scholarly journals Gross primary production and light response parameters of four Southern Plains ecosystems estimated using long-term CO2-flux tower measurements

2003 ◽  
Vol 17 (2) ◽  
pp. n/a-n/a ◽  
Author(s):  
Tagir G. Gilmanov ◽  
Shashi B. Verma ◽  
Phillip L. Sims ◽  
Tilden P. Meyers ◽  
James A. Bradford ◽  
...  
Keyword(s):  
Primary Production ◽  
Gross Primary Production ◽  
Light Response ◽  
Co2 Flux ◽  
Southern Plains ◽  
Flux Tower

scholarly journals Modeling gross primary production of irrigated and rain-fed maize using MODIS imagery and CO2 flux tower data

2011 ◽  
Vol 151 (12) ◽  
pp. 1514-1528 ◽  
Author(s):  
Joshua L. Kalfas ◽  
Xiangming Xiao ◽  
Diana X. Vanegas ◽  
Shashi B. Verma ◽  
Andrew E. Suyker
Keyword(s):  
Primary Production ◽  
Gross Primary Production ◽  
Co2 Flux ◽  
Flux Tower ◽  
Modis Imagery

scholarly journals Uncertainty analysis of gross primary production partitioned from net ecosystem exchange measurements

2016 ◽  
Vol 13 (5) ◽  
pp. 1409-1422 ◽  
Author(s):  
Rahul Raj ◽  
Nicholas Alexander Samuel Hamm ◽  
Christiaan van der Tol ◽  
Alfred Stein
Keyword(s):  
Primary Production ◽  
Gross Primary Production ◽  
Empirical Relationship ◽  
Net Ecosystem Exchange ◽  
Light Response ◽  
Uncertainty Assessment ◽  
Forest Site ◽  
Posterior Distributions ◽  
Flux Tower ◽  
Mcmc Simulation

Abstract. Gross primary production (GPP) can be separated from flux tower measurements of net ecosystem exchange (NEE) of CO2. This is used increasingly to validate process-based simulators and remote-sensing-derived estimates of simulated GPP at various time steps. Proper validation includes the uncertainty associated with this separation. In this study, uncertainty assessment was done in a Bayesian framework. It was applied to data from the Speulderbos forest site, The Netherlands. We estimated the uncertainty in GPP at half-hourly time steps, using a non-rectangular hyperbola (NRH) model for its separation from the flux tower measurements. The NRH model provides a robust empirical relationship between radiation and GPP. It includes the degree of curvature of the light response curve, radiation and temperature. Parameters of the NRH model were fitted to the measured NEE data for every 10-day period during the growing season (April to October) in 2009. We defined the prior distribution of each NRH parameter and used Markov chain Monte Carlo (MCMC) simulation to estimate the uncertainty in the separated GPP from the posterior distribution at half-hourly time steps. This time series also allowed us to estimate the uncertainty at daily time steps. We compared the informative with the non-informative prior distributions of the NRH parameters and found that both choices produced similar posterior distributions of GPP. This will provide relevant and important information for the validation of process-based simulators in the future. Furthermore, the obtained posterior distributions of NEE and the NRH parameters are of interest for a range of applications.

scholarly journals Uncertainty analysis of gross primary production partitioned from net ecosystem exchange measurements

2015 ◽  
Vol 12 (16) ◽  
pp. 13967-14002
Author(s):  
R. Raj ◽  
N. A. S. Hamm ◽  
C. van der Tol ◽  
A. Stein
Keyword(s):  
Primary Production ◽  
Prior Distribution ◽  
Gross Primary Production ◽  
Empirical Relationship ◽  
Net Ecosystem Exchange ◽  
Light Response ◽  
Forest Site ◽  
Posterior Distributions ◽  
Flux Tower ◽  
Mcmc Simulation

Abstract. Gross primary production (GPP), separated from flux tower measurements of net ecosystem exchange (NEE) of CO2, is used increasingly to validate process-based simulators and remote sensing-derived estimates of simulated GPP at various time steps. Proper validation should include the uncertainty associated with this separation at different time steps. This can be achieved by using a Bayesian framework. In this study, we estimated the uncertainty in GPP at half hourly time steps. We used a non-rectangular hyperbola (NRH) model to separate GPP from flux tower measurements of NEE at the Speulderbos forest site, The Netherlands. The NRH model included the variables that influence GPP, in particular radiation, and temperature. In addition, the NRH model provided a robust empirical relationship between radiation and GPP by including the degree of curvature of the light response curve. Parameters of the NRH model were fitted to the measured NEE data for every 10-day period during the growing season (April to October) in 2009. Adopting a Bayesian approach, we defined the prior distribution of each NRH parameter. Markov chain Monte Carlo (MCMC) simulation was used to update the prior distribution of each NRH parameter. This allowed us to estimate the uncertainty in the separated GPP at half-hourly time steps. This yielded the posterior distribution of GPP at each half hour and allowed the quantification of uncertainty. The time series of posterior distributions thus obtained allowed us to estimate the uncertainty at daily time steps. We compared the informative with non-informative prior distributions of the NRH parameters. The results showed that both choices of prior produced similar posterior distributions GPP. This will provide relevant and important information for the validation of process-based simulators in the future. Furthermore, the obtained posterior distributions of NEE and the NRH parameters are of interest for a range of applications.

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.

The potential of carbonyl sulfide as a proxy for gross primary production at flux tower sites

2011 ◽  
Vol 116 (G4) ◽  
Author(s):  
J. M. Blonquist ◽  
S. A. Montzka ◽  
J. W. Munger ◽  
D. Yakir ◽  
A. R. Desai ◽  
...  
Keyword(s):  
Primary Production ◽  
Gross Primary Production ◽  
Carbonyl Sulfide ◽  
Flux Tower

scholarly journals Observed and modelled ecosystem respiration and gross primary production of a grassland in southwestern France

2010 ◽  
Vol 7 (1) ◽  
pp. 429-462 ◽  
Author(s):  
C. Albergel ◽  
J.-C. Calvet ◽  
A.-L. Gibelin ◽  
S. Lafont ◽  
J.-L. Roujean ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Primary Production ◽  
Root Zone ◽  
Ecosystem Respiration ◽  
Gross Primary Production ◽  
Net Ecosystem Exchange ◽  
Co2 Flux ◽  
Single Equation ◽  
Complex Model ◽  
Area Index

Abstract. In this work, a simple representation of the soil moisture effect on the ecosystem respiration is implemented into the A-gs version of the Interactions between Soil, Biosphere, and Atmosphere (ISBA) model. It results in an improvement of the modelled CO2 flux over a grassland, in southwestern France. The former temperature-only dependent respiration formulation used in ISBA-A-gs is not able to model the limitation of the respiration under dry conditions. In addition to soil moisture and soil temperature, the only parameter required in this formulation is the ecosystem respiration parameter Re25. It can be estimated by the mean of eddy covariance measurements of turbulent nighttime CO2 flux (i.e. ecosystem respiration). The resulting correlation between observed and modelled net ecosystem exchange is r2=0.63 with a bias of −2.18 μmol m−2 s−1. It is shown that when CO2 observations are not available, it is possible to use a more complex model, able to represent the heterotrophic respiration and all the components of the autotrophic respiration, to estimate Re25 with similar results. The modelled ecosystem respiration estimates are provided by the Carbon Cycle (CC) version of ISBA (ISBA-CC). ISBA-CC is a version of ISBA able to simulate all the respiration components whereas ISBA-A-gs uses a single equation for ecosystem respiration. ISBA-A-gs is easier to handle and more convenient than ISBA-CC for practical use in atmospheric or hydrological models. Surface water and energy flux observations as well as gross primary production (GPP) estimates are compared with model outputs. The dependence of GPP to air temperature is investigated. The observed GPP is less sensitive to temperature than the modelled GPP. Finally, the simulations of the ISBA-A-gs model are analysed over a seven year period (2001–2007). Modelled soil moisture and leaf area index (LAI) are confronted with the observed root-zone soil moisture content (m3 m−3), and with LAI estimates derived from surface reflectance measurements.

scholarly journals Long-term drainage reduces CO<sub>2</sub> uptake and increases CO<sub>2</sub> emission on a Siberian floodplain due to shifts in vegetation community and soil thermal characteristics

2016 ◽  
Vol 13 (14) ◽  
pp. 4219-4235 ◽  
Author(s):  
Min Jung Kwon ◽  
Martin Heimann ◽  
Olaf Kolle ◽  
Kristina A. Luus ◽  
Edward A. G. Schuur ◽  
...  
Keyword(s):  
Primary Production ◽  
Ecosystem Respiration ◽  
Gross Primary Production ◽  
Thermal Characteristics ◽  
Growing Season ◽  
Co2 Uptake ◽  
Vegetation Community ◽  
Uptake Rates ◽  
Soil Temperatures

Abstract. With increasing air temperatures and changing precipitation patterns forecast for the Arctic over the coming decades, the thawing of ice-rich permafrost is expected to increasingly alter hydrological conditions by creating mosaics of wetter and drier areas. The objective of this study is to investigate how 10 years of lowered water table depths of wet floodplain ecosystems would affect CO2 fluxes measured using a closed chamber system, focusing on the role of long-term changes in soil thermal characteristics and vegetation community structure. Drainage diminishes the heat capacity and thermal conductivity of organic soil, leading to warmer soil temperatures in shallow layers during the daytime and colder soil temperatures in deeper layers, resulting in a reduction in thaw depths. These soil temperature changes can intensify growing-season heterotrophic respiration by up to 95 %. With decreased autotrophic respiration due to reduced gross primary production under these dry conditions, the differences in ecosystem respiration rates in the present study were 25 %. We also found that a decade-long drainage installation significantly increased shrub abundance, while decreasing Eriophorum angustifolium abundance resulted in Carex sp. dominance. These two changes had opposing influences on gross primary production during the growing season: while the increased abundance of shrubs slightly increased gross primary production, the replacement of E. angustifolium by Carex sp.  significantly decreased it. With the effects of ecosystem respiration and gross primary production combined, net CO2 uptake rates varied between the two years, which can be attributed to Carex-dominated plots' sensitivity to climate. However, underlying processes showed consistent patterns: 10 years of drainage increased soil temperatures in shallow layers and replaced E. angustifolium by Carex sp., which increased CO2 emission and reduced CO2 uptake rates. During the non-growing season, drainage resulted in 4 times more CO2 emissions, with high sporadic fluxes; these fluxes were induced by soil temperatures, E. angustifolium abundance, and air pressure.

scholarly journals Modeling Gross Primary Production of a Typical Coastal Wetland in China Using MODIS Time Series and CO2 Eddy Flux Tower Data

2018 ◽  
Vol 10 (5) ◽  
pp. 708 ◽  
Author(s):  
Xiaoming Kang ◽  
Liang Yan ◽  
Xiaodong Zhang ◽  
Yong Li ◽  
Dashuan Tian ◽  
...  
Keyword(s):  
Time Series ◽  
Primary Production ◽  
Coastal Wetland ◽  
Gross Primary Production ◽  
Eddy Flux ◽  
Flux Tower

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).

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