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 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).

scholarly journals Estimation of Terrestrial Global Gross Primary Production (GPP) with Satellite Data-Driven Models and Eddy Covariance Flux Data

2018 ◽  
Vol 10 (9) ◽  
pp. 1346 ◽  
Author(s):  
Joanna Joiner ◽  
Yasuko Yoshida ◽  
Yao Zhang ◽  
Gregory Duveiller ◽  
Martin Jung ◽  
...  
Keyword(s):  
Primary Production ◽  
Eddy Covariance ◽  
Satellite Data ◽  
Gross Primary Production ◽  
Satellite Observation ◽  
Data Driven ◽  
Spatial And Temporal Variability ◽  
Light Use Efficiency ◽  
High Productivity ◽  
Use Efficiency

We estimate global terrestrial gross primary production (GPP) based on models that use satellite data within a simplified light-use efficiency framework that does not rely upon other meteorological inputs. Satellite-based geometry-adjusted reflectances are from the MODerate-resolution Imaging Spectroradiometer (MODIS) and provide information about vegetation structure and chlorophyll content at both high temporal (daily to monthly) and spatial (∼1 km) resolution. We use satellite-derived solar-induced fluorescence (SIF) to identify regions of high productivity crops and also evaluate the use of downscaled SIF to estimate GPP. We calibrate a set of our satellite-based models with GPP estimates from a subset of distributed eddy covariance flux towers (FLUXNET 2015). The results of the trained models are evaluated using an independent subset of FLUXNET 2015 GPP data. We show that variations in light-use efficiency (LUE) with incident PAR are important and can be easily incorporated into the models. Unlike many LUE-based models, our satellite-based GPP estimates do not use an explicit parameterization of LUE that reduces its value from the potential maximum under limiting conditions such as temperature and water stress. Even without the parameterized downward regulation, our simplified models are shown to perform as well as or better than state-of-the-art satellite data-driven products that incorporate such parameterizations. A significant fraction of both spatial and temporal variability in GPP across plant functional types can be accounted for using our satellite-based models. Our results provide an annual GPP value of ∼140 Pg C year - 1 for 2007 that is within the range of a compilation of observation-based, model, and hybrid results, but is higher than some previous satellite observation-based estimates.

scholarly journals Global cropland monthly Gross Primary Production in the year 2000

2014 ◽  
Vol 11 (2) ◽  
pp. 3465-3488
Author(s):  
T. Chen ◽  
G. R. van der Werf ◽  
N. Gobron ◽  
E. J. Moors ◽  
A. J. Dolman
Keyword(s):  
Primary Production ◽  
Survey Data ◽  
Land Surface ◽  
Gross Primary Production ◽  
Co2 Exchange ◽  
Light Use Efficiency ◽  
Natural Ecosystems ◽  
Use Efficiency ◽  
Crop Types ◽  
Year 2000

Abstract. Croplands cover about 12% of the ice-free terrestrial land surface. Compared with natural ecosystems, croplands have distinct characteristics due to anthropogenic influences. Their global gross primary production (GPP) is not well constrained and estimates vary between 8.2 and 14.2 Pg C yr−1. We quantified global cropland GPP using a light use efficiency (LUE) model, employing satellite observations and survey data of crop types and distribution. A novel step in our analysis was to assign a maximum light use efficiency estimate (ϵ*GPP) to each of the 26 different crop types, instead of taking a uniform value as done in the past. These ϵ*GPP values were calculated based on flux tower CO2 exchange measurements and a literature survey of field studies, and ranged from 1.20 g CMJ−1 to 2.96 g CMJ−1. Global cropland GPP was estimated to be 11.05 Pg C yr−1 in the year 2000. Maize contributed most to this (1.55 Pg C yr−1), and the continent of Asia contributed most with 38.9% of global cropland GPP. In the continental United States, annual cropland GPP (1.28 Pg C yr−1) was close to values reported previously (1.24 Pg C yr−1) constrained by harvest records, but our estimates of ϵ*GPP values were much higher. Our results are sensitive to satellite information and survey data on crop type and extent, but provide a consistent and data-driven approach to generate a look-up table of ϵ*GPP for the 26 crop types for potential use in other vegetation models.

scholarly journals P-model v1.0: An optimality-based light use efficiency model for simulating ecosystem gross primary production

2019 ◽  
Author(s):  
Benjamin D. Stocker ◽  
Han Wang ◽  
Nicholas G. Smith ◽  
Sandy P. Harrison ◽  
Trevor F. Keenan ◽  
...  
Keyword(s):  
Primary Production ◽  
Satellite Data ◽  
Vegetation Type ◽  
Meteorological Data ◽  
Gross Primary Production ◽  
Satellite Monitoring ◽  
Light Use Efficiency ◽  
Spatial And Temporal Scales ◽  
Wide Range ◽  
Use Efficiency

Abstract. Terrestrial photosynthesis is the basis for vegetation growth and drives the land carbon cycle. Accurately simulating gross primary production (GPP, ecosystem-level apparent photosynthesis) is key for satellite monitoring and Earth System Model predictions under climate change. While robust models exist for describing leaf-level photosynthesis, predictions diverge due to uncertain photosynthetic traits and parameters which vary on multiple spatial and temporal scales. Here, we describe and evaluate a gross primary production (GPP, photosynthesis per unit ground area) model, the P-model, that combines the Farquhar-von Caemmerer-Berry model for C3 photosynthesis with an optimality principle for the carbon assimilation-transpiration trade-off, and predicts a multi-day average light use efficiency (LUE) for any climate and C3 vegetation type. The model is forced here with satellite data for the fraction of absorbed photosynthetically active radiation and site-specific meteorological data and is evaluated against GPP estimates from a globally distributed network of ecosystem flux measurements. Although the P-model requires relatively few inputs and prescribed parameters, the R2 for predicted versus observed GPP based on the full model setup is 0.75 (8-day mean, 131 sites) – better than some state-of-the-art satellite data-driven light use efficiency models. The R2 is reduced to 0.69 when not accounting for the reduction in quantum yield at low temperatures and effects of low soil moisture on LUE. The R2 for the P-model-predicted LUE is 0.37 (means by site) and 0.53 (means by vegetation type). The P-model provides a simple but powerful method for predicting – rather than prescribing – light use efficiency and simulating terrestrial photosythesis across a wide range of conditions. The model is available as an R package (rpmodel).

scholarly journals Direct effect of aerosols on solar radiation and gross primary production in boreal and hemiboreal forests

2018 ◽  
Vol 18 (24) ◽  
pp. 17863-17881 ◽  
Author(s):  
Ekaterina Ezhova ◽  
Ilona Ylivinkka ◽  
Joel Kuusk ◽  
Kaupo Komsaare ◽  
Marko Vana ◽  
...  
Keyword(s):  
Solar Radiation ◽  
Primary Production ◽  
Direct Effect ◽  
Gross Primary Production ◽  
Global Radiation ◽  
Diffuse Radiation ◽  
Light Use Efficiency ◽  
Diffuse Fraction ◽  
Aerosol Loading ◽  
Use Efficiency

Abstract. The effect of aerosol loading on solar radiation and the subsequent effect on photosynthesis is a relevant question for estimating climate feedback mechanisms. This effect is quantified in the present study using ground-based measurements from five remote sites in boreal and hemiboreal (coniferous and mixed) forests of Eurasia. The diffuse fraction of global radiation associated with the direct effect of aerosols, i.e. excluding the effect of clouds, increases with an increase in the aerosol loading. The increase in the diffuse fraction of global radiation from approximately 0.11 on days characterized by low aerosol loading to 0.2–0.27 on days with relatively high aerosol loading leads to an increase in gross primary production (GPP) between 6 % and 14 % at all sites. The largest increase in GPP (relative to days with low aerosol loading) is observed for two types of ecosystems: a coniferous forest at high latitudes and a mixed forest at the middle latitudes. For the former ecosystem the change in GPP due to the relatively large increase in the diffuse radiation is compensated for by the moderate increase in the light use efficiency. For the latter ecosystem, the increase in the diffuse radiation is smaller for the same aerosol loading, but the smaller change in GPP due to this relationship between radiation and aerosol loading is compensated for by the higher increase in the light use efficiency. The dependence of GPP on the diffuse fraction of solar radiation has a weakly pronounced maximum related to clouds.

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