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Peer-reviewedCC BY-NC-NDCarbon balance and management

Improved GPP modeling reveals rising precipitation influence in typical grasslands of Northern China's Hulun Lake Basin.

  • Tong Sun1,2
  • Nan Shan1,3
  • Xuehe Lu4
  • Bingshuai Cao1,3
  • Wenjing Li1,3
  • Qian Zhang5
  • Qing Huang6
  • Haidong Zhang7
  • Qianghao Zeng8
  • Suwan Chen2
8 affiliations
  1. 1Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment of the People's Republic of China, Nanjing, 210042, China.
  2. 2School of Geography Science and Geomatics Engineering, Suzhou University of Science and Technology, Suzhou, 215000, China.
  3. 3Inner Mongolia Hulun Lake (Wetland) Comprehensive Monitoring Station for Ecological Quality, Hulunbuir, 021000, China.
  4. 4School of Geography Science and Geomatics Engineering, Suzhou University of Science and Technology, Suzhou, 215000, China. [email protected].
  5. 5College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao, 266590, China.
  6. 6School of Environmental Science, Nanjing Xiaozhuang University, Nanjing, 211171, Jiangsu, China.
  7. 7Institute of Agricultural Sciences in Taihu Area of Jiangsu, Suzhou, 215155, China.
  8. 8East China Agricultural Science and Technology Center, Chinese Academy of Agricultural Sciences, Suzhou, 215300, China.
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Abstract

The Hulun Lake Basin in northern China harbors extensive temperate grasslands, whose carbon uptake and ecosystem functioning are effectively represented by gross primary productivity (GPP). However, the long-term response of grassland GPP to regional climate change remains insufficiently understood, due to the limited ability of global models to capture local ecosystem variability. This study improved GPP estimation in the Hulun Lake Basin by integrating additional grassland flux observations from China into a Random Forest model (MCF). We subsequently examined the spatiotemporal dynamics of GPP and identified the dominant climatic drivers and their long-term trends under changing climate conditions. Results show that the MCF model significantly outperformed the model trained solely on FLUXNET 2015 Tier 2 data in both accuracy and trend patterns of grassland GPP. A 23-year MCF simulation in the Hulun Lake Basin showed a clear west-east GPP gradient, averaging 676.36 g C m- 2 yr- 1 and totaling 70.13 Tg C yr- 1. Most of the region exhibited increasing GPP trends, with an average growth rate of 6.85 g C m- 2 yr- 1, resulting in an annual total increase of approximately 0.45 Tg C yr- 1. SHAP analysis demonstrated that shifts in precipitation and temperature are reshaping the dominant factors contributing to GPP increases. Structural equation modeling further revealed that precipitation enhanced GPP through both direct and indirect pathways, with the direct effect accounting for 63.6% of the total effect, while the remaining 36.4% was mediated primarily through NIRv. In particular, over large areas of the central-eastern basin, increasing precipitation has gradually replaced shortwave radiation as the primary driver of GPP enhancement. This study provides new insights into how temperate grassland GPP responds to climate change, offering a scientific basis for region-specific ecosystem management and policy decisions.

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Cite

BibTeXRIS
BibTeX
@article{sun2026improved,
  title = {{Improved GPP modeling reveals rising precipitation influence in typical grasslands of Northern China's Hulun Lake Basin.}},
  author = {Tong Sun and Nan Shan and Xuehe Lu and Bingshuai Cao and Wenjing Li and Qian Zhang and Qing Huang and Haidong Zhang and Qianghao Zeng and Suwan Chen},
  journal = {Carbon balance and management},
  year = {2026},
  month = jul,
  doi = {10.1186/s13021-026-00491-9},
  url = {https://europepmc.org/article/MED/42955579},
  note = {CC BY-NC-ND},
}
RIS
TY  - JOUR
TI  - Improved GPP modeling reveals rising precipitation influence in typical grasslands of Northern China's Hulun Lake Basin.
AU  - Tong Sun
AU  - Nan Shan
AU  - Xuehe Lu
AU  - Bingshuai Cao
AU  - Wenjing Li
AU  - Qian Zhang
AU  - Qing Huang
AU  - Haidong Zhang
AU  - Qianghao Zeng
AU  - Suwan Chen
PY  - 2026
DA  - 2026/07/26
JO  - Carbon balance and management
DO  - 10.1186/s13021-026-00491-9
UR  - https://europepmc.org/article/MED/42955579
AB  - The Hulun Lake Basin in northern China harbors extensive temperate grasslands, whose carbon uptake and ecosystem functioning are effectively represented by gross primary productivity (GPP). However, the long-term response of grassland GPP to regional climate change remains insufficiently understood, due to the limited ability of global models to capture local ecosystem variability. This study improved GPP estimation in the Hulun Lake Basin by integrating additional grassland flux observations from China into a Random Forest model (MCF). We subsequently examined the spatiotemporal dynamics of GPP and identified the dominant climatic drivers and their long-term trends under changing climate conditions. Results show that the MCF model significantly outperformed the model trained solely on FLUXNET 2015 Tier 2 data in both accuracy and trend patterns of grassland GPP. A 23-year MCF simulation in the Hulun Lake Basin showed a clear west-east GPP gradient, averaging 676.36 g C m- 2 yr- 1 and totaling 70.13 Tg C yr- 1. Most of the region exhibited increasing GPP trends, with an average growth rate of 6.85 g C m- 2 yr- 1, resulting in an annual total increase of approximately 0.45 Tg C yr- 1. SHAP analysis demonstrated that shifts in precipitation and temperature are reshaping the dominant factors contributing to GPP increases. Structural equation modeling further revealed that precipitation enhanced GPP through both direct and indirect pathways, with the direct effect accounting for 63.6% of the total effect, while the remaining 36.4% was mediated primarily through NIRv. In particular, over large areas of the central-eastern basin, increasing precipitation has gradually replaced shortwave radiation as the primary driver of GPP enhancement. This study provides new insights into how temperate grassland GPP responds to climate change, offering a scientific basis for region-specific ecosystem management and policy decisions.
SN  - 1750-0680
ER  -