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Modelling Ecological Hazards and Causal Factors in the Yellow River Basin’s Key Tributaries: A Case Study of the Kuye River Basin and Its Future Outlook

Author

Listed:
  • Yihan Wu

    (College of Desert Control Science and Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China)

  • Fucang Qin

    (College of Desert Control Science and Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China
    Inner Mongolia Forestry Research Institute, Hohhot 010010, China)

  • Xiaoyu Dong

    (College of Desert Control Science and Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China)

  • Long Li

    (College of Desert Control Science and Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China
    Key Laboratory of Desert Ecosystem Conservation and Restoration, State Forestry and Grass Land Administration of China, Hohhot 010018, China)

Abstract

The Kuye River is the second largest tributary of the middle Yellow River. (1) Background: The Kuye River Basin, a typical erosion area of the Loess Plateau region, faces significant environmental challenges and intense human activities. Balancing environmental sustainability and economic development in this region is urgent. (2) Methods: This study analyses the phenomena, evolutionary processes, driving mechanisms, and future development trends. We assess ecological risks and drivers of land use change using data from 2000, 2005, 2010, 2015, and 2022. (3) Results: Farmland, grassland, and construction land are the main land use types, accounting for 85.63% of the total area. Construction land increased by 7.95 times over 22 years, mainly due to the conversion of woodland, grassland, and farmland. The landscape pattern increased in patches from 4713 in 2000 to 6522 in 2022. Patch density decreased from 0.0945 to 0.0771 between 2000 and 2015, then rose to 0.0788 in 2022. Post-2015, increased human intervention and urban development led to significant landscape fragmentation and higher ecological risk, expected to persist until 2030. Geographical detector analysis identified distance from roads, distance from cities, night light, and precipitation as key factors influencing landscape ecological risk. The interaction of anthropogenic disturbance with other factors showed a non-linear increase in risk, with combined factors having a greater impact than individual ones. (4) Conclusions: The Kuye River Basin’s landscape ecological risk is influenced by both natural conditions and human activities. To achieve sustainability, it is essential to protect critical areas, regulate development, and improve the adaptive management of ecological risks through innovative policies, integrated regulations, and technological solutions for ecosystem restoration. These findings provide empirical evidence to support decision-making and underscore the need for comprehensive strategies to mitigate ecological risks and promote sustainable development in the Kuye River Basin.

Suggested Citation

  • Yihan Wu & Fucang Qin & Xiaoyu Dong & Long Li, 2024. "Modelling Ecological Hazards and Causal Factors in the Yellow River Basin’s Key Tributaries: A Case Study of the Kuye River Basin and Its Future Outlook," Sustainability, MDPI, vol. 16(16), pages 1-35, August.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:16:p:6977-:d:1456437
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    References listed on IDEAS

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    1. Meng, Cen & Liu, Huanyao & Li, Yuyuan & Shen, Jianlin & Li, Xi & Wu, Jinshui, 2022. "Effects of environmental and agronomic factors on pond water quality within an intensive agricultural landscape in subtropical southern China," Agricultural Water Management, Elsevier, vol. 274(C).
    2. Lingyu Meng & Rongjin Yang & Meiying Sun & Le Zhang & Xiuhong Li, 2023. "Regional Sustainable Strategy Based on the Coordination of Ecological Security and Economic Development in Yunnan Province, China," Sustainability, MDPI, vol. 15(9), pages 1-20, May.
    3. Robert Pontius & Wideke Boersma & Jean-Christophe Castella & Keith Clarke & Ton Nijs & Charles Dietzel & Zengqiang Duan & Eric Fotsing & Noah Goldstein & Kasper Kok & Eric Koomen & Christopher Lippitt, 2008. "Comparing the input, output, and validation maps for several models of land change," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 42(1), pages 11-37, March.
    4. Nansha Sun & Qiong Chen & Fenggui Liu & Qiang Zhou & Wenxin He & Yuanyuan Guo, 2023. "Land Use Simulation and Landscape Ecological Risk Assessment on the Qinghai-Tibet Plateau," Land, MDPI, vol. 12(4), pages 1-14, April.
    5. Jian Peng & Minli Zong & Yi'na Hu & Yanxu Liu & Jiansheng Wu, 2015. "Assessing Landscape Ecological Risk in a Mining City: A Case Study in Liaoyuan City, China," Sustainability, MDPI, vol. 7(7), pages 1-23, June.
    6. Rares Halbac-Cotoara-Zamfir & Gloria Polinesi & Francesco Chelli & Luca Salvati & Leonardo Bianchini & Alvaro Marucci & Andrea Colantoni, 2022. "Found in Complexity, Lost in Fragmentation: Putting Soil Degradation in a Landscape Ecology Perspective," IJERPH, MDPI, vol. 19(5), pages 1-16, February.
    7. Yangfan Zhou & Lijie Pu & Ming Zhu, 2020. "Coastal Landscape Vulnerability Analysis in Eastern China—Based on Land-Use Change in Jiangsu Province," IJERPH, MDPI, vol. 17(5), pages 1-18, March.
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