A new assessment of European forests carbon exchanges by eddy fluxes and artificial neural network spatialization

Papale, Dario; Valentini, Riccardo

doi:10.1046/j.1365-2486.2003.00609.x

Cited by 507 publications

(398 citation statements)

References 10 publications

(9 reference statements)

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“…In the dataset, the half-hourly GPP were calculated by the net ecosystem exchange and ecosystem respiration. The data gaps associated with equipment failures and unsuitable micrometeorological conditions were filled using the Artificial Neural Network method (Papale and Valentini, 2003) and/or the Marginal Distribution Sampling method (Reichstein et al, 2005). The half-hourly GPP was summed to obtain daily GPP and then averaged to 8-day interval in order to keep accordance with the temporal resolution of MODIS data.…”

Section: Datamentioning

confidence: 99%

Comparison of four light use efficiency models for estimating terrestrial gross primary production

Zhang

Zhou

Fan

et al. 2015

Ecological Modelling

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a b s t r a c tLight use efficiency (LUE) models that with different structures (i.e., methods to address environmental stresses on LUE) have been widely used to estimate terrestrial gross primary production (GPP) because of their theoretical soundness and practical conveniences. However, a systematic validation of those models with field observations across diverse ecosystems is still lacking and whether the model can be further improved by structural optimization remains unclear. Using GPP estimates at global 51 eddy covariance flux towers that cover a wide climate range and diverse vegetation types, we evaluated the performances of the four major LUE models (i.e., Carnegie-Ames-Stanford approach (CASA), Global Production Efficiency Model (GLO-PEM), Vegetation Photosynthesis Model (VPM), and Eddy Covariance-Light Use Efficiency (EC-LUE)) and examined the possible further improvement of the better-performed model(s) via model structural optimization. Our results showed that the GLO-PEM, VPM, and EC-LUE exhibited the similar capabilities in simulating GPP (explained around 68% of the total variations) and overall performed better than CASA (58%). Nevertheless, the EC-LUE and VPM were the optimal ones because they required less model inputs than the GLO-PEM. For the two optimal models, we found that the minimum method is better than the multiplication approach to integrate multiple environmental stresses on LUE. Moreover, we found that the VPM can be further improved by incorporating the constraint of water vapor deficit (VPD s ). We suggested that a modified VPM by using minimum method and adding VPD s may be the best model in estimating large-scale GPP if high-quality remote sensing data available, otherwise, the modified models with the water stress reflected by VPD s only is optimal.

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Section: Datamentioning

confidence: 99%

Comparison of four light use efficiency models for estimating terrestrial gross primary production

Zhang

Zhou

Fan

et al. 2015

Ecological Modelling

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“…To minimize the uncertainty derived from assigning different model structures for the study sites, the artificial neural network (ANN) model with a feed-forward back propagation algorithm and sigmoid transfer functions (Papale and Valentini, 2003;Melesse and Hanley, 2005;Moffat et al, 2007) was applied to fill the gaps and partition net ecosystem exchange of CO 2 (NEE) into gross primary productivity (GPP) and ecosystem respiration (RE). The ANN model was trained and validated separately for each year's daytime and nighttime data.…”

Section: Preprocessing and Gap-fillingmentioning

confidence: 99%

Biotic and climatic controls on interannual variability in carbon fluxes across terrestrial ecosystems

Shao

Zhou

Luo

et al. 2015

Agricultural and Forest Meteorology

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a b s t r a c tInterannual variability (IAV, represented by standard deviation) in net ecosystem exchange of CO 2 (NEE) is mainly driven by climatic drivers and biotic variations (i.e., the changes in photosynthetic and respiratory responses to climate), the effects of which are referred to as climatic (CE) and biotic effects (BE), respectively. Evaluating the relative contributions of CE and BE to the IAV in carbon (C) fluxes and understanding their controlling mechanisms are critical in projecting ecosystem changes in the future climate. In this study, we applied statistical methods with flux data from 65 sites located in the Northern Hemisphere to address this issue. Our results showed that the relative contribution of BE (CnBE) and CE (CnCE) to the IAV in NEE was 57% ± 14% and 43% ± 14%, respectively. The discrepancy in the CnBE among sites could be largely explained by water balance index (WBI). Across water-stressed ecosystems, the CnBE decreased with increasing aridity (slope = 0.18% mm −1 ). In addition, the CnBE tended to increase and the uncertainty reduced as timespan of available data increased from 5 to 15 years. Inter-site variation of the IAV in NEE mainly resulted from the IAV in BE (72%) compared to that in CE (37%). Interestingly, positive correlations between BE and CE occurred in grasslands and dry ecosystems (r > 0.45, P < 0.05) but not in other ecosystems. These results highlighted the importance of BE in determining the IAV in NEE and the ability of ecosystems to regulate C fluxes under climate change might decline when the ecosystems experience more severe water stress in the future.

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“…Vertical fluxes were computed on 30-min average windows, and corrected for tilt angles, temperature, and water vapor fluctuation effects (Detto and Katul 2007). Gaps due to data loss and quality check filtering were filled using a trained Artificial Neural Network (Papale and Valentini 2003). Cows ($100) were an irregular presence at this site.…”

Section: Eddy Covariancementioning

confidence: 99%

Large Greenhouse Gas Emissions from a Temperate Peatland Pasture

et al. 2011

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Agricultural drainage is thought to alter greenhouse gas emissions from temperate peatlands, with CH 4 emissions reduced in favor of greater CO 2 losses. Attention has largely focussed on C trace gases, and less is known about the impacts of agricultural conversion on N 2 O or global warming potential. We report greenhouse gas fluxes (CH 4 , CO 2 , N 2 O) from a drained peatland in the Sacramento-San Joaquin River Delta, California, USA currently managed as a rangeland (that is, pasture). This ecosystem was a net source of CH 4 (25.8 ± 1.4 mg CH 4 -C m -2 d -1 ) and N 2 O (6.4 ± 0.4 mg N 2 O-N m -2 d -1 ). Methane fluxes were comparable to those of other managed temperate peatlands, whereas N 2 O fluxes were very high; equivalent to fluxes from heavily fertilized agroecosystems and tropical forests. Ecosystem scale CH 4 fluxes were driven by ''hotspots'' (drainage ditches) that accounted for less than 5% of the land area but more than 84% of emissions. Methane fluxes were unresponsive to seasonal fluctuations in climate and showed minimal temporal variability. Nitrous oxide fluxes were more homogeneously distributed throughout the landscape and responded to fluctuations in environmental variables, especially soil moisture. Elevated CH 4 and N 2 O fluxes contributed to a high overall ecosystem global warming potential (531 g CO 2 -C equivalents m -2 y -1 ), with non-CO 2 trace gas fluxes offsetting the atmospheric ''cooling'' effects of photoassimilation. These data suggest that managed Delta peatlands are potentially large regional sources of greenhouse gases, with spatial heterogeneity in soil moisture modulating the relative importance of each gas for ecosystem global warming potential.

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A new assessment of European forests carbon exchanges by eddy fluxes and artificial neural network spatialization

Cited by 507 publications

References 10 publications

Comparison of four light use efficiency models for estimating terrestrial gross primary production

Comparison of four light use efficiency models for estimating terrestrial gross primary production

Biotic and climatic controls on interannual variability in carbon fluxes across terrestrial ecosystems

Large Greenhouse Gas Emissions from a Temperate Peatland Pasture

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