Multi-time scale co-integration forecast of annual runoff in the source area of the Yellow River

In order to reveal the multi-time scale of rainfall, runoff and sediment in the source area of the Yellow River and improve the accuracy of annual runoff forecast, the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) method is introduced to decompose the measured rainfall...

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Autores principales:	Jinping Zhang, Hongbin Li, Bin Sun, Hongyuan Fang
Formato:	article
Lenguaje:	EN
Publicado:	IWA Publishing 2021
Materias:	ceemdan co-integration multi-time scale runoff forecast source area of the yellow river Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/620ec27bcd8049fc96774104f42acaf5
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spelling	oai:doaj.org-article:620ec27bcd8049fc96774104f42acaf52021-11-05T18:40:47ZMulti-time scale co-integration forecast of annual runoff in the source area of the Yellow River2040-22442408-935410.2166/wcc.2020.137https://doaj.org/article/620ec27bcd8049fc96774104f42acaf52021-02-01T00:00:00Zhttp://jwcc.iwaponline.com/content/12/1/101https://doaj.org/toc/2040-2244https://doaj.org/toc/2408-9354In order to reveal the multi-time scale of rainfall, runoff and sediment in the source area of the Yellow River and improve the accuracy of annual runoff forecast, the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) method is introduced to decompose the measured rainfall, runoff and sediment data series of the Tangnahai hydrological station in the source area of the Yellow River of China. With the co-integration theory, two new error correction models (ECMs) for the forecast of annual runoff in the source area of the Yellow River are constructed. The application of these two methods solves the problem of pseudo-regression caused by nonlinearity and non-stationary of hydrological time series. The results show that rainfall, runoff and sediment in the source area of the Yellow River have multi-time scales and the component sequences have co-integration relationships. For two new ECMs, the CEEMDAN component ECM has better forecast accuracy than the original sequence one. The relative error of all forecasted values is less than 15% except 2009, and the accuracy has reached level A. HIGHLIGHTS The research on the multi-time scale change law of hydrological variables reveals the multi periodic change law of hydrological variables and provides a scientific basis for the rational development of water resources.; The non-stationary and nonlinear processing of hydrological variables can avoid spurious regression and make the result more accurate.; Study on the co-integration relationship of rainfall, runoff and sediment.; Study on multi-time scale dynamic relationship among rainfall, runoff and sediment.; Multi-time scale prediction of river runoff provides a technical reference for the effective protection and scientific operation of water resources.;Jinping ZhangHongbin LiBin SunHongyuan FangIWA Publishingarticleceemdanco-integrationmulti-time scalerunoff forecastsource area of the yellow riverEnvironmental technology. Sanitary engineeringTD1-1066Environmental sciencesGE1-350ENJournal of Water and Climate Change, Vol 12, Iss 1, Pp 101-115 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	ceemdan co-integration multi-time scale runoff forecast source area of the yellow river Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350
spellingShingle	ceemdan co-integration multi-time scale runoff forecast source area of the yellow river Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350 Jinping Zhang Hongbin Li Bin Sun Hongyuan Fang Multi-time scale co-integration forecast of annual runoff in the source area of the Yellow River
description	In order to reveal the multi-time scale of rainfall, runoff and sediment in the source area of the Yellow River and improve the accuracy of annual runoff forecast, the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) method is introduced to decompose the measured rainfall, runoff and sediment data series of the Tangnahai hydrological station in the source area of the Yellow River of China. With the co-integration theory, two new error correction models (ECMs) for the forecast of annual runoff in the source area of the Yellow River are constructed. The application of these two methods solves the problem of pseudo-regression caused by nonlinearity and non-stationary of hydrological time series. The results show that rainfall, runoff and sediment in the source area of the Yellow River have multi-time scales and the component sequences have co-integration relationships. For two new ECMs, the CEEMDAN component ECM has better forecast accuracy than the original sequence one. The relative error of all forecasted values is less than 15% except 2009, and the accuracy has reached level A. HIGHLIGHTS The research on the multi-time scale change law of hydrological variables reveals the multi periodic change law of hydrological variables and provides a scientific basis for the rational development of water resources.; The non-stationary and nonlinear processing of hydrological variables can avoid spurious regression and make the result more accurate.; Study on the co-integration relationship of rainfall, runoff and sediment.; Study on multi-time scale dynamic relationship among rainfall, runoff and sediment.; Multi-time scale prediction of river runoff provides a technical reference for the effective protection and scientific operation of water resources.;
format	article
author	Jinping Zhang Hongbin Li Bin Sun Hongyuan Fang
author_facet	Jinping Zhang Hongbin Li Bin Sun Hongyuan Fang
author_sort	Jinping Zhang
title	Multi-time scale co-integration forecast of annual runoff in the source area of the Yellow River
title_short	Multi-time scale co-integration forecast of annual runoff in the source area of the Yellow River
title_full	Multi-time scale co-integration forecast of annual runoff in the source area of the Yellow River
title_fullStr	Multi-time scale co-integration forecast of annual runoff in the source area of the Yellow River
title_full_unstemmed	Multi-time scale co-integration forecast of annual runoff in the source area of the Yellow River
title_sort	multi-time scale co-integration forecast of annual runoff in the source area of the yellow river
publisher	IWA Publishing
publishDate	2021
url	https://doaj.org/article/620ec27bcd8049fc96774104f42acaf5
work_keys_str_mv	AT jinpingzhang multitimescalecointegrationforecastofannualrunoffinthesourceareaoftheyellowriver AT hongbinli multitimescalecointegrationforecastofannualrunoffinthesourceareaoftheyellowriver AT binsun multitimescalecointegrationforecastofannualrunoffinthesourceareaoftheyellowriver AT hongyuanfang multitimescalecointegrationforecastofannualrunoffinthesourceareaoftheyellowriver
_version_	1718444104413609984

Multi-time scale co-integration forecast of annual runoff in the source area of the Yellow River

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