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Sensitivity Analysis of Biome-BGC for Gross Primary Production of a Rubber Plantation Ecosystem: A Case Study of Hainan Island, China

Author

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  • Junyi Liu

    (College of Ecology and Environment, Hainan University, Haikou 570228, China
    Rubber Research Institute, Chinese Academy of Tropical Agricultural Sciences, Haikou 571101, China
    Hainan Danzhou Tropical Agro-Ecosystem National Observation and Research Station, Danzhou 571737, China)

  • Zhixiang Wu

    (College of Ecology and Environment, Hainan University, Haikou 570228, China
    Rubber Research Institute, Chinese Academy of Tropical Agricultural Sciences, Haikou 571101, China
    Hainan Danzhou Tropical Agro-Ecosystem National Observation and Research Station, Danzhou 571737, China)

  • Siqi Yang

    (College of Ecology and Environment, Hainan University, Haikou 570228, China
    Rubber Research Institute, Chinese Academy of Tropical Agricultural Sciences, Haikou 571101, China
    Hainan Danzhou Tropical Agro-Ecosystem National Observation and Research Station, Danzhou 571737, China)

  • Chuan Yang

    (Rubber Research Institute, Chinese Academy of Tropical Agricultural Sciences, Haikou 571101, China
    Hainan Danzhou Tropical Agro-Ecosystem National Observation and Research Station, Danzhou 571737, China)

Abstract

Accurate monitoring of forest carbon flux and its long-term response to meteorological factors is important. To accomplish this task, the model parameters need to be optimized with respect to in situ observations. In the present study, the extended Fourier amplitude sensitivity test (eFAST) method was used to optimize the sensitive ecophysiological parameters of the Biome BioGeochemical Cycles model. The model simulation was integrated from 2010 to 2020. The results showed that using the eFAST method quantitatively improved the model output. For instance, the R 2 increased from 0.53 to 0.72. Moreover, the root-mean-square error was reduced from 1.62 to 1.14 gC·m −2 ·d −1 . In addition, it was reported that the carbon flux outputs of the model were highly sensitive to various parameters, such as the canopy average specific leaf area and canopy light extinction coefficient. Moreover, long-term meteorological factor analysis showed that rainfall dominated the trend of gross primary production (GPP) of the study area, while extreme temperatures restricted the GPP. In conclusion, the eFAST method can be used in future studies. Furthermore, eFAST could be applied to other biomes in response to different climatic conditions.

Suggested Citation

  • Junyi Liu & Zhixiang Wu & Siqi Yang & Chuan Yang, 2022. "Sensitivity Analysis of Biome-BGC for Gross Primary Production of a Rubber Plantation Ecosystem: A Case Study of Hainan Island, China," IJERPH, MDPI, vol. 19(21), pages 1-13, October.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:21:p:14068-:d:956306
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    2. Jing Wang & Liang Feng & Paul I. Palmer & Yi Liu & Shuangxi Fang & Hartmut Bösch & Christopher W. O’Dell & Xiaoping Tang & Dongxu Yang & Lixin Liu & ChaoZong Xia, 2020. "Publisher Correction: Large Chinese land carbon sink estimated from atmospheric carbon dioxide data," Nature, Nature, vol. 588(7837), pages 19-19, December.
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    4. Sun, Qingling & Li, Baolin & Zhang, Tao & Yuan, Yecheng & Gao, Xizhang & Ge, Jinsong & Li, Fei & Zhang, Zhijun, 2017. "An improved Biome-BGC model for estimating net primary productivity of alpine meadow on the Qinghai-Tibet Plateau," Ecological Modelling, Elsevier, vol. 350(C), pages 55-68.
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