Institute of BioEconomy, National Research Council of Italy,
Sassari, 07100, Italy. 73Department of Earth, Environment, and Physics, Worcester State University, Worcester, MA,
01602, USA. 74Department of Matter and Energy Fluxes, Global Change Research Institute of the Czech Academy of
Sciences, Brno, 60300, Czech Republic. 75ElObeid Research Station, Agricultural Research Corporation, ElObeid,
51111, Sudan. 76Airborne Research Australia, TERN Ecosystem Processes Central Node, Paraeld, 5106, Australia.
77Department of Botany, Program in Ecology, University of Wyoming, 1000 E. Univ. Ave, Laramie, WY, 82071, USA.
78Institute of BioEconomy, National Research Council of Italy, Rome, 00100, Italy. 79Research Centre for Forestry and
Wood, Council for Agricultural Research and Economics, Rome, 00166, Italy. 80Geoscience Australia, Canberra, 2601,
Australia. 81Department of Geosciences and Natural Resource Management, University of Copenhagen,
Copenhagen, 1350, Denmark. 82Energy Analysis & Environmental Impacts Division, Lawrence Berkeley National
Laboratory, Berkeley, CA, 94720, USA. 83USDA Forest Service, Rocky Mountain Research Station, Fort Collins, CO,
80526, USA. 84Institute of BioEconomy, National Research Council of Italy, Firenze, 50145, Italy. 85School of Natural
Resources, University of Nebraska-Lincoln, Lincoln, NE, 68583, USA. 86Max Planck Institute for Biogeochemistry,
Jena, 03641, Germany. 87Department of Biology, Virginia Commonwealth University, Richmond, VA, 23284, USA.
88Department of Earth System Science, University of California, Irvine, CA, 92697, USA. 89Agrosphere (IBG3),
Forschungszentrum Jülich, Jülich, 52428, Germany. 90Department of Ecology, University of Innsbruck, Innsbruck,
6020, Austria. 91International Joint Research Laboratory for Global Change Ecology, School of Life Sciences, Henan
University, Kaifeng, 450000, China. 92Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, 110016,
China. 93Department of Forest Ecosystems and Society, Oregon State University, Corvallis, OR, 97333, USA.
94College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100190, China.
95School of Ecosystem and Forest Sciences, The University of Melbourne, Creswick, VIC3363, Australia. 96Research
Institute for the Environment and Livelihoods, Charles Darwin University, Darwin, 0909, Australia. 97Department of
Environmental Engineering, Technical University of Denmark (DTU), Kongens Lyngby, 2800, Denmark. 98Institute for
Agro-Environmental Sciences, National Agriculture and Food Research Organization, Tsukuba, 305-8604, Japan.
99Wageningen Environmental Research, Wageningen University and Research, Wageningen, 6708PB, The
Netherlands. 100Key Laboratory of Vegetation Ecology, Ministry of Education, Northeast Normal University,
Changchun, 130024, China. 101Research Faculty of Agriculture, Hokkaido University, Sapporo, 060-8589, Japan.
102GI-Core, Hokkaido University, Sapporo, 060-0808, Japan. 103Geography and Environmental Management,
Waterloo, ON, N2L3G1, Canada. 104Institute of Meteorology and Climate Research, Karlsruhe Institute of
Technology, Garmisch-Partenkirchen, 82467, Germany. 105Bioclimatology, University of Goettingen, Goettingen,
37077, Germany. 106Centre of Biodiversity and Sustainable Land Use (CBL), University of Goettingen, Goettingen,
37077, Germany. 107Department of Geography, The University of British Columbia, Vancouver, BC, V6T1Z2, Canada.
108Research Institute for Global Change, Institute of Arctic Climate and Environment Research, Japan Agency for
Marine-Earth Science and Technology, Yokoama, 236-0001, Japan. 109Biological Sciences, University of Adelaide,
Adelaide, SA5064, Australia. 110Graduate School of Agriculture, Kyoto University, Kyoto, 606-8502, Japan.
111Graduate School of Bioagricultural Sciences, Nagoya University, Nagoya, 4648601, Japan. 112Department of
Applied Physics, University of Granada, Granada, 18071, Spain. 113Water systems and Global Change group,
Wageningen University, Wageningen, 6500, The Netherlands. 114A.N. Severtsov Institute of Ecology and Evolution,
Russian Academy of Sciences, Moscow, 119071, Russia. 115Head Oce, Integrated Carbon Observation System
(ICOS ERIC), Helsinki, 00560, Finland. 116Natural Resources Institute Finland, Helsinki, 00790, Finland. 117Key
Laboratory of Adaptation and Evolution of Plateau Biota, Northwest Institute of Plateau Biology, Chinese Academy
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of Sciences, Xining, 810008, China. 118Centre for Tropical Environmental Sustainability Studies, James Cook
University, Cairns, 4878, Australia. 119CEFE, CNRS, Univ Montpellier, Montpellier, 34293, France. 120Forestry and
Environment Division, Forest Research Institute Malaysia (FRIM), Kepong, 52109, Malaysia. 121Institute for
Atmosphere and Earth System Research/Physics, University of Helsinki, Helsinki, 00560, Finland. 122Department of
Botany, School of Natural Sciences, Trinity College Dublin, Dublin, D02PN40, Ireland. 123German Meteorological
Service (DWD), Centre for Agrometeorological Research, Braunschweig, 38116, Germany. 124Micrometeorology,
University of Bayreuth, Bayreuth, 95440, Germany. 125Bayreuth Center of Ecology and Environmental Research,
95448, Bayreuth, Germany. 126CSIRO Land and Water, Floreat, 6014, Australia. 127Department of Agriculture,
University of Sassari, Sassari, 07100, Italy. 128Oulanka research station, University of Oulu, Kuusamo, 93900, Finland.
129Dept. Biological Sciences, Wellesley College, Wellesley, MA, 02481, USA. 130Research Institute on Terrestrial
Ecosystems, National Research Council of Italy, Monterotondo Scalo, 00015, Italy. 131Department of Geography and
Planning, Queen’s University, Kingston, ON, K7L3N6, Canada. 132Environmental Analytics NZ, Ltd. Raumati South,
Paraparaumu, 5032, New Zealand. 133Mazingira Centre, International Livestock Research Institute (ILRI), Nairobi,
00100, Kenya. 134NOAA/OAR/Air Resources Laboratory, 325 Broadway, Boulder, CO, 80303, USA. 135Atmospheric
Sciences Research Center, State University of New York at Albany, Albany, NY, 12203, USA. 136Forest Department of
South Tyrol, Bolzano, 39100, Italy. 137Department of Ecology and Evolutionary Biology, University of Arizona, Tucson,
AZ, 85721, USA. 138Faculty of Science and Technology, Free University of Bolzano, Bolzano, 39100, Italy.
139Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA. 140IHE Delft,
Delft, 2611, The Netherlands. 141Faculty of Science, VU Amsterdam, Amsterdam, 1081, The Netherlands.
142University Grenoble Alpes, IRD, CNRS, IGE, Grenoble, 38000, France. 143School of Engineering and Applied
Sciences, Harvard University, Cambridge, MA, 02138, USA. 144Department of Earth and Planetary Sciences, Harvard
University, Cambridge, MA, 02138, USA. 145School of Forestry and Resource Conservation, National Taiwan
University, Taipei, 0617, Taiwan. 146International Arctic Research Center, University of Alaska Fairbanks, Fairbanks,
AK, 99775, USA. 147Environment and Climate, Research Institute for Nature and Forest, Geraardsbergen, 9500,
Belgium. 148Department of Ecosystem Science and Management, Texas A&M University, College Station, TX, 77843,
USA. 149Research Institute for the Environment and Livelihoods, Charles Darwin University, Casuarina, 0810,
Australia. 150Grupo de Estudios Ambientales, Instituto de Matemática Aplicada San Luis (UNSL & CONICET), San
Luis, D5700HHW, Argentina. 151Facultad de Ciencias Agropecuarias (UNER), Oro Verde, 3100, Argentina.
152Eco&Sols, Univ Montpellier-CIRAD-INRA-IRD-Montpellier SupAgro, Montpellier, 34060, France. 153O’Neill School
of Public and Environmental Affairs, Indiana University Bloomington, Bloomington, IN, 47405, USA. 154Global
Change Research Group, Dept. Biology, San Diego State University, San Diego, CA, 92182, USA. 155Department of
Geography, College of Life and Environmental Sciences, University of Exeter, Exeter, EX44RJ, United Kingdom.
156Department of Agroecology, Aarhus University, Tjele, 8830, Denmark. 157iCLIMATE, Aarhus University, Tjele, 8830,
Denmark. 158Department of Geology, Wayne State University, Detroit, MI, 48202, USA. 159Department of
Geosciences, University of Oslo, Oslo, 0315, Norway. 160Department of Geography, University of Zurich, Zurich,
8057, Switzerland. 161Department of Forest Ecology and Management, Swedish University of Agricultural Sciences,
Umeå, 90183, Sweden. 162Hawkesbury Institute for the Environment, Western Sydney University, Penrith, 2751,
Australia. 163Department of Biology, Indiana University Bloomington, Bloomington, IN, 47401, USA. 164CSIRO Land
and Water, Wembley, 6913, Australia. 165Instituto de Clima y Agua, Instituto Nacional de Tecnologia Agropecuaria
(INTA), Buenos Aires, 1686, Argentina. 166Department Computational Hydrosystems, Helmholtz Centre for
Environmental Research UFZ, Leipzig, 04318, Germany. 167Center for Global Change & Earth Observations, Michigan
State University, East Lansing, MI, 48823, USA. 168School of Life Science and Engineering, Southwest University of
Science and Technology, Mianyang, 621010, China. 169Departamento de Química e Física, Universidade Federal da
Paraiba, Areia, PB, 58397-000, Brazil. 170Remote Sensing and Geoinformatics, GFZ German Research Centre for
Geosciences, Potsdam, 14473, Germany. 171Andalusian Institute for Earth System Research (CEAMA-IISTA),
Granada, 18006, Spain. 172Ciencias del Agua y Medioambiente, Instituto Tecnológico de Sonora, Ciudad Obregón,
85000, Mexico. 173Geographical Institute, University of Cologne, Cologne, 50923, Germany. 174Department of
Industry, Innovation and Science, Geoscience Australia, Canberra, 2609, Australia. 175Southwest Watershed Research
Center, USDA-ARS, Tucson, AZ, 85719, USA. 176Institute of Atmospheric Physics of the Czech Academy of Sciences,
Prague, 14100, Czech Republic. 177Department of Ecology, University of Granada, Granada, 18071, Spain. 178National
Hulunber Grassland Ecosystem Observation and Research Station & Institute of Agricultural Resources and Regional
Planning, Chinese Academy of Agricultural Sciences, Beijing, 100081, China. 179School of Science, Edith Cowan
University, Joondalup, 6027, Australia. 180Sentek Pty Ltd, Stepney, SA5069, Australia. 181National Ecological
Observatory Network Program, Boulder, CO, 80301, USA. 182Kansai Research Center, Forestry and Forest Products
Research Institute, Kyoto, 612-0855, Japan. 183College of Urban and Environmental Sciences, Peking University,
Beijing, 100871, China. 184School of Earth, Atmosphere and Environment, Monash University, Clayton, 3800,
Australia. 185Space Science and Engineering Center, University of Wisconsin-Madison, Madison, WI, 53706, USA.
186Terrasystem srl, Viterbo, 01100, Italy. 187USDA Forest Service, Rocky Mountain Research Station, Missoula, MT,
59808, USA. 188Meteorology and Air Quality group, Wageningen University, 6500, Wageningen, The Netherlands.
189Fenner School of Environment and Society, Australian National University Canberra, Canberra, ACT, 2600,
Australia. 190Earth and Life Institute, Université Catholique de Louvain, Louvain, 1348, Belgium. 191Department of
Civil Engineering, Monash University, Clayton, 3800, Australia. 192CSIRO Land and Water, Canberra, 2601, Australia.
193School of Earth and Environmental Sciences, The University of Queensland, St Lucia, 4072, Australia. 194South
China Botanical Garden, Chinese Academy of Sciences, Guangzhou, 510650, China. 195College of Applied
Meteorology, Nanjing University of Information Science & Technology, Nanjing, 210044, China. 196Department of
Animal and Plant Sciences, University of Sheeld, Sheeld, S102TN, United Kingdom. 197Deceased: Ray Leuning.
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... Eddy covariance flux towers continuously measure the net ecosystem exchanges (NEE) of carbon dioxide between the land and the atmosphere and partition it into ecosystem respiration and gross primary production (GPP) at relatively high accuracy (Moncrieff et al., 1996;Pastorello and Hörtnagl, 2020;Tagesson et al., 2016). For regions with relatively homogeneous vegetation conditions and with little disturbances, interannual changes in eddy covariance carbon fluxes at the site level could be representative of the climate-related biomass carbon variations in the surrounding region. ...
... The annual GPP values were obtained by summing up the daily data, and the daily data was obtained from the averaged values of daytime GPP (GPP_DT_VUT_REF) and nighttime GPP (GPP_NT_VUT_REF) from the variable USTAR threshold (VUT) method (Pastorello and Hörtnagl, 2020), both of which were all aggregated from half-hour GPP estimates with good quality (QC < 1). ...
Full-text available
Vegetation optical depth (VOD) from satellite passive microwave sensors has enabled monitoring of aboveground biomass carbon dynamics by building a relationship with static carbon maps over space and then applying this relationship to VOD time series. However, uncertainty in this relationship arises from changes in water stress, as VOD is mainly determined by vegetation water content, which varies at diurnal to interannual scales, and depends on changes in both biomass and relative moisture content. Here, we studied the reliability of using VOD from various microwave frequencies and temporal aggregation methods for estimating decadal biomass carbon dynamics at the global scale. We used the VOD diurnal variations to represent the magnitude of vegetation water content buffering caused by climatic variations for a constant amount of dry biomass carbon. This magnitude of VOD diurnal variations was then used to evaluate the likelihood of VOD decadal variations in reflecting decadal dry biomass carbon changes. We found that SMOS-IC L-VOD and LPDR X-VOD can be reliably used to estimate decadal carbon dynamics for 76.7% and 69.9% of the global vegetated land surface, respectively, yet cautious use is warranted for some areas such as the eastern Amazon rainforest. Moreover, the annual VOD aggregated from the 95% percentile of the nighttime VOD retrievals was proved to be the most suitable parameter for estimating decadal biomass carbon dynamics among the temporal aggregation methods. Finally, we validated the use of annual VOD for estimating interannual carbon dynamics by comparing VOD changes between adjacent years against eddy covariance estimations of gross primary production from flux sites over several land cover classes across the globe. Despite the large difference in spatial scales between them, the positive correlation obtained supports the capability of satellite VOD in quantifying interannual carbon dynamics.
... The diurnal SIF was compared with GPP from the FLUXNET2015 dataset (Pastorello et al., 2020) to evaluate solar geometry effects on the SIF ~ GPP relationships. The locations of the six sites used can be found in Fig. 1, and details can be found in Table 1. ...
Solar induced fluorescence efficiency (SIF) provides a unique chance to capture the dynamics of photosynthesis or gross primary production (GPP). However, the relationship between GPP and SIF observed by sensors (SIFobs) is affected by the solar-target-viewing geometry. Although the effects of viewing geometry on SIFobs and its relationship with GPP have been recognized, solar geometry effects on SIFobs have been ignored and less studied in recent decades. This study used SIFobs from the Orbiting Carbon Observatory-3 (OCO-3) and the TROPOspheric Monitoring Instrument (TROPOMI) to investigate solar geometry effects on GPP-SIFobs relationships at the diurnal scale. Furthermore, we derived total canopy SIF emission (SIFtotal) that should be insensitive to solar-target-viewing geometry in theory. Our results indicated that SIFtotal can effectively mitigate solar geometry effects on SIFobs (including SIF observed at nadir, SIFnadir) and its relationship with GPP from six flux towers. After mitigating the solar angle effects in OCO-3 SIFnadir, the correlation (R²) with GPP increased from 0.73 to 0.87 for SIFnadir to 0.84–0.95 for SIFtotal. The correlations between TROPOMI SIFobs and GPP at three high-latitude sites were also clearly improved by TROPOMI SIFtotal. In addition, the diurnal pattern of SIFtotal was more consistent with that of GPP compared to SIFnadir or SIFobs. This study aims to reveal solar geometry effects on SIF measurements, which should be considered when studying the SIF-GPP relationship especially at the diurnal scale with large variations in solar geometry.
... In this study, ET estimations require measurements from FLUXNET2015 dataset (Pastorello et al., 2020) FLUXNET were used to validate the ET model. ...
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Evapotranspiration (ET) plays an important role in land surface processes and global climate. ET cannot be observed directly at a large scale, and its spatial distribution is mainly obtained by ET models at present. However, the application of existing ET models is greatly hampered by a number of plant functional type (PFT)-specific empirical parameters and the difficulty in partitioning the contribution of ET from the canopy and soil. To this end, an eco-evolutionary optimality-based evapotranspiration model (EEOET) was developed in this study with some schemes used in the Noah-MP land surface model. The principle of eco-evolutionary optimality expresses the underlying acclimation/adaption of plants to diverse environments and thus greatly reduces the number of PFT-specific parameters. The Noah-MP provides schemes to partition the energy between the canopy and soil and to calculate photosynthetic and ET resistances. Owing to the above, the EEOET model, which has enhanced mechanisms and fewer PFT-specific parameters, requires inputs of daily land surface temperature, vegetation-related data (leaf area index and fractional vegetation), and soil information (soil texture and soil water content) in addition to routine meteorological data. The model output is daily ET. Global evaluation across 74 FLUXNET sites indicates that the model is capable of reasonably estimating ET for all PFTs and is also comparable to existing models that require more tunable parameters. In addition, the EEOET model is less sensitive to uncertainties in the input data than other models, making it a more robust solution for estimating ET under various conditions.
... Eddy covariance (EC) is a widely used method to measure ecosystem-scale greenhouse gas fluxes (Baldocchi, 2003;Sulkava et al., 2011;Pastorello et al., 2020). The method is nondestructive and allows continuous monitoring of surfaceatmosphere exchange fluxes at high temporal frequency (Baldocchi et al., 1988;Lee et al., 2005;Burba and Anderson, 2010;Aubinet et al., 2012). ...
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Large changes in the Arctic carbon balance are expected as warming linked to climate change threatens to destabilize ancient permafrost carbon stocks. The eddy co-variance (EC) method is an established technique to quantify net losses and gains of carbon between the biosphere and atmosphere at high spatiotemporal resolution. Over the past decades, a growing network of terrestrial EC tower sites has been established across the Arctic, but a comprehensive assessment of the network's representativeness within the heterogeneous Arctic region is still lacking. This creates additional uncertainties when integrating flux data across sites, for example when upscaling fluxes to constrain pan-Arctic carbon budgets and changes therein. This study provides an inventory of Arctic (here > = 60 • N) EC sites, which has also been made available online (, last access: 25 January 2022). Our database currently comprises 120 EC sites, but only 83 are listed as active, and just 25 of these active sites remain operational throughout the winter. To map the representativeness of this EC network, we evaluated the similarity between environmental conditions observed at the tower locations and those within the larger Arc-tic study domain based on 18 bioclimatic and edaphic variables. This allows us to assess a general level of similarity between ecosystem conditions within the domain, while not necessarily reflecting changes in greenhouse gas flux rates directly. We define two metrics based on this representativeness score: one that measures whether a location is represented by an EC tower with similar characteristics (ER1) and a second for which we assess if a minimum level of representation for statistically rigorous extrapolation is met (ER4). We find that while half of the domain is represented by at least one tower, only a third has enough towers in similar locations to allow reliable extrapolation. When we consider methane measurements or year-round (including wintertime) measurements, the values drop to about 1/5 and 1/10 of the domain, respectively. With the majority of sites located in Fennoscandia and Alaska, these regions were assigned the highest level of network representativeness, while large parts of Siberia and patches of Canada were classified as underrep-resented. Across the Arctic, mountainous regions were particularly poorly represented by the current EC observation network. We tested three different strategies to identify new site locations or upgrades of existing sites that optimally enhance the representativeness of the current EC network. While 15 new sites can improve the representativeness of the pan-Arctic network by 20 %, upgrading as few as 10 existing sites to capture methane fluxes or remain active during winter-time can improve their respective ER1 network coverage by Published by Copernicus Publications on behalf of the European Geosciences Union. 560 M. M. T. A. Pallandt et al.: Representativeness assessment of the pan-Arctic eddy covariance site network 28 % to 33 %. This targeted network improvement could be shown to be clearly superior to an unguided selection of new sites, therefore leading to substantial improvements in network coverage based on relatively small investments.
... Among the datasets of the in situ evaporation measurements, the FLUXNET network (, last access: 20 November 2021) provides eddy-covariance data from about 500 stations worldwide within FLUXNET2015 dataset (Pastorello et al., 2020) and still acts as the main driver in advancing evaporation research Jung et al., 2011;Mauder et al., 2018). Evaporation measurements are still scarcely available due to high costs and the problem of large-scale representability (e.g. in comparison to discharge measurements). ...
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The dissertation presents the Global BROOK90 framework, which has been developed at the Chair of Meteorology, TU Dresden by the candidate and co-authors. Global BROOK90 allows modelling the water balance components globally for the local scale of ‘hydrological response units’ in a fully automatic mode. It combines recent advances in global datasets with a physically based model. The framework possesses a vast application range with a special focus on the non-expert users and data scarce regions. To prove the applicability of the framework for different climates, landscapes, soil types and orography, an extensive validation was necessary. Two important components of the water balance – runoff and evaporation– were compared with measured data from all over the globe. Results indicated that considering its build-up and scope, Global BROOK90 performs well on the desired local scale. Certainly, the described approach has substantial shortcomings, thus simulation results must always be treated through the prism of the uncertainties. These limitations result not only from model limitations itself, but also from the input datasets, which were used for parameterization and forcing. Therefore, in this study main uncertainties are addressed allowing the end-user an outlook on their potential impact on the modelling results.
Water budgets and climate are related in many ways and at all scales. Therefore, we expect climate change to trigger changes in all water budget components at any scale. For Central Europe observed and projected climate change indicates higher variability of precipitation, while evapotranspiration (ET) should increase due to higher temperatures, yielding lower and more variable infiltration and runoff. However, evidence in ET records is limited, as long-term measurements of ET are methodologically challenging and as factors other than climate are changing in parallel, like vegetation and land use. In this study, we take advantage of long-term hydro-meteorological data from the small research catchment Wernersbach (4.6 km², dominated by Norway spruce) in operation since 1967 and from two eddy-covariance (EC) flux towers, all located in the Tharandt Forest, Germany. The tower DE-Tha is located a few kilometres east of the catchment, is spruce dominated and in operation since 1996. After a wind break of a spruce stand (situated inside the catchment) and planting of deciduous oaks, the tower DE-Hzd was set up in 2009. For the first time, we report systematically about observation, correction methods and metadata of the long data series of the observatory, represented by the Wernersbach catchment and the EC flux towers. Climate change signals in the region are mirrored in the Tharandt Forest records. They show rising air temperature with a breakpoint around 1988 and complex changes in solar radiation associated to a regional peak in air pollution around the same time. The catchment and both towers did not show any systematic differences in climate or meteorological data, allowing us to address observed changes in the water budget components as related to (i) climate change, (ii) change in vegetation, and (iii) different responses due to different soil and hydrogeological characteristics as well as methodological aspects. The catchment term ET plus storage, derived from precipitation minus runoff, showed the expected high variability with a significant increase over the more than 50 years of operation. The flux-tower DE-Tha showed much lower inter-annual variability in ET with an average annual total of 486 mm (1997 to 2019), but no significant trend. For the same period, average catchment ET was 734 mm/yr. The younger flux-tower DE-Hzd showed ET values in between, closer to catchment ET at the very dry end of the ten-year record (2010 to 2019). An analysis of decadal trends in a Budyko framework at catchment level revealed the dominating response of ET to land use or vegetation change until around 1990. The climate induced change of ET increased in the last decades, on the one hand directly due to an increased atmospheric demand. On the other hand, extreme weather events exerted harmful effects on vegetation, especially triggered by two dry years at the end of the record. Furthermore, we found that the mean annual tower ET was about 250 mm lower than catchment ET despite the careful correction for energy balance closure. We attribute this difference to soil and to a lesser extend to vegetation characteristics, but also to methodological uncertainties. There is evidence from interception and transpiration measurements at the flux tower as well as from water budget modelling that a major contribution of this difference is related to an insufficient EC² closure correction during interception events. A careful consideration of rain events and evaporation from interception is recommended when addressing ET of similar evergreen forests in a humid climate, as EC records might be generally too low. This illustrates the necessity of redundant and complementary measurements when dealing with large system complexity.
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Climate warming has become a great challenge for global sustainable development. Under the Paris Agreement, every country must present a climate action plan in five-yearly cycles, a National Determined Contributions (NDC) report will be presented using a standard inventory approach for each country since 2020, and all countries will engage in the global stocktake every five years to assess countries’ NDC progress since 2023. The 49th session of the Intergovernmental Panel on Climate Change (IPCC 49) recommend a ‘top-down’ inversion approach to account greenhouse gas (GHG) emission based on space-borne atmospheric measurements. The European Union, the United States, Japan, and Canada are vigorously developing MVS (Monitoring & Verification Support) capabilities for accounting GHG emissions using satellite remote sensing. Here, we aimed to give a detailed review on the methods and progresses of satellite-based inversion for global stocktaking, and highlight the challenges and perspectives for satellite remote sensing for global stocktaking in China. Firstly, Earth observation for atmospheric GHG, including ground-based observation networks and GHG satellites, were summarized. Compared to ground-based observations, satellite remote sensing has been providing more and more accurate and higher resolution global GHG detection. In the next five years, 13 GHG satellites will be launched, with resolutions ranging from 25 m to 100 km. Secondly, the progresses of satellite remote sensing of ecosystem carbon fluxes were reviewed. There are three kinds of methods to estimate global carbon fluxes, including: the assimilation inversion method (also named as the “top-down” method), that uses atmospheric chemical transmission model and ground-based or satellite observations of atmospheric GHG to invert carbon flux; the modelling simulation method (also named as the “bottom-up” method) that uses the process model to estimate terrestrial and marine ecosystems carbon fluxes; the data-driven machine learning method that uses remote sensing datasets and metrological datasets to model the carbon uptakes of terrestrial and marine ecosystems. However, the uncertainty in the estimation results of all these top-down or bottom-up methods is still huge at regional or global scale. Thirdly, the researches on satellite monitoring of anthropogenic GHG emissions were summarized. Satellite remote sensing has been an important platform for realizing large-scale, long-term observations of anthropogenic GHG emissions. Although the current accuracy of the satellite-based observations does not fully meet the requirements of the global stocktake, satellite remote sensing has become a promising tool for verifying hot-spot, city, national and global anthropogenic emissions. Finally, the current capability of satellite remote sensing to support global carbon monitoring was assessed, and the Chinese carbon satellite future program was proposed. According the preliminary simulations based on Observation System Simulation Experiments (OSSE), the China’s next generation carbon satellite (TanSat-2) are presented. Similar to CO2M project supported by European Union, TanSat-2 will give global accurate retrieval of GHGs (1 ppm for CO2 and 10 ppb for CH4), pollution gases (1.0×1015 molecules/cm2 for NO2, 10% for CO) and solar-induced chlorophyll fluorescence (SIF) (0.25 mw m-2·nm-1·sr-1) with a swath of 1000 km and a resolution 500 m resolution, which will provide unprecedented imaging capabilities for estimating GHG emissions. Satellite remote sensing plays extremely role in build the MVS capability for global stocktake, we provide a reference for the roadmap of the Chinese carbon monitoring program based on the preliminary OSSE simulations. It is absolutely necessary to integrate satellite remote sensing, in-situ observations, big data, carbon assimilation to achieve high precision, high-resolution scientific data on GHG fluxes at hot-spot, regional and global scales, and to effectively distinguish and quantify the flux contributions of anthropogenic GHG emissions and terrestrial carbon sinks /sources.
Various models are available for global and regional scale evapotranspiration (ET) estimation. The performance of these models generally differs due to their differences in driving data, model structure (mathematical representations of the ET process), and parameter estimation. Model comparison is the most straightforward way to identify the strengths, weaknesses, and uncertainty sources of a model. In this study, three widely used remote sensing ET models were considered: the air-relative-humidity-based two-source (ARTS) model, Priestley-Taylor Jet Propulsion Laboratory (PT-JPL) model, and Penman-Monteith-Leuning (PML) model. The three models were evaluated based on measurements from 12 eddy-covariance (EC) towers and water-balance-based ET estimates from 286 basins. The evaluation results indicate that the PML model performs best on both the site and basin scales. The advantage of the PML model may be due to (i) the land-cover-based parameter configuration and (ii) the consideration of differences in the responses of soil evaporation and transpiration to soil water deficit. We also investigated the consistencies and differences between the models in simulating the spatiotemporal variation and component partitioning of ET, that is, interception loss (Ei), soil evaporation (Es), and transpiration (Et). The three models showed high consistency in estimating nationwide multi-year average ET (416.3−438.2 mm/yr) and its spatial pattern but also showed large discrepancies in ET trends (0.10−0.98 mm/yr²) and component partitioning. The PT-JPL model considerably overestimates the ratio of Ei/ET, thereby underestimating the ratio of Es/ET because of the negative correlation between Ei and Es. The ARTS model showed better applicability in grasslands than in croplands or forestlands. This may be because its parameter value (constant for all biomes) and the water constraint scheme are more suitable for grasslands. Finally, we propose specific modifications to address the potential issues of each model.
Accurate estimates of carbon, water and energy fluxes between the Earth surface and the atmosphere are crucial for enhancing our understanding of ecosystem–climate interactions. Such estimates can be made by combining remote sensing derived land surface parameters with climate reanalysis data. We analysed to what degree generic (plant functional type (PFT)-independent) satellite-derived vegetation properties and climate reanalysis data can explain land surface fluxes and to what extent the PFT-specific information extends the flux simulations. For this purpose, we used the Soil Canopy Observation, Photochemistry and Energy fluxes (SCOPE) model, which combines radiative transfer in plant leaves and vegetation canopies with photosynthesis and energy balance in a single model representation of the vegetation. We evaluated the performance of SCOPE in simulating fluxes by comparison to 63 eddy covariance sites representing 10 PFTs. We varied the sources of maximum carboxylation capacity (Vcmax25) and BallBerrySlope values (default vs literature), the seasonality of Vcmax25 and the meteorological forcing (locally measured and climate reanalysis). The average performance of daily flux in terms of root-mean-square error (RMSE) was 2.3 ± 0.8 μmol CO2 m⁻² s⁻¹ (R2= 0.74 ± 0.12) for gross primary productivity (GPP), 24 ± 8 W m⁻² (R2= 0.68 ± 0.16) for latent heat flux (λE) and 50 ± 15 W m⁻² (R20.47±0.17) for sensible heat flux (H). The inter-site variability of the annual accumulated GPP flux was captured well with seasonally varying PFT-specific Vcmax25 (R2= 0.74, RMSE = 308 g C m⁻² yr⁻¹ and bias = −68 g C m⁻² yr⁻¹). The annual accumulated evapotranspiration (ET) was overestimated (R2= 0.31, RMSE = 101 mm yr⁻¹ and bias = 37 mm yr⁻¹), mainly in the ecosystems with subtropical Mediterranean climate, for which the soil resistance to evaporation from porous space (rss) had to be constrained from soil moisture content (SMC) or land surface temperature (LST). Overall, the study demonstrates that SCOPE model can simulate ecosystem flux with high accuracy without site-specific calibration of its parameters.