IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i16p6977-d1456437.html
   My bibliography  Save this article

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 1-34, August.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:16:p:6977-:d:1456437
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/16/6977/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/16/16/6977/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yang, Yuanyuan & Bao, Wenkai & Liu, Yansui, 2020. "Scenario simulation of land system change in the Beijing-Tianjin-Hebei region," Land Use Policy, Elsevier, vol. 96(C).
    2. Youjung Kim & Galen Newman, 2019. "Climate Change Preparedness: Comparing Future Urban Growth and Flood Risk in Amsterdam and Houston," Sustainability, MDPI, vol. 11(4), pages 1-24, February.
    3. Aritta Suwarno & Meine van Noordwijk & Hans-Peter Weikard & Desi Suyamto, 2018. "Indonesia’s forest conversion moratorium assessed with an agent-based model of Land-Use Change and Ecosystem Services (LUCES)," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 23(2), pages 211-229, February.
    4. Yuanyuan Yang & Shuwen Zhang & Jiuchun Yang & Xiaoshi Xing & Dongyan Wang, 2015. "Using a Cellular Automata-Markov Model to Reconstruct Spatial Land-Use Patterns in Zhenlai County, Northeast China," Energies, MDPI, vol. 8(5), pages 1-21, May.
    5. Qi’ang Du & Hongbo Li & Yanyan Fu & Xintian Fu & Rui Wang & Tingting Jia, 2023. "More Green, Better Funding? Exploring the Dynamics between Corporate Bank Loans and Trade Credit," Sustainability, MDPI, vol. 15(13), pages 1-20, June.
    6. Bonoua Faye & Guoming Du & Edmée Mbaye & Chang’an Liang & Tidiane Sané & Ruhao Xue, 2023. "Assessing the Spatial Agricultural Land Use Transition in Thiès Region, Senegal, and Its Potential Driving Factors," Land, MDPI, vol. 12(4), pages 1-20, March.
    7. Rifat, Shaikh Abdullah Al & Liu, Weibo, 2022. "Predicting future urban growth scenarios and potential urban flood exposure using Artificial Neural Network-Markov Chain model in Miami Metropolitan Area," Land Use Policy, Elsevier, vol. 114(C).
    8. Jing Yang & Feng Shi & Yizhong Sun & Jie Zhu, 2019. "A Cellular Automata Model Constrained by Spatiotemporal Heterogeneity of the Urban Development Strategy for Simulating Land-use Change: A Case Study in Nanjing City, China," Sustainability, MDPI, vol. 11(15), pages 1-19, July.
    9. Brian Pickard & Joshua Gray & Ross Meentemeyer, 2017. "Comparing Quantity, Allocation and Configuration Accuracy of Multiple Land Change Models," Land, MDPI, vol. 6(3), pages 1-21, August.
    10. Di Liu & Xiaoying Liang & Hai Chen & Hang Zhang & Nanzhao Mao, 2018. "A Quantitative Assessment of Comprehensive Ecological Risk for a Loess Erosion Gully: A Case Study of Dujiashi Gully, Northern Shaanxi Province, China," Sustainability, MDPI, vol. 10(9), pages 1-16, September.
    11. Ju-Sung Lee & Tatiana Filatova & Arika Ligmann-Zielinska & Behrooz Hassani-Mahmooei & Forrest Stonedahl & Iris Lorscheid & Alexey Voinov & J. Gareth Polhill & Zhanli Sun & Dawn C. Parker, 2015. "The Complexities of Agent-Based Modeling Output Analysis," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 18(4), pages 1-4.
    12. Zhang, Yan & Chang, Xia & Liu, Yanfang & Lu, Yanchi & Wang, Yiheng & Liu, Yaolin, 2021. "Urban expansion simulation under constraint of multiple ecosystem services (MESs) based on cellular automata (CA)-Markov model: Scenario analysis and policy implications," Land Use Policy, Elsevier, vol. 108(C).
    13. Margaret Gitau & Nathaniel Bailey, 2012. "Multi-Layer Assessment of Land Use and Related Changes for Decision Support in a Coastal Zone Watershed," Land, MDPI, vol. 1(1), pages 1-27, December.
    14. Xiaoli Hu & Xin Li & Ling Lu, 2018. "Modeling the Land Use Change in an Arid Oasis Constrained by Water Resources and Environmental Policy Change Using Cellular Automata Models," Sustainability, MDPI, vol. 10(8), pages 1-14, August.
    15. Yaya Jin & Jiahe Ding & Yue Chen & Chaozheng Zhang & Xianhui Hou & Qianqian Zhang & Qiankun Liu, 2023. "Urban Land Expansion Simulation Considering the Increasing versus Decreasing Balance Policy: A Case Study in Fenghua, China," Land, MDPI, vol. 12(12), pages 1-21, November.
    16. Charlotte Shade & Peleg Kremer, 2019. "Predicting Land Use Changes in Philadelphia Following Green Infrastructure Policies," Land, MDPI, vol. 8(2), pages 1-19, February.
    17. repec:ris:cieodp:2013_019 is not listed on IDEAS
    18. Wu, Wei & Yeager, Kevin M. & Peterson, Mark S. & Fulford, Richard S., 2015. "Neutral models as a way to evaluate the Sea Level Affecting Marshes Model (SLAMM)," Ecological Modelling, Elsevier, vol. 303(C), pages 55-69.
    19. Zeng, Lijun & Guo, Jiaqi & Wang, Bingcheng & Lv, Jun & Wang, Qin, 2019. "Analyzing sustainability of Chinese coal cities using a decision tree modeling approach," Resources Policy, Elsevier, vol. 64(C).
    20. Chengge Jiang & Lingzhi Wang & Wenhua Guo & Huiling Chen & Anqi Liang & Mingying Sun & Xinyao Li & Hichem Omrani, 2024. "Spatio-Temporal Evolution and Multi-Scenario Simulation of Non-Grain Production on Cultivated Land in Jiangsu Province, China," Land, MDPI, vol. 13(5), pages 1-21, May.
    21. Robert Gilmore Pontius, 2018. "Criteria to Confirm Models that Simulate Deforestation and Carbon Disturbance," Land, MDPI, vol. 7(3), pages 1-14, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:16:y:2024:i:16:p:6977-:d:1456437. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.