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Corrected GCM data through CMFD data to analysis future runoff changes in the source region of the Yangtze River, China

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Abstract

The source region of the Yangtze River (SRYR) is located in the hinterland of the Tibetan Plateau (TP). Due to the harsh climate environment, there are few meteorological stations in its territory, and the measured meteorological data are also less. To overcome the impact of lack of measured meteorological data, the China Meteorological Forcing Dataset (CMFD) was used to couple with the meteorological data, to make the meteorological data more representative of the average climate state of the SRYR. Furthermore, we used the Soil and Water Assessment Tool (SWAT) to verify interpolation effect of meteorological data. Then the six global climate models (GCMs) were integrated through deep learning (DL) algorithm to make it closer to the changes in weather data after correction. The future water resource changes of SRYR were obtained by coupling the SWAT model with the integrated meteorological data. The results show that the CMFD dataset has a high precision in the SRYR, and that the average relative error (RE), coefficient of determination (R2), Nash–Sutcliffe efficiency (Ens) with the measured temperature and precipitation are 10.66%, 0.99, and 0.93, respectively. The CMFD dataset can be used for meteorological data integration. After the meteorological data integration, the Ens between simulated runoff and observed runoff increased from 0.64 to 0.70, and the RE reduced from 13.4 to 10.3%. Under the future climate scenario, the runoff in the SRYR shows a decreasing trend, the average annual runoff decreased by 11.89%, and the distribution of runoff during the year changes is even more dramatic. This shows that the SRYR faces the risk of water resources reduction, which will bring challenges to water resources management in the SRYR. This study provides a new idea for meteorological data correction.

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Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

The forcing dataset used in this study was developed by Data Assimilation and Modeling Center for Tibetan Multi-spheres, Institute of Tibetan Plateau Research, Chinese Academy of Sciences.

Funding

This study is financially supported by National Natural Science Foundation of China (Grant No. U1911204, 51861125203), National Key R&D Program of China (2021YFC3001000).

Author information

Authors and Affiliations

  1. School of Civil Engineering, Sun Yat-sen University, Guangzhou, 510275, China

    Haoyu Jin, Xiaohong Chen, Ruida Zhong, Tongtiegang Zhao, Zhiyong Liu & Xinjun Tu

  2. Guangdong Engineering Technology Research Center of Water Security Regulation and Control for Southern China, Sun Yat-sen University, Guangzhou, 510275, China

    Haoyu Jin, Xiaohong Chen, Ruida Zhong, Tongtiegang Zhao, Zhiyong Liu & Xinjun Tu

  3. Key Laboratory of Water Cycle and Water Security in Southern China of Guangdong High Education Institute, Sun Yat-sen University, Guangzhou, 510275, China

    Haoyu Jin, Xiaohong Chen, Ruida Zhong, Tongtiegang Zhao, Zhiyong Liu & Xinjun Tu

  4. Dongjiang Water Source Project Management Division of Shenzhen, Shenzhen, 518036, China

    Yingjie Pan

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  1. Haoyu Jin
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Correspondence to Xiaohong Chen.

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Jin, H., Chen, X., Zhong, R. et al. Corrected GCM data through CMFD data to analysis future runoff changes in the source region of the Yangtze River, China. Environ Earth Sci 81, 527 (2022). https://doi.org/10.1007/s12665-022-10655-8

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