Comparison of hydrological model ensemble forecasting based on multiple members and ensemble methods

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 (th...

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Autores principales:	Wang Jie, Wang Guoqing, Elmahdi Amgad, Bao Zhenxin, Yang Qinli, Shu Zhangkang, Song Mingming
Formato:	article
Lenguaje:	EN
Publicado:	De Gruyter 2021
Materias:	hydrological model ensemble forecasting method comparison flood forecasting Geology QE1-996.5
Acceso en línea:	https://doaj.org/article/f834d02144124745835ed37488c39ae9
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id	oai:doaj.org-article:f834d02144124745835ed37488c39ae9
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spelling	oai:doaj.org-article:f834d02144124745835ed37488c39ae92021-12-05T14:10:48ZComparison of hydrological model ensemble forecasting based on multiple members and ensemble methods2391-544710.1515/geo-2020-0239https://doaj.org/article/f834d02144124745835ed37488c39ae92021-04-01T00:00:00Zhttps://doi.org/10.1515/geo-2020-0239https://doaj.org/toc/2391-5447Ensemble 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.Wang JieWang GuoqingElmahdi AmgadBao ZhenxinYang QinliShu ZhangkangSong MingmingDe Gruyterarticlehydrological modelensemble forecastingmethod comparisonflood forecastingGeologyQE1-996.5ENOpen Geosciences, Vol 13, Iss 1, Pp 401-415 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	hydrological model ensemble forecasting method comparison flood forecasting Geology QE1-996.5
spellingShingle	hydrological model ensemble forecasting method comparison flood forecasting Geology QE1-996.5 Wang Jie Wang Guoqing Elmahdi Amgad Bao Zhenxin Yang Qinli Shu Zhangkang Song Mingming Comparison of hydrological model ensemble forecasting based on multiple members and ensemble methods
description	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.
format	article
author	Wang Jie Wang Guoqing Elmahdi Amgad Bao Zhenxin Yang Qinli Shu Zhangkang Song Mingming
author_facet	Wang Jie Wang Guoqing Elmahdi Amgad Bao Zhenxin Yang Qinli Shu Zhangkang Song Mingming
author_sort	Wang Jie
title	Comparison of hydrological model ensemble forecasting based on multiple members and ensemble methods
title_short	Comparison of hydrological model ensemble forecasting based on multiple members and ensemble methods
title_full	Comparison of hydrological model ensemble forecasting based on multiple members and ensemble methods
title_fullStr	Comparison of hydrological model ensemble forecasting based on multiple members and ensemble methods
title_full_unstemmed	Comparison of hydrological model ensemble forecasting based on multiple members and ensemble methods
title_sort	comparison of hydrological model ensemble forecasting based on multiple members and ensemble methods
publisher	De Gruyter
publishDate	2021
url	https://doaj.org/article/f834d02144124745835ed37488c39ae9
work_keys_str_mv	AT wangjie comparisonofhydrologicalmodelensembleforecastingbasedonmultiplemembersandensemblemethods AT wangguoqing comparisonofhydrologicalmodelensembleforecastingbasedonmultiplemembersandensemblemethods AT elmahdiamgad comparisonofhydrologicalmodelensembleforecastingbasedonmultiplemembersandensemblemethods AT baozhenxin comparisonofhydrologicalmodelensembleforecastingbasedonmultiplemembersandensemblemethods AT yangqinli comparisonofhydrologicalmodelensembleforecastingbasedonmultiplemembersandensemblemethods AT shuzhangkang comparisonofhydrologicalmodelensembleforecastingbasedonmultiplemembersandensemblemethods AT songmingming comparisonofhydrologicalmodelensembleforecastingbasedonmultiplemembersandensemblemethods
_version_	1718371756335431680

Comparison of hydrological model ensemble forecasting based on multiple members and ensemble methods

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