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Runoff Prediction Under Extreme Precipitation and Corresponding Meteorological Conditions

Author

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  • Jinping Zhang

    (Zhengzhou University
    Chinese Academy of Meteorological Sciences
    Zhengzhou University)

  • Dong Wang

    (Zhengzhou University)

  • Yuhao Wang

    (Hohai University)

  • Honglin Xiao

    (Zhengzhou University)

  • Muxiang Zeng

    (Zhengzhou University)

Abstract

In order to more reasonably predict runoff under extreme precipitation and corresponding meteorological conditions, and explore the influences of annual precipitation and extreme precipitation on the runoff process, this paper proposes an improved precipitation stochastic simulation model and combine it with Weather Generator based on Dry and Wet Spells (WGDWS) and Soil and Water Assessment Tool (SWAT) model. Taking a typical mountainous basin in North China, the basin above the Wangkuai Reservoir, as the study area, the daily precipitation process and corresponding meteorological data for six extreme precipitation scenarios are generated as inputs of the SWAT model to predict monthly runoff. The results reveal that the annual runoff under the six scenarios is 5.41 m3/s, 5.95 m3/s, 6.57 m3/s, 7.02 m3/s, 7.74 m3/s and 8.04 m3/s, with maximum monthly runoff of 18.10 m3/s, 21.71 m3/s, 21.94 m3/s, 32.69 m3/s, 34.33 m3/s, 43.72 m3/s, respectively. For the same annual precipitation, the extreme precipitation magnitude has a significant effect on annual runoff, but this impact weakens as annual precipitation increases, and the influence on monthly runoff is reflected mainly in August. Moreover, under the same extreme precipitation conditions, the annual runoff increases by approximately 10% if the annual precipitation increases by 100 mm, and the influence on monthly runoff is reflected only in July. The coupling of the improved precipitation stochastic simulation, WGDWS and SWAT model not only presents a technical reference for water conservancy project operation management and water resource management under extreme precipitation scenarios, but also provides a new idea for predicting runoff under extreme precipitation and corresponding meteorological conditions.

Suggested Citation

  • Jinping Zhang & Dong Wang & Yuhao Wang & Honglin Xiao & Muxiang Zeng, 2023. "Runoff Prediction Under Extreme Precipitation and Corresponding Meteorological Conditions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(9), pages 3377-3394, July.
    • Handle: RePEc:spr:waterr:v:37:y:2023:i:9:d:10.1007_s11269-023-03506-z
    DOI: 10.1007/s11269-023-03506-z
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    References listed on IDEAS

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    1. M. Bermúdez & L. Cea & E. Van Uytven & P. Willems & J.F. Farfán & J. Puertas, 2020. "A Robust Method to Update Local River Inundation Maps Using Global Climate Model Output and Weather Typing Based Statistical Downscaling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(14), pages 4345-4362, November.
    2. Wenying Zeng & Songbai Song & Yan Kang & Xuan Gao & Rui Ma, 2022. "Response of Runoff to Meteorological Factors Based on Time-Varying Parameter Vector Autoregressive Model with Stochastic Volatility in Arid and Semi-Arid Area of Weihe River Basin," Sustainability, MDPI, vol. 14(12), pages 1-12, June.
    3. Bing-Chen Jhong & Ching-Pin Tung, 2018. "Evaluating Future Joint Probability of Precipitation Extremes with a Copula-Based Assessing Approach in Climate Change," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(13), pages 4253-4274, October.
    4. Hao Yang & Weide Li, 2023. "Data Decomposition, Seasonal Adjustment Method and Machine Learning Combined for Runoff Prediction: A Case Study," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(1), pages 557-581, January.
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    Cited by:

    1. Bing Yan & Yicheng Gu & En Li & Yi Xu & Lingling Ni, 2024. "Runoff Prediction of Tunxi Basin under Projected Climate Changes Based on Lumped Hydrological Models with Various Model Parameter Optimization Strategies," Sustainability, MDPI, vol. 16(16), pages 1-21, August.

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