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

2001 ◽  
Vol 11 (1) ◽  
pp. 239 ◽  
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

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

2001 ◽  
Vol 11 (1) ◽  
pp. 239-252 ◽  
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

scholarly journals Technical note: Dynamic INtegrated Gap-filling and partitioning for OzFlux (DINGO)

2017 ◽  
Vol 14 (6) ◽  
pp. 1457-1460 ◽  
Author(s):  
Jason Beringer ◽  
Ian McHugh ◽  
Lindsay B. Hutley ◽  
Peter Isaac ◽  
Natascha Kljun
Keyword(s):  
Primary Productivity ◽  
Active Sites ◽  
Ecosystem Respiration ◽  
Technical Note ◽  
Gross Primary Productivity ◽  
Quality Data ◽  
Gap Filling ◽  
Continental Scale ◽  
Flux Data ◽  
Processing Pathways

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.

Inferring ecosystem-level rates of gross primary productivity, respiration, and evapotranspiration with automatic light-dark measurement chambers

2021 ◽  
Author(s):  
Klaus Steenberg Larsen ◽  
Johannes Wilhelmus Maria Pullens ◽  
Linsey Avila ◽  
Sander Bruun ◽  
Ji Chen ◽  
...  
Keyword(s):  
Primary Productivity ◽  
Ecosystem Respiration ◽  
Gross Primary Productivity ◽  
Eddy Flux ◽  
Dark Mode ◽  
Dark Chamber ◽  
Closed Chamber ◽  
Flux Measurements ◽  
Ecosystem Level ◽  
Set Up

<p>In experimental ecosystem ecology, plot sizes are most often too small to apply eddy flux techniques and estimation of ecosystem gas exchange rates relies on various chamber measurement technologies. Furthermore, drained areas often results in increased growth of trees which complicates application of eddy flux measurements on small plots.</p><p>We combined ECO<sub>2</sub>FluX ecosystem-level automatic chambers (prenart.dk) with an LI-8100/LI-8150 multiplexer systems (licor.com) in a range of Danish and Norwegian ecosystems experiments spanning agriculture, grassland/heathlands and peatland ecosystems. The automatic closed, none-steady state chambers each cover an area of 3,117 cm<sup>2</sup> (63 cm diameter), are 80 cm tall (volume: 250L), and are capable of switching automatically between transparent and darkened mode, enabling separation of light-sensitive and light-indifferent processes in the ecosystems covered. For CO<sub>2</sub> fluxes, net exchange (NEE) was estimated as the flux in transparent mode, ecosystem respiration (R<sub>E</sub>) in darkened mode, while Gross Ecosystem Productivity (GPP) was estimated as NEE – R<sub>E</sub>.</p><p>Chambers were set up to measure gas concentrations every second using enclosure times of 4-5 minutes, first in light mode and 10-30 minutes later in dark mode, with 3-48 repetitions per day. The longest time series spans 5 years of measurements and contain >60,000 point measurements.</p><p>In this presentation, we will present an analysis of the ability of the light-dark chamber data to infer ecosystem-level rates of gross primary productivity, respiration, net CO<sub>2</sub> exchange, and evapotranspiration. In the two Norwegian peatland sites, flux measurements may be compared directly with eddy flux measurements. We also compare the rates of the direct estimates of GPP from the light-dark chamber measurements to estimates inferred from using the light (NEE) measurements only followed by applying methodologies normally used for eddy flux measurements. This comparison may help constrain potential biases in both the closed chamber and eddy flux techniques. Finally, we investigate the ability of using such closed chambers to estimate ecosystem evapotranspiration rates at the plot scale. Such application may be useful for estimating the effects on evapotranspiration in field-scale experiments manipulating the ecosystem water balance either directly or indirectly.</p>

scholarly journals Dynamic INtegrated Gap-filling and partitioning for OzFlux (DINGO)

2016 ◽  
Author(s):  
Jason Beringer ◽  
Ian McHugh ◽  
Lindsay B. Hutley ◽  
Peter Isaac ◽  
Natascha Kljun
Keyword(s):  
Primary Productivity ◽  
Active Sites ◽  
Ecosystem Respiration ◽  
Gross Primary Productivity ◽  
Carbon Accounting ◽  
Quality Data ◽  
Gap Filling ◽  
Continental Scale ◽  
Flux Data ◽  
Robust Measurements

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 and gross primary productivity and subsequently provides diagnostics and results. We outline the processing pathways and methodologies that are applied in DINGO (v12a) 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; and 5) diagnostic, summary and results outputs. Opportunities remain for DINGO to incorporate robust measurements of uncertainty for application in land management and carbon accounting. In addition, footprint information is crucial in understanding and interpreting the scale and spatial influence of flux measurements. Both these features are scheduled for the next release (v13) but are detailed here. 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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