Basic and extensible post-processing of eddy covariance flux data with REddyProc

Wutzler, Thomas; Moffat, Antje Maria; Migliavacca, Mirco; Knauer, Jürgen; Sickel, Kerstin; Šigut, Ladislav; Menzer, Olaf; Reichstein, Markus

doi:10.5194/bg-15-5015-2018

Cited by 647 publications

(529 citation statements)

References 34 publications

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“…In brief, the nighttime flux (Rg < 10 W/m 2 ) of NEE (i.e., only respiration) is extrapolated from nighttime to daytime through a temperature response function, which is based on short term temperature sensitivities (for details see [56]). The u*-threshold, gap-filling, and partitioning was performed with the R package REddyproc [57].…”

Section: Ec Data Processing and Flux Partitioning To Gppmentioning

confidence: 99%

Using Near-Infrared-Enabled Digital Repeat Photography to Track Structural and Physiological Phenology in Mediterranean Tree–Grass Ecosystems

Luo

El-Madany

Filippa³

et al. 2018

Remote Sensing

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Tree-grass ecosystems are widely distributed. However, their phenology has not yet been fully characterized. The technique of repeated digital photographs for plant phenology monitoring (hereafter referred as PhenoCam) provide opportunities for long-term monitoring of plant phenology, and extracting phenological transition dates (PTDs, e.g., start of the growing season). Here, we aim to evaluate the utility of near-infrared-enabled PhenoCam for monitoring the phenology of structure (i.e., greenness) and physiology (i.e., gross primary productivity-GPP) at four tree-grass Mediterranean sites. We computed four vegetation indexes (VIs) from PhenoCams: (1) green chromatic coordinates (GCC), (2) normalized difference vegetation index (CamNDVI), (3) near-infrared reflectance of vegetation index (CamNIRv), and (4) ratio vegetation index (CamRVI). GPP is derived from eddy covariance flux tower measurement. Then, we extracted PTDs and their uncertainty from different VIs and GPP. The consistency between structural (VIs) and physiological (GPP) phenology was then evaluated. CamNIRv is best at representing the PTDs of GPP during the Green-up period, while CamNDVI is best during the Dry-down period. Moreover, CamNIRv outperforms the other VIs in tracking growing season length of GPP. In summary, the results show it is promising to track structural

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Section: Ec Data Processing and Flux Partitioning To Gppmentioning

confidence: 99%

Using Near-Infrared-Enabled Digital Repeat Photography to Track Structural and Physiological Phenology in Mediterranean Tree–Grass Ecosystems

Luo

El-Madany

Filippa³

et al. 2018

Remote Sensing

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“…We applied two gap-filling approaches (a) mean diurnal course (MDC; Dengel, Levy, Grace, Jones, & Skiba, 2011;Sakabe et al, 2018;Wong et al, 2018) and (b) marginal distribution sampling (MDS; Alberto et al, 2014;Dalmagro et al, 2019;Tang et al, 2018) using the REddyProc package (Wutzler et al, 2018). The MDC is a simple interpolation technique where the missing value is replaced with the averaged value of the adjacent days at exactly that time of day (Falge et al, 2001).…”

Section: Eddy Covariance Data Processingmentioning

confidence: 99%

“…The emissions were similar from both methods at the natural forest (Mann-Whitney test; p = .34, Finkelstein and Sims (2001). The standard deviation of three different flux values derived from friction velocity (u*) thresholds of 5th, 50th, and 95th percentiles were applied as an uncertainty due to u* threshold (σ 2 ) using the REddyProc package (Wutzler et al, 2018).…”

Section: Eddy Covariance Data Processingmentioning

confidence: 99%

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Impact of forest plantation on methane emissions from tropical peatland

Deshmukh¹,

Julius²,

Evans

et al. 2020

Global Change Biology

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Tropical peatlands are a known source of methane (CH4) to the atmosphere, but their contribution to atmospheric CH4 is poorly constrained. Since the 1980s, extensive areas of the peatlands in Southeast Asia have experienced land‐cover change to smallholder agriculture and forest plantations. This land‐cover change generally involves lowering of groundwater level (GWL), as well as modification of vegetation type, both of which potentially influence CH4 emissions. We measured CH4 exchanges at the landscape scale using eddy covariance towers over two land‐cover types in tropical peatland in Sumatra, Indonesia: (a) a natural forest and (b) an Acacia crassicarpa plantation. Annual CH4 exchanges over the natural forest (9.1 ± 0.9 g CH4 m−2 year−1) were around twice as high as those of the Acacia plantation (4.7 ± 1.5 g CH4 m−2 year−1). Results highlight that tropical peatlands are significant CH4 sources, and probably have a greater impact on global atmospheric CH4 concentrations than previously thought. Observations showed a clear diurnal variation in CH4 exchange over the natural forest where the GWL was higher than 40 cm below the ground surface. The diurnal variation in CH4 exchanges was strongly correlated with associated changes in the canopy conductance to water vapor, photosynthetic photon flux density, vapor pressure deficit, and air temperature. The absence of a comparable diurnal pattern in CH4 exchange over the Acacia plantation may be the result of the GWL being consistently below the root zone. Our results, which are among the first eddy covariance CH4 exchange data reported for any tropical peatland, should help to reduce the uncertainty in the estimation of CH4 emissions from a globally important ecosystem, provide a more complete estimate of the impact of land‐cover change on tropical peat, and develop science‐based peatland management practices that help to minimize greenhouse gas emissions.

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“…This MDS method via looking up the site-specific influencing variables has been shown as a reliable method for gap-filling CH 4 flux data (Helbig et al, 2017;Hommeltenberg et al, 2014). The gap-filled net CO 2 fluxes were then separated into total respiration (RE) and gross primary production (GPP) using a nighttime-based partitioning method (Reichstein et al, 2005) implemented in the REddyProc online tool (Wutzler et al, 2018).…”

Section: Eddy Covariance Measurementsmentioning

confidence: 99%

The Net Landscape Carbon Balance—Integrating terrestrial and aquatic carbon fluxes in a managed boreal forest landscape in Sweden

Chi

Nilsson

Laudon

et al. 2020

Global Change Biology

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The boreal biome exchanges large amounts of carbon (C) and greenhouse gases (GHGs) with the atmosphere and thus significantly affects the global climate. A managed boreal landscape consists of various sinks and sources of carbon dioxide (CO2), methane (CH4), and dissolved organic and inorganic carbon (DOC and DIC) across forests, mires, lakes, and streams. Due to the spatial heterogeneity, large uncertainties exist regarding the net landscape carbon balance (NLCB). In this study, we compiled terrestrial and aquatic fluxes of CO2, CH4, DOC, DIC, and harvested C obtained from tall‐tower eddy covariance measurements, stream monitoring, and remote sensing of biomass stocks for an entire boreal catchment (~68 km2) in Sweden to estimate the NLCB across the land–water–atmosphere continuum. Our results showed that this managed boreal forest landscape was a net C sink (NLCB = 39 g C m−2 year−1) with the landscape–atmosphere CO2 exchange being the dominant component, followed by the C export via harvest and streams. Accounting for the global warming potential of CH4, the landscape was a GHG sink of 237 g CO2‐eq m−2 year−1, thus providing a climate‐cooling effect. The CH4 flux contribution to the annual GHG budget increased from 0.6% during spring to 3.2% during winter. The aquatic C loss was most significant during spring contributing 8% to the annual NLCB. We further found that abiotic controls (e.g., air temperature and incoming radiation) regulated the temporal variability of the NLCB whereas land cover types (e.g., mire vs. forest) and management practices (e.g., clear‐cutting) determined their spatial variability. Our study advocates the need for integrating terrestrial and aquatic fluxes at the landscape scale based on tall‐tower eddy covariance measurements combined with biomass stock and stream monitoring to develop a holistic understanding of the NLCB of managed boreal forest landscapes and to better evaluate their potential for mitigating climate change.

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Basic and extensible post-processing of eddy covariance flux data with REddyProc

Cited by 647 publications

References 34 publications

Using Near-Infrared-Enabled Digital Repeat Photography to Track Structural and Physiological Phenology in Mediterranean Tree–Grass Ecosystems

Using Near-Infrared-Enabled Digital Repeat Photography to Track Structural and Physiological Phenology in Mediterranean Tree–Grass Ecosystems

Impact of forest plantation on methane emissions from tropical peatland

The Net Landscape Carbon Balance—Integrating terrestrial and aquatic carbon fluxes in a managed boreal forest landscape in Sweden

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