Estimating daytime ecosystem respiration from eddy-flux data

Abstract. Standardised, quality-controlled and robust data from flux networks underpin the understanding of ecosystem processes and tools necessary to support the management of natural resources, including water, carbon and nutrients for environmental and production benefits. The Australian regional flux network (OzFlux) currently has 23 active sites and aims to provide a continental-scale national research facility to monitor and assess Australia's terrestrial biosphere and climate for improved predictions. Given the need for standardised and effective data processing of flux data, we have developed a software suite, called the Dynamic INtegrated Gap-filling and partitioning for OzFlux (DINGO), that enables gap-filling and partitioning of the primary fluxes into ecosystem respiration (Fre) and gross primary productivity (GPP) and subsequently provides diagnostics and results. We outline the processing pathways and methodologies that are applied in DINGO (v13) to OzFlux data, including (1) gap-filling of meteorological and other drivers; (2) gap-filling of fluxes using artificial neural networks; (3) the u* threshold determination; (4) partitioning into ecosystem respiration and gross primary productivity; (5) random, model and u* uncertainties; and (6) diagnostic, footprint calculation, summary and results outputs. DINGO was developed for Australian data, but the framework is applicable to any flux data or regional network. Quality data from robust systems like DINGO ensure the utility and uptake of the flux data and facilitates synergies between flux, remote sensing and modelling.

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Seasonal variation in ecosystem parameters derived from FLUXNET data

Biogeosciences Discussions ◽

10.5194/bgd-6-2863-2009 ◽

2009 ◽

Vol 6 (2) ◽

pp. 2863-2912 ◽

Cited By ~ 10

Author(s):

M. Groenendijk ◽

M. K. van der Molen ◽

A. J. Dolman

Keyword(s):

Carbon Dioxide ◽

Seasonal Variation ◽

Ecosystem Respiration ◽

Vegetation Type ◽

Correlation Coefficients ◽

Carbon Dioxide Flux ◽

Model Parameters ◽

Flux Data ◽

Quality Checks ◽

Parameter Values

Abstract. The carbon dioxide sink is in a complex way related to weather and climate. In order to better understand the relationship and feedbacks, we present a methodology to simulate observed carbon dioxide flux data with a simple vegetation model (5PM) with weekly varying model parameters. The model parameters explain the interaction between vegetation and seasonal climate more general than the flux data. Two parameters (Rref and E0) are related to ecosystem respiration and three parameters (Jm, α and λ) to photosynthesis and transpiration. We quantified the weekly variability of ecosystem parameters as a function of vegetation type and climate region. After statistical quality checks 121 FLUXNET sites were available for analysis of the weekly varying model parameters. The simulations of these sites have high correlation coefficients (r2=0.6 to 0.8) between the observed and simulated carbon and water fluxes. With weekly parameters we determined average seasonal cycles for the different combinations of vegetation type and climate regions (PFTs). The variation between PFTs is large, which provides an excellent dataset to study the differences in ecosystem characteristics. In general we observed that needleleaf forests and grasslands in warmer climates have relatively constant parameter values during the year. Broadleaf forests in all climate regions have large seasonal variation for each of the five parameters. In boreal regions parameter values are always lower than in temperate regions. A large seasonality of the model parameters indicates a strong relation between vegetation and climate. This suggests that climate change will have the largest impact on the terrestrial carbon fluxes in boreal regions and for deciduous forests, and less for grasslands and evergreen forests.

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GROSS PRIMARY PRODUCTIVITY IN DUKE FOREST: MODELING SYNTHESIS OF CO2EXPERIMENT AND EDDY–FLUX DATA

Ecological Applications ◽

10.1890/1051-0761(2001)011[0239:gppidf]2.0.co;2 ◽

2001 ◽

Vol 11 (1) ◽

pp. 239-252 ◽

Cited By ~ 5

Author(s):

Yiqi Luo ◽

Belinda Medlyn ◽

Dafeng Hui ◽

David Ellsworth ◽

James Reynolds ◽

...

Keyword(s):

Primary Productivity ◽

Gross Primary Productivity ◽

Eddy Flux ◽

Flux Data ◽

Forest Modeling

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Processing Arctic Eddy-Flux Data Using a Simple Carbon-Exchange Model Embedded in the Ensemble Kalman Filter

Ecological Applications ◽

10.1890/09-0876 ◽

2009 ◽

pp. 100319061507001 ◽

Cited By ~ 1

Author(s):

Edward Rastetter ◽

Mathew Williams ◽

Kevin Griffin ◽

Bonnie Kwiatkowski ◽

Gabrielle Tomasky ◽

...

Keyword(s):

Kalman Filter ◽

Ensemble Kalman Filter ◽

Exchange Model ◽

Carbon Exchange ◽

Eddy Flux ◽

Flux Data

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A Method for Estimating Annual Cumulative Soil/Ecosystem Respiration and CH4 Flux from Sporadic Data Collected Using the Chamber Method

Atmosphere ◽

10.3390/atmos10100623 ◽

2019 ◽

Vol 10 (10) ◽

pp. 623

Author(s):

Meng Yang ◽

Guirui Yu ◽

Nianpeng He ◽

John Grace ◽

Qiufeng Wang ◽

...

Keyword(s):

Ecosystem Respiration ◽

Estimation Method ◽

Terrestrial Ecosystems ◽

Soil Ecosystem ◽

Flux Chamber ◽

Ch4 Flux ◽

Continuous Sampling ◽

Chamber Method ◽

Flux Data ◽

Annual Flux

Measurements of greenhouse gas fluxes over many ecosystems have been made as part of the attempt to quantify global carbon and nitrogen cycles. In particular, annual flux observations are of great value for regional flux assessments, as well as model development and optimization. The chamber method is a popular approach for soil/ecosystem respiration and CH4 flux observations of terrestrial ecosystems. However, in situ flux chamber measurements are usually made with non-continuous sampling. To date, efficient methods for the application of such sporadic data to upscale temporally and obtain annual cumulative fluxes have not yet been determined. To address this issue, we tested the adequacy of non-continuous sampling using multi-source data aggregation. We collected 330 site-years monthly soil/ecosystem respiration and 154 site-years monthly CH4 flux data in China, all obtained using the chamber method. The data were randomly divided into a training group and verification group. Fluxes of all possible sampling months of a year, i.e., 4094 different month combinations were used to obtain the annual cumulative flux. The results showed a good linear relationship between the monthly flux and the annual cumulative flux. The flux obtained during the warm season from May to October generally played a more important role in annual flux estimations, as compared to other months. An independent verification analysis showed that the monthly flux of 1 to 4 months explained up to 67%, 89%, 94%, and 97% of the variability of the annual cumulative soil/ecosystem respiration and 92%, 99%, 99%, and 99% of the variability of the annual cumulative CH4 flux. This study supports the use of chamber-observed sporadic flux data, which remains the most commonly-used method for annual flux estimating. The flux estimation method used in this study can be used as a guide for designing sampling programs with the intention of estimating the annual cumulative flux.

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Gross Primary Productivity in Duke Forest: Modeling Synthesis of CO 2 Experiment and Eddy-Flux Data

Ecological Applications ◽

10.2307/3061070 ◽

2001 ◽

Vol 11 (1) ◽

pp. 239 ◽

Cited By ~ 5

Author(s):

Yiqi Luo ◽

Belinda Medlyn ◽

Dafeng Hui ◽

David Ellsworth ◽

James Reynolds ◽

...

Keyword(s):

Primary Productivity ◽

Gross Primary Productivity ◽

Eddy Flux ◽

Flux Data ◽

Forest Modeling

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New Gap-Filling Strategies for Long-Period Flux Data Gaps Using a Data-Driven Approach

Atmosphere ◽

10.3390/atmos10100568 ◽

2019 ◽

Vol 10 (10) ◽

pp. 568 ◽

Cited By ~ 2

Author(s):

Minseok Kang ◽

Kazuhito Ichii ◽

Joon Kim ◽

Yohana M. Indrawati ◽

Juhan Park ◽

...

Keyword(s):

Machine Learning ◽

Ecosystem Respiration ◽

Gross Primary Production ◽

Data Driven ◽

Series Data ◽

Gap Filling ◽

Long Period ◽

Flux Data ◽

Data Gaps ◽

Data Driven Approach

In the Korea Flux Monitoring Network, Haenam Farmland has the longest record of carbon/water/energy flux measurements produced using the eddy covariance (EC) technique. Unfortunately, there are long gaps (i.e., gaps longer than 30 days), particularly in 2007 and 2014, which hinder attempts to analyze these decade-long time-series data. The open source and standardized gap-filling methods are impractical for such long gaps. The data-driven approach using machine learning and remote-sensing or reanalysis data (i.e., interpolating/extrapolating EC measurements via available networks temporally/spatially) for estimating terrestrial CO2/H2O fluxes at the regional/global scale is applicable after appropriate modifications. In this study, we evaluated the applicability of the data-driven approach for filling long gaps in flux data (i.e., gross primary production, ecosystem respiration, net ecosystem exchange, and evapotranspiration). We found that using a longer training dataset in the machine learning generally produced better model performance, although there was a greater possibility of missing interannual variations caused by ecosystem state changes (e.g., changes in crop variety). Based on the results, we proposed gap-filling strategies for long-period flux data gaps and used them to quantify the annual sums with uncertainties in 2007 and 2014. The results from this study have broad implications for long-period gap-filling at other sites, and for the estimation of regional/global CO2/H2O fluxes using a data-driven approach.

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A Critical Zone Approach to Carbon Fluxes in the Arctic Tundra

10.5194/egusphere-egu2020-2956 ◽

2020 ◽

Author(s):

Mariasilvia Giamberini ◽

Ilaria Baneschi ◽

Matteo Lelli ◽

Marta Magnani ◽

Brunella Raco ◽

...

Keyword(s):

Active Layer ◽

Ecosystem Respiration ◽

Carbon Fluxes ◽

Arctic Tundra ◽

Critical Zone ◽

Climate Feedback ◽

The Arctic ◽

Svalbard Archipelago ◽

Accumulation Chamber ◽

Flux Data

<p>Arctic tundra is currently undergoing significant changes induced by the effects of a rapid temperature rise, that in the Arctic is about twice as fast as in the rest of the world. The response of the system composed by the permafrost active layer, soil and vegetation is especially relevant. In fact, it is still unclear whether the system will turn from a carbon sink to a carbon source, owing to the interplay of two opposite phenomena: the increasing time span of the growing season, favouring Net Ecosystem Production (NEP), and the increasing soil temperatures, favouring degradation of organic matter through heterotrophic respiration (HR) and then creating a positive climate feedback. In this work, we analyse soil-vegetation-atmosphere CO<sub>2</sub> flux data of a field campaign conducted in the Bayelva river basin, Spitzbergen, in the Svalbard Archipelago (NO) during summer 2019, measured by a portable accumulation chamber. We use a &#8220;Critical Zone&#8221; perspective, considering the multiple interactions between biotic and abiotic components. We measured the Net Ecosystem Exchange (NEE) and Ecosystem Respiration (ER) along a slope gradient at different degrees of soil humidity and active layer depths, relating flux data to climate and environmental parameters, soil physical-chemical parameters and vegetation type. The statistical empirical relationships between variables are analysed to identify the main drivers of carbon exchanges. An empirical data-driven model is built to describe the coupled dynamics of soil, vegetation, water and atmosphere that contributes to budgeting the carbon cycle in the Arctic Critical Zone. A comparison of the carbon fluxes obtained with the accumulation chamber method and an Eddy Covariance tower located in the same area is also addressed.</p>

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