Runoff Prediction under Different Precipitation Scenarios Based on SWAT Model and Stochastic Simulation of Precipitation
Publication: Journal of Hydrologic Engineering
Volume 27, Issue 5
Abstract
To predict runoff under different annual precipitation and reflect the impact of annual precipitation and its inner-annual distribution on the runoff process, the soil and water assessment tool (SWAT) model is combined with the stochastic simulation of precipitation based on the torrential rain analysis. Taking the watershed above Wangkuai Reservoir in China as an example, the SWAT model is constructed. and the stochastic simulation models of precipitation under three annual precipitation states are established. Then, based on the torrential rain analysis, five precipitation scenarios with annual precipitation of 300, 600, and 900 mm are assumed, and the daily precipitation process of each scenario is generated as the input of the SWAT model. The results are as follows: the SWAT model has a very good performance for monthly runoff simulation; the maximum monthly runoffs of the five precipitation scenarios are 5.99, 7.09, 9.14, 17.48, and , respectively, and the annual runoffs are 2.24, 3.02, 3.30, 4.75, and , respectively. When the annual precipitation is about 600 mm, the influence of precipitation inner-annual distribution on the monthly runoff process is mainly reflected in July to October. When the annual precipitation is about 900 mm, the influence is mainly reflected in August. This study provides a new idea for runoff prediction and provides reference for the study of the rainfall–runoff uncertainty relationship. Moreover, the improved precipitation stochastic simulation and its combination with the SWAT model can be applied to the study of other basins for further discovery.
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Data Availability Statement
Some or all data, models, or code used during the study were provided by a third party (the measured data of runoff and precipitation, topographic, land use/cover, soil, and hydrometeorological data). Direct request for these materials may be made to the provider as indicated in the Acknowledgments.
Acknowledgments
This research is supported by the National Key R & D Program of China (Grant No. 2021YFC3200205), the National Key R & D Program of China (Grant No. 2018YFC0406501), the Natural Sciences Foundation of Henan Province (Grant No. 212300410404), the Open Grants of the State Key Laboratory of Severe Weather (Grant No. 2021LASW-A15), Program for Innovative Talents (in Science and Technology) at University of Henan Province (Grant No. 18HASTIT014), and Foundation for University Youth Key Teacher of Henan Province (Grant No. 2017GGJS006).
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Journal of Hydrologic Engineering
Volume 27 • Issue 5 • May 2022
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© 2022 American Society of Civil Engineers.
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Received: Apr 21, 2021
Accepted: Feb 8, 2022
Published online: Mar 15, 2022
Published in print: May 1, 2022
Discussion open until: Aug 15, 2022
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