Photosynthetic activity of plankton and assessment of gross primary production from measurements of CO2 concentration in surface and bottom water of the Lake Baikal coastal zone

2018 ◽  
Author(s):  
Mikhail V. Panchenko ◽  
Dmitrii A. Pestunov ◽  
Valentina M. Domysheva ◽  
Mariya V. Sakirko ◽  
Artem M. Shamrin
Keyword(s):  
Primary Production ◽  
Coastal Zone ◽  
Lake Baikal ◽  
Bottom Water ◽  
Photosynthetic Activity ◽  
Gross Primary Production ◽  
Co2 Concentration

scholarly journals Biophsyical constraints on gross primary production by the terrestrial biosphere

2014 ◽  
Vol 11 (20) ◽  
pp. 5987-6001 ◽  
Author(s):  
H. Wang ◽  
I. C. Prentice ◽  
T. W. Davis
Keyword(s):  
Primary Production ◽  
Optimality Theory ◽  
Vegetation Cover ◽  
Gross Primary Production ◽  
Co2 Flux ◽  
Co2 Concentration ◽  
Water Shortage ◽  
C3 Plants ◽  
Environmental Controls ◽  
Green Vegetation

Abstract. Persistent divergences among the predictions of complex carbon-cycle models include differences in the sign as well as the magnitude of the response of global terrestrial primary production to climate change. Such problems with current models indicate an urgent need to reassess the principles underlying the environmental controls of primary production. The global patterns of annual and maximum monthly terrestrial gross primary production (GPP) by C3 plants are explored here using a simple first-principles model based on the light-use efficiency formalism and the Farquhar model for C3 photosynthesis. The model is driven by incident photosynthetically active radiation (PAR) and remotely sensed green-vegetation cover, with additional constraints imposed by low-temperature inhibition and CO2 limitation. The ratio of leaf-internal to ambient CO2 concentration in the model responds to growing-season mean temperature, atmospheric dryness (indexed by the cumulative water deficit, Δ E) and elevation, based on an optimality theory. The greatest annual GPP is predicted for tropical moist forests, but the maximum (summer) monthly GPP can be as high, or higher, in boreal or temperate forests. These findings are supported by a new analysis of CO2 flux measurements. The explanation is simply based on the seasonal and latitudinal distribution of PAR combined with the physiology of photosynthesis. By successively imposing biophysical constraints, it is shown that partial vegetation cover – driven primarily by water shortage – represents the largest constraint on global GPP.

scholarly journals Improved estimate of global gross primary production for reproducing its long-term variation, 1982–2017

2020 ◽  
Vol 12 (4) ◽  
pp. 2725-2746
Author(s):  
Yi Zheng ◽  
Ruoque Shen ◽  
Yawen Wang ◽  
Xiangqian Li ◽  
Shuguang Liu ◽  
...  
Keyword(s):  
Primary Production ◽  
Environmental Variables ◽  
Gross Primary Production ◽  
Co2 Concentration ◽  
Atmospheric Co2 ◽  
Interannual Variations ◽  
Light Use Efficiency ◽  
Long Term Changes ◽  
Use Efficiency

Abstract. Satellite-based models have been widely used to simulate vegetation gross primary production (GPP) at the site, regional, or global scales in recent years. However, accurately reproducing the interannual variations in GPP remains a major challenge, and the long-term changes in GPP remain highly uncertain. In this study, we generated a long-term global GPP dataset at 0.05∘ latitude by 0.05∘ longitude and 8 d interval by revising a light use efficiency model (i.e., EC-LUE model). In the revised EC-LUE model, we integrated the regulations of several major environmental variables: atmospheric CO2 concentration, radiation components, and atmospheric vapor pressure deficit (VPD). These environmental variables showed substantial long-term changes, which could greatly impact the global vegetation productivity. Eddy covariance (EC) measurements at 95 towers from the FLUXNET2015 dataset, covering nine major ecosystem types around the globe, were used to calibrate and validate the model. In general, the revised EC-LUE model could effectively reproduce the spatial, seasonal, and annual variations in the tower-estimated GPP at most sites. The revised EC-LUE model could explain 71 % of the spatial variations in annual GPP over 95 sites. At more than 95 % of the sites, the correlation coefficients (R2) of seasonal changes between tower-estimated and model-simulated GPP are larger than 0.5. Particularly, the revised EC-LUE model improved the model performance in reproducing the interannual variations in GPP, and the averaged R2 between annual mean tower-estimated and model-simulated GPP is 0.44 over all 55 sites with observations longer than 5 years, which is significantly higher than those of the original EC-LUE model (R2=0.36) and other LUE models (R2 ranged from 0.06 to 0.30 with an average value of 0.16). At the global scale, GPP derived from light use efficiency models, machine learning models, and process-based biophysical models shows substantial differences in magnitude and interannual variations. The revised EC-LUE model quantified the mean global GPP from 1982 to 2017 as 106.2±2.9 Pg C yr−1 with the trend 0.15 Pg C yr−1. Sensitivity analysis indicated that GPP simulated by the revised EC-LUE model was sensitive to atmospheric CO2 concentration, VPD, and radiation. Over the period of 1982–2017, the CO2 fertilization effect on the global GPP (0.22±0.07 Pg C yr−1) could be partly offset by increased VPD (-0.17±0.06 Pg C yr−1). The long-term changes in the environmental variables could be well reflected in global GPP. Overall, the revised EC-LUE model is able to provide a reliable long-term estimate of global GPP. The GPP dataset is available at https://doi.org/10.6084/m9.figshare.8942336.v3 (Zheng et al., 2019).

scholarly journals Biophysical constraints on gross primary production by the terrestrial biosphere

2014 ◽  
Vol 11 (2) ◽  
pp. 3209-3240 ◽  
Author(s):  
H. Wang ◽  
I. C. Prentice ◽  
T. W. Davis
Keyword(s):  
Primary Production ◽  
Optimality Theory ◽  
Vegetation Cover ◽  
Gross Primary Production ◽  
Co2 Flux ◽  
Co2 Concentration ◽  
Water Shortage ◽  
C3 Plants ◽  
Environmental Controls ◽  
Green Vegetation

Abstract. Persistent divergences among the predictions of complex carbon cycle models include differences in the sign as well as the magnitude of the response of global terrestrial primary production to climate change. This and other problems with current models indicate an urgent need to re-assess the principles underlying the environmental controls of primary production. The global patterns of annual and maximum monthly terrestrial gross primary production (GPP) by C3 plants are explored here using a simple first-principles model based on the light-use efficiency formalism and the Farquhar model for C3 photosynthesis. The model is driven by incident photosynthetically active radiation (PAR) and remotely sensed green vegetation cover, with additional constraints imposed by low-temperature inhibition and CO2 limitation. The ratio of leaf-internal to ambient CO2 concentration in the model responds to growing-season mean temperature, atmospheric dryness (indexed by the cumulative water deficit, ΔE) and elevation, based on optimality theory. The greatest annual GPP is predicted for tropical moist forests, but the maximum (summer) monthly GPP can be as high or higher in boreal or temperate forests. These findings are supported by a new analysis of CO2 flux measurements. The explanation is simply based on the seasonal and latitudinal distribution of PAR combined with the physiology of photosynthesis. By successively imposing biophysical constraints, it is shown that partial vegetation cover – driven primarily by water shortage – represents the largest constraint on global GPP.

scholarly journals Improved estimate of global gross primary production for reproducing its long-term variation, 1982–2017

2019 ◽  
Author(s):  
Yi Zheng ◽  
Ruoque Shen ◽  
Yawen Wang ◽  
Xiangqian Li ◽  
Shuguang Liu ◽  
...  
Keyword(s):  
Primary Production ◽  
Environmental Variables ◽  
Gross Primary Production ◽  
Co2 Concentration ◽  
Global Scale ◽  
Interannual Variations ◽  
Long Term Changes ◽  
Fertilization Effect ◽  
Global Vegetation

Abstract. Satellite-based models have been widely used to simulate vegetation gross primary production (GPP) at site, regional, or global scales in recent years. However, accurately reproducing the interannual variations in GPP remains a major challenge, and the long-term changes in GPP remain highly uncertain. In this study, we generated a long-term global GPP dataset at 0.05° latitude by 0.05° longitude at 8-day interval by revising a light use efficiency model (i.e. EC-LUE). In the revised EC-LUE model, we integrated the regulations of several major environmental variables: atmospheric CO2 concentration, radiation components, and atmospheric vapor pressure deficit (VPD). These environmental variables showed substantial long-term changes, which could greatly impact the global vegetation productivity. Eddy covariance (EC) measurements at 84 towers from the FLUXNET2015 dataset, covering nine major ecosystem types of the globe, were used to calibrate and validate the model. The revised EC-LUE model could explain 83 % and 68 % of the spatial variations in the annual GPP at 42 calibration and 43 validation sites, respectively. In particular, the revised EC-LUE model could very well reproduce (~ 74 % sites R2 > 0.5; averaged R2 = 0.65) the interannual variations in GPP at 51 sites with observations greater than 5-years. At global scale, sensitivity analysis indicated that the long-term changes of environmental variables could be well reflected in the global GPP dataset. The CO2 fertilization effect on the global GPP (0.14 ± 0.001 Pg C yr−1) could be offset by the increased VPD (−0.16 ± 0.02 Pg C yr−1). The global GPP derived from different datasets exist substantial uncertainty in magnitude and interannual variations. The magnitude of global summed GPP simulated by the revised EC-LUE model was comparable to other global models. While the revised EC-LUE model has a unique superiority in simulating the interannual variations in GPP at both site level and global scales. The revised EC-LUE model provides a reliable long-term estimate of global GPP because of integrating the important environmental variables. The dataset is available at https://doi.org/10.6084/m9.figshare.8942336.v1 (Zheng et al., 2019).

Composition, biomass, and photosynthetic activity of the benthic algal communities in a littoral zone of Lake Baikal in summer

Limnology ◽  
2002 ◽  
Vol 3 (3) ◽  
pp. 175-180 ◽  
Author(s):  
K. Nozaki ◽  
H. Morino ◽  
H. Munehara ◽  
V. G. Sideleva ◽  
K. Nakai ◽  
...  
Keyword(s):  
Lake Baikal ◽  
Littoral Zone ◽  
Photosynthetic Activity ◽  
Algal Communities

scholarly journals Estimation of crop gross primary production (GPP): fAPARchl versus MOD15A2 FPAR

2014 ◽  
Vol 153 ◽  
pp. 1-6 ◽  
Author(s):  
Qingyuan Zhang ◽  
Yen-Ben Cheng ◽  
Alexei I. Lyapustin ◽  
Yujie Wang ◽  
Feng Gao ◽  
...  
Keyword(s):  
Primary Production ◽  
Gross Primary Production

scholarly journals Soil drying weakens the positive effect of climate factors on global gross primary production

2021 ◽  
Vol 129 ◽  
pp. 107953
Author(s):  
Huan Chen ◽  
Xiaoyong Bai ◽  
Yangbing Li ◽  
Qin Li ◽  
Luhua Wu ◽  
...  
Keyword(s):  
Primary Production ◽  
Gross Primary Production ◽  
Soil Drying ◽  
Climate Factors ◽  
Positive Effect
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