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<p>The Qinling Mountains plays an important geographical and climatic divide between northern and southern China, influencing vegetation dynamics and carbon sequestration. We investigate the spatiotemporal variations in net primary productivity (NPP) and its driving mechanisms from 2000 to 2022 using MODIS NPP data, climatic factors (temperature, precipitation, sunshine hours), land-use types, and DEM in the Qinling Shaanxi section of China. The findings show that (1) the annual mean NPP is 597.5 gC/m² in the Qinling Mountains, with higher NPP at 657 gC/m² over the south slope than the north slope during 2000–2022. NPP varies obvious among land-use types, ranking as: forest (612.67 gC/m²) &gt; shrub (591.06 gC/m²) &gt; grassland (566.83 gC/m²) &gt; cropland (552.16 gC/m²) &gt; water (486.29 gC/m²) &gt; impervious (463.32 gC/m²) &gt; barren (342.75 gC/m²). (2) NPP shows an overall increasing trend (457.3–682.7 gC/m²), peaking in 2015 and reaching its lowest in 2001. (3) The Climatic mean temperature, precipitation, and sunshine hours range from 10.5 to 11.9&#xa0;°C, from 617.6 to 1237.2&#xa0;mm, and from 1734.2 to 2394.4&#xa0;h, exhibiting spatial heterogeneity, higher temperatures in low-altitude areas, decreasing precipitation from south to north, and greater sunshine hours in the northeast and southwest. (4) Temperature is the dominant climatic driver of NPP, followed by precipitation, while sunshine hours had minimal influence. Land-use type changes, particularly cropland-to-forest conversion, significantly boosted NPP (contributing 91,430.61 t/a during 2016–2022), whereas urban expansion reduced regional NPP. These findings underscore the need for balanced ecological conservation and economic development in the Qinling Mountains, including curbing urban sprawl to maintain vegetation health. Future research will assess climate change impacts on vegetation ecology to enhance adaptive strategies.</p>

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Spatio-Temporal Variations and the Climatic and Human Activity Driving Factor Analysis of Net Primary Productivity(2000–2022) in the Qinling Shaanxi Section of China

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  • 发表日期：

    2025年10月29日

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  • Yuan Yuan,
  • Guanrong Huang,
  • Ting Zhao,
  • Xiangrong Zhang,
  • Mengtao Zheng,
  • Lei Lei

摘要

The Qinling Mountains plays an important geographical and climatic divide between northern and southern China, influencing vegetation dynamics and carbon sequestration. We investigate the spatiotemporal variations in net primary productivity (NPP) and its driving mechanisms from 2000 to 2022 using MODIS NPP data, climatic factors (temperature, precipitation, sunshine hours), land-use types, and DEM in the Qinling Shaanxi section of China. The findings show that (1) the annual mean NPP is 597.5 gC/m² in the Qinling Mountains, with higher NPP at 657 gC/m² over the south slope than the north slope during 2000–2022. NPP varies obvious among land-use types, ranking as: forest (612.67 gC/m²) > shrub (591.06 gC/m²) > grassland (566.83 gC/m²) > cropland (552.16 gC/m²) > water (486.29 gC/m²) > impervious (463.32 gC/m²) > barren (342.75 gC/m²). (2) NPP shows an overall increasing trend (457.3–682.7 gC/m²), peaking in 2015 and reaching its lowest in 2001. (3) The Climatic mean temperature, precipitation, and sunshine hours range from 10.5 to 11.9 °C, from 617.6 to 1237.2 mm, and from 1734.2 to 2394.4 h, exhibiting spatial heterogeneity, higher temperatures in low-altitude areas, decreasing precipitation from south to north, and greater sunshine hours in the northeast and southwest. (4) Temperature is the dominant climatic driver of NPP, followed by precipitation, while sunshine hours had minimal influence. Land-use type changes, particularly cropland-to-forest conversion, significantly boosted NPP (contributing 91,430.61 t/a during 2016–2022), whereas urban expansion reduced regional NPP. These findings underscore the need for balanced ecological conservation and economic development in the Qinling Mountains, including curbing urban sprawl to maintain vegetation health. Future research will assess climate change impacts on vegetation ecology to enhance adaptive strategies.