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Forecasting Future Vegetation Dynamics under SSP/RCP Pathways under Spatially Changing Climate and Human Activities Conditions

Author

Listed:
  • Wei Yang

    (Hubei Water Resources Research Institute, Wuhan 430070, China
    Hubei Soil and Water Conservation Engineering Research Center, Wuhan 430070, China)

  • Xinquan Su

    (College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China)

  • Lu Li

    (Hubei Water Resources Research Institute, Wuhan 430070, China
    Hubei Soil and Water Conservation Engineering Research Center, Wuhan 430070, China)

  • Bing Yu

    (Hubei Water Resources Research Institute, Wuhan 430070, China
    Hubei Soil and Water Conservation Engineering Research Center, Wuhan 430070, China)

  • Xiao Chen

    (College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China)

  • Zhibang Luo

    (College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China)

  • Wenyv Chu

    (College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China)

  • Wenting Zhang

    (College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China)

Abstract

Vegetation dynamics result from the interaction between human activities and climate change. Numerous studies have investigated the contributions of human activities and climate change to vegetation cover dynamics using statistical methods. However, these studies have not focused much on the spatially non-stationary effects of human activities on vegetation cover changes and future trends. Taking the Three Gorges Reservoir (TGR) area as the case study area, it was divided into 32 combinations by considering the spatially varying effects of five factors related to human activity and climate change, including gross domestic product (GDP), population, land use change, precipitation, and temperature. Regression in terms of pixels was then performed for each combination at the pixel scale. The result showed that from 2001 to 2020, the annual average normalized digital vegetation index (NDVI) in the TGR area exhibited an upward trend (slope = 0.0051, p < 0.01), with the mean NDVI increasing from 0.53 to 0.64. Compared with the regression with climate variables, the proposed model improved the R 2 value from 0.2567 to 0.6484, with the p -value in the t -test reduced from 0.2579 to 0.0056. It indicated that changes in vegetation were dominated by human activities and climate change in 48.77% and 3.19% of the TGR area, respectively, and 43.70% of the vegetation coverage was dominated by both human activities and climate change. This study also predicted the future NDVI according to the shared socioeconomic pathways (SSPs) and representative concentration pathway (RCP) scenarios provided by the Intergovernmental Panel on Climate Change. It suggests that, assuming future regional policies are the same as the historical policies in the TGR, the SSP5–8.5 scenario would have the highest and fastest growth in average NDVI, with the average NDVI increasing from 0.68 to 0.89, because of the large increase in the GDP, lower population in this scenario, and adequate hydrothermal conditions.

Suggested Citation

  • Wei Yang & Xinquan Su & Lu Li & Bing Yu & Xiao Chen & Zhibang Luo & Wenyv Chu & Wenting Zhang, 2024. "Forecasting Future Vegetation Dynamics under SSP/RCP Pathways under Spatially Changing Climate and Human Activities Conditions," Sustainability, MDPI, vol. 16(14), pages 1-25, July.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:14:p:6188-:d:1438840
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    References listed on IDEAS

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