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.

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.

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 Non-stomatal processes reduce gross primary productivity in temperate forest ecosystems during severe edaphic drought

2020 ◽  
Vol 375 (1810) ◽  
pp. 20190527 ◽  
Author(s):  
Louis Gourlez de la Motte ◽  
Quentin Beauclaire ◽  
Bernard Heinesch ◽  
Mathias Cuntz ◽  
Lenka Foltýnová ◽  
...  
Keyword(s):  
Primary Productivity ◽  
Temperate Forest ◽  
Forest Ecosystems ◽  
Stomatal Closure ◽  
Gross Primary Productivity ◽  
Severe Drought ◽  
Continental Scale ◽  
European Forest ◽  
Ecosystem Flux ◽  
The Impact

Severe drought events are known to cause important reductions of gross primary productivity ( GPP ) in forest ecosystems. However, it is still unclear whether this reduction originates from stomatal closure (Stomatal Origin Limitation) and/or non-stomatal limitations (Non-SOL). In this study, we investigated the impact of edaphic drought in 2018 on GPP and its origin (SOL, NSOL) using a dataset of 10 European forest ecosystem flux towers. In all stations where GPP reductions were observed during the drought, these were largely explained by declines in the maximum apparent canopy scale carboxylation rate V CMAX,APP (NSOL) when the soil relative extractable water content dropped below around 0.4. Concurrently, we found that the stomatal slope parameter ( G 1 , related to SOL) of the Medlyn et al . unified optimization model linking vegetation conductance and GPP remained relatively constant. These results strengthen the increasing evidence that NSOL should be included in stomatal conductance/photosynthesis models to faithfully simulate both GPP and water fluxes in forest ecosystems during severe drought. This article is part of the theme issue ‘Impacts of the 2018 severe drought and heatwave in Europe: from site to continental scale’.

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

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

Atmosphere ◽  
2019 ◽  
Vol 10 (10) ◽  
pp. 568 ◽  
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.

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 Sensitivity of gross primary productivity to climatic drivers during the summer drought of 2018 in Europe

2020 ◽  
Vol 375 (1810) ◽  
pp. 20190747 ◽  
Author(s):  
Zheng Fu ◽  
Philippe Ciais ◽  
Ana Bastos ◽  
Paul C. Stoy ◽  
Hui Yang ◽  
...  
Keyword(s):  
Primary Productivity ◽  
Vapour Pressure Deficit ◽  
Photosynthetic Photon Flux Density ◽  
Gross Primary Productivity ◽  
Environmental Drivers ◽  
Photon Flux ◽  
Photon Flux Density ◽  
Severe Drought ◽  
Summer Drought ◽  
Continental Scale

In summer 2018, Europe experienced a record drought, but it remains unknown how the drought affected ecosystem carbon dynamics. Using observations from 34 eddy covariance sites in different biomes across Europe, we studied the sensitivity of gross primary productivity (GPP) to environmental drivers during the summer drought of 2018 versus the reference summer of 2016. We found a greater drought-induced decline of summer GPP in grasslands (−38%) than in forests (−10%), which coincided with reduced evapotranspiration and soil water content (SWC). As compared to the ‘normal year’ of 2016, GPP in different ecosystems exhibited more negative sensitivity to summer air temperature (Ta) but stronger positive sensitivity to SWC during summer drought in 2018, that is, a stronger reduction of GPP with soil moisture deficit. We found larger negative effects of Ta and vapour pressure deficit (VPD) but a lower positive effect of photosynthetic photon flux density on GPP in 2018 compared to 2016, which contributed to reduced summer GPP in 2018. Our results demonstrate that high temperature-induced increases in VPD and decreases in SWC aggravated drought impacts on GPP. This article is part of the theme issue ‘Impacts of the 2018 severe drought and heatwave in Europe: from site to continental scale’.

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