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Tighten the bolts and nuts on GPP estimations from sites to the globe: an assessment of LUE models and supporting data fields
  • +13
  • Zhao Wang,
  • Shuguang Liu,
  • Ying-Ping Wang,
  • Ruben Valbuena,
  • Yiping Wu,
  • Mykola Kutia,
  • Shuqing Zhao,
  • Wenping Yuan,
  • Yi Zheng,
  • Yu Zhu,
  • Weizhi Lu,
  • Meifang Zhao,
  • Xi Peng,
  • Haiqiang Gao,
  • Shuailong Feng,
  • Yi Shi
Zhao Wang
Central South University of Forestry and Technology
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Shuguang Liu
Central South University of Forestry and Technology
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Ying-Ping Wang
CSIRO
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Ruben Valbuena
Bangor University
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Mykola Kutia
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Shuqing Zhao
Peking University
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Wenping Yuan
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Yu Zhu
Central South University of Forestry and Technology
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Weizhi Lu
Central South University of Forestry and Technology
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Meifang Zhao
Central South University of Forestry and Technology
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Xi Peng
Central South University of Forestry and Technology
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Haiqiang Gao
Central South University of Forestry and Technology
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Shuailong Feng
Central South University of Forestry and Technology
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Yi Shi
Central South University of Forestry and Technology
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Abstract

Gross primary production (GPP) determines the amounts of carbon and energy that enter terrestrial ecosystems. However, the tremendous uncertainty of the GPP still hinders the reliability of the GPP estimates and therefore understanding of the global carbon cycle. In this study, using observations from global eddy covariance (EC) flux towers, we appraised the performance of 22 widely used GPP models and quality of major spatial data layers that drive the models. Results show that the global GPP products generated by the 22 models varied greatly in the means (from 92.7 to 178.9 Pg C yr-1), trends (from -0.25 to 0.84 Pg C yr-1). Model structures (i.e., light use efficiency models, machine learning models, and process-based biophysical models) are an important aspect contributing to the large uncertainty. In addition, various biases in currently available spatial datasets have found (e.g., only 57% of the observed variation in photosynthetically active radiation was explained by the spatial dataset), which contributed greatly affects global GPP estimates. Our analysis indicates that the model development did not converge GPP simulations with the advance of time. Moving forward, research into efficacy of model structures and the precision of input data may be more important than the development of new models for global GPP estimation.