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Water Conservation and Ecological Water Requirement Prediction of Mining Area in Arid Region Based on RS-GIS and InVEST: A Case Study of Bayan Obo Mine in Baotou, China

Author

Listed:
  • Qian-Qian Wang

    (School of Resources and Architectural Engineering, Gannan University of Science and Technology, Ganzhou 341000, China)

  • Cheng-Xin Geng

    (School of Energy and Environment, Inner Mongolia University of Science and Technology, Inner Mongolia Autonomous Region, Baotou 014010, China)

  • Lu Wang

    (Ganjiang Innovation Academy, Chinese Academy of Sciences, Ganzhou 341000, China)

  • Ting-Ting Zheng

    (School of Energy and Environment, Inner Mongolia University of Science and Technology, Inner Mongolia Autonomous Region, Baotou 014010, China)

  • Qing-Hong Jiang

    (School of Energy and Environment, Inner Mongolia University of Science and Technology, Inner Mongolia Autonomous Region, Baotou 014010, China)

  • Tong Yang

    (School of Energy and Environment, Inner Mongolia University of Science and Technology, Inner Mongolia Autonomous Region, Baotou 014010, China)

  • Yong-Qi Liu

    (School of Energy and Environment, Inner Mongolia University of Science and Technology, Inner Mongolia Autonomous Region, Baotou 014010, China)

  • Zhe Wang

    (School of Energy and Environment, Inner Mongolia University of Science and Technology, Inner Mongolia Autonomous Region, Baotou 014010, China)

Abstract

The overexploitation of mineral resources in northwestern China has resulted in severe ecological degradation and even desertification in certain mining areas. To support ecological restoration in these arid mining regions, we conducted a study on water conservation and ecological water demand using Bayan Obo as a case study. Based on remote sensing, geographic information systems, and the Integrated Valuation of Ecosystem Services and Trade-offs InVEST model, our study found that the mining area has lost its capacity for water production, with the water source conservation showing negative values. In addition, precipitation levels are far lower than evapotranspiration, making it difficult to retain precipitation. We predicted ecological water demand for the planning years (2025, 2030, and 2035) by combining qualitative and quantitative forecasting methods, with 2019 serving as the base year. The results indicated a downward trend in natural ecological water demand, while artificial ecological water demand exhibited the opposite trend. Changes in natural grassland and artificial green areas in the mining region were identified as the main drivers of changes in ecological water demand.

Suggested Citation

  • Qian-Qian Wang & Cheng-Xin Geng & Lu Wang & Ting-Ting Zheng & Qing-Hong Jiang & Tong Yang & Yong-Qi Liu & Zhe Wang, 2023. "Water Conservation and Ecological Water Requirement Prediction of Mining Area in Arid Region Based on RS-GIS and InVEST: A Case Study of Bayan Obo Mine in Baotou, China," Sustainability, MDPI, vol. 15(5), pages 1-17, February.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:5:p:4238-:d:1081722
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    References listed on IDEAS

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    1. Paredes, P. & Pereira, L.S. & Almorox, J. & Darouich, H., 2020. "Reference grass evapotranspiration with reduced data sets: Parameterization of the FAO Penman-Monteith temperature approach and the Hargeaves-Samani equation using local climatic variables," Agricultural Water Management, Elsevier, vol. 240(C).
    2. Barton, Louise & Flottmann, Samuel J. & Stefanovia, Katia T. & Colmer, Timothy D., 2020. "Approaches to scheduling water allocations to kikuyugrass grown on a water repellent soil in a drying-climate," Agricultural Water Management, Elsevier, vol. 230(C).
    3. Hu, Xuhua & Chen, Mengting & Liu, Dong & Li, Dan & Jin, Li & Liu, Shaohui & Cui, Yuanlai & Dong, Bin & Khan, Shahbaz & Luo, Yufeng, 2021. "Reference evapotranspiration change in Heilongjiang Province, China from 1951 to 2018: The role of climate change and rice area expansion," Agricultural Water Management, Elsevier, vol. 253(C).
    4. K. Cheng & Q. Fu & J. Meng & T. X. Li & W. Pei, 2018. "Analysis of the Spatial Variation and Identification of Factors Affecting the Water Resources Carrying Capacity Based on the Cloud Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(8), pages 2767-2781, June.
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