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Analysis of the impact of the Xiaolangdi Reservoir on the runoff of the Yellow River downstream based on CEEMDAN-multiscale information entropy
Water resources are vital to the development of human society, and mastering the law of runoff changes is the basis for achieving sustainable use of water resources. To study the impact of reservoir construction on the changes of downstream river runoff, this paper decomposes the runoff before and a...
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Published in: | Water science and technology 2023-08, Vol.88 (4), p.1058-1073 |
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description | Water resources are vital to the development of human society, and mastering the law of runoff changes is the basis for achieving sustainable use of water resources. To study the impact of reservoir construction on the changes of downstream river runoff, this paper decomposes the runoff before and after reservoir construction using the CEEMDAN method based on the runoff data from the Huayuankou hydrological station. The fluctuation characteristics of each decomposition series of runoff before and after reservoir construction and the intra-annual variation pattern of runoff are also analyzed by combining multi-time information entropy and coefficient of variation. The results show that after the operation of the Xiaolangdi Reservoir, the annual runoff variation cycle tends to be flat, and the monthly runoff cycle is significantly reduced. After reservoir construction, the entropy values of each IMF and Res of runoff become larger, the complexity and randomness of runoff changes increase, and predictability decreases. Before and after the operation of the Xiaolangdi Reservoir, the coefficient of variation of runoff were 0.28–1 and 0.38–0.83, the distribution of runoff was more uniform, and the percentage of runoff in the flood season was reduced from 51.51 to 39.89%. |
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To study the impact of reservoir construction on the changes of downstream river runoff, this paper decomposes the runoff before and after reservoir construction using the CEEMDAN method based on the runoff data from the Huayuankou hydrological station. The fluctuation characteristics of each decomposition series of runoff before and after reservoir construction and the intra-annual variation pattern of runoff are also analyzed by combining multi-time information entropy and coefficient of variation. The results show that after the operation of the Xiaolangdi Reservoir, the annual runoff variation cycle tends to be flat, and the monthly runoff cycle is significantly reduced. After reservoir construction, the entropy values of each IMF and Res of runoff become larger, the complexity and randomness of runoff changes increase, and predictability decreases. Before and after the operation of the Xiaolangdi Reservoir, the coefficient of variation of runoff were 0.28–1 and 0.38–0.83, the distribution of runoff was more uniform, and the percentage of runoff in the flood season was reduced from 51.51 to 39.89%.</description><identifier>ISSN: 0273-1223</identifier><identifier>EISSN: 1996-9732</identifier><identifier>DOI: 10.2166/wst.2023.256</identifier><language>eng</language><publisher>London: IWA Publishing</publisher><subject>Annual runoff ; Annual variations ; Coefficient of variation ; Construction ; Dams ; Decomposition ; Downstream ; Entropy ; Entropy (Information theory) ; Evolution ; Hydrologic data ; Hydrology ; Impact analysis ; lower yellow river ; Neural networks ; Reservoir construction ; River discharge ; River ecology ; River flow ; Rivers ; Runoff ; Runoff cycle ; Sustainable use ; Time series ; Water resources ; Water shortages ; Water use ; xiaolangdi reservoir</subject><ispartof>Water science and technology, 2023-08, Vol.88 (4), p.1058-1073</ispartof><rights>Copyright IWA Publishing Aug 2023</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c400t-a6da535655954e588d98fbdb777817b01ae9656d5a510485eb681ad53c7bd3ee3</citedby><cites>FETCH-LOGICAL-c400t-a6da535655954e588d98fbdb777817b01ae9656d5a510485eb681ad53c7bd3ee3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Zhang, Xianqi</creatorcontrib><creatorcontrib>Qiao, Wenbao</creatorcontrib><creatorcontrib>Huang, Jiafeng</creatorcontrib><creatorcontrib>Shi, Jingwen</creatorcontrib><creatorcontrib>Zhang, Minghui</creatorcontrib><title>Analysis of the impact of the Xiaolangdi Reservoir on the runoff of the Yellow River downstream based on CEEMDAN-multiscale information entropy</title><title>Water science and technology</title><description>Water resources are vital to the development of human society, and mastering the law of runoff changes is the basis for achieving sustainable use of water resources. To study the impact of reservoir construction on the changes of downstream river runoff, this paper decomposes the runoff before and after reservoir construction using the CEEMDAN method based on the runoff data from the Huayuankou hydrological station. The fluctuation characteristics of each decomposition series of runoff before and after reservoir construction and the intra-annual variation pattern of runoff are also analyzed by combining multi-time information entropy and coefficient of variation. The results show that after the operation of the Xiaolangdi Reservoir, the annual runoff variation cycle tends to be flat, and the monthly runoff cycle is significantly reduced. After reservoir construction, the entropy values of each IMF and Res of runoff become larger, the complexity and randomness of runoff changes increase, and predictability decreases. Before and after the operation of the Xiaolangdi Reservoir, the coefficient of variation of runoff were 0.28–1 and 0.38–0.83, the distribution of runoff was more uniform, and the percentage of runoff in the flood season was reduced from 51.51 to 39.89%.</description><subject>Annual runoff</subject><subject>Annual variations</subject><subject>Coefficient of variation</subject><subject>Construction</subject><subject>Dams</subject><subject>Decomposition</subject><subject>Downstream</subject><subject>Entropy</subject><subject>Entropy (Information theory)</subject><subject>Evolution</subject><subject>Hydrologic data</subject><subject>Hydrology</subject><subject>Impact analysis</subject><subject>lower yellow river</subject><subject>Neural networks</subject><subject>Reservoir construction</subject><subject>River discharge</subject><subject>River ecology</subject><subject>River flow</subject><subject>Rivers</subject><subject>Runoff</subject><subject>Runoff cycle</subject><subject>Sustainable use</subject><subject>Time series</subject><subject>Water resources</subject><subject>Water shortages</subject><subject>Water use</subject><subject>xiaolangdi reservoir</subject><issn>0273-1223</issn><issn>1996-9732</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>DOA</sourceid><recordid>eNpdkctu1DAUhiNEJYbCjgeIxIYFmfoSX7IcDQOtVKhUtRKsrJP4pHiUxIPtdDRPwSvj6VAWrCz7fP7tc76ieEfJklEpL_YxLRlhfMmEfFEsaNPIqlGcvSwWhCleUcb4q-J1jFtCiOI1WRS_VxMMh-hi6fsy_cTSjTvo0vPuuwM_wPRgXXmLEcOjd6H001MtzJPv-2fyBw6D35e37hFDaf1-iikgjGULEe3xynqz-fpp9a0a5yG52MGQ35p6H0ZILpdxSsHvDm-Ksx6GiG__rufF_efN3fqyur75crVeXVddTUiqQFoQXEghGlGj0No2um9tq5TSVLWEAjZSSCtAUFJrga3UFKzgnWotR-TnxdUp13rYml1wI4SD8eDM04EPDwZCct2AxrattIioWcfrnnZaSiSia5qaUpVDc9aHU9Yu-F8zxmTG3GCeB0zo52iYlqQmkmid0ff_oVs_h6zgSDXZIVdSZerjieqCjzFg_--DlJijaZNNm6Npk03zP2SnnS8</recordid><startdate>20230815</startdate><enddate>20230815</enddate><creator>Zhang, Xianqi</creator><creator>Qiao, Wenbao</creator><creator>Huang, Jiafeng</creator><creator>Shi, Jingwen</creator><creator>Zhang, Minghui</creator><general>IWA Publishing</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7QH</scope><scope>7UA</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8FE</scope><scope>8FG</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>BKSAR</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>F1W</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>H96</scope><scope>H97</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>L.G</scope><scope>L6V</scope><scope>M0S</scope><scope>M1P</scope><scope>M7S</scope><scope>PCBAR</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>7X8</scope><scope>DOA</scope></search><sort><creationdate>20230815</creationdate><title>Analysis of the impact of the Xiaolangdi Reservoir on the runoff of the Yellow River downstream based on CEEMDAN-multiscale information entropy</title><author>Zhang, Xianqi ; Qiao, Wenbao ; Huang, Jiafeng ; Shi, Jingwen ; Zhang, Minghui</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c400t-a6da535655954e588d98fbdb777817b01ae9656d5a510485eb681ad53c7bd3ee3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Annual runoff</topic><topic>Annual variations</topic><topic>Coefficient of variation</topic><topic>Construction</topic><topic>Dams</topic><topic>Decomposition</topic><topic>Downstream</topic><topic>Entropy</topic><topic>Entropy (Information theory)</topic><topic>Evolution</topic><topic>Hydrologic data</topic><topic>Hydrology</topic><topic>Impact analysis</topic><topic>lower yellow river</topic><topic>Neural networks</topic><topic>Reservoir construction</topic><topic>River discharge</topic><topic>River ecology</topic><topic>River flow</topic><topic>Rivers</topic><topic>Runoff</topic><topic>Runoff cycle</topic><topic>Sustainable use</topic><topic>Time series</topic><topic>Water resources</topic><topic>Water shortages</topic><topic>Water use</topic><topic>xiaolangdi reservoir</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhang, Xianqi</creatorcontrib><creatorcontrib>Qiao, Wenbao</creatorcontrib><creatorcontrib>Huang, Jiafeng</creatorcontrib><creatorcontrib>Shi, Jingwen</creatorcontrib><creatorcontrib>Zhang, Minghui</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Aqualine</collection><collection>Water Resources Abstracts</collection><collection>ProQuest Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Earth, Atmospheric & Aquatic Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 3: Aquatic Pollution & Environmental Quality</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>ProQuest Engineering Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>PML(ProQuest Medical Library)</collection><collection>Engineering Database</collection><collection>Earth, Atmospheric & Aquatic Science Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Engineering collection</collection><collection>MEDLINE - Academic</collection><collection>Directory of Open Access Journals</collection><jtitle>Water science and technology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhang, Xianqi</au><au>Qiao, Wenbao</au><au>Huang, Jiafeng</au><au>Shi, Jingwen</au><au>Zhang, Minghui</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Analysis of the impact of the Xiaolangdi Reservoir on the runoff of the Yellow River downstream based on CEEMDAN-multiscale information entropy</atitle><jtitle>Water science and technology</jtitle><date>2023-08-15</date><risdate>2023</risdate><volume>88</volume><issue>4</issue><spage>1058</spage><epage>1073</epage><pages>1058-1073</pages><issn>0273-1223</issn><eissn>1996-9732</eissn><abstract>Water resources are vital to the development of human society, and mastering the law of runoff changes is the basis for achieving sustainable use of water resources. To study the impact of reservoir construction on the changes of downstream river runoff, this paper decomposes the runoff before and after reservoir construction using the CEEMDAN method based on the runoff data from the Huayuankou hydrological station. The fluctuation characteristics of each decomposition series of runoff before and after reservoir construction and the intra-annual variation pattern of runoff are also analyzed by combining multi-time information entropy and coefficient of variation. The results show that after the operation of the Xiaolangdi Reservoir, the annual runoff variation cycle tends to be flat, and the monthly runoff cycle is significantly reduced. After reservoir construction, the entropy values of each IMF and Res of runoff become larger, the complexity and randomness of runoff changes increase, and predictability decreases. Before and after the operation of the Xiaolangdi Reservoir, the coefficient of variation of runoff were 0.28–1 and 0.38–0.83, the distribution of runoff was more uniform, and the percentage of runoff in the flood season was reduced from 51.51 to 39.89%.</abstract><cop>London</cop><pub>IWA Publishing</pub><doi>10.2166/wst.2023.256</doi><tpages>16</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Annual runoff Annual variations Coefficient of variation Construction Dams Decomposition Downstream Entropy Entropy (Information theory) Evolution Hydrologic data Hydrology Impact analysis lower yellow river Neural networks Reservoir construction River discharge River ecology River flow Rivers Runoff Runoff cycle Sustainable use Time series Water resources Water shortages Water use xiaolangdi reservoir |
title | Analysis of the impact of the Xiaolangdi Reservoir on the runoff of the Yellow River downstream based on CEEMDAN-multiscale information entropy |
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