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An improved Biome-BGC model for estimating net primary productivity of alpine meadow on the Qinghai-Tibet Plateau

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  • Sun, Qingling
  • Li, Baolin
  • Zhang, Tao
  • Yuan, Yecheng
  • Gao, Xizhang
  • Ge, Jinsong
  • Li, Fei
  • Zhang, Zhijun

Abstract

The process-based Biome-BGC (BioGeochemical Cycles) model is widely used to simulate the storage and flux of water, carbon, and nitrogen within an ecosystem. However, the Biome-BGC model neglects the existence of underground persistent portion of perennial grasses as well as functional differences between the fast-cycling and persistent roots, which are assumed to negatively impact the accurate estimation of net primary productivity (NPP) of alpine meadow on the Qinghai-Tibet Plateau. This study presents the Perennial Deciduous Grasses (PDG) NPP model developed under the framework of Biome-BGC version 4.2. In the PDG NPP model, underground roots of perennial grasses were partitioned into two portions, the fast-cycling portion and the persistent portion. These two portions were represented by different turnover fractions, carbon to nitrogen ratios, and allocation ratios in the PDG NPP model. Processes primarily involving allocation, maintenance respiration, growth respiration, litterfall and mortality, and transfer growth were modified to account for the differences between fast-cycling and persistent roots. Measured NPP including both aboveground NPP (ANPP) and belowground NPP (BNPP) at Zhenqin Station in Qinghai Province, China, was used to validate estimates of the PDG NPP model. The results showed that the PDG NPP model has a higher estimation accuracy of NPP than the Biome-BGC model. The root mean square error (RMSE) and mean absolute percent error (MAPE) of total NPP estimated from the PDG NPP model were 15.1gC/m2/yr and 12.1%, respectively, whereas those from the Biome-BGC model were 49.8gC/m2/yr and 39.9%, respectively. Both RMSE and MAPE of ANPP and BNPP estimates based on the Biome-BGC model were approximately 3 times those based on the PDG NPP model. Improvements of NPP estimation were mainly attributed to the decreasing estimates of gross primary productivity which was caused by the increase of water stress and decrease of soil N availability simulated during growing seasons. Therefore, the PDG NPP model is more reflective of the limitations of water and nutrients to vegetation growth and productivity when applied to the Qinghai-Tibet Plateau.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:ecomod:v:350:y:2017:i:c:p:55-68
    DOI: 10.1016/j.ecolmodel.2017.01.025
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    References listed on IDEAS

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    1. Hidy, D. & Barcza, Z. & Haszpra, L. & Churkina, G. & Pintér, K. & Nagy, Z., 2012. "Development of the Biome-BGC model for simulation of managed herbaceous ecosystems," Ecological Modelling, Elsevier, vol. 226(C), pages 99-119.
    2. Di Vittorio, Alan V. & Anderson, Ryan S. & White, Joseph D. & Miller, Norman L. & Running, Steven W., 2010. "Development and optimization of an Agro-BGC ecosystem model for C4 perennial grasses," Ecological Modelling, Elsevier, vol. 221(17), pages 2038-2053.
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    1. Chaobin Zhang & Ying Zhang & Jianlong Li, 2019. "Grassland Productivity Response to Climate Change in the Hulunbuir Steppes of China," Sustainability, MDPI, vol. 11(23), pages 1-15, November.
    2. Qifei Han & Geping Luo & Chaofan Li & Shoubo Li, 2018. "Response of Carbon Dynamics to Climate Change Varied among Different Vegetation Types in Central Asia," Sustainability, MDPI, vol. 10(9), pages 1-15, September.
    3. 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.
    4. Cho, Nanghyun & Agossou, Casimir & Kim, Eunsook & Lim, Jong-Hwan & Seo, Jeong-Wook & Kang, Sinkyu, 2023. "Machine-learning modeling on tree mortality and growth reduction of temperate forests with climatic and ecophysiological parameters," Ecological Modelling, Elsevier, vol. 483(C).

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