Runoff Prediction Under Extreme Precipitation and Corresponding Meteorological Conditions

Jinping Zhang, Dong Wang, Yuhao Wang (), Honglin Xiao and Muxiang Zeng
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Jinping Zhang: Zhengzhou University
Dong Wang: Zhengzhou University
Yuhao Wang: Hohai University
Honglin Xiao: Zhengzhou University
Muxiang Zeng: Zhengzhou University

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2023, vol. 37, issue 9, No 3, 3377-3394

Abstract: 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.

Keywords: SWAT model; WGDWS; Extreme precipitation; Improved precipitation stochastic simulation; Runoff prediction (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s11269-023-03506-z

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