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Analysis of Runoff Variation and Future Trends in a Changing Environment: Case Study for Shiyanghe River Basin, Northwest China

Author

Listed:
  • Yiqing Shao

    (College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China)

  • Zengchuan Dong

    (College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China)

  • Jinyu Meng

    (College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China)

  • Shujun Wu

    (College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China)

  • Yao Li

    (College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China)

  • Shengnan Zhu

    (College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China)

  • Qiang Zhang

    (Jiangsu Province Hydrology and Water Resources Investigation Bureau, Nanjing 210029, China)

  • Ziqin Zheng

    (College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China)

Abstract

Changes in the hydrological cycle and water resources are inevitable consequences of environmental change, and runoff is an important element of the hydrological cycle. Therefore, the assessment of runoff changes is crucial for water resources management and socio-economic development. As an inland river basin in the arid zone of northwest China, the Shiyang River Basin is very vulnerable to environmental changes. Consequently, this study evaluated the past runoff evolution of the Shiyang River basin using a variety of statistical tools. In addition, the improved Soil and Water Assessment Tool (SWAT) was used to predict runoff trends from 2019 to 2050 under potential future climate change and land use projection scenarios in the future for the Shiyang River Basin. In the inland river basins, water resources mainly come from headwaters of the rivers in the upper mountainous regions, where they are more sensitive. Therefore, this study not only examined the mainstream of the Shiyang River, but also the six tributaries in the upper stream. The results indicate that the mainstream of the Shiyang River Basin and its six upstream tributaries all showed declining trends from the 1950s to 2019, and most of the rivers will continue to insignificantly decrease until 2050. Furthermore, there are two main timescales for runoff in the past as well as future: one is around 40 years and another is 20–30 years. In the meantime, the Shiyang River and its tributaries have relatively consistent change characteristics. The results of this study will provide assistance to basin management agencies in developing more appropriate water resource management plans.

Suggested Citation

  • Yiqing Shao & Zengchuan Dong & Jinyu Meng & Shujun Wu & Yao Li & Shengnan Zhu & Qiang Zhang & Ziqin Zheng, 2023. "Analysis of Runoff Variation and Future Trends in a Changing Environment: Case Study for Shiyanghe River Basin, Northwest China," Sustainability, MDPI, vol. 15(3), pages 1-23, January.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:3:p:2173-:d:1045497
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    References listed on IDEAS

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    1. Xiaoyan Gong & Jianmin Bian & Yu Wang & Zhuo Jia & Hanli Wan, 2019. "Evaluating and Predicting the Effects of Land Use Changes on Water Quality Using SWAT and CA–Markov Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(14), pages 4923-4938, November.
    2. Corso, G. & Kuhn, P.S. & Lucena, L.S. & Thomé, Z.D., 2003. "Seismic ground roll time–frequency filtering using the gaussian wavelet transform," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 318(3), pages 551-561.
    3. Kotapati Narayana Loukika & Venkata Reddy Keesara & Venkataramana Sridhar, 2021. "Analysis of Land Use and Land Cover Using Machine Learning Algorithms on Google Earth Engine for Munneru River Basin, India," Sustainability, MDPI, vol. 13(24), pages 1-15, December.
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