Estimating the dynamics and driving factors of gross primary productivity over the Chinese Loess Plateau by the modified vegetation photosynthesis model

Gross primary productivity (GPP) is a key parameter in research on the global carbon cycle and changes. Understanding the spatiotemporal dynamics and influencing factors of GPP on the Loess Plateau (LP) helps identify the health status of ecosystems, thereby enabling the implementation of effective conservation and restoration measures. In this study, we used a modified vegetation photosynthesis model (VPM) to simulate a long-term series of GPP in the LP from 2001 to 2022, and the impacts of different land-use patterns and meteorological factors on GPP were investigated using a transition matrix, linear regression, and partial correlation analyses. The findings suggested that the modified simulation yielded a more reliable performance (coefficient of determination ( R 2 ) = 0.89, root mean square error (RMSE) = 143.47 gC·m −2 ·yr −1 ) and was suitable for further research endeavors. (1) The GPP on the LP significantly increased by 232.65 TgC from 2001 to 2022. The southeastern region contributed more than the northwestern region, and the GPP exhibited higher multi-year averages and growth rates below 1000 m elevation. (2) Forests in the southeastern region of the LP, characterized by a heightened growth rate, will influence future spatial variations in GPP increases across the LP. Despite the decline in grassland and cultivated land areas, substantial land coverage has significantly contributed to the overall GPP growth. Urbanization encroaching on cultivated land has emerged as a key contributor to the decline in GPP in low-altitude regions. (3) Air temperature was the main physical driving force for GPP change in the LP. Additionally, the GPP in forested regions exhibited a negative correlation with rainfall, whereas the GPP in areas undergoing the return of cropland to forest–grassland and cropland reclamation correlated negatively with solar radiation. (4) The attribution analysis indicated that the surge in vegetation GPP on the LP was collectively driven by human activities and meteorological changes, with human activities dominating these changes by 61.41 %. This study deepens the understanding of terrestrial ecology in semi-humid regions and provides scientific insights for implementing ecological governance strategies in the LP. • The revised photosynthesis model has been demonstrated to perform better on the Loess Plateau in China. • precipitation has constrained the growth of vegetation productivity. • Urbanization is a significant factor leading to the decline in GPP in low-altitude areas. • Human activities dominate the changes in GPP.

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Publication Details

Journal
Ecological Informatics
Published
2024-09-27
DOI
https://doi.org/10.1016/j.ecoinf.2024.102838
Citations
11
Primary Topic
Plant Water Relations and Carbon Dynamics
Type
article
Field-Weighted Citation Impact
3.11
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article

Estimating the dynamics and driving factors of gross primary productivity over the Chinese Loess Plateau by the modified vegetation photosynthesis model

Enjun Gong, Jun Wang, Zhihui Wang, Jing Zhang
11 citations
Ecological Informatics
Plant Water Relations and Carbon Dynamics
3.11
article

Estimating the dynamics and driving factors of gross primary productivity over the Chinese Loess Plateau by the modified vegetation photosynthesis model

Enjun Gong, Jun Wang, Zhihui Wang, Jing Zhang
article en
11 citations

Abstract

Gross primary productivity (GPP) is a key parameter in research on the global carbon cycle and changes. Understanding the spatiotemporal dynamics and influencing factors of GPP on the Loess Plateau (LP) helps identify the health status of ecosystems, thereby enabling the implementation of effective conservation and restoration measures. In this study, we used a modified vegetation photosynthesis model (VPM) to simulate a long-term series of GPP in the LP from 2001 to 2022, and the impacts of different land-use patterns and meteorological factors on GPP were investigated using a transition matrix, linear regression, and partial correlation analyses. The findings suggested that the modified simulation yielded a more reliable performance (coefficient of determination ( R 2 ) = 0.89, root mean square error (RMSE) = 143.47 gC·m −2 ·yr −1 ) and was suitable for further research endeavors. (1) The GPP on the LP significantly increased by 232.65 TgC from 2001 to 2022. The southeastern region contributed more than the northwestern region, and the GPP exhibited higher multi-year averages and growth rates below 1000 m elevation. (2) Forests in the southeastern region of the LP, characterized by a heightened growth rate, will influence future spatial variations in GPP increases across the LP. Despite the decline in grassland and cultivated land areas, substantial land coverage has significantly contributed to the overall GPP growth. Urbanization encroaching on cultivated land has emerged as a key contributor to the decline in GPP in low-altitude regions. (3) Air temperature was the main physical driving force for GPP change in the LP. Additionally, the GPP in forested regions exhibited a negative correlation with rainfall, whereas the GPP in areas undergoing the return of cropland to forest–grassland and cropland reclamation correlated negatively with solar radiation. (4) The attribution analysis indicated that the surge in vegetation GPP on the LP was collectively driven by human activities and meteorological changes, with human activities dominating these changes by 61.41 %. This study deepens the understanding of terrestrial ecology in semi-humid regions and provides scientific insights for implementing ecological governance strategies in the LP. • The revised photosynthesis model has been demonstrated to perform better on the Loess Plateau in China. • precipitation has constrained the growth of vegetation productivity. • Urbanization is a significant factor leading to the decline in GPP in low-altitude areas. • Human activities dominate the changes in GPP.

Ecological InformaticsVol. 83
Chang'an University (CN), Northwest University (CN), Yellow River Institute of Hydraulic Research (CN)
Openalex Percentile: Top 8%
Plant Water Relations and Carbon Dynamics
3.11
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