Comparison of hydrological model ensemble forecasting based on multiple members and ensemble methods
Abstract: Ensemble hydrologic forecasting which takes advantages of multiple hydrologic models has made much contribution to water resource management. In this study, four hydrological models (the Xin’anjiang model (XAJ), Simhyd, GR4J, and artificial neural network (ANN) models) and three ensemble methods (the simple average, black box-based, and binomial-based methods) were applied and compared to simulate the hydrological process during 1979–1983 in three representative catchments (Daixi, Hengtangcun, and Qiaodongcun). The results indicate that for a single model, the XAJ model and the GR4J model performed relatively well with averaged Nash and Sutcliffe efficiency coefficient (NSE) values of 0.78 and 0.83, respectively. For the ensemble models, the results show that the binomial-based ensemble method (dynamic weight) outperformed with water volume error reduced by 0.8% and NSE value increased by 0.218. The best performance on runoff forecasting occurs in the Hengtang catchment by integrating four hydrologic models based on binomial ensemble method, achieving the water volume error of 2.73% and NSE value of 0.923. Finding would provide scientific support to water engineering design and water resources management in the study areas.
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Bibliographic citation
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Comparison of hydrological model ensemble forecasting based on multiple members and ensemble methods ; volume:13 ; number:1 ; year:2021 ; pages:401-415 ; extent:15
Open Geosciences ; 13, Heft 1 (2021), 401-415 (gesamt 15)
- Creator
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Wang, Jie
Wang, Guoqing
Elmahdi, Amgad
Bao, Zhenxin
Yang, Qinli
Shu, Zhangkang
Song, Mingming
- DOI
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10.1515/geo-2020-0239
- URN
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urn:nbn:de:101:1-2501051555125.339982059778
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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15.08.2025, 7:24 AM CEST
Data provider
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Wang, Jie
- Wang, Guoqing
- Elmahdi, Amgad
- Bao, Zhenxin
- Yang, Qinli
- Shu, Zhangkang
- Song, Mingming
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