B031-0016
Tighten the Bolts and Nuts on GPP Estimations from Sites to the Globe: An Assessment of LUE Models and Supporting Data Fields

Wednesday, 9 December 2020
Poster
Zhao Wang1, Shuguang Liu2, Yingping Wang3, Ruben Valbuena4, Yiping Wu5, Mykola Kutia6, Shuqing Zhao7, Wenping Yuan8, Yi Zheng8, Yu Zhu9, Weizhi Lu Sr.1,9, Meifang Zhao10, Xi Peng9, Haiqiang Gao11 and shuai Long Feng1, (1)Central South University of Forestry and Technology, Changsha, China, (2)Faculty of Life Science and Technology, and National Engineering Laboratory for Applied Technology in Forestry & Ecology in Southern China, Central South University of Forestry and Technology, Changsha, China, (3)CSIRO, Ocean and Atmosphere Flagship, Aspendale, Australia, (4)School of Natural Sciences, Thoday Building, Deiniol Road, Bangor University, Gwynedd, LL57 2UW, UK., Bangor, United Kingdom, (5)Xi'an Jiaotong University, Department of Earth & Environmental Sciences, Xi'an, China, (6)School of Natural Sciences, Thoday Building, Deiniol Road, Bangor University, Gwynedd, LL57 2UW, UK, Bangor, United Kingdom, (7)Peking University, College of Urban and Environmental Sciences, Beijing, China, (8)Sun Yat-Sen University, Guangzhou, China, (9)College of Life Science and Technology, Central South University of Forestry and Technology, Changsha, China, (10)College of Life Science and Technology, Central South University of Forestry and Technology, Cahngsha, China, (11)Central South University of Forestry and Technology, National Engineering Laboratory for Applied Technology of Forestry & Ecology in South China, Changsha, China
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.
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