scholarly journals Comparison of MODIS gross primary production estimates for forests across the U.S.A. with those generated by a simple process model, 3-PGS

2007 ◽  
Vol 109 (4) ◽  
pp. 500-509 ◽  
Author(s):  
J.M. Nightingale ◽  
N.C. Coops ◽  
R.H. Waring ◽  
W.W. Hargrove
Keyword(s):  
Primary Production ◽  
Process Model ◽  
Gross Primary Production ◽  
Simple Process

scholarly journals The inter-annual variability of Africa's ecosystem productivity: a multi-model analysis

2008 ◽  
Vol 5 (5) ◽  
pp. 4035-4069 ◽  
Author(s):  
U. Weber ◽  
M. Jung ◽  
M. Reichstein ◽  
C. Beer ◽  
M. Braakhekke ◽  
...  
Keyword(s):  
Primary Production ◽  
Interannual Variability ◽  
Carbon Balance ◽  
Process Model ◽  
Ecosystem Respiration ◽  
Gross Primary Production ◽  
Principal Component ◽  
Carbon Fluxes ◽  
Atmospheric Modeling ◽  
Eastern Africa

Abstract. We are comparing spatially explicit process-model based estimates of the terrestrial carbon balance and its components over Africa and confront them with remote sensing based proxies of vegetation productivity and atmospheric inversions of land-atmosphere net carbon exchange. Particular emphasis is on characterizing the patterns of interannual variability of carbon fluxes and analyzing the factors and processes responsible for it. For this purpose simulations with the terrestrial biosphere models ORCHIDEE, LPJ-DGVM, LPJ-Guess and JULES have been performed using a standardized modeling protocol and a uniform set of corrected climate forcing data. While the models differ concerning the absolute magnitude of carbon fluxes, we find several robust patterns of interannual variability among the models. Models exhibit largest interannual variability in southern and eastern Africa, regions which are primarily covered by herbaceous vegetation. Interannual variability of the net carbon balance appears to be more strongly influenced by gross primary production than by ecosystem respiration. A principal component analysis indicates that moisture is the main driving factor of interannual gross primary production variability for those regions. On the contrary in a large part of the inner tropics radiation appears to be limiting in two models. These patterns are corroborated by remotely sensed vegetation properties from the SeaWiFS satellite sensor. Inverse atmospheric modeling estimates of surface carbon fluxes are less conclusive at this point, implying the need for a denser network of observation stations over Africa.

scholarly journals The interannual variability of Africa's ecosystem productivity: a multi-model analysis

2009 ◽  
Vol 6 (2) ◽  
pp. 285-295 ◽  
Author(s):  
U. Weber ◽  
M. Jung ◽  
M. Reichstein ◽  
C. Beer ◽  
M. C. Braakhekke ◽  
...  
Keyword(s):  
Primary Production ◽  
Interannual Variability ◽  
Carbon Balance ◽  
Process Model ◽  
Ecosystem Respiration ◽  
Gross Primary Production ◽  
Principal Component ◽  
Carbon Fluxes ◽  
Atmospheric Modeling ◽  
Eastern Africa

Abstract. We are comparing spatially explicit process-model based estimates of the terrestrial carbon balance and its components over Africa and confront them with remote sensing based proxies of vegetation productivity and atmospheric inversions of land-atmosphere net carbon exchange. Particular emphasis is on characterizing the patterns of interannual variability of carbon fluxes and analyzing the factors and processes responsible for it. For this purpose simulations with the terrestrial biosphere models ORCHIDEE, LPJ-DGVM, LPJ-Guess and JULES have been performed using a standardized modeling protocol and a uniform set of corrected climate forcing data. While the models differ concerning the absolute magnitude of carbon fluxes, we find several robust patterns of interannual variability among the models. Models exhibit largest interannual variability in southern and eastern Africa, regions which are primarily covered by herbaceous vegetation. Interannual variability of the net carbon balance appears to be more strongly influenced by gross primary production than by ecosystem respiration. A principal component analysis indicates that moisture is the main driving factor of interannual gross primary production variability for those regions. On the contrary in a large part of the inner tropics radiation appears to be limiting in two models. These patterns are partly corroborated by remotely sensed vegetation properties from the SeaWiFS satellite sensor. Inverse atmospheric modeling estimates of surface carbon fluxes are less conclusive at this point, implying the need for a denser network of observation stations over Africa.

scholarly journals Global variability of carbon use efficiency in terrestrial ecosystems

2019 ◽  
Author(s):  
Xiaolu Tang ◽  
Nuno Carvalhais ◽  
Catarina Moura ◽  
Bernhard Ahrens ◽  
Sujan Koirala ◽  
...  
Keyword(s):  
Primary Production ◽  
Process Model ◽  
Net Primary Production ◽  
Gross Primary Production ◽  
Driving Factors ◽  
Spatial And Temporal Variability ◽  
Carbon Use Efficiency ◽  
Environmental Controls ◽  
C Cycle ◽  
Use Efficiency

Abstract. Vegetation carbon use efficiency (CUE) is a key measure of carbon (C) transfer from the atmosphere to terrestrial biomass, and indirectly reflects how much C is released through autotrophic respiration from the vegetation to the atmosphere. Diagnosing the variability of CUE with climate and other environmental factors is fundamental to understand its driving factors, and to further fill the current gaps in knowledge about the environmental controls on CUE. Thus, to study CUE variability and its driving factors, this study established a global database of site-year CUE based on observations from 188 field measurement sites for five ecosystem types – forest, grass, wetland, crop and tundra. The spatial pattern of CUE was predicted from global climate and soil variables using Random Forest, and compared with estimates from Dynamic Global Vegetation Models (DGVMs) from the TRENDY model ensemble. Globally, we found two prominent CUE gradients in ecosystem types and latitude, that is, CUE varied with ecosystem types, being the highest in wetlands and lowest in grassland, and CUE decreased with latitude with the lowest CUE in tropics, and the highest CUE in higher latitude regions. CUE varied greatly between data-derived CUE and TRENDY-CUE, but also among TRENDY models. Both data-derived and TRENDY-CUE challenged the constant value of 0.5 for CUE, independent of environmental controls. However, given the role of CUE in controlling the spatial and temporal variability of the terrestrial biosphere C cycle, these results emphasize the need to better understand the biotic and abiotic controls on CUE to reduce the uncertainties in prognostic land-process model simulations. Finally, this study proposed a new estimate of net primary production based on CUE and gross primary production, offering another benchmark for net primary production comparison for global carbon modelling.

scholarly journals A simplified gross primary production and evapotranspiration model for boreal coniferous forests – is a generic calibration sufficient?

2015 ◽  
Vol 8 (7) ◽  
pp. 5089-5137 ◽  
Author(s):  
F. Minunno ◽  
M. Peltoniemi ◽  
S. Launiainen ◽  
M. Aurela ◽  
A. Lindroth ◽  
...  
Keyword(s):  
Primary Production ◽  
Process Model ◽  
Gross Primary Production ◽  
Regional Scale ◽  
Model Complexity ◽  
Model Parameters ◽  
Water Fluxes ◽  
Site Specific ◽  
Bayesian Model Comparison ◽  
Wide Range

Abstract. The problem of model complexity has been lively debated in environmental sciences as well as in the forest modelling community. Simple models are less input demanding and their calibration involves a lower number of parameters, but they might be suitable only at local scale. In this work we calibrated a simplified ecosystem process model (PRELES) to data from multiple sites and we tested if PRELES can be used at regional scale to estimate the carbon and water fluxes of Boreal conifer forests. We compared a multi-site (M-S) with site-specific (S-S) calibrations. Model calibrations and evaluations were carried out by the means of the Bayesian method; Bayesian calibration (BC) and Bayesian model comparison (BMC) were used to quantify the uncertainty in model parameters and model structure. To evaluate model performances BMC results were combined with more classical analysis of model-data mismatch (M-DM). Evapotranspiration (ET) and gross primary production (GPP) measurements collected in 10 sites of Finland and Sweden were used in the study. Calibration results showed that similar estimates were obtained for the parameters at which model outputs are most sensitive. No significant differences were encountered in the predictions of the multi-site and site-specific versions of PRELES with exception of a site with agricultural history (Alkkia). Although PRELES predicted GPP better than evapotranspiration, we concluded that the model can be reliably used at regional scale to simulate carbon and water fluxes of Boreal forests. Our analyses underlined also the importance of using long and carefully collected flux datasets in model calibration. In fact, even a single site can provide model calibrations that can be applied at a wider spatial scale, since it covers a wide range of variability in climatic conditions.

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

Post-fire co-stimulation of gross primary production and ecosystem respiration in a meadow grassland on the Tibetan Plateau

2021 ◽  
Vol 303 ◽  
pp. 108388
Author(s):  
Dong Wang ◽  
Ji Chen ◽  
Andrew J. Felton ◽  
Longlong Xia ◽  
Yuefang Zhang ◽  
...  
Keyword(s):  
Tibetan Plateau ◽  
Primary Production ◽  
Ecosystem Respiration ◽  
Gross Primary Production ◽  
The Tibetan Plateau ◽  
Stimulation Of

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

scholarly journals Can vegetation index track the interannual variation in gross primary production of temperate deciduous forests?

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Fan Liu ◽  
Chuankuan Wang ◽  
Xingchang Wang
Keyword(s):  
Primary Production ◽  
Vegetation Index ◽  
Near Infrared ◽  
Deciduous Forest ◽  
Gross Primary Production ◽  
Growing Season ◽  
Temperate Deciduous Forest ◽  
Near Infrared Reflectance ◽  
Modis Ndvi ◽  
Temperate Deciduous

Abstract Background Vegetation indices (VIs) by remote sensing are widely used as simple proxies of the gross primary production (GPP) of vegetation, but their performances in capturing the inter-annual variation (IAV) in GPP remain uncertain. Methods We evaluated the performances of various VIs in tracking the IAV in GPP estimated by eddy covariance in a temperate deciduous forest of Northeast China. The VIs assessed included the normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI), and the near-infrared reflectance of vegetation (NIRv) obtained from tower-radiometers (broadband) and the Moderate Resolution Imaging Spectroradiometer (MODIS), respectively. Results We found that 25%–35% amplitude of the broadband EVI tracked the start of growing season derived by GPP (R2: 0.56–0.60, bias < 4 d), while 45% (or 50%) amplitudes of broadband (or MODIS) NDVI represented the end of growing season estimated by GPP (R2: 0.58–0.67, bias < 3 d). However, all the VIs failed to characterize the summer peaks of GPP. The growing-season integrals but not averaged values of the broadband NDVI, MODIS NIRv and EVI were robust surrogates of the IAV in GPP (R2: 0.40–0.67). Conclusion These findings illustrate that specific VIs are effective only to capture the GPP phenology but not the GPP peak, while the integral VIs have the potential to mirror the IAV in GPP.

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