Net primary production and ecosystem carbon flux of Brazilian tropical savanna ecosystems from eddy covariance and inventory methods

2022 ◽  
Author(s):  
George Louis Vourlitis ◽  
Osvaldo Borges Pinto Jr. ◽  
Higo José Dalmagro ◽  
Paulo Arruda ◽  
Francisco de Almeida Lobo ◽  
...  
Keyword(s):  
Primary Production ◽  
Eddy Covariance ◽  
Carbon Flux ◽  
Net Primary Production ◽  
Tropical Savanna ◽  
Ecosystem Carbon

scholarly journals How drought severity constrains gross primary production(GPP) and its partitioning among carbon pools in a <i>Quercus ilex</i> coppice?

2014 ◽  
Vol 11 (23) ◽  
pp. 6855-6869 ◽  
Author(s):  
S. Rambal ◽  
M. Lempereur ◽  
J. M. Limousin ◽  
N. K. Martin-StPaul ◽  
J. M. Ourcival ◽  
...  
Keyword(s):  
Primary Production ◽  
Water Deficit ◽  
Eddy Covariance ◽  
Quercus Ilex ◽  
Net Primary Production ◽  
Gross Primary Production ◽  
Carbon Fluxes ◽  
Stem Growth ◽  
Drought Severity ◽  
Mediterranean Ecosystem

Abstract. The partitioning of photosynthates toward biomass compartments plays a crucial role in the carbon (C) sink function of forests. Few studies have examined how carbon is allocated toward plant compartments in drought-prone forests. We analyzed the fate of gross primary production (GPP) in relation to yearly water deficit in an old evergreen Mediterranean Quercus ilex coppice severely affected by water limitations. Carbon fluxes between the ecosystem and the atmosphere were measured with an eddy covariance flux tower running continuously since 2001. Discrete measurements of litterfall, stem growth and fAPAR allowed us to derive annual productions of leaves, wood, flowers and acorns, and an isometric relationship between stem and belowground biomass has been used to estimate perennial belowground growth. By combining eddy covariance fluxes with annual net primary productions (NPP), we managed to close a C budget and derive values of autotrophic, heterotrophic respirations and carbon-use efficiency (CUE; the ratio between NPP and GPP). Average values of yearly net ecosystem production (NEP), GPP and Reco were 282, 1259 and 977 g C m−2. The corresponding aboveground net primary production (ANPP) components were 142.5, 26.4 and 69.6 g C m−2 for leaves, reproductive effort (flowers and fruits) and stems, respectively. NEP, GPP and Reco were affected by annual water deficit. Partitioning to the different plant compartments was also impacted by drought, with a hierarchy of responses going from the most affected – the stem growth – to the least affected – the leaf production. The average CUE was 0.40, which is well in the range for Mediterranean-type forest ecosystems. CUE tended to decrease less drastically in response to drought than GPP and NPP did, probably due to drought acclimation of autotrophic respiration. Overall, our results provide a baseline for modeling the inter-annual variations of carbon fluxes and allocation in this widespread Mediterranean ecosystem, and they highlight the value of maintaining continuous experimental measurements over the long term.

scholarly journals Comparison of Machine Learning Methods to Up-Scale Gross Primary Production

2021 ◽  
Vol 13 (13) ◽  
pp. 2448
Author(s):  
Tao Yu ◽  
Qiang Zhang ◽  
Rui Sun
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Primary Production ◽  
Eddy Covariance ◽  
Carbon Flux ◽  
Gross Primary Production ◽  
Regional Scale ◽  
Learning Methods ◽  
Flux Tower ◽  
Machine Learning Methods

Eddy covariance observation is an applicable way to obtain accurate and continuous carbon flux at flux tower sites, while remote sensing technology could estimate carbon exchange and carbon storage at regional and global scales effectively. However, it is still challenging to up-scale the field-observed carbon flux to a regional scale, due to the heterogeneity and the unstable air conditions at the land surface. In this paper, gross primary production (GPP) from ground eddy covariance systems were up-scaled to a regional scale by using five machine learning methods (Cubist regression tree, random forest, support vector machine, artificial neural network, and deep belief network). Then, the up-scaled GPP were validated using GPP at flux tower sites, weighted GPP in the footprint, and MODIS GPP products. At last, the sensitivity of the input data (normalized difference vegetation index, fractional vegetation cover, shortwave radiation, relative humidity and air temperature) to the precision of up-scaled GPP was analyzed, and the uncertainty of the machine learning methods was discussed. The results of this paper indicated that machine learning methods had a great potential in up-scaling GPP at flux tower sites. The validation of up-scaled GPP, using five machine learning methods, demonstrated that up-scaled GPP using random forest obtained the highest accuracy.

scholarly journals The response of seagrass (<i>Posidonia oceanica</i>) meadow metabolism to CO<sub>2</sub> levels and hydrodynamic exchange determined with aquatic eddy covariance

2018 ◽  
Author(s):  
Dirk Koopmans ◽  
Moritz Holtappels ◽  
Arjun Chennu ◽  
Miriam Weber ◽  
Dirk de Beer
Keyword(s):  
Primary Production ◽  
Eddy Covariance ◽  
Metabolic Activity ◽  
Net Primary Production ◽  
Gross Primary Production ◽  
Open Water ◽  
Posidonia Oceanica ◽  
Oxygen Depletion ◽  
Light Levels ◽  
Hydrodynamic Exchange

Abstract. We investigated light, water velocity, and CO2 as drivers of primary production in Mediterranean seagrass (Posidonia oceanica) meadows and neighboring bare sands using the aquatic eddy covariance technique. Study locations included an open-water meadow and a nearshore meadow, the nearshore meadow being exposed to greater hydrodynamic exchange. A third meadow was located at a CO2 vent. We found that, despite the oligotrophic environment, the meadows had a remarkably high metabolic activity, up to 20 times higher than the surrounding sands. They were strongly autotrophic, with net production half of gross primary production. Thus, P. oceanica meadows are oases of productivity in an unproductive environment. Secondly, we found that turbulent oxygen fluxes above the meadow can be significantly higher in the afternoon than in the morning at the same light levels. This hysteresis can be explained by the replenishment of nighttime-depleted oxygen within the meadow during the morning. Oxygen depletion and replenishment within the meadow do not contribute to turbulent O2 flux. The hysteresis disappeared when fluxes were corrected for the O2 storage within the meadow and, consequently, accurate metabolic rate measurements require measurements of meadow oxygen content. We further argue that oxygen-depleted waters in the meadow provide a source of CO2 and inorganic nutrients for fixation, especially in the morning. Contrary to expectation, meadow metabolic activity at the CO2 vent was lower than at the other sites, with negligible net primary production.

Gross and aboveground net primary production at Canadian forest carbon flux sites

2013 ◽  
Vol 174-175 ◽  
pp. 54-64 ◽  
Author(s):  
T.S. Zha ◽  
A.G. Barr ◽  
P.-Y. Bernier ◽  
M.B. Lavigne ◽  
J.A. Trofymow ◽  
...  
Keyword(s):  
Primary Production ◽  
Carbon Flux ◽  
Net Primary Production ◽  
Forest Carbon ◽  
Aboveground Net Primary Production
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