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Modeling China's terrestrial ecosystem gross primary productivity with BEPS model : Parameter sensitivity analysis and model calibration

Xing, Xiuli ; Wu, Mousong ; Zhang, Wenxin LU orcid ; Ju, Weimin ; Tagesson, Torbern LU ; He, Wei ; Wang, Songhan ; Wang, Jun ; Hu, Lu and Yuan, Shu , et al. (2023) In Agricultural and Forest Meteorology 343.
Abstract

Terrestrial ecosystems are the largest sink for carbon, and their ecosystem gross primary productivity (GPP) regulates variations in atmospheric carbon dioxide (CO2) concentrations. Current process-based ecosystem models used for estimating GPP are subject to large uncertainties due to poorly constrained parameter values. In this study, we implemented a global sensitivity analysis (GSA) on parameters in the Boreal Ecosystem Productivity Simulator (BEPS) considering the parameters’ second-order impacts. We also applied the generalized likelihood estimation (GLUE) method, which is flexible for a multi-parameter calibration, to optimize the GPP simulation by BEPS for 10 sites covering 7 plant functional types (PFT) over China.... (More)

Terrestrial ecosystems are the largest sink for carbon, and their ecosystem gross primary productivity (GPP) regulates variations in atmospheric carbon dioxide (CO2) concentrations. Current process-based ecosystem models used for estimating GPP are subject to large uncertainties due to poorly constrained parameter values. In this study, we implemented a global sensitivity analysis (GSA) on parameters in the Boreal Ecosystem Productivity Simulator (BEPS) considering the parameters’ second-order impacts. We also applied the generalized likelihood estimation (GLUE) method, which is flexible for a multi-parameter calibration, to optimize the GPP simulation by BEPS for 10 sites covering 7 plant functional types (PFT) over China. Our optimized results significantly reduced the uncertainty of the simulated GPP over all the sites by 17 % to 82 % and showed that the GPP is sensitive to not only the photosynthesis-related parameters but also the parameters related to the soil water uptake as well as to the energy balance. The optimized GPP across South China showed that the mix forest, shrub, and grass have a higher GPP and are more controlled by the soil water availability. This study showed that the GLUE method together with the GSA scheme could constrain the ecosystem model well when simulating GPP across multiple ecosystems and provide a reasonable estimate of the spatial and temporal distribution of the ecosystem GPP over China. We call for more observations from more sites, as well as data on plant traits, to be collected in China in order to better constrain ecosystem carbon cycle modeling and understand its response to climate change.

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organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Ecosystem modeling, Global sensitivity analysis, Gross primary productivity, Satellite-data-driven, Uncertainty analysis
in
Agricultural and Forest Meteorology
volume
343
article number
109789
publisher
Elsevier
external identifiers
  • scopus:85175711607
ISSN
0168-1923
DOI
10.1016/j.agrformet.2023.109789
language
English
LU publication?
yes
additional info
Funding Information: This study was supported by the National Natural Science Foundation of China ( 42141005 , 42371486 , 42277453 , 42111530184 , 41901266 ), the National Key Research and Development Program of China ( 2020YFA0607504 ), the Research Funds for the Frontiers Science Center for Critical Earth Material Cycling, Nanjing University (Grant No: 090414380031, 020914380115). Zhang W. acknowledged grant from the Swedish Research Council VR 2020-05338. We thank Tilo Ziehn for providing the RS-HDMR software. Publisher Copyright: © 2023 Elsevier B.V.
id
dc07c4d0-7c4d-4a7c-b50f-e3266ce551dd
date added to LUP
2023-11-17 09:20:02
date last changed
2023-12-15 09:12:34
@article{dc07c4d0-7c4d-4a7c-b50f-e3266ce551dd,
  abstract     = {{<p>Terrestrial ecosystems are the largest sink for carbon, and their ecosystem gross primary productivity (GPP) regulates variations in atmospheric carbon dioxide (CO<sub>2</sub>) concentrations. Current process-based ecosystem models used for estimating GPP are subject to large uncertainties due to poorly constrained parameter values. In this study, we implemented a global sensitivity analysis (GSA) on parameters in the Boreal Ecosystem Productivity Simulator (BEPS) considering the parameters’ second-order impacts. We also applied the generalized likelihood estimation (GLUE) method, which is flexible for a multi-parameter calibration, to optimize the GPP simulation by BEPS for 10 sites covering 7 plant functional types (PFT) over China. Our optimized results significantly reduced the uncertainty of the simulated GPP over all the sites by 17 % to 82 % and showed that the GPP is sensitive to not only the photosynthesis-related parameters but also the parameters related to the soil water uptake as well as to the energy balance. The optimized GPP across South China showed that the mix forest, shrub, and grass have a higher GPP and are more controlled by the soil water availability. This study showed that the GLUE method together with the GSA scheme could constrain the ecosystem model well when simulating GPP across multiple ecosystems and provide a reasonable estimate of the spatial and temporal distribution of the ecosystem GPP over China. We call for more observations from more sites, as well as data on plant traits, to be collected in China in order to better constrain ecosystem carbon cycle modeling and understand its response to climate change.</p>}},
  author       = {{Xing, Xiuli and Wu, Mousong and Zhang, Wenxin and Ju, Weimin and Tagesson, Torbern and He, Wei and Wang, Songhan and Wang, Jun and Hu, Lu and Yuan, Shu and Zhu, Tingting and Wang, Xiaorong and Ran, Youhua and Li, Sien and Wang, Chunyu and Jiang, Fei}},
  issn         = {{0168-1923}},
  keywords     = {{Ecosystem modeling; Global sensitivity analysis; Gross primary productivity; Satellite-data-driven; Uncertainty analysis}},
  language     = {{eng}},
  month        = {{12}},
  publisher    = {{Elsevier}},
  series       = {{Agricultural and Forest Meteorology}},
  title        = {{Modeling China's terrestrial ecosystem gross primary productivity with BEPS model : Parameter sensitivity analysis and model calibration}},
  url          = {{http://dx.doi.org/10.1016/j.agrformet.2023.109789}},
  doi          = {{10.1016/j.agrformet.2023.109789}},
  volume       = {{343}},
  year         = {{2023}},
}