Methane (CH4) emissions from natural landscapes constitute
roughly half of global CH4 contributions to the atmosphere, yet large
uncertainties remain in the absolute magnitude and the seasonality of
emission quantities and drivers. Eddy covariance (EC) measurements of
CH4 flux are ideal for constraining ecosystem-scale CH4
emissions due to quasi-continuous and high-temporal-resolution CH4
flux measurements, coincident carbon dioxide, water, and energy flux
measurements, lack of ecosystem disturbance, and increased availability of
datasets over the last decade. Here, we (1) describe the newly published
dataset, FLUXNET-CH4 Version 1.0, the first open-source global dataset of
CH4 EC measurements (available at
https://fluxnet.org/data/fluxnet-ch4-community-product/, last access: 7 April 2021). FLUXNET-CH4
includes half-hourly and daily gap-filled and non-gap-filled aggregated
CH4 fluxes and meteorological data from 79 sites globally: 42
freshwater wetlands, 6 brackish and saline wetlands, 7 formerly drained
ecosystems, 7 rice paddy sites, 2 lakes, and 15 uplands. Then, we (2) evaluate FLUXNET-CH4 representativeness for freshwater wetland coverage
globally because the majority of sites in FLUXNET-CH4 Version 1.0 are
freshwater wetlands which are a substantial source of total atmospheric
CH4 emissions; and (3) we provide the first global estimates of the
seasonal variability and seasonality predictors of freshwater wetland
CH4 fluxes. Our representativeness analysis suggests that the
freshwater wetland sites in the dataset cover global wetland bioclimatic
attributes (encompassing energy, moisture, and vegetation-related
parameters) in arctic, boreal, and temperate regions but only sparsely
cover humid tropical regions. Seasonality metrics of wetland CH4
emissions vary considerably across latitudinal bands. In freshwater wetlands
(except those between 20∘ S to 20∘ N) the spring onset
of elevated CH4 emissions starts 3 d earlier, and the CH4
emission season lasts 4 d longer, for each degree Celsius increase in mean
annual air temperature. On average, the spring onset of increasing CH4
emissions lags behind soil warming by 1 month, with very few sites experiencing
increased CH4 emissions prior to the onset of soil warming. In
contrast, roughly half of these sites experience the spring onset of rising
CH4 emissions prior to the spring increase in gross primary
productivity (GPP). The timing of peak summer CH4 emissions does not
correlate with the timing for either peak summer temperature or peak GPP.
Our results provide seasonality parameters for CH4 modeling and
highlight seasonality metrics that cannot be predicted by temperature or GPP
(i.e., seasonality of CH4 peak). FLUXNET-CH4 is a powerful new resource
for diagnosing and understanding the role of terrestrial ecosystems and
climate drivers in the global CH4 cycle, and future additions of sites
in tropical ecosystems and site years of data collection will provide added
value to this database. All seasonality parameters are available at
10.5281/zenodo.4672601 (Delwiche et al., 2021).
Additionally, raw FLUXNET-CH4 data used to extract seasonality parameters
can be downloaded from https://fluxnet.org/data/fluxnet-ch4-community-product/ (last access: 7 April 2021), and a complete
list of the 79 individual site data DOIs is provided in Table 2 of this paper.
Introduction
Methane (CH4) has a global warming potential that is 28 times larger
than carbon dioxide (CO2) on a 100-year timescale (Myhre et al.,
2013), and its atmospheric concentration has increased by > 1000 ppb since 1800 (Etheridge et al., 1998). While atmospheric CH4
concentrations are substantially lower than those of CO2, CH4 has
contributed 20 %–25 % as much radiative forcing as CO2 since 1750
(Etminan et al., 2016). Despite its importance to global climate change,
natural CH4 sources and sinks remain poorly constrained and with
uncertain attribution to the various biogenic and anthropogenic sources
(Saunois et al., 2016, 2020). Bottom-up and top-down estimates differ by 154 Tg yr-1 (745 versus 591 Tg yr-1, respectively); much of this difference arises
from natural sources (Saunois et al., 2020). Vegetated wetlands and inland
water bodies account for most natural CH4 emissions, as well as the
majority of uncertainty in bottom-up emission estimates (Saunois et al.,
2016). Better diagnosis and prediction of terrestrial CH4 sources to
the atmosphere requires high frequency and continuous measurements of
CH4 exchange across a continuum of time (hours to years) and space
(meters to kilometers) scales.
Tower-based eddy covariance (EC) measurements providing ecosystem-scale
CH4 fluxes at high temporal resolution across years are coupled with
measurements of key CH4 drivers such as temperature, water, and recent
substrate input (inferred from CO2 flux) and thus help constrain
bottom-up CH4 budgets and improve CH4 predictions. Although EC
towers began measuring CO2 fluxes in the late 1970s (Desjardins, 1974;
Anderson et al., 1984), and some towers began measuring CH4 in the
1990s (Verma et al., 1992), most CH4 flux EC measurements began within
the last decade (2010s). Given that many EC CH4 sites are relatively
new, the flux community has only recently compiled them for global synthesis
efforts (e.g., Chang et al., 2021) and is still working to standardize
CH4 flux measurements and establish gap-filling protocols (Nemitz et
al., 2018; Knox et al., 2019). Furthermore, the growth of EC networks for
CH4 fluxes has sometimes taken place in a relatively ad hoc fashion, often at
sites that were already measuring CO2 fluxes or where higher CH4 fluxes were expected, potentially introducing bias. The representativeness
and spatial distribution of CO2 flux tower networks have been assessed
to evaluate their ability to upscale fluxes regionally (Hargrove et al., 2003;
Hoffman et al., 2013; Papale et al., 2015; Villarreal et al., 2018, 2019)
and globally (Jung et al., 2009, 2020). However, a relatively sparse
coverage of CH4 flux towers prompts the question of how well the
current observation network provides a sufficient sampling of global or
ecosystem-specific bioclimatic conditions.
Broad-scale wetland CH4 seasonality estimates, such as when fluxes
increase, peak, and decrease and the predictors of seasonality, remain
relatively unconstrained across wetlands globally. These key seasonality
metrics vary considerably across high-emitting systems such as wetlands and
other aquatic systems (Desjardins, 1974; Dise, 1992; Melloh and Crill, 1996;
Wik et al., 2013; Zona et al., 2016; Treat et al., 2018). The few continuous
CH4 flux datasets across representative site years make it difficult to
establish trends in seasonal dynamics, though monthly or annually aggregated
estimates of CH4 fluxes from different seasons do exist for high
latitudes (Zona et al., 2016; Treat et al., 2018). Seasonal variability in
freshwater wetland CH4 fluxes is expected to be driven by changes in
air (TA) and soil temperature (ST), soil moisture (including water table dynamics),
and recent carbon substrate availability, which influence the rates of
CH4 production and consumption (Lai, 2009; Bridgham et al., 2013; Dean
et al., 2018). Temperature has widely been found to strongly affect CH4
flux (Chu et al., 2014; Yvon-Durocher et al., 2014; Sturtevant et al.,
2016), but the relationship is complex (Chang et al., 2020) and varies
seasonally (Koebsch et al., 2015; Helbig et al., 2017). CH4 flux is
also driven by inundation depth since anoxic conditions are typically
necessary for methanogenesis (Lai, 2009; Bridgham et al., 2013), though
CH4 production under bulk-oxic conditions has been observed (Angle et
al., 2017). Substrate availability influences CH4 production potential
and is linked with gross primary productivity (GPP) because recent
photosynthate fuels methanogenesis, though this relationship can vary by
ecosystem type, plant functional type, and biome (Megonigal et al., 1999;
Chanton et al., 2008; Hatala et al., 2012; Lai et al., 2014; Malhotra and
Roulet, 2015; Sturtevant et al., 2016). In process models, the seasonality
of CH4 emissions from wetlands globally is primarily constrained by
inundation (Poulter et al., 2017) with secondary within-wetland influences
from temperature and availability of carbon (C) substrates (Melton et al.,
2013; Castro-Morales et al., 2018). Bottom-up and top-down global CH4
estimates continue to disagree on total CH4 flux magnitudes and
seasonality, including the timing of annual peak emissions (Spahni et al.,
2011; Saunois et al., 2020). Thus, the variability in and predictors of wetland
CH4 seasonality globally remain a knowledge gap that high-frequency and
long-term EC data can help fill.
Here, we first describe version 1.0 of the FLUXNET-CH4 dataset (available at
https://fluxnet.org/data/fluxnet-ch4-community-product/, last access: 7 April 2021). Version 1.0 of the
dataset expands and formalizes the publication of data scattered among
regional flux networks as described previously in Knox et al. (2019).
FLUXNET-CH4 includes half-hourly and daily gap-filled and non-gap-filled
aggregated CH4 fluxes and meteorological data from 79 sites globally:
42 freshwater wetlands, 6 brackish and saline wetlands, 7 formerly drained
ecosystems, 7 rice paddy sites, 2 lakes, and 15 upland ecosystems.
FLUXNET-CH4 includes an additional two wetland sites (RU-Vrk and SE-St1), but
they are not available under the CC BY 4.0 data policy and thus are excluded
from this analysis. Since the majority of sites in FLUXNET-CH4 Version 1.0
(hereafter referred to solely as “FLUXNET-CH4”) are freshwater wetlands,
which are a substantial source of total atmospheric CH4 emissions, we
use the subset of data from freshwater wetlands to evaluate the
representativeness of freshwater wetland coverage in the FLUXNET-CH4 dataset
relative to wetlands globally, and we provide the first assessment of global
variability in and predictors of freshwater wetland CH4 flux seasonality.
We quantify a suite of CH4 seasonality metrics and evaluate temperature
and GPP (a proxy for recent substrate input) as predictors of seasonality
across four latitudinal bands (northern, temperate, subtropical, and
tropical). Due to a lack of high-temporal-resolution water table data at all
sites, our analyses are unable to evaluate the critical role of water table
on CH4 seasonality. Here we provide parameters for better understanding
and modeling seasonal variability in freshwater wetland CH4 fluxes and
generate new hypotheses and data resources for future syntheses.
MethodsFLUXNET-CH4 datasetHistory and data description
The FLUXNET-CH4 dataset was initiated by the Global Carbon Project (GCP) in
2017 to better constrain the global CH4 budget (https://www.globalcarbonproject.org/methanebudget/index.htm, last access: 6 July 2021). Beginning
with a kick-off meeting in May 2018 in Washington DC, hosted by Stanford
University, we coordinated with the AmeriFlux Management Project, the
European Ecosystem Fluxes Database, and the Integrated Carbon Observation System Ecosystem Thematic Centre
(ICOS-ETC) to avoid duplication of efforts as most sites are part of
different regional networks (albeit with different data products). We
collected and standardized data for FLUXNET-CH4 with assistance from the
regional flux networks, AmeriFlux's “Year of Methane”, FLUXNET, the EU's
Readiness of ICOS for Necessities of Integrated Global Observations (RINGO)
project, and a US Geological Survey Powell Center working group.
FLUXNET-CH4 is a community-led project, so while we developed it with
assistance from FLUXNET, we do not necessarily use standard FLUXNET data
variables, formats, or methods.
FLUXNET-CH4 includes gap-filled half-hourly CH4 fluxes and
meteorological variables. Gaps in meteorological variables (TA – air
temperature; SW_IN – incoming shortwave radiation;
LW_IN – incoming longwave radiation; VPD – vapor pressure
deficient; PA – pressure; P – precipitation; WS – wind speed) were filled
with the ERA-Interim (ERA-I) reanalysis product (Vuichard and Papale, 2015).
We used the REddyProc package (Wutzler et al., 2018) to filter flux values
with low friction velocity (u∗) based on relating nighttime
u∗ to fill gaps in CO2, latent heat, and sensible heat
fluxes and to partition net CO2 fluxes into gross primary production
(GPP) and ecosystem respiration (RECO) using both the daytime (Lasslop et
al., 2010) and nighttime (Reichstein et al., 2005) approaches. Data gaps of
CH4 flux were filled using artificial neural network (ANN) methods
first described in Knox et al. (2015, 2019) and
summarized here in Sect. 2.1.2. Gap-filled data for gaps exceeding 2
months are provided and flagged for quality. Please see Table B1 for
variable description and units, as well as quality flag information. For the
seasonality analysis in this paper we excluded data from gaps exceeding 2
months, and we encourage future users of FLUXNET-CH4 to critically evaluate
gap-filled values from long data gaps before including them in analyses
(Dengel et al., 2013; Kim et al., 2020).
In addition to half-hourly data, the FLUXNET-CH4 Version 1.0 release also
contains a full set of daily mean values for all parameters except wind
direction and precipitation. Daily precipitation is included as the daily
sum of the half-hourly data, and daily average wind direction is not
included.
Gap-filling methods and uncertainty estimates
As described in Knox et al. (2015, 2019), the ANN
routine used to gap-fill the CH4 data was optimized for
generalizability and representativeness. To avoid biasing the ANN toward
environmental conditions with typically better data coverage (e.g.,
summertime and daytime measurements), the explanatory data were divided
into a maximum of 15 clusters using a k-means clustering algorithm. Data
used to train, test, and validate the ANN were proportionally sampled from
these clusters. For generalizability, the simplest ANN architecture with
good performance (< 5 % gain in model accuracy for additional
increases in architecture complexity) was selected for 20 extractions of the
training, test, and validation data. Within each extraction, each tested ANN
architecture was reinitialized 10 times, and the initialization with the
lowest root-mean-square error was selected to avoid local minima. The median
of the 20 predictions was used to fill each gap. A standard set of variables
available across all sites was used to gap-fill CH4 fluxes (Dengel et
al., 2013), which included the previously mentioned meteorological variables
TA, SW_IN, WS, and PA and sine and cosine functions to represent
seasonality. These meteorological variables were selected for their
relevance to CH4 exchange and were gap-filled using the ERA-I
reanalysis data. Other variables related to CH4 flux (e.g., water table
depth, WTD, and soil temperature, TS) were not included as explanatory
variables as they were not available across all sites or had large gaps that
could not be filled using the ERA-I reanalysis data (Knox et al., 2019). The
ANN gap-filling was performed using MATLAB (Mathworks 2018a, version 9.4.0).
While the median of the 20 predictions was used to fill each gap, the spread
of the predictions was used to provide a measure of uncertainty resulting
from the ANN gap-filling procedure. Specifically, the combined annual
gap-filling and random uncertainty was calculated from the variance of the
cumulative sums of the 20 ANN predictions (Knox et al., 2015; Anderson et
al., 2016; Oikawa et al., 2017). The (non-cumulative) variance of the 20 ANN
predictions was also used to provide gap-filling uncertainty for each
half-hourly gap-filled value. While this output is useful for data–model
comparisons, it cannot be used to estimate cumulative annual gap-filling
error because gap-filling error is not random, which is why the cumulative
sums of the 20 ANN predictions are used to estimate annual gap-filling
error.
Random errors in EC fluxes follow a double exponential (Laplace)
distribution with the standard deviation varying with flux magnitude
(Richardson et al., 2006, 2012). For half-hourly CH4
flux measurements, random error was estimated using the residuals of the
median ANN predictions, providing a conservative “upper limit” estimate of
the random flux uncertainty (Moffat et al., 2007; Richardson et al., 2008).
The annual cumulative uncertainty at 95 % confidence was estimated by
adding the cumulative gap-filling and random measurement uncertainties in
quadrature (Richardson and Hollinger, 2007; Anderson et al., 2016). Annual
uncertainties in CH4 flux for individual site years are provided in
Table B2. Throughout this paper, we include uncertainties on individual site
years when discussing single years of data. In sites with multiple years of
data, we report the standard deviation of the multiple years.
Dataset structure and site metadata
FLUXNET-CH4 contains two comma-separated data files per site at half-hourly
and daily resolutions which are available for download at
https://fluxnet.org/data/fluxnet-ch4-community-product/ (last access: 7 April 2021), along with a file
containing select site metadata. Each site has a unique FLUXNET-CH4 DOI. All
data from the 79 sites used in this analysis are available under CC BY 4.0
(https://creativecommons.org/licenses/by/4.0/, last access: 6 July 2021) copyright
license (FLUXNET-CH4 has an additional two sites available under the FLUXNET
Tier 2 license (https://fluxnet.org/data/data-policy/, last access: 6 July 2021), though
these sites are not included in our analysis).
Metadata (Table B3) include site coordinates, ecosystem classification based
on site literature, presence/absence and dominance for specific vegetation
types, and DOI link, as well as calculated data such as annual and quarterly
CH4 flux values. FLUXNET-CH4 Version 1.0 sites were classified based
on site-specific literature as fen, bog, swamp, marsh, salt marsh, lake,
mangrove, rice paddy field, wet tundra, upland, or drained ecosystems that
previously could have been wetlands, seasonally flooded pastures, or
agricultural areas. To the extent possible, we followed classification
systems of previous wetland CH4 syntheses (Olefeldt et al., 2013;
Turetsky et al., 2014; Treat et al., 2018). Drained systems are former
wetlands that have subsequently been drained but may maintain a relatively
shallow water table, which can contribute to occasional methane emissions,
although we do not have specific water table depth information at all
drained sites. Upland ecosystems are further divided into alpine meadows,
grasslands, needleleaf forests, mixed forest, crops, tundra, and urban.
Freshwater wetland classifications follow hydrological definitions of bog
(ombrotrophic), fen (minerotrophic), wet tundra, marshes, and swamps and
were designated as per the primary literature on the site. For all sites,
vegetation was classified for the presence or absence of brown mosses (all
species from the division Bryophyta except those in the class Sphagnopsida),
Sphagnum mosses (any species from class Sphagnopsida), ericaceous shrubs, trees (of
any height), and aerenchymatous species (mostly order Poales but includes
exceptions). These categories closely follow Treat et al. (2018) except
that aerenchymatous species had to be expanded beyond Cyperaceae to
incorporate wetlands globally. Presence/absence of vegetation groups was
designated based on species lists in primary literature from the site. Out
of the vegetation groups present, the dominant (most abundant) group is also
reported and is based on information provided by lead site investigators.
In addition to the variable description table (Table B1) and the site
metadata (Table B3), we provide several more tables to complement our
analysis. Table B4 includes the climatic data used in the representativeness
analysis. Table B5 provides seasonality parameters for CH4 flux, air
temperature, soil temperature (from the probe closest to the ground
surface), and GPP. For sites with multiple soil temperature probes, the full
set of soil temperature parameters are in Table B6. Table B7 contains the
soil temperature probe depths. Table B2 contains the annual CH4 flux
and uncertainty. All Appendix B tables are also available at 10.5281/zenodo.4672601 (Delwiche et al., 2021).
Annual CH4 fluxes
Annual CH4 fluxes were calculated from gap-filled data for site years
with data gaps shorter than 2 consecutive months or for sites above
20∘ N where > 2-month data gaps occurred outside of the
highest CH4-emission months of 1 May through 31 October. Since we did
not sum gap-filled values for > 2 month-gaps during the winter,
annual sums from these years will be an underestimate since winter fluxes
can be important (Zona et al., 2016; Treat et al., 2018). Several sites had
less than 1 year of data, and we report gap-filled CH4 flux annual
sums for sites with between 6 months and 1 year of data (BW-Gum = 228 d, CH-Oe2 = 200 d, JP-Swl = 210 d, and US-EDN = 182 d). While
these sums will be an underestimate of annual CH4 flux since they do
not span a full year (and we therefore do not use them in the seasonality
analysis), their relative magnitude can still be informative. For example,
site JP-SWL is a lake site, and even with less than 1 year of data the
summed CH4 flux of 66 g C m-2 is relatively high (Taoka et
al., 2020). In addition to sites with short time series, the annual CH4 sum for site ID-Pag represents 365 d spanning June 2016 to June 2017.
Subset analysis on freshwater wetland CH4 flux
In addition to the FLUXNET-CH4-wide description of site class distributions
and annual CH4 fluxes, we also include a subset analysis on freshwater
wetlands given that it is the dominant ecosystem type in our dataset and an
important global CH4 source (Saunois et al., 2016). First, we analyze
freshwater wetland representativeness and subsequently the seasonality of
their CH4 emissions. Freshwater wetlands included in the seasonality
and representativeness analysis are indicated in Table B3, column
“IN_SEASONALITY_ANALYSIS”.
To compare the FLUXNET-CH4 site distribution to the global wetland
distribution, we evaluated their representativeness in the entire global
wetland cover along four bioclimatic gradients. Only freshwater wetland
sites were included in this analysis. Coastal sites were excluded because
salinity, an important control on CH4 production, could not be
evaluated across the tower network due to a lack of global gridded salinity
data (Bartlett et al., 1987; Poffenbarger et al., 2011). The four
bioclimatic variables used were mean annual air temperature (MAT), latent
heat flux (LE), enhanced vegetation index (EVI), and simple ratio water
index (SRWI; data sources in Table B4). We use EVI because it is a more
direct measurement than GPP from global gridded products and is considered a
reasonable proxy for GPP (Sims et al., 2006). Together, these environmental
variables account for, or are, proxies for key controls of CH4
production, oxidation at the surface, and transport (Bridgham et al., 2013).
We use a principal components analysis (PCA) to visualize the site
distribution across the four environmental drivers at once. For this
analysis, we consider the annual average bioclimatic conditions over
2003–2015. In the PCA output, we evaluate the coverage of the 42 freshwater
sites over 0.25∘ grid cells containing > 5 % wetland
mean cover in Wetland Area and Dynamics for Methane Modeling (WAD2M; Zhang
et al., 2020, 2021) for the same time period.
Global dissimilarity and constituency analysis
To further identify geographical gaps in the coverage of the FLUXNET-CH4
Version 1.0 network, we quantified the dissimilarity of global wetlands from
the tower network, using a similar approach to that taken for CO2 flux
towers (Meyer and Pebesma, 2020). We calculated the 4-dimensional Euclidean
distance from the four bioclimatic variables between every point at the land
surface to every tower location at the FLUXNET-CH4 network. We then divided
these distances by the average distance between towers to produce a
dissimilarity index. Dissimilarity scores < 1 represent areas whose
nearest tower is closer than the average distance among towers, while areas
with scores > 1 are more distant. Lastly, we identified the
importance of an individual tower in the network by estimating the
geographical area to which it is most analogous in bioclimate space. We
divided the world's land surface according to closest towers in bioclimatic
space. The area to which each tower is nearest is defined as the tower's
constituency.
Wetland CH4 seasonality
To examine freshwater wetland CH4 seasonality across the global range
of sites in FLUXNET-CH4, we extracted seasonality parameters for CH4,
temperature, and GPP using TIMESAT, a software package designed to analyze
seasonality of environmental systems (Jönsson and Eklundh, 2002, 2004;
Eklundh and Jönsson, 2015). TIMESAT
calculates several seasonality parameters, including baseline flux, peak
flux, and the slope of spring flux increase and fall decrease (Fig. 1). We
also calculate parameters such as amplitude (peak flux minus baseline, which is
the average of spring and fall baselines; “e” - ((“a” + “b”)/2) in
Fig. 1) and relative peak timing ((“g” - “f”)/(“h” - “f”) in
Fig. 1). TIMESAT uses a double-logistic fitting function to create a series
of localized fits centered on data minima and maxima. Localized fits are
determined by minimizing a merit function with the Levenberg–Marquardt
method (Madsen et al., 2004; Nielsen, 1999). These localized fits are then
merged using a global function to create a smooth fit over the full time
interval. To fit CH4 time series in TIMESAT, we used gap-filled data
after removing gaps exceeding 2 months. We do not report TIMESAT
parameters when large gaps occur during the spring CH4 emissions'
increase, peak, or fall decrease.
TIMESAT parameter description. (a, b) Base values (TIMESAT
reports the average of these two values), (c, d) slopes of seasonal
curves (lines drawn between 20 % and 80 % of the amplitude), (e) peak
value, and day of year (DOY) for the start (f), peak (g), and end (h) of the
elevated methane (CH4) emission season. Data points are the mean daily
gap-filled CH4 fluxes from site JP-Bby in 2015.
We estimate “start of elevated emission season” when CH4 emissions
begin to increase in the spring ( “f” in Fig. 1), and “end of elevated
emission season” when the period of elevated CH4 flux ends in the fall
(“h” in Fig. 1), as the intercept between the TIMESAT fitted baseline
parameter and shoulder-season slope (similar to Gu et al., 2009). To extract
seasonality parameters with TIMESAT, sites need a sufficiently pronounced
seasonality, a sufficiently long time period, and minimal data gaps (we note
that while TIMESAT is capable of fitting two peaks per year, all the
freshwater wetland sites have a single annual peak). We excluded site years
in restored wetlands when wetlands were still under construction. Of the 42
freshwater wetland sites in FLUXNET-CH4 Version 1.0, 36 had sufficient data
series to extract seasonality parameters. These 36 wetlands had 141
site years of data in total, which we fit with the double-logistic fitting
method which followed site data well (representative examples in Fig. 2).
For extratropical sites in the Southern Hemisphere, we shifted all data by
182 d so that maximum solar insolation seasonality would be congruent
across the globe.
Examples of TIMESAT fits for two FLUXNET-CH4 sites, (a) RU-Che and
(b) JP-Bby. Methane (CH4) flux data showing daily average flux tower
data, with several high outliers excluded to improve the plot (dark gray),
gap-filled values (light gray), and TIMESAT-fitted curve (dark red line) for
sites JP-Bby and RU-Che. TIMESAT captures the size and shape of peaks (note
different scale on y axes). CH4= methane.
We also used TIMESAT to extract seasonality metrics for GPP, partitioned
using the daytime-based approach (Lasslop et al., 2010) (GPP_DT), air temperature (TA), and soil temperature (TS_1,
TS_2, etc). For sites where winter soil temperatures fall
significantly below 0 ∘C, TIMESAT fits a soil temperature “start
of elevated season” date to periods when the soil is still frozen. In order
for TIMESAT to define the soil temperature seasonality within the thawed
season, we converted all negative soil temperatures to zero (simply removing
these values results in too many missing values for TIMESAT to fit). Many
sites have more than one soil temperature probe, so we extracted separate
seasonality metrics from each individual probe (although we used the metrics
from the shallowest temperature probe in our analysis). Table B4 contain the
TIMESAT seasonality parameters used in the seasonality analysis. We did not
include water table depth in the seasonality analysis because many sites
either lack water table depth measurements or have sparse data.
We regressed the CH4 seasonality parameters from TIMESAT against annual
temperature, annual water table depth, and TIMESAT seasonality parameters
for air temperature, soil temperature, and GPP (proxy for recent carbon
input available as substrate) using linear mixed-effect modeling with the
lmer command (with site as a random effect) from the R (R Core Team, 2018,
version 3.6.2) package lmerTest (Kuznetsova et al., 2017). For these
regressions we present the marginal R2 outputs from lmer, which represent
the variance explained only by the fixed effects. Mixed-effect modeling was
necessary to account for the non-independence between measurements taken at
the same site during different years (Zona et al., 2016; Treat et al.,
2018). We also compared how seasonality metrics varied across latitudinal
bands by dividing sites into northern (> 60∘ N),
temperate (between 40 and 60∘ N), subtropical
(absolute value between 20 and 40∘ N latitude, with site
NZ-KOP being the only Southern Hemisphere site), and tropical (absolute
value below 20∘ N). Site-year totals for the northern, temperate,
subtropical, and tropical bands were n=57, 36, 39, and 9, respectively.
We used the Kruskal–Wallis test to establish whether groups (either across
quarters or across latitudes) were from similar distributions and the post
hoc multiple comparison “Dwass, Steel, Critchlow, and Fligner” procedure
for inter-group comparisons. Kruskal–Wallis and post hoc tests were
implemented in Python Version 3.7.4, using stats from scipy for
Kruskal–Wallis and posthoc_dscf from scikit_posthocs.
We also compared quarterly CH4 flux sums by dividing data into
quarterly periods: January–February–March (JFM), April–May–June (AMJ),
July–August–September (JAS), and October–November–December (OND). For the
sake of simplicity, we chose to compare quarterly periods rather than
site-specific growing/non-growing season periods so that all time periods
would be the same length. Quarterly sums were computed from the gap-filled
CH4 fluxes when the longest continuous data gap within the quarter did
not exceed 30 d, leading to site-year counts of 67, 92, 95, and 72 for JFM,
AMJ, JAS, and OND, respectively. We compared quarterly CH4 fluxes
across latitudinal bands both for the total CH4 flux and for the
quarterly percentage of the annual CH4 flux. Quarterly statistics were
also conducted with the Kruskal–Wallis test and the post hoc multiple
comparison “Dwass, Steel, Critchlow, and Fligner” procedure implemented in
Python. Quarterly values are provided in Table B3, and the sum of mean
quarterly CH4 flux does not always equal mean annual CH4 flux
because some quarters either do not have data or have data gaps that exceed
30 d.
Results and discussionFLUXNET-CH4 datasetDataset description
Version 1.0 of the FLUXNET-CH4 dataset contains 79 unique sites, 293 total
site years of data, and 201 site years with sufficient data to estimate
annual CH4 emissions. A synthesis paper, published prior to the public
data release of FLUXNET-CH4 Version 1.0, had 60 unique sites and 139
site years with annual CH4 emission estimates (Knox et al., 2019).
Freshwater wetlands make up the majority of sites (n=42), and the
dataset also includes five salt marshes and one mangrove wetland. Notable
additions to FLUXNET-CH4 from the previous unpublished dataset used in Knox
et al. (2019) include six tropical sites (between 20∘ S and
20∘ N), including one site in South America, two sites in southern
Africa, and three sites in Southeast Asia. The 15 upland sites include six
needleleaf forests, three crop sites (excluding rice), two alpine meadows,
one grassland, one mixed forest, one tundra, and one urban site. The drained
sites represent former wetlands that have been artificially drained for use
as grasslands (n=3) or croplands (n=3). FLUXNET-CH4 sites span the
globe, though they are concentrated in North America and Europe (Fig. 3). Table B3 includes characteristics of all sites in the dataset.
Global map of FLUXNET-CH4 Version 1.0 site locations colored by
site type. Panels (a)–(d) show sites that were too closely located to
distinguish in the global map.
Sites represent a range of ecosystem types, latitudes, median fluxes, and
seasonality patterns (Table 1). Across all FLUXNET-CH4 sites (including
non-wetland sites), mean average annual CH4 flux is positively skewed
with a median flux of 9.5 g C m-2 yr-1, a mean flux of 16.9 g C m-2 yr-1, and numerous annual CH4 fluxes exceeding 60 g C m-2 yr-1. Marshes and swamps have the highest median flux, and
upland, salt marsh, and tundra sites have the lowest (Fig. 4). Lake
emissions are highly variable due to one high-flux lake site (JP-SWL). Flux
data at many sites show strong seasonality in CH4 emissions, but data
coverage is also lower outside the growing season (Table 1). Data coverage
is lowest during the JFM quarter (on average 20 % of half-hourly time
periods contain flux data), reflecting the predominance of Northern
Hemisphere sites and the practical difficulties in maintaining EC tower
sites during colder winter months (Table 1). Bogs, fens, and marshes have
pronounced seasonality, with fluxes being highest in the AMJ and JAS
quarters. In contrast, CH4 fluxes from uplands, drained sites, and salt
marshes are more uniform and low year-round.
Histogram of annual methane fluxes (FCH4, g C m-2 yr-1) grouped by site type.
Summary table of sites grouped by ecosystem class reporting annual
mean flux (Ann_Flux) and standard deviation from interannual
variability (Ann_Flux_SD), site years of data,
percent data cover per quarter, and median (med.) flux across site class.
JFM signifies January–February–March, AMJ April–May–June, JAS July–August–September, and OND October–November–December.
The FLUXNET-CH4 Version 1.0 dataset contains 42 freshwater wetlands that
span 37∘ S to 69∘ N, including bogs, fens, wet tundra,
marshes, and swamps, and a range of annual CH4 emission rates (Fig. 4). The majority of freshwater wetlands in our dataset emit 0–20 g C m-2 yr-1, with 10 emitting 20–60 g C m-2 yr-1, and one
more than 60 g C m-2 yr-1. Differences in annual CH4 flux
among wetland types is partially driven by temperature (which is often
linked to site type), with mean annual air temperature explaining 51 % of
the variance between sites (Fig. 5, exponential relationship). The global
relationship between annual methane emissions and temperature can be
described using a Q10 relationship where Q10=R2/R1((T2-T1)/10), with R2 and R1 being the CH4 emission rates
at temperatures T2 and T1, respectively (temperature in ∘C). The
Q10 based on Fig. 5 data is 2.57. We also note that annual CH4 flux from individual biomes may have different relationships with
temperature, as previous work has shown biome-specific trends in CH4
flux with environmental drivers (Abdalla et al., 2016). However, there
currently are not enough data points in each biome category to compare
relationships between mean annual CH4 flux and temperature. Annual
CH4 flux is not correlated with mean annual water table depth in
FLUXNET-CH4, unlike in Knox et al. (2019), which used a subset of the
FLUXNET-CH4 sites in which CH4 flux was correlated with water table depth
only for sites with water table below ground for 90 % of measured days
(r2=0.31, p< 0.05, n=27 site years). Freshwater wetland
seasonality is further described in Sect. 3.3.
Relationship between mean annual wetland methane (CH4) flux
(g C m-2 yr-1, logarithmic scale) and mean annual air temperature
(∘C) for each freshwater wetland site, with wetland type
indicated by symbol. Markers represent individual site means, with vertical
error bars representing the standard deviation of interannual variability.
Upland, rice, and urban CH4 characteristics
Upland agricultural sites are characterized by a lack of seasonal pattern in
CH4 emissions, relatively low flux, and sometimes negative daily flux
(i.e., CH4 uptake) averages. All of the upland non-agricultural sites
in FLUXNET-CH4 Version 1.0 are net (albeit weak) CH4 sources except for
the needleleaf forest site US-Ho1, which has a mean annual CH4 flux of
-0.1±0.1 g C m-2 yr-1 (see Table B3 for site acronyms and
metadata). The average agricultural site emissions are 1.3±0.8 g C m-2 yr-1, and non-agricultural site emissions are 1.6±1.2 g C m-2 yr-1 across sites.
Rice sites (n=7) have average annual emissions across all sites of 16.7±7.7 g C m-2 yr-1 and are characterized by strong seasonal
patterns, with either one or more CH4 emission peaks per year depending
on the number of rice seasons and field water management. One peak is
typically observed during the reproductive period for the continuously
flooded sites with one rice season (i.e., US-HRC, JP-MSE) (Iwata et al.,
2018; Runkle et al., 2019; Hwang et al., 2020). For sites with only one rice
season but with single or multiple drainage and re-flooding periods, a
secondary peak may appear before the reproductive peak (i.e., KR-CRK,
IT-Cas, and US-HRA; Meijide et al., 2011; Runkle et al., 2019; Hwang et al.,
2020). Two reproductive peaks appear for sites with two rice seasons (i.e.,
PH-RiF), and each reproductive peak may be accompanied by a secondary peak
due to drainage events (Alberto et al., 2015). Even sites with one
continuously flooded rice season may experience a second peak if the field
is flooded during the fallow season to provide habitat for migrating birds
(e.g., US-Twt; Knox et al., 2016).
The dataset has 1 year of urban data from site UK-LBT in London, England.
UK-LBT observes CH4 fluxes from a 190 m tall communications tower in
the center of London and has a mean annual CH4 flux of 46.5±5.6 g C m-2 yr-1. This flux is more than twice as high as the mean
annual CH4 flux across all FLUXNET-CH4 sites, 16.9 g C m-2 yr-1. The London site has higher CH4 emissions in the winter
compared to summer, which is attributed to a seasonal increase in natural
gas usage (Helfter et al., 2016.)
Saltwater and mangrove wetland CH4 characteristics
Three of the five saltwater wetlands in FLUXNET-CH4 (US-Edn, US-MRM, and
US-Srr) have a very low mean annual CH4 flux (see Table B2 for
individual site-year CH4 flux sums and associated uncertainty) and
minimal seasonality. Two other FLUXNET-CH4 saltwater sites (US-La1 and
US-StJ) have significantly higher fluxes, with annual sums of 12.6±0.6 and 9.6±1.0 g C m-2 yr-1, respectively, while the
mangrove site HK-MPM has annual mean fluxes of 11.1±0.5 g C m-2 yr-1. This range of CH4 fluxes across different saltwater
ecosystems could be valuable for exploring the effect of salinity and
different biogeochemical pathways of CH4 production, oxidation, and
transport of CH4 (Bartlett et al., 1987; Poffenbarger et al., 2011).
Saltwater wetlands along the coast have unique CH4 dynamics
attributable to the presence of abundant electron acceptors, most
importantly sulfates which inhibit methanogenesis (Pattnaik et al., 2000;
Mishra et al., 2003; Weston et al., 2006) but at low concentrations can
have no effect (Chambers et al., 2011) or even increase methanogenesis
(Weston et al., 2011). In fact, estuarine wetlands with moderate salinity
can still be significant sources of CH4 (Liu et al., 2020). Even under
sulfate-rich conditions, high CH4 production can be found via
methylotrophic methanogenesis (Dalcin Martins et al., 2017; Seyfferth et al.,
2020,) or because the processes of sulfate reduction and methanogenesis are
spatially separated (Koebsch et al., 2019). Consequently, representing the
biophysical drivers of ecosystem-scale CH4 fluxes in non-freshwater
wetlands is challenging and may represent a combination of competing or
confounding effects (Vazquez-Lule and Vargas, 2021).
Freshwater wetland representativeness
We evaluated the representativeness of freshwater wetland sites in the
FLUXNET-CH4 Version 1.0 dataset against wetlands globally, based on
bioclimatic conditions of our sites. When evaluating bioclimatic variables
individually, the distribution of freshwater wetlands across the network was
significantly different from the global distribution (alpha > 0.05; two-tailed Kolmogorov–Smirnov tests; see Table B4). We exclude
wetlands classified as “salt marsh” in this representativeness analysis
and the seasonality analysis below because of the unique CH4 flux
dynamics in saltwater ecosystems (as discussed in Sect. 3.1.4), though we
note that some of the coastal wetlands included in the freshwater analysis
periodically experience brackish water (i.e., US-Myb, US-Sne).
When considering the four bioclimatic variables, MAT, LE, EVI, and SRWI in a
PCA, we found that our tower network generally samples the bioclimatic
conditions of global wetland cover, but some noticeable gaps remain (Fig. 6). Three clusters of the world's wetland-dense regions are identified but
are not equally sampled by the network. A cluster of low-temperature
wetlands is sampled by a large number of high-latitude sites. The other two
wetland clusters are not as well sampled: a high-temperature and LE cluster
is represented only by two towers (ID-Pag and MY-MLM), while drier and
temperate and subtropical wetlands including large swathes of the Sahel in
Africa only have a site in Botswana (BW-Npw) as their closest analog tower.
Principal component analysis displaying the distribution of
freshwater wetland sites (points) along the two main principal components
together accounting for 91.9 % of variance. Tower sites are represented as
points with shapes indicating their wetland type and color shade
representing the annual methane (CH4) flux (gray points represent sites
for which < 6 months of flux data were available to estimate annual
budget). Sites codes are labeled in blue text for selected sites deviating
from average conditions. Loading variables are represented by the arrows:
mean annual temperature (MAT), simple ratio water index (SRWI), latent heat
flux (LE), and enhanced vegetation index (EVI). The background shades of gray
are a qualitative representation of the density of global wetland pixels and
their distribution in the PCA climate space, with darker color representing
higher densities (excluding Greenland and Antarctica). Only grid cells with
> 5 % average wetland fraction according to the WAD2M over
2000–2018 are included (Zhang et al., 2020).
Evaluating the bioclimatic dissimilarity of global wetlands to the
FLUXNET-CH4 network shows the least captured regions are in the
tropics (Fig. 7a). Sparse coverage in the tropics also means that the few
existing towers occupy a critical place in the network, particularly as
tropical wetlands are the largest CH4 emitters (Bloom et al., 2017;
Poulter et al., 2017). Highly dissimilar wetlands are limited in extent and
distributed across all latitudes, but the average dissimilarity is higher in
north temperate (55 to 65∘) and tropical
(-5 to 5∘) latitudes (Fig. 7b). To evaluate the
importance of individual towers in the network, we estimated the
geographical area to which it is most analogous in bioclimate space (Fig. 7c). We found that some towers have disproportionately large constituencies
(i.e., wetland areas that share the same closest bioclimatic analog tower).
Towers in Indonesia (ID-Pag), Brazilian Pantanal (BR-Npw), and Botswana
floodplains (BW-Nxr) represent the closest climate analog for much of the
tropics (678 000, 300 000, and 284 000 km2, respectively), while CA-SCB
represents a vast swath (291 000 km2) of boreal and arctic regions
(Fig. 7d).
(a) Distance in bioclimatic space between global land surface and
the FLUXNET-CH4 Version 1.0 tower network (gray areas indicate no mapped
wetlands). The Euclidean distance was computed on the four bioclimatic
variables and was then standardized by the average distance within the network.
Most of the land surface has a dissimilarity score lower than 1, meaning
these areas are closer than the average tower distance (lower dissimilarity
score means a similar bioclimate to that represented by towers in the
network). However, this pattern reflects more the sparsity of the tower
network than a similarity of the land surface to the network. Areas with
< 5 % coverage by wetlands were excluded to focus on wetland-dense
regions. (b) Latitudinal distribution of dissimilarity score, (c) map of the
four largest tower constituencies, and (d) scatterplot of wetland area in each
tower constituency plotted against the average dissimilarity score (point)
and ± standard deviation (error bar).
Our assessment of wetland CH4 tower coverage determines the ability
of our dataset to represent global wetland distributions and highlights some
clear representation gaps in the network, particularly in tropical and humid
regions. Other geographic regions such India, China, and Australia, where
towers exist but are not included in the current network, should be
prioritized when expanding the network even though they are not among the
most distant areas to the current network. Similar representativeness
assessments have been developed for CO2 tower networks to identify gaps
and priorities for expansion (Jung et al., 2009). To improve the geographic
coverage of the network for representing global-scale fluxes, locations for
new tower sites can be targeted to cover bio-climatically distant areas from
the current network (Villarreal et al., 2019). Candidate regions for
expansion that are both high CH4 emitting (Saunois et al., 2020) and located in under-sampled climates are the following: African Sahel, Amazon Basin,
Congo Basin, and Southeast Asia. Climatic conditions over boreal and arctic
biomes are generally better represented (primarily at lower elevations), but
there is scope to expand the network in wetland-dense regions like the
Hudson Bay Lowlands and North Siberian Lowlands. Moreover, establishing
sites in other ecosystem types, especially lakes and reservoirs (see Deemer
et al., 2016; Bastviken et al., 2011; Matthews et al., 2020) in most climatic
zones would help capture CH4 fluxes from these ecosystems.
Understanding the representativeness of the network is essential when
inferring general patterns of flux magnitude, seasonality, and drivers from
the tower data (Villarreal et al., 2018). We produced a first-order
representativeness of average bioclimatic conditions, but temporal
representativeness (across seasons, climate anomalies, and extreme events) is
particularly needed given the episodic nature of CH4 fluxes (Chu et
al., 2017; Mahecha et al., 2017; Göckede et al., 2019).
Assessing representation of wetland CH4 sites is complicated by the
fact that wetlands occupy only a fraction of most landscapes (except wetland-dense regions such as North Siberian Lowlands, Hudson Bay Lowlands, Congo
Basin, etc.) and that not all relevant factors affecting CH4 production
and consumption could be considered in our analysis. For instance, our
assessment of representation did not consider wetland types as such maps are
limited by the inherent difficulties in remotely sensing wetland features
(Gallant, 2015). The attribution of representativeness is further
complicated by the fact that many EC tower locations are subject to
small-scale variability within the field of view, or footprint, of the
sensor. Consequently, the individual time steps within EC flux time series
may represent a mixture of different wetland types or different fractions
of wetland contribution to the total CH4 flux, varying with wind
direction, atmospheric stability, or season (Chu et al., 2021). This further
complicates upscaling efforts. Additionally, this representativeness
analysis did not apply weights to the drivers to reflect their varying
influence on CH4 flux. Such weights can be included in future versions
as they are generated by a cross-validated machine learning approach (Jung
et al., 2020). Future efforts could include the dissimilarity index from
this analysis as a metric of extrapolation in a CH4 flux upscaling
effort.
Freshwater wetland flux seasonalitySeasonal flux comparisons by latitudinal bands
CH4 flux and seasonality varied substantially across latitudinal
bands (northern, temperate, subtropical, and tropical) (Fig. 8). Annual
CH4 fluxes for temperate, and subtropical sites were significantly
higher than for northern sites (8.7 ± 5.0, 29.7 ± 25.2, 40.1 ± 14.6, and 24.5 ± 20.7 g C m-2 yr-1 for northern,
temperate, subtropical, and tropical, respectively, and p< 0.0001 using
Kruskal–Wallis and post hoc comparisons; Fig. 8a), and tropical sites were
similar to all other latitudinal bands likely because of their small sample
size. The ratio of seasonal amplitude to peak flux provides a measure of the
relative seasonal increase in emissions compared with baseline, in which a
ratio of 0 indicates no seasonal change in amplitude, a ratio of 1
indicates the off-season flux is zero, and values over 1 mean the
off-season baseline CH4 fluxes were negative (i.e., uptake). Average
amplitude to peak flux ratios were similar across all latitudinal bands (0.9 ± 0.1, 0.9 ± 0.1, 0.9 ± 0.1, and 1.0 ± 0.7 for
northern, temperate, subtropical, and tropical, respectively; Fig. 8b). The
spring increase in CH4 emissions began later in northern sites compared
with temperate and subtropical sites (end of May versus April, respectively, and
p=0.001; Fig. 8c), while tropical sites vary widely in elevated emission
season start date. Northern sites also had shorter elevated CH4 flux
season lengths (138 ± 24 d) compared to temperate sites (162 ± 32 d), and both were shorter than subtropical sites (209 ± 43 d; p< 0.0001; Fig. 8e). On average, CH4 flux peaked
earlier for temperate sites compared to northern (p=0.008) and
subtropical sites (p=0.02; middle to late July compared with early August;
Fig. 8f), while tropical sites again vary widely. Given their unique
seasonality and low number of site years (n=9), tropical systems are
discussed separately in Sect. 3.3.3 and are not included in the comparisons in
the remainder of this section. While our results on CH4 seasonality
corroborate expected trends for these latitudinal bands, they provide some
of the first estimates of CH4 seasonality parameters and ranges
across a global distribution of sites.
(a) Annual methane (CH4) flux (g C m-2 yr-1), (b) ratio of seasonal amplitude to seasonal peak, where values of 0 indicate
uniform annual CH4 flux, values of 1 indicate zero off-season fluxes,
and values exceeding 1 indicate negative off-season fluxes, (c) CH4
flux (FCH4) elevated emission season start by day of year (DOY), (d) FCH4 elevated emission season end by DOY, (e) length of elevated CH4
flux season (days), and (f) DOY of peak FCH4. Northern (dark blue,
solid line), temperate (blue, dashed line), sub-tropical (green, dot-dash
line), and tropical (light green, solid line) wetlands plotted using the
kernel density function. Each panel has lines that represent latitudinal
bands as follows: northern (> 60∘), temperate (between
40 and 60∘), subtropical (between 20 and
40∘), and tropical (< 20∘), though the
site-year totals vary between these groups (n=57, n=36, n=39,
and n=9 respectively). All total CH4 flux values and elevated
season start values are positive, and the apparent continuation of the data
distribution into negative values is an artifact of the kernel density
function. Southern Hemisphere sites below 20∘ S were shifted by
182 d to make summer the middle of the year for comparability with
Northern Hemisphere sites.
We found that latitudinal groups showed strong differences in absolute
CH4 flux across quarters and narrower differences in percentage of
annual CH4 flux (Fig. 9a versus 9b). Thus, the AMJ quarter had a
similar relative contribution to the annual CH4 flux across latitudes
regardless of the absolute annual CH4 flux. CH4 fluxes (Fig. 9a)
were highest during JAS for northern, temperate, and subtropical sites and
highest in AMJ and JAS for temperate sites (p< 0.01). Though
CH4 fluxes in northern sites are most commonly measured during warm
summer months (Sachs et al., 2010; Parmentier et al., 2011), fluxes in JFM
and OND (50 % of the yearly duration) on average make up 18.1 ± 3.6 %, 15.3 ± 0.1 %, and 31.2 ± 0.1 % (northern,
temperate, and subtropical, respectively) of annual emissions. This pattern
indicates that a substantial fraction of annual CH4 fluxes occur
during cooler months. The contribution of non-growing season CH4 emissions to annual CH4 fluxes has previously been described for
arctic and boreal regions (Zona et al., 2016; Treat et al., 2018), and our
analysis suggests comparable contributions in temperate and subtropical
systems for the same quarterly periods.
(a) Quarterly contribution to total annual CH4 flux (in g C m-2) and (b) percentage of annual CH4 flux. Sites were divided
into northern (> 60∘ N), temperate (40–60∘ N), and subtropical (20–40∘ N).
Quarters with continuous data gaps exceeding 30 d were excluded. We used
the following quarterly periods: January–February–March (JFM),
April–May–June (AMJ), July–August–September (JAS), and
October–November–December (OND). Tropical sites are discussed separately in
Sect. 3.3.3 because of their unique seasonality and low number of sites.
Predictors of CH4 flux phenology
The start of the elevated CH4 flux season, and how long the elevated
flux season lasts, correlated strongly with mean annual air temperature
(Fig. 10; p< 0.0001 for each). Methane flux began to increase
roughly 2 months earlier in the warmest systems (mean annual temperature
> 20 ∘C) compared to the coldest (mean annual
temperature near -10 ∘C), though several of the warmer sites had
high variability. Our data suggest that the CH4 season started 2.8 ± 0.5 d earlier for every degree Celsius increase in mean annual
temperature (Fig. 10a). In contrast, the end of the CH4 emission season
was not correlated with mean annual temperature, but a positive trend
existed despite high variability at the warmest and coldest sites (Fig. 10b). The
high variability seen in the end of the CH4 season at northern sites is
important to note and would likely be better resolved by incorporating other
seasonality or phenological characteristics, such as moisture, active layer
depth, and plant community composition (e.g., Kittler et al., 2017). Plants
with aerenchymatous tissue, for example, influence the timing of
plant-mediated CH4 flux and are a key source of uncertainty when predicting CH4 seasonality for northern wetlands (Xu et al., 2016; Kwon
et al., 2017). Despite the relative lack of trend with season end date, the
season length was still positively correlated with mean annual temperature,
with the warmest sites having roughly 3 more months of seasonally
elevated CH4 emissions than the coldest sites (Fig. 10c). CH4 season length increased 3.6 ± 0.6 d for every degree Celsius
increase in mean annual temperature (note that these relationships are
correlations, and we cannot disentangle causality with this analysis).
Temperature is highly correlated with other parameters (i.e., radiation,
days of snow cover, etc.), so CH4 flux is also likely to correlate
with other environmental parameters.
The (a) start of the elevated methane (CH4) emission season
(y=-2.8x+130, with “x” in ∘C and “y” in day of year, DOY), (b) the end of the elevated emission season in DOY, and (c) the
length of the emission season with mean annual site air temperature (y=3.6x+176.6, with “x” in ∘C and “y” in days). Each point
represents a site year of data, and all reported r2 values are significant to p< 0.0001. Tropical sites are discussed separately in Sect. 3.3.3.
Although the spring onset of increasing CH4 emissions correlated with
mean annual air temperature, on average it lagged behind the spring increase in the
shallowest soil temperatures by 31 ± 40 d (Fig. 11; lag is
significantly different than zero, and p< 0.001), with very few
instances of CH4 emissions beginning before seasonal soil temperatures
increase (and by 20 ± 50 d for the deepest temperature probes). In
contrast, for roughly half of the sites, CH4 emission increased prior
to seasonal GPP (a proxy for fresh substrate availability) increases. This
suggests that the initiation of increased CH4 fluxes at the beginning
of the season was not limited by availability of substrate derived from
recent photosynthates. Additionally, the onset of CH4 fluxes tended to
occur closer to the onset of soil temperature increase for cooler
temperature sites (sites with later start dates tend to be cooler; Fig. 11a). This result is likely attributable to the direct influence of
increased temperature on microbial processes (Chadburn et al., 2020), as
well as the indirect influences of snowmelt, both via release of CH4 from the snowpack and a higher water table leading to more CH4
production (Hargreaves et al., 2001; Tagesson et al., 2012; Mastepanov et
al., 2013; Helbig et al., 2017). These observed trends hold for the entire
temperature or GPP range of freshwater wetland sites but are not
necessarily applicable within individual latitudinal bands.
Relationship between the onset of the methane
(CH4) emission season to (a) the beginning of the air warming by day of
year (DOY), (b) soil warming at the shallowest probe depth per site by DOY,
and (c) gross primary productivity (GPP) increase for the subset of sites
with soil temperature data by DOY. Each point represents a site year of
data. Dashed lines represent a 1:1 relationship, and solid lines are significant
(p< 0.05) regression fits. On average, the CH4 emission season
lags behind the soil temperature increase by 31 ± 40 d and is more
synchronous with GPP.
In contrast with the CH4 season start timing, the timing of the
CH4 peak did not correlate with the timing of either the soil
temperature peak or the GPP peak (Fig. A1). For 63 % of the sites, the
average timing of peak CH4 emissions lagged behind the soil temperature peak,
and at 83 % of the sites average peak CH4 lagged behind peak GPP (Fig. A1).
Although there was no simple relationship between absolute CH4 peak
timing and the environmental drivers we investigated, there was a
correlation (p=0.0005) between the relative timing of peak CH4
compared to season onset (calculated as described in Sect. 2.3) and mean
annual air temperature (Fig. 12a). For cooler sites, the peak of seasonal
CH4 emissions occurred closer to the onset of the CH4 emission
season than the end of the season, resulting in an asymmetrical seasonal
CH4 flux shape that is illustrated in Fig. 2a. Soil temperature also
peaked earlier in the season for cooler wetlands, though the relationship is
not as pronounced (p=0.009; Fig. 12b). In contrast, GPP peaked later in
the season for cooler wetlands (p=0.009, Fig. 12c). Previous work on
Arctic sites (sites US-Ivo, US-Beo, US-Atq, US-Bes, and RU-CH2) highlighted
the asymmetrical annual CH4 peak, with higher fall emissions being
attributed to the “zero curtain” period when soil below the surface
remains thawed for an extended period of time due to snow insulation (Zona
et al., 2016; Kittler et al., 2017). Furthermore, soils can stay above the
“zero curtain” range for an extended time into the fall and winter (Helbig
et al., 2017), which may also be caused by snow insulation. The rapid onset
of emissions in the spring following snowmelt could be attributed to the
release of accumulated CH4 (Friborg et al., 1997), and other high
latitude sites have seen similarly sharp increases in CH4 emissions at
snowmelt (Dise, 1992; Windsor, 1992). However, not all studies in high
latitudes have observed asymmetrical CH4 emission peaks, pointing to
the inherent complexity of these ecosystems (Rinne et al., 2007; Tagesson et
al., 2012).
Site-year peak methane (CH4) emissions (a) and peak soil
temperature (b) occur earlier in the season for sites with lower mean annual
temperatures. (c) Gross primary productivity (GPP) tends to peak earlier in
the season for warmer sites, though the trend is weak. All r2 values
are significant at p< 0.001. Each point represents a site year of
data.
Site identification (SITE_ID), data DOI, and DOI
reference for each FLUXNET-CH4 site.
SITE_IDDOIDOI_REFERENCEAT-Neu10.18140/FLX/1669365Wohlfahrt (2020)BR-Npw10.18140/FLX/1669368Vourlitis et al. (2020)BW-Gum10.18140/FLX/1669370Helfter (2020a)BW-Nxr10.18140/FLX/1669518Helfter (2020b)CA-SCB10.18140/FLX/1669613Sonnentag and Helbig (2020a)CA-SCC10.18140/FLX/1669628Sonnentag and Helbig (2020b)CH-Cha10.18140/FLX/1669629Merbold et al. (2020a)CH-Dav10.18140/FLX/1669630Merbold et al. (2020b)CH-Oe210.18140/FLX/1669631Maier et al. (2020)CN-Hgu10.18140/FLX/1669632Niu and Chen (2020)DE-Dgw10.18140/FLX/1669633Sachs and Wille (2020a)DE-Hte10.18140/FLX/1669634Koebsch and Jurasinski (2020)DE-SfN10.18140/FLX/1669635Schmid and Klatt (2020)DE-Zrk10.18140/FLX/1669636Sachs and Wille (2020b)FI-Hyy10.18140/FLX/1669637Mammarella et al. (2020)FI-Lom10.18140/FLX/1669638Lohila et al. (2020)FI-Si210.18140/FLX/1669639Vesala et al. (2020a)FI-Sii10.18140/FLX/1669640Vesala et al. (2020b)FR-LGt10.18140/FLX/1669641Jacotot et al. (2020)HK-MPM10.18140/FLX/1669642Lai (2020)ID-Pag10.18140/FLX/1669643Sakabe et al. (2020)IT-BCi10.18140/FLX/1669644Famulari (2020)IT-Cas10.18140/FLX/1669645Manca and Goded (2020)JP-BBY10.18140/FLX/1669646Ueyama et al. (2020)JP-Mse10.18140/FLX/1669647Iwata (2020a)JP-SwL10.18140/FLX/1669648Iwata (2020b)KR-CRK10.18140/FLX/1669649Ryu et al. (2020)MY-MLM10.18140/FLX/1669650Wong et al. (2020)NL-Hor10.18140/FLX/1669651Dolman et al. (2020a)NZ-Kop10.18140/FLX/1669652Campbell and Goodrich (2020)PH-RiF10.18140/FLX/1669653Alberto and Wassmann (2020)RU-Ch210.18140/FLX/1669654Goeckede (2020)RU-Che10.18140/FLX/1669655Merbold (2020)RU-Cok10.18140/FLX/1669656Dolman et al. (2020b)RU-Fy210.18140/FLX/1669657Varlagin (2020)SE-Deg10.18140/FLX/1669659Nilsson and Peichl (2020)UK-LBT10.18140/FLX/1670207Helfter (2020c)US-A0310.18140/FLX/1669661Billesbach and Sullivan (2020a)US-A1010.18140/FLX/1669662Billesbach and Sullivan (2020b)US-Atq10.18140/FLX/1669663Zona and Oechel (2020a)US-Beo10.18140/FLX/1669664Zona and Oechel (2020b)US-Bes10.18140/FLX/1669665Zona and Oechel (2020c)US-Bi110.18140/FLX/1669666Rey-Sanchez et al. (2020a)US-Bi210.18140/FLX/1669667Rey-Sanchez et al. (2020b)US-BZB10.18140/FLX/1669668Euskirchen and Edgar (2020a)US-BZF10.18140/FLX/1669669Euskirchen and Edgar (2020b)US-BZS10.18140/FLX/1669670Euskirchen and Edgar (2020c)US-CRT10.18140/FLX/1669671Chen and Chu (2020a)US-DPW10.18140/FLX/1669672Hinkle and Bracho (2020)US-EDN10.18140/FLX/1669673Oikawa (2020)US-EML10.18140/FLX/1669674Schuur (2020)US-Ho110.18140/FLX/1669675Richardson and Hollinger (2020)US-HRA10.18140/FLX/1669676Runkle et al. (2020)US-HRC10.18140/FLX/1669677Reba et al. (2020)US-ICs10.18140/FLX/1669678Euskirchen et al. (2020)
Continued.
SITE_IDDOIDOI_REFERENCEUS-Ivo10.18140/FLX/1669679Zona and Oechel (2020d)US-LA110.18140/FLX/1669680Holm et al. (2020a)US-LA210.18140/FLX/1669681Holm et al. (2020b)US-Los10.18140/FLX/1669682Desai (2020a)US-MAC10.18140/FLX/1669683Sparks (2020)US-MRM10.18140/FLX/1669684Schäfer (2020)US-Myb10.18140/FLX/1669685Matthes et al. (2020)US-NC410.18140/FLX/1669686Noormets et al. (2020)US-NGB10.18140/FLX/1669687Torn and Dengel (2020a)US-NGC10.18140/FLX/1669688Torn and Dengel (2020b)US-ORv10.18140/FLX/1669689Bohrer and Morin (2020)US-OWC10.18140/FLX/1669690Bohrer et al. (2020)US-PFa10.18140/FLX/1669691Desai (2020b)US-Snd10.18140/FLX/1669692Detto et al. (2020)US-Sne10.18140/FLX/1669693Shortt et al. (2020)US-Srr10.18140/FLX/1669694Windham-Myers et al. (2020)US-StJ10.18140/FLX/1669695Vazquez-Lule and Vargas (2020)US-Tw110.18140/FLX/1669696Valach et al. (2020a)US-Tw310.18140/FLX/1669697Chamberlain et al. (2020)US-Tw410.18140/FLX/1669698Eichelmann et al. (2020)US-Tw510.18140/FLX/1669699Valach et al. (2020b)US-Twt10.18140/FLX/1669700Knox et al. (2020)US-Uaf10.18140/FLX/1669701Iwata et al. (2020)US-WPT10.18140/FLX/1669702Chen and Chu (2020b)Uniqueness of tropical wetlands
Tropical wetlands typically do not experience the large swings in
temperature and GPP that contribute to CH4 flux seasonality in
temperate and northern sites. Indeed, the relatively constant high
temperatures and high GPP in tropical ecosystems may lead to the lower ratio
between seasonal amplitude and peak CH4 flux compared with temperate
and northern sites (Fig. 8b). Tropical flux sites have historically been
under-studied, leading to a lack of synthesized information about these
ecosystems. FLUXNET-CH4 has five tropical wetland sites (latitudes between
20∘ S and 20∘ N) and one tropical rice site,
representing 13 site years of data. These sites are especially insightful as
they provide the first estimates of CH4 fluxes from large, tropical,
seasonal floodplain systems.
We found a broad range of annual CH4 fluxes across tropical sites in
FLUXNET-CH4 Version 1.0. Annual CH4 flux emissions from two Southeast
Asian flooded peat forests were relatively low, 0.01 ± 0.1 and 9.5 ± 0.6 g C m-2 yr-1 for ID-PAG and MY-MLM, respectively,
which is consistent with annual CH4 fluxes measured at another peat
forest in Indonesia (Deshmukh et al., 2020). In contrast, mean annual
CH4 flux for a seasonally flooded swamp in the Brazilian Pantanal
region (BR-NPW) was over twice as high as MY-MLM, at 19.2 ± 2.5 g C m-2 yr-1. Similarly high annual CH4 fluxes were observed at
the two Botswana swamp sites in the Okavango Delta (51.7 ± 10.6 and
47.3 ± 3.7 g C m-2 yr-1 for BW-GUM and BW-NXR,
respectively), one of which is seasonally inundated and surrounded by
grassland (BW-NXR) and the other a permanently flooded lagoon covered in
a floating papyrus mat (BW-GUM). The relatively low fluxes found at the two
Southeast Asian peat forest sites indicate that these ecosystems may be
smaller CH4 sources than expected given their location in the humid
tropics. Even the higher-emitting tropical sites in Brazil and Botswana are
still well within the range of annual CH4 flux typical in cooler
latitudes (Fig. 1).
In addition to having highly variable CH4 flux magnitudes, the
tropical sites differ from each other in their seasonality. CH4 flux
hit a minimum around July for two sites (BW-GUM, latitude 18.965∘ S, and MY-MLM, latitude 1.46∘ N), while CH4 flux increased
through July and the subsequent months for the other Botswana site, BW-NXR
(latitude 19.548∘ S). Site ID-Pag (latitude 2.32∘ S)
had minimal seasonality, whereas the flooded forest site in Brazil (BR-NPW,
latitude 16.49∘ S) had near-zero fluxes from approximately July
to January and consistently high fluxes for the remainder of the year. The
rice site PH-RiF (latitude 14.14∘ N) had two annual CH4 flux
peaks, which is consistent with some other rice sites and likely reflects
management practices. Baseline CH4 flux values also differed, with
the two Botswana sites having the highest off-season fluxes (29 and 133 nmol m-2 s-1 for BW-NXR and BW-GUM, respectively, estimated by
TIMESAT), MY-MLM having an intermediate baseline CH4 flux (16 nmol m-2 s-1, estimated by TIMESAT), and the remainder of the sites
having essentially zero flux at baseline. While more tropical wetland data
will be needed to extract broad-scale conclusions about these ecosystems,
the six tropical sites in FLUXNET-CH4 provide an important starting point
for synthesis studies and highlight tropical wetland CH4 variability.
Data availability
Half-hourly and daily aggregations are available for download at https://fluxnet.org/data/fluxnet-ch4-community-product/ (for citations, please cite this study), along with a
table containing site metadata compiled from Table B3. Variable descriptions
and units are provided in Table B1 and at https://fluxnet.org/data/fluxnet-ch4-community-product/ (last access: 7 April 2021). Each site has a
unique FLUXNET-CH4 DOI as listed in Table B3. All site data used in this
analysis are available under the CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/, last access: 6 July 2021) copyright policy (two additional sites in FLUXNET-CH4 are available under the more restrictive
Tier 2 data policy, https://fluxnet.org/data/data-policy/ (last access: 6 July 2021);
these sites are not used in our analysis). The individual site DOIs are
provided below in Table 2. All seasonality parameters used in these analyses
are available at 10.5281/zenodo.4672601
(Delwiche et al., 2021).
Conclusions
The breadth and scope of CH4 flux data in the FLUXNET-CH4 dataset make
it possible to study the global patterns of CH4 fluxes, particularly
for global freshwater wetlands which release a substantial fraction of
atmospheric CH4. To help data users understand seasonal patterns within
the dataset, we provide the first global estimates of CH4 flux
patterns and predictors in CH4 seasonality using freshwater wetland
data. In the seasonality analysis, we find that, on average, the seasonal
increase in CH4 emissions begins about 3 months earlier and lasts
about 4 months longer at the warmest sites compared with the coolest
sites. We also find that the beginning of the CH4 emission season lags
behind the beginning of seasonal soil warming by approximately 1 month with
almost no instances of CH4 emissions increasing before temperature
increases. Additionally, roughly half the sites have CH4 emissions
increasing prior to GPP increase, highlighting the importance of substrate
versus temperature limitations on wetland CH4 emissions. Furthermore,
relative to warmer climates, wetland CH4 emissions in cooler climates
increase faster in the warming season and decrease slower in the cooling
season. This phenomenon has previously been noted on a regional scale, and we
show that it persists at the global scale. Constraining the seasonality of
CH4 fluxes on a global scale can help improve the accuracy of global
wetland models.
FLUXNET-CH4 is an important new resource for the research community, but
critical data gaps and opportunities remain. The current FLUXNET-CH4 dataset
is biased towards sites in boreal and temperate regions, which influence the
relationships presented in our analyses. Tropical ecosystems are estimated
to account for 64 % of potential natural CH4 emissions (< 30∘ N; Saunois et al., 2020) but only account for 13 % of the
FLUXNET-CH4 sites in the dataset. Unsurprisingly, tropical sites in our
network do not represent the range of bioclimatic wetland conditions present
in the tropics. Therefore, while maintaining flux towers in tropical
ecosystems is challenging, it is necessary to further constrain the global
CH4 cycle. Coastal wetlands are also poorly represented in FLUXNET-CH4
even though there is evidence of substantial CH4 emissions from these
ecosystems, and so better representation across salinity gradients is warranted.
Lastly, the average time series for FLUXNET-CH4 Version 1.0 is relatively
short, only 3.7 site years on average compared with 7.2 for CO2 sites
in FLUXNET (Pastorello et al., 2020). Adding additional site years of data
from existing sites, as a complement to adding new sites, will increase the
community's ability to explain interannual variability in CH4 emissions
and seasonality. Nevertheless, FLUXNET-CH4 is an important and unprecedented
resource with which to diagnose and understand drivers of the global
CH4 cycle.
Peak methane (CH4) flux timing versus peak gross primary
productivity (GPP) timing (a) and peak soil temperature timing by day of
year (b). Points represent site average, and error bars represent standard
deviations. Dotted line represents 1:1 relationship.
FLUXNET-CH4 data variables
This web page describes data variables and file formatting for the
FLUXNET-CH4 Community Product.
Data variable: base names
Base names indicate fundamental quantities that are either measured or
calculated/derived. They can also indicate quantified quality information.
Data variable: qualifiers
Qualifiers are suffixes appended to variable base names that provide
additional information about the variable. For example, the _DT qualifier in the variable label GPP_DT indicates that
gross primary production (GPP) has been partitioned using the flux
partitioning method from Lasslop et al. (2010).
Multiple qualifiers can be added, and they must follow the order in which
they are presented here.
Qualifiers: general
General qualifiers indicate additional information about a variable.
_F: variable has been gap-filled by the
FLUXNET-CH4 team. Gaps in meteorological variables, including air
temperature (TA), incoming shortwave (SW_IN) and longwave
(LW_IN) radiation, vapor pressure deficit (VPD), pressure
(PA), precipitation (P), and wind speed (WS), were filled with ERA-Interim
(ERA-I) reanalysis data (Vuichard and Papale, 2015). Other variables were
filled using the multidimensional scaling (MDS) approach in REddyProc (see Delwiche et al., 2021, for
more details).
_DT: variable is acquired using the flux
partitioning method from Lasslop et al. (2010), with values estimated by
fitting the light-response curve.
_NT: variable is acquired using the flux
partitioning method from Reichstein et al. (2005), with values estimated
from nighttime data and extrapolated to daytime.
_RANDUNC: random uncertainty is introduced from
several different sources including errors associated with the flux
measurement system (gas analyzer, sonic anemometer, data acquisition system,
flux calculations), errors associated with turbulent transport, and
statistical errors relating to the location and activity of the sites of
flux exchange (“footprint heterogeneity”) (Hollinger and Richardson, 2005).
_ANNOPTLM: gap-filled variable uses an
artificial neural net routine from Matlab with the Levenberg–Marquardt
algorithm as the training function and parameters optimized across runs
(more detail in Knox et al., 2016, 2019).
_UNC: uncertainty is introduced from ANNOPTLM
gap-filling routine, as described in Knox et al. (2016, 2019).
_QC: this reports quality checks on FCH4 gap-filled
data (_ANNOPTLM) based on length of data gap: 1 signifies data gap
shorter than 2 months, and 3 signifies data gap exceeding 2 months which could lead to
poor-quality gap-filled data.
Qualifiers: positional (_V)
Positional qualifiers are used to indicate relative positions of
observations at the site. For FLUXNET-CH4, positional qualifiers are used to
distinguish soil temperature probes for sites with more than one probe.
Probe depths for each positional qualifier per site are included in the
metadata file included with data download and also in Table B7 of Delwiche
et al. (2021). For sites where the original database file release in
AmeriFlux, AsiaFlux, or EuroFlux contains multiple probes at the same
_V depth, we average values and report only the average for
each _V position. The one exception to this is site US-UAF
where the original positional qualifier from the data we downloaded from
AmeriFlux had different depths for the same qualifier. We still averaged the
probe data, so _V qualifiers from US-UAF represent an average
of more than one depth.
Missing data
Missing data are reported using -9999. Data for all days in a leap year are
reported.
Data variable names, descriptions, and units.
VariableDescriptionUnitsTIMEKEEPINGTIMESTAMP_STARTISO time stamp start of averaging period, used in half-hourly dataYYYYMMDDHHMMTIMESTAMP_ENDISO time stamp end of averaging period, used in half-hourly dataYYYYMMDDHHMMTIMESTAMPISO time stamp used in daily aggregation filesYYYYMMDDMET_RADSW_INShortwave radiation, incomingW m-2SW_OUTShortwave radiation, outgoingW m-2LW_INLongwave radiation, incomingW m-2LW_OUTLongwave radiation, outgoingW m-2PPFD_INPhotosynthetic photon flux density, incomingµmolphotonm-2s-1PPFD_OUTPhotosynthetic photon flux density, outgoingµmolphotonm-2s-1NETRADNet radiationW m-2MET_WINDUSTARFriction velocitym s-1WDWind directionDecimal degreesWSWind speedm s-1HEATHSensible heat turbulent flux (with storage term if provided by site principal investigator)W m-2LELatent heat turbulent flux (with storage term if provided by site principal investigator)W m-2GSoil heat fluxW m-2MET_ATMPAAtmospheric pressurekPaTAAir temperature∘CVPDVapor pressure deficithPaRHRelative humidity, range 0–100%MET_PRECIPPPrecipitationmmPRODUCTSNEENet ecosystem exchangeµmolCO2m-2s-1GPPGross primary productivityµmolCO2m-2s-1RECOEcosystem respirationµmolCO2m-2s-1GASESFCH4Methane (CH4) turbulent flux (no storage correction)nmol CH4 m-2 s-1MET_SOILTSSoil temperature∘CWTDWater table depth (negative values indicate below the surface)m
Annual methane flux sum and uncertainty, annual mean soil temperature, and annual mean water table depth. Column headers are explained after the table.
* Data from ID-Pag spans 365 d
from June 2016 to June 2017. Annual methane flux for each year is the sum of these 365 d, with uncertainty being calculated separately for each year.
ColumnDescriptionSITE_IDSite identification code as assigned by regional flux data networkYearData yearAnn_Flux_g_C_m-2Total annual methane flux (g C m-2)Ann_Flux_Uncertainty_g_C_m-2Gap-filling and random uncertainty associated with annual flux (g C m-2)Mean_Soil_Temp_CAnnual mean soil temperature (degree C). For sites with multiple probes, we use the probe closest to the surface.Mean_Water_Table_Depth_mAnnual mean water table depth (m)
Metadata and select data for FLUXNET-CH4 sites.
(a)SITESITESITECOUNTRYLATLONDATAYEARYEARUTCORIGINAL_DATA_ID_NAME_PERSONNEL_DOI_START_END_OFFSET_SOURCE1AT-NeuNeustiftGeorg WohlfahrtAustria47.11711.31810.18140/FLX/1669365201020121EuroFlux2BR-NpwNorthern Pantanal WetlandGeorge VourlitisBrazil-16.498-56.41210.18140/FLX/166936820132016-4AmeriFlux3BW-GumGumaCarole HelfterBotswana-18.96522.37110.18140/FLX/1669370201820182EuroFlux4BW-NxrNxaragaCarole HelfterBotswana-19.54823.17910.18140/FLX/1669518201820182EuroFlux5CA-SCBScotty Creek BogOliver Sonnentag, Manuel HelbigCanada61.309-121.29810.18140/FLX/166961320142017-7AmeriFlux6CA-SCCScotty Creek LandscapeOliver Sonnentag, Manuel HelbigCanada61.308-121.29910.18140/FLX/166962820132016-7AmeriFlux7CH-ChaChamauNina BuchmannSwitzerland47.2108.41010.18140/FLX/1669629201220161EuroFlux8CH-DavDavosNina BuchmannSwitzerland46.8159.85610.18140/FLX/1669630201620171EuroFlux9CH-Oe2Oensingen cropNina BuchmannSwitzerland47.2867.73410.18140/FLX/1669631201820181EuroFlux10CN-HguHongyuanShuli Niu, Weinan ChenChina32.845102.59010.18140/FLX/1669632201520178EuroFlux11DE-DgwDagowseeTorsten SachsGermany53.15113.05410.18140/FLX/1669633201520181EuroFlux12DE-HteHuetelmoorGerald JurasinskiGermany54.21012.17610.18140/FLX/1669634201120181EuroFlux13DE-SfNSchechenfilz NordHans Peter SchmidGermany47.80611.32810.18140/FLX/1669635201220141EuroFlux14DE-ZrkZarnekowTorsten SachsGermany53.87612.88910.18140/FLX/1669636201320181EuroFlux15FI-HyyHyytialaTimo Vesala, Ivan MammarellaFinland61.84724.29510.18140/FLX/1669637201620162EuroFlux16FI-LomLompolojankkaAnnalea LohilaFinland67.99724.20910.18140/FLX/1669638200620102EuroFlux17FI-Si2Siikaneva-2 BogTimo Vesala, Ivan Mammarella, Eeva-Stiina TuittilaFinland61.83724.19710.18140/FLX/1669639201220162EuroFlux18FI-SiiSiikanevaTimo Vesala, Ivan Mammarella,Eeva-Stiina TuittilaFinland61.83324.19310.18140/FLX/1669640201320182EuroFlux19FR-LGtLa GuetteAdrien Jacotot, Sébastien Gogo, Fatima Laggoun-Défarge, Laurent PerdereauFrance47.3232.28410.18140/FLX/1669641201720181EuroFlux20HK-MPMMai Po MangroveDerrick Lai, Jiangong LiuHong Kong22.498114.02910.18140/FLX/1669642201620188EuroFlux21ID-PagPalangkaraya undrained forestTakashi HiranoIndonesia-2.320113.90010.18140/FLX/1669643201620177EuroFlux22IT-BCiBorgo CioffiVincenzo MagliuloItaly40.52414.95710.18140/FLX/1669644201720181EuroFlux23IT-CasCastellaroGiovanni Manca, Ignacio Goded, Carsten Gruening, Ana MeijideItaly45.0708.71810.18140/FLX/1669645200920101EuroFlux24JP-BBYBibai bogMasahito UeyamaJapan43.323141.81110.18140/FLX/1669646201520189AsiaFlux25JP-MseMase rice paddy fieldAkira MiyataJapan36.054140.02710.18140/FLX/1669647201220129AsiaFlux26JP-SwLSuwa LakeHiroki IwataJapan36.047138.10810.18140/FLX/1669648201620169AsiaFlux27KR-CRKCheorwon Rice paddyYoungryel Ryu, Minseok KangKorea38.201127.25110.18140/FLX/1669649201520189AsiaFlux28MY-MLMMaludam National ParkAngela C. I. Tang, Guan Xhuan Wong, Lulie MellingMalaysia1.454111.14910.18140/FLX/1669650201420158AsiaFlux29NL-HorHorstermeerHan DolmanNetherlands52.2405.07110.18140/FLX/1669651200720091EuroFlux30NZ-KopKopuataiDave CampbellNew Zealand-37.388175.55410.18140/FLX/16696522012201513OzFlux31PH-RiFPhilippines Rice InstitutefloodedMa. Carmelita AlbertoPhilippines14.141121.26510.18140/FLX/1669653201220148EuroFlux32RU-Ch2Chersky referenceMatthias GoeckedeRussia68.617161.35110.18140/FLX/16696542014201611EuroFlux33RU-CheCherskiMatthias GoeckedeRussia68.613161.34110.18140/FLX/16696552014201611EuroFlux34RU-CokChokurdakhHan DolmanRussia70.829147.49410.18140/FLX/16696562008201611EuroFlux35RU-Fy2Fyodorovskoye dry spruceAndrej VarlaginRussia56.44832.90210.18140/FLX/1669657201520183EuroFlux36SE-DegDegeroMatthias Peichl, Mats NilssonSweden64.18219.55710.18140/FLX/1669659201420181EuroFlux37UK-LBTLondon_BTCarole HelfterUK51.522-0.13910.18140/FLX/1670207201120140EuroFlux38US-A03ARM-AMF3-OliktokRyan Sullivan, David Cook, DavidBillesbachUSA70.495-149.88210.18140/FLX/166966120152018-9AmeriFlux39US-A10ARM-NSA-BarrowRyan Sullivan, David Cook, DavidBillesbachUSA71.324-156.61510.18140/FLX/166966220122018-9AmeriFlux40US-AtqAtqasukDonatella ZonaUSA70.470-157.40910.18140/FLX/166966320132016-9AmeriFlux41US-BeoBarrow EnvironmentalObservatory (BEO) towerDonatella ZonaUSA71.281-156.61210.18140/FLX/166966420132014-8AmeriFlux42US-BesBarrow-Bes (BiocomplexityExperiment South tower)Donatella ZonaUSA71.281-156.59710.18140/FLX/166966520132015-8AmeriFlux43US-Bi1Bouldin Island AlfalfaDennis BaldocchiUSA38.099-121.49910.18140/FLX/166966620162018-8AmeriFlux44US-Bi2Bouldin Island cornDennis BaldocchiUSA38.109-121.53510.18140/FLX/166966720172018-8AmeriFlux45US-BZBBonanza Creek Thermokarst BogEugenie EuskirchenUSA64.696-148.32110.18140/FLX/166966820142016-9AmeriFlux46US-BZFBonanza Creek Rich FenEugenie EuskirchenUSA64.704-148.31310.18140/FLX/166966920142016-9AmeriFlux47US-BZSBonanza Creek Black SpruceEugenie EuskirchenUSA64.696-148.32410.18140/FLX/166967020152016-9AmeriFlux48US-CRTCurtice Walter-BergercroplandJiquen Chen, Housen ChuUSA41.628-83.34710.18140/FLX/166967120112012-5AmeriFlux49US-DPWDisney Wilderness Preserve WetlandCharles Ross Hinkle, Rosvel Bracho, Scott Graham, Brian BenscoterUSA28.052-81.43610.18140/FLX/166967220132017-5AmeriFlux
Continued.
SITESITESITECOUNTRYLATLONDATAYEARYEARUTCORIGINAL_DATA_ID_NAME_PERSONNEL_DOI_START_END_OFFSET_SOURCE50US-EDNEden Landing EcologicalReservePatty OikawaUSA37.616-122.11410.18140/FLX/166967320182018-8AmeriFlux51US-EMLEight Mile Lake Permafrostthaw gradient, Healy AlaskaTed SchuurUSA63.878-149.25410.18140/FLX/166967420152018-9AmeriFlux52US-Ho1Howland Forest (main tower)Andrew Richardson, David HollingerUSA45.204-68.74010.18140/FLX/166967520122018-5AmeriFlux53US-HRAHumnoke Farm Rice Field– Field ABenjamin RunkleUSA34.585-91.75210.18140/FLX/166967620172017-6AmeriFlux54US-HRCHumnoke Farm Rice Field – Field CBenjamin RunkleUSA34.589-91.75210.18140/FLX/166967720172017-6AmeriFlux55US-ICsImnavait Creek Watershed Wet Sedge TundraEugenie EuskirchenUSA68.606-149.31110.18140/FLX/166967820142016-9AmeriFlux56US-IvoIvotukDonatella ZonaUSA68.487-155.75010.18140/FLX/166967920132016-9AmeriFlux57US-LA1Pointe-aux-Chenes BrackishMarshKen KraussUSA29.501-90.44510.18140/FLX/166968020112012-6AmeriFlux58US-LA2Salvador WMA FreshwaterMarshKen KraussUSA29.859-90.28710.18140/FLX/166968120112013-6AmeriFlux59US-LosLost CreekAnkur DesaiUSA46.083-89.97910.18140/FLX/166968220142018-6AmeriFlux60US-MACMacArthur Agro-EcologyJed Sparks, Sam ChamberlainUSA27.163-81.18710.18140/FLX/166968320132015-5AmeriFlux61US-MRMMarsh Resource Meadowlands Mitigation BankKarina SchäferUSA40.816-74.04410.18140/FLX/1669684201220135AmeriFlux62US-MybMayberry WetlandDennis BaldocchiUSA38.050-121.76510.18140/FLX/166968520102018-8AmeriFlux63US-NC4NC_AlligatorRiverAsko NoormetsUSA35.788-75.90410.18140/FLX/166968620122016-5AmeriFlux64US-NGBNGEE Arctic BarrowMargaret TornUSA71.280-156.60910.18140/FLX/166968720122018-9AmeriFlux65US-NGCNGEE Arctic CouncilMargaret TornUSA64.861-163.70110.18140/FLX/166968820172018-9AmeriFlux66US-ORvOlentangy River Wetland Research ParkGil BohrerUSA40.020-83.01810.18140/FLX/166968920112015-5AmeriFlux67US-OWCOld Woman CreekGil BohrerUSA41.380-82.51210.18140/FLX/166969020152016-5AmeriFlux68US-PFaPark Falls/WLEFAnkur DesaiUSA45.946-90.27210.18140/FLX/166969120102018-6AmeriFlux69US-SndSherman IslandDennis BaldocchiUSA38.037-121.75410.18140/FLX/166969220102015-8Ameriflux70US-SneSherman Island RestoredWetlandDennis BaldocchiUSA38.037-121.75510.18140/FLX/166969320162018-8AmeriFlux71US-SrrSuisun marsh - Rush RanchLisamarie Windham-MyersUSA38.201-122.02610.18140/FLX/166969420142017-8AmeriFlux72US-StJSt Jones ReserveRodrigo VargasUSA39.088-75.43710.18140/FLX/166969520162016-5AmeriFlux73US-Tw1Twitchell Wetland West PondDennis BaldocchiUSA38.107-121.64710.18140/FLX/166969620112018-8AmeriFlux74US-Tw3Twitchell AlfalfaDennis BaldocchiUSA38.116-121.64710.18140/FLX/166969720132014-8AmeriFlux75US-Tw4Twitchell East End WetlandDennis BaldocchiUSA38.103-121.64110.18140/FLX/166969820132018-8AmeriFlux76US-Tw5East Pond WetlandDennis BaldocchiUSA38.107-121.64310.18140/FLX/166969920182018-8AmeriFlux77US-TwtTwitchell IslandDennis BaldocchiUSA38.109-121.65310.18140/FLX/166970020092017-8AmeriFlux78US-UafUniversity of Alaska, FairbanksMasahito UeyamaUSA64.866-147.85610.18140/FLX/166970120112018-9AmeriFlux79US-WPTWinous Point North MarshJiquen Chen, Housen ChuUSA41.465-82.99610.18140/FLX/166970220112013-5AmeriFlux
ColumnDescriptionSITE_IDSite identification code as assigned by regional flux data networkSITE_NAMESite name determined by site personnelSITE_PERSONNELPeople associated with site FLUXNET-CH4 dataCOUNTRYSite countryLATLatitudeLONLongitudeDATA_DOIDOI link for site FLUXNET-CH4 dataYEAR_STARTYear data beginYEAR_ENDYear data endUTC_OFFSETSite data offset from coordinated universal time (in hours)ORIGINAL_DATA_SOURCERegional network hosting the site's methane data that were incorporated into FLUXNET-CH4SITE_CLASSIFICATIONSite classification based on the literature description of sitesUPLAND_CLASSFor upland sites, category of upland typeIGBPInternational Geosphere–Biosphere Programme (IGBP) ecosystem surface classificationKOPPENKoppen climate zone abbreviationMEAN_ANNUAL_TEMP_C_WORLDCLIMMean annual temperature from WorldClim2 Global Climate DataMEAN_ANNUAL_PRECIP_MM_WORLDCLIMMean annual precipitation from WorldClim2 Global Climate DataMOSS_BROWNPresence/absence (1/0) brown moss. Presence/absence designated by Avni Malhotra using site literatureMOSS_SPHAGNUMPresence/absence (1/0) sphagnum moss. Presence/absence designated by Avni Malhotra using site literatureAERENCHYMATOUSPresence/absence (1/0) aerenchymatous vegetation. Presence/absence designated by Avni Malhotra using site literatureERI_SHRUBPresence/absence (1/0) ericaceous shrubs. Presence/absence designated by Avni Malhotra using site literatureTREEPresence/absence (1/0) trees. Presence/absence designated by Avni Malhotra using site literatureDOM_VEGDominant vegetation type in tower footprint. Dom_veg provided to Avni Malhotra by site personnel via survey, except 15 sites where principal investigators did not answer and Avni Malhotra estimated dominant vegetation type based on site literatureIN_SEASONALITY_ANALYSISIs site in freshwater wetland seasonality analysis? 1 = yes, 0 = no.Mean_Air_Temp_CMean annual air temperature (C)Mean_Air_Temp_stdev_CStandard deviation of annual air temperature (C)Ann_Flux_g_CH4-C_m-2Mean annual methane flux (g CH4-C m-2 yr1)Ann_Flux_stdev_g_CH4-C_m-2Standard deviation of annual methane flux (g CH4-C m-2 yr-1)JFM_flux_g_CH4-C_m-2Mean methane flux in January, February, March (g CH4-C m-2 yr-1)JFM_flux_stdev_g_CH4-C_m-2Standard deviation of methane flux in January, February, March (g CH4-C m-2 yr-1)AMJ_flux_g_CH4-C_m-2Mean methane flux in April, May, June (g CH4-C m-2 yr-1)AMJ_flux_stdev_g_CH4-C_m-2Standard deviation of methane flux in April, May, June (g CH4-C m-2 yr-1)JAS_flux_g_CH4-C_m-2Mean methane flux in July, August, September (g CH4-C m-2 yr-1)JAS_flux_stdev_g_CH4-C_m-2Standard deviation of methane flux in July, August, September (g CH4-C m-2 yr-1)OND_flux_g_CH4-C_m-2Mean methane flux in October, November, December (g CH4-C m-2 yr-1)OND_flux_stdev_g_CH4-C_m-2Standard deviation of methane flux in October, November, December (g CH4-C m-2 yr-1)SOIL_TEMP_PROBE_DEPTHSDepth of soil temperature probe (m), with negative values being under the surface
Table of bioclimatic predictor data used in the principal component analysis (PCA) of Fig. 6.
ColumnDescriptionSITE_IDSite identification code as assigned by regional flux data networkEnhanced_Vegetation_Index_(EVI)Enhanced vegetation index (unitless) from MOD13A3 (Didan, 2015), 2001–2018 monthly dataWong_Simple_Ratio_Water_Index_(SRWI)Simple ratio water index (unitless) from MOD09A1 (Vermote, 2015), ∼ 2001–2018 monthly dataLatent_Heat_(LE)Latent heat (in W m-2) from FLUXCOM (Jung et al., 2019), 2003–2013 monthly dataMean_Annual_Temperature_(MAT)Mean annual temperature (C) from BioClim (Fick and Hijman, 2017), 2001–2018 monthly data
Seasonality parameters estimated using TIMESAT software for methane flux (FCH4), gross primary productivity (GPP), air temperature (TA), and soil temperature (TS, for shallowest probe at each site).
ColumnDescriptionSITE_IDSite identification code as assigned by regional flux data networkYearData yearStart_FCH4_(DOY)Season start for elevated methane fluxes (DOY), point “f” in Fig. 1End_FCH4_(DOY)Season end for elevated methane fluxes (DOY), point “h” in Fig. 1Base_value_FCH4_(nmolCH4/m2/s)Baseline methane flux during non-elevated season (nmol CH4 m-2 s-1), average of points “a” and “b” in Fig. 1Ampl_FCH4_(nmolCH4/m2/s)Amplitude of methane flux during elevated flux season (nmol CH4 m-2 s-1), difference between point “e” in Fig. 1 and Base_value_FCH4Peak_FCH4_(DOY)Day of maximum elevated methane flux (DOY), point “g” in Fig. 1Peak_value_FCH4_(nmolCH4/m2/s)Maximum value of methane flux (nmol CH4 m-2 s-1), point “e” in Fig. 1Start_GPP_DT_(DOY)Season start for elevated GPP_DT (DOY), point “f” in Fig. 1End_GPP_DT_(DOY)Season end for elevated GPP_DT fluxes (DOY), point “h” in Fig. 1Base_value_GPP_DT_(µmolCO2/m2/s)Baseline GPP_DT flux during non-elevated season (µmol CO2 m-2 s-1), average of points “a” and “b” in Fig. 1Ampl_GPP_DT_(µmolCO2/m2/s)Amplitude of GPP_DT flux during elevated flux season (µmol CO2 m-2 s-1), difference between point “e” in Fig. 1 and Base_value_GPP_DTPeak_GPP_DT_(DOY)Day of maximum elevated GPP_DT flux (DOY), point “g” in Fig. 1Peak_value_GPP_DT_(µmolCO2/m2/s)Maximum value of GPP_DT flux (µmol CO2 m-2 s-1), point “e” in Fig. 1Probe_nameTemperature probe name as given in data filesSoil_temp_depth_mDepth of soil temperature probe (m), with negative values being under the surfaceStart_TS_(DOY)Season start for elevated TS (DOY), point “f” in Fig. 1End_TS_(DOY)Season end for elevated TS (DOY), point “h” in Fig. 1Base_value_TS_(C)Baseline TS during non-elevated season (C), average of points “a” and “b” in Fig. 1Ampl_TS_(C)Amplitude of TS during elevated temperature season (C), difference between point “e” in Fig. 1 and Base_value_TSPeak_TS_(DOY)Day of maximum elevated TS (DOY), point “g” in Fig. 1Peak_value_TS_(C)Maximum value of TS (C), point “e” in Fig. 1Start_TA_(DOY)Season start for elevated TA (DOY), point “f” in Fig. 1End_TA_(DOY)Season end for elevated TA (DOY), point “h” in Fig. 1Base_value_TA_(C)Baseline TA during non-elevated season (C), average of points “a” and “b” in Fig. 1Ampl_TA_(C)Amplitude of TA during elevated temperature season (C), difference between point “e” in Fig. 1 and Base_value_TAPeak_TA_(DOY)Day of maximum elevated TA (DOY), point “g” in Fig. 1Peak_value_TA_(C)Maximum value of TA (C), point “e” in Fig. 1
Seasonality parameters estimated using TIMESAT software for soil temperature (TS, from every probe).
ColumnDescriptionSITE_IDSite identification code as assigned by regional flux data networkYearData yearProbe_nameTemperature probe name as given in data filesSoil_temp_depth_mDepth of soil temperature probe (m), with negative values being under the surfaceStart_TS_(DOY)Season start for elevated TS (DOY), point “f” in Fig. 1End_TS_(DOY)Season end for elevated TS (DOY), point “h” in Fig. 1Base_value_TS_(C)Baseline TS during non-elevated season (C), average of points “a” and “b” in Fig. 1Ampl_TS_(C)Amplitude of TS during elevated temperature season (C), difference between point “e” in Fig. 1 and Base_value_TSPeak_TS_(DOY)Day of maximum elevated TS (DOY), point “g” in Fig. 1Peak_value_TS_(C)Maximum value of TS (C), point “e” in Fig. 1
SITE_IDYearProbe nameSoil_temp_depth_mAdditional_notes225US-OWCTS_1-0.05226US-OWCTS_2-0.3227US-PFA228US-SNDTS_1-0.08229US-SNDTS_2-0.16230US-SNDTS_3231US-SNDTS_4232US-SNDTS_5233US-SNDTS_6234US-SNETS_1-0.01235US-SNETS_2-0.02236US-SNETS_3-0.08237US-SNETS_4-0.16238US-SNETS_5-0.32239US-SRRTS_1240US-SRRTS_2241US-SRRTS_3242US-SRRTS_4243US-SRRTS_5244US-STJTS_2-0.05245US-STJTS_3-0.1246US-TW1TS_1-0.02247US-TW1TS_2-0.04248US-TW1TS_3-0.08249US-TW1TS_4-0.16250US-TW1TS_5-0.32251US-TW3TS_1-0.02252US-TW3TS_2-0.04253US-TW3TS_3-0.08254US-TW3TS_4-0.16255US-TW3TS_5-0.32256US-TW4TS_1-0.02257US-TW4TS_2-0.04258US-TW4TS_3-0.08259US-TW4TS_4-0.16260US-TW4TS_5-0.32261US-TW5TS_1-0.02262US-TW5TS_2-0.1263US-TW5TS_3-0.02264US-TW5TS_4-0.08265US-TW5TS_5-0.16266US-TWTTS_1-0.02267US-TWTTS_2-0.04268US-TWTTS_3-0.08269US-TWTTS_4-0.16270US-TWTTS_5-0.32271US-UAFTS_1-0.09average of 3 depths: -0.15, -0.02, -0.1272US-UAFTS_2-0.18333average of 3 depths: -0.3, -0.05, -0.2273US-UAFTS_3-0.28333average of 3 depths: -0.3, -0.05, -0.2274US-UAFTS_4-0.36667average of 3 depths: -0.5, -0.2, -0.4275US-UAFTS_5-0.5average of 2 depths: -0.7, -0.3276US-UAFTS_6-0.6average of 2 depths: -0.8, -0.4277US-UAFTS_7-0.75average of 2 depths: -1, -0.5278US-UAFTS_8-0.925average of 2 depths: -1.25, 0.6,279US-UAFTS_9-1280US-WPTTS_1-0.1281US-WPTTS_2-0.3
ColumnDescriptionSITE_IDSite identification code as assigned by regional flux data networkYearWhen relevant, information about time span of probe location; if blank, assume constant probe depthProbe_nameTemperature probe name as given in data filesSoil_temp_depth_mDepth of soil temperature probe (m), with negative values being under the surfaceAdditional_notesWhen relevant, additional information about site
Author contributions
KBD oversaw the data release, performed the seasonality
analysis, gathered metadata, and prepared the manuscript with contributions
from all co-authors. SHK gathered and standardized the data, and
gap-filled the CH4 flux data. AM prepared the manuscript and
gathered metadata. EFC did the representativeness
analysis and prepared the manuscript. GM gathered data and
prepared the manuscript. RBJ oversaw the data collection,
processing, analysis, and release. DC and YWC
oversaw the FLUXNET-CH4 dataset release on https://fluxnet.org (last access: 6 July 2021). DP,
EC, and CT did the data collection, curation, and
pre-processing for all of the sites outside North and South America.
The remaining co-authors contributed eddy-covariance data to FLUXNET-CH4 dataset
and/or participated in editing the manuscript.
Competing interests
The authors declare that they have no conflict of interest.
Disclaimer
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
We acknowledge primary support from the Gordon and Betty Moore Foundation
(grant GBMF5439, “Advancing Understanding of the Global Methane Cycle”;
Stanford University) and from the John Wesley Powell Center for Analysis and
Synthesis of the US Geological Survey (“Wetland FLUXNET Synthesis for
Methane” working group). Benjamin R. K. Runkle was supported by the US
National Science Foundation CBET CAREER Award 1752083. Ankur R. Desai
acknowledges support of the DOE AmeriFlux Network Management Project.
Masahito Ueyama was supported by ArCS II (JPMXD1420318865) and JSPS KAKENHI
(20K21849). Dario Papale and Nina Buchmann acknowledge the support of the
RINGO (GA 730944) H2020 EU project. Nina Buchmann and Kathrin Fuchs
acknowledge the SNF project M4P (40FA40_154245/1) and
InnoFarm (407340_172433). Nina Buchmann acknowledges support
from the SNF for ICOS-CH phases 1 and 2 (20FI21_148992,
20FI20_173691). Carlo Trotta acknowledges the support of the
E-SHAPE (GA 820852) H2020 EU project. William J. Riley was supported by the
US Department of Energy, BER, RGCM, RUBISCO project under contract no.
DEAC02-05CH11231. Jessica Turner acknowledges support from NSF GRFP
(DGE-1747503) and NTL LTER (DEB-1440297). Minseok Kang was supported by the
National Research Foundation of Korea (NRF-2018 R1C1B6002917). Carole
Helfter acknowledges the support of the UK Natural Environment Research
Council (the Global Methane Budget project, grant number NE/N015746/1).
Rodrigo Vargas acknowledges support from the National Science Foundation
(1652594). Dennis Baldocchi acknowledges the California Department of Water
Resources for a funding contract from the California Department of Fish and
Wildlife and the United States Department of Agriculture (NIFA grant
#2011-67003-30371), as well as the US Department of Energy's Office of
Science (AmeriFlux contract #7079856) for funding the AmeriFlux core
sites. US-A03 and US-A10 are operated by the Atmospheric Radiation
Measurement (ARM) user facility, a US Department of Energy's Office of
Science user facility managed by the Biological and Environmental Research
Program.
Work at ANL was supported by the US Department of Energy's Office of
Science and Office of Biological and Environmental Research under contract
DE-AC02-06CH11357. Any use of trade, firm, or product names is for
descriptive purposes only and does not imply endorsement by the US
government. The CH-Dav, DE-SfN, FI-Hyy, FI-Lom, FI-Sii, FR-LGt, IT-BCi,
SE-Deg, and SE-Sto sites are part of the ICOS European Research
Infrastructure. Oliver Sonnentag acknowledges funding by the Canada Research
Chairs, Canada Foundation for Innovation Leaders Opportunity Fund, and
Natural Sciences and Engineering Research Council Discovery Grant programs
for work at CA-SCC and CA-SCB. Benjamin Poulter acknowledges support from
the NASA Carbon Cycle and Ecosystems program. Derrick Lai acknowledges the
support of the Research Grants Council of the Hong Kong Special
Administrative Region, China (project no. CUHK 458913). We thank Nathaniel
Goenawan for his help with the representativeness analysis.
Financial support
This research has been supported by the Gordon and Betty Moore Foundation (grant no. GBMF5439), the John Wesley Powell Center for Analysis and Synthesis of the US Geological Survey, the National Science Foundation (grant nos. 1752083, DGE-1747503, and 1652594), the ArCS II (grant no. JPMXD1420318865), the JSPS KAKENHI (grant no. 20K21849), the RINGO (grant no. GA 730944), the SNF (grant nos. 40FA40_154245/1, 20FI21_148992, and 20FI20_173691), the InnoFarm (grant no. 407340_172433), the E-SHAPE (grant no. GA 820852), the US Department of Energy (grant nos. DEAC02-05CH11231, 7079856, and DE-AC02-06CH11357), the NTL LTER (grant no. DEB-1440297), the National Research Foundation of Korea (grant no. NRF-2018 R1C1B6002917), the UK Natural Environment Research Council (grant no. NE/N015746/1), the California Department of Fish and Wildlife (grant no. 2011-67003-30371), the Canada Research Chairs, the Canada Foundation for Innovation Leaders Opportunity Fund, the Natural Sciences and Engineering Research Council Discovery Grant programs, and the NASA Carbon Cycle and Ecosystems program.
Review statement
This paper was edited by David Carlson and reviewed by two anonymous referees.
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