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Spatiotemporal Differentiation and Its Attribution of the Ecosystem Service Trade-Off/Synergy in the Yellow River Basin

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Listed:
  • Huiying Sun

    (State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China)

  • Zhenhua Di

    (State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China)

  • Piling Sun

    (School of Geography and Tourism, Qufu Normal University, Rizhao 276826, China)

  • Xueyan Wang

    (State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China)

  • Zhenwei Liu

    (State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China)

  • Wenjuan Zhang

    (State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China)

Abstract

Clarifying the spatio-temporal patterns of ecosystem services trade-off/synergy relationships (ESTSs) and their attribution in the Yellow River Basin is crucial to constructing ecological civilization in China. This study first analyzed the spatio-temporal change of ecosystem services (ESs) including the water yield, soil conservation, carbon sequestration, and habitat quality in the Yellow River Basin during 2000–2020 based on the InVEST and RUSLE models. Then, the spatial autocorrelation methods were used to quantify the spatio-temporal differentiation of ESTSs, and the Geo-detector method was employed to identify the contributions of driving factors associated with the natural, social-economic, and regional policy aspects of the ESTSs. Finally, the random forest and analysis of variance methods were used to validate the reasonability of major driving factors obtained by the Geo-detector. The main findings include: (1) In 2000–2020, water yield, soil conservation, and habitat quality increased, and carbon sequestration decreased. The ESs had a spatial pattern of high in the east and low in the west. (2) Overall, there were synergistic relationships between the four Ess. In the spatial distribution of ESTSs, the expansion of the synergy zone and trade-off zone occupied the majority. The synergy zones tended to be concentrated in the northwest and southeast of the study area. In contrast, the trade-off zones were more scattered than the synergy zone, mainly focused on the east-central and southwestern parts of the Yellow River Basin. (3) Geo-detector and random forest both showed that natural factors had a strong explanatory power on ESTSs, in which NDVI is a key driver. Both the results of Geo-detector and the analysis of variance showed that the interactions between natural factors exerted the most significant influence on ESTSs, followed by the interaction between natural factors and socio-economic factors.

Suggested Citation

  • Huiying Sun & Zhenhua Di & Piling Sun & Xueyan Wang & Zhenwei Liu & Wenjuan Zhang, 2024. "Spatiotemporal Differentiation and Its Attribution of the Ecosystem Service Trade-Off/Synergy in the Yellow River Basin," Land, MDPI, vol. 13(3), pages 1-26, March.
    • Handle: RePEc:gam:jlands:v:13:y:2024:i:3:p:369-:d:1357198
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    References listed on IDEAS

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    1. Xiaoyi Zhang & Yichen Ruan & Weihao Xuan & Haijun Bao & Zhenhong Du, 2023. "Risk assessment and spatial regulation on urban ground collapse based on geo-detector: a case study of Hangzhou urban area," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 118(1), pages 525-543, August.
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    Cited by:

    1. Zhongyi Ding & Yuxin Wang & Liang Ma & Jintan Yang & Huping Hou & Jing Wang & Jinting Xiong & Shaoliang Zhang, 2024. "Investigating Ecosystem Service Trade-Offs and Synergies: The Need for Correlations and Driving Factors in the Upper Fen River Basin of Shanxi Province, China," Land, MDPI, vol. 13(11), pages 1-17, November.

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