A River Network‐Based Hierarchical Model for Deriving Flood Frequency Distributions and Its Application to the Upper Yangtze Basin
In flood frequency analysis, expanding the additional information with hydrological reasoning beyond local at‐site flood samples can be very useful for improving the accuracy of flood frequency distribution (FFD) estimation as well as reflecting a better understanding of flood characteristics. In th...
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description | In flood frequency analysis, expanding the additional information with hydrological reasoning beyond local at‐site flood samples can be very useful for improving the accuracy of flood frequency distribution (FFD) estimation as well as reflecting a better understanding of flood characteristics. In this study, a river network‐based hierarchical model is developed to estimate the FFDs in the Upper Yangtze basin by making full use of the hydrological reasoning information of both the flood dependence within the river network and reservoir regulation. Under this hierarchical model, a covariate analysis based on the generalized additive model for location, scale and shape is performed to obtain the conditional distribution of the interested flood variable given both its upstream flood variables and the reservoir index quantifying reservoir regulation; and then, the FFD of the interested flood variable is derived by combining its conditional distribution with the probability distribution of the upstream flood variables. The application to the Upper Yangtze basin indicates that the proposed hierarchical model suggests a satisfactory performance in FFD estimation. It is also found that the reservoir regulation, especially that of the Three Gorges Reservoir, is of great significance in reducing the flood magnitude in the basin. Compared to the conventional FFD estimation method that directly fits the assumed theoretical probability distributions to the at‐site flood samples, the hierarchical model incorporating the flood dependence within the river network exhibits an advantage in capturing the effect of reservoir regulation on the floods as well as in reducing the uncertainty in flood quantile estimation.
Key Points
A river network‐based hierarchical model is developed to derive flood frequency distributions in the Upper Yangtze basin
The reservoir regulation especially that of the Three Gorges Reservoir remarkably reduces the flood magnitude in the Upper Yangtze basin
The hierarchical model incorporating the flood dependence within river network improves estimation quality of flood frequency distributions |
doi_str_mv | 10.1029/2020WR029374 |
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Key Points
A river network‐based hierarchical model is developed to derive flood frequency distributions in the Upper Yangtze basin
The reservoir regulation especially that of the Three Gorges Reservoir remarkably reduces the flood magnitude in the Upper Yangtze basin
The hierarchical model incorporating the flood dependence within river network improves estimation quality of flood frequency distributions</description><identifier>ISSN: 0043-1397</identifier><identifier>EISSN: 1944-7973</identifier><identifier>DOI: 10.1029/2020WR029374</identifier><language>eng</language><publisher>WASHINGTON: Amer Geophysical Union</publisher><subject>Additives ; Canyons ; Distribution ; Environmental Sciences ; Environmental Sciences & Ecology ; Flood frequency ; Flood frequency analysis ; flood frequency distribution ; Flood magnitude ; Floods ; Frequency analysis ; Frequency distribution ; hierarchical model ; Hydrology ; Life Sciences & Biomedicine ; Limnology ; Marine & Freshwater Biology ; Modelling ; Physical Sciences ; Probability distribution ; Probability theory ; Reasoning ; reservoir effect ; Reservoirs ; River discharge ; River networks ; Rivers ; Science & Technology ; Upstream ; Water Resources ; Yangtze River</subject><ispartof>Water resources research, 2021-08, Vol.57 (8), p.n/a, Article 2020</ispartof><rights>2021. American Geophysical Union. All Rights Reserved.</rights><rights>info:eu-repo/semantics/openAccess</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>true</woscitedreferencessubscribed><woscitedreferencescount>16</woscitedreferencescount><woscitedreferencesoriginalsourcerecordid>wos000688205400060</woscitedreferencesoriginalsourcerecordid><citedby>FETCH-LOGICAL-a3921-3461075815cdd4ae68bb1d4305b5bf13a0258fae9cb21f566d602e6713893003</citedby><cites>FETCH-LOGICAL-a3921-3461075815cdd4ae68bb1d4305b5bf13a0258fae9cb21f566d602e6713893003</cites><orcidid>0000-0002-6390-5646 ; 0000-0003-4826-5350 ; 0000-0001-6990-2414</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1029%2F2020WR029374$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1029%2F2020WR029374$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>230,315,781,785,886,1418,11519,26572,27929,27930,39263,45579,45580,46473,46897</link.rule.ids></links><search><creatorcontrib>Jiang, Cong</creatorcontrib><creatorcontrib>Xiong, Lihua</creatorcontrib><creatorcontrib>Xu, Chong‐Yu</creatorcontrib><creatorcontrib>Yan, Lei</creatorcontrib><title>A River Network‐Based Hierarchical Model for Deriving Flood Frequency Distributions and Its Application to the Upper Yangtze Basin</title><title>Water resources research</title><addtitle>WATER RESOUR RES</addtitle><description>In flood frequency analysis, expanding the additional information with hydrological reasoning beyond local at‐site flood samples can be very useful for improving the accuracy of flood frequency distribution (FFD) estimation as well as reflecting a better understanding of flood characteristics. In this study, a river network‐based hierarchical model is developed to estimate the FFDs in the Upper Yangtze basin by making full use of the hydrological reasoning information of both the flood dependence within the river network and reservoir regulation. Under this hierarchical model, a covariate analysis based on the generalized additive model for location, scale and shape is performed to obtain the conditional distribution of the interested flood variable given both its upstream flood variables and the reservoir index quantifying reservoir regulation; and then, the FFD of the interested flood variable is derived by combining its conditional distribution with the probability distribution of the upstream flood variables. The application to the Upper Yangtze basin indicates that the proposed hierarchical model suggests a satisfactory performance in FFD estimation. It is also found that the reservoir regulation, especially that of the Three Gorges Reservoir, is of great significance in reducing the flood magnitude in the basin. Compared to the conventional FFD estimation method that directly fits the assumed theoretical probability distributions to the at‐site flood samples, the hierarchical model incorporating the flood dependence within the river network exhibits an advantage in capturing the effect of reservoir regulation on the floods as well as in reducing the uncertainty in flood quantile estimation.
Key Points
A river network‐based hierarchical model is developed to derive flood frequency distributions in the Upper Yangtze basin
The reservoir regulation especially that of the Three Gorges Reservoir remarkably reduces the flood magnitude in the Upper Yangtze basin
The hierarchical model incorporating the flood dependence within river network improves estimation quality of flood frequency distributions</description><subject>Additives</subject><subject>Canyons</subject><subject>Distribution</subject><subject>Environmental Sciences</subject><subject>Environmental Sciences & Ecology</subject><subject>Flood frequency</subject><subject>Flood frequency analysis</subject><subject>flood frequency distribution</subject><subject>Flood magnitude</subject><subject>Floods</subject><subject>Frequency analysis</subject><subject>Frequency distribution</subject><subject>hierarchical model</subject><subject>Hydrology</subject><subject>Life Sciences & Biomedicine</subject><subject>Limnology</subject><subject>Marine & Freshwater Biology</subject><subject>Modelling</subject><subject>Physical Sciences</subject><subject>Probability distribution</subject><subject>Probability theory</subject><subject>Reasoning</subject><subject>reservoir effect</subject><subject>Reservoirs</subject><subject>River discharge</subject><subject>River networks</subject><subject>Rivers</subject><subject>Science & Technology</subject><subject>Upstream</subject><subject>Water Resources</subject><subject>Yangtze River</subject><issn>0043-1397</issn><issn>1944-7973</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>HGBXW</sourceid><sourceid>3HK</sourceid><recordid>eNqNkc9u1DAQxi0EEkvhxh1LHCEw_pv4uKQsrVRAWhVVnCwncVqXYKe2t9Vy4sAD8Iw8CV62IE6Ik0fWb77vmxmEHhN4QYCqlxQonK1LxWp-By2I4ryqVc3uogUAZxVhqr6PHqR0CUC4kPUCfVvitbu2Eb-z-SbETz--fn9lkh3wkbPRxP7C9WbCb8NgJzyGiA9tdNfOn-PVFMKAV9Febazvt_jQpRxdt8ku-ISNH_BxTng5z1NR2H3iHHC-sPjDPBe7j8af5y8WFzPnH6J7o5mSfXT7HqDT1evT9qg6ef_muF2eVIYpSirGJYFaNET0w8CNlU3XkYEzEJ3oRsIMUNGMxqq-o2QUUg4SqJU1YY1iAOwAPdnL9rGEdV77EI0m0AiqFVW0LsTTPTHHUOZKWV-GTfQlk6ZCciJpcSjU8986IaVoRz1H99nEbdHSu0Povw9R8Gd7_MZ2YUy9K_uyf1oAQDYNBcF31S5k8_906_Kv3bZh43NpZbetbrLbf4bSZ-t2TQUXhP0EqOWp7w</recordid><startdate>202108</startdate><enddate>202108</enddate><creator>Jiang, Cong</creator><creator>Xiong, Lihua</creator><creator>Xu, Chong‐Yu</creator><creator>Yan, Lei</creator><general>Amer Geophysical Union</general><general>John Wiley & Sons, Inc</general><scope>BLEPL</scope><scope>DTL</scope><scope>HGBXW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QH</scope><scope>7QL</scope><scope>7T7</scope><scope>7TG</scope><scope>7U9</scope><scope>7UA</scope><scope>8FD</scope><scope>C1K</scope><scope>F1W</scope><scope>FR3</scope><scope>H94</scope><scope>H96</scope><scope>KL.</scope><scope>KR7</scope><scope>L.G</scope><scope>M7N</scope><scope>P64</scope><scope>3HK</scope><orcidid>https://orcid.org/0000-0002-6390-5646</orcidid><orcidid>https://orcid.org/0000-0003-4826-5350</orcidid><orcidid>https://orcid.org/0000-0001-6990-2414</orcidid></search><sort><creationdate>202108</creationdate><title>A River Network‐Based Hierarchical Model for Deriving Flood Frequency Distributions and Its Application to the Upper Yangtze Basin</title><author>Jiang, Cong ; Xiong, Lihua ; Xu, Chong‐Yu ; Yan, Lei</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a3921-3461075815cdd4ae68bb1d4305b5bf13a0258fae9cb21f566d602e6713893003</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Additives</topic><topic>Canyons</topic><topic>Distribution</topic><topic>Environmental Sciences</topic><topic>Environmental Sciences & Ecology</topic><topic>Flood frequency</topic><topic>Flood frequency analysis</topic><topic>flood frequency distribution</topic><topic>Flood magnitude</topic><topic>Floods</topic><topic>Frequency analysis</topic><topic>Frequency distribution</topic><topic>hierarchical model</topic><topic>Hydrology</topic><topic>Life Sciences & Biomedicine</topic><topic>Limnology</topic><topic>Marine & Freshwater Biology</topic><topic>Modelling</topic><topic>Physical Sciences</topic><topic>Probability distribution</topic><topic>Probability theory</topic><topic>Reasoning</topic><topic>reservoir effect</topic><topic>Reservoirs</topic><topic>River discharge</topic><topic>River networks</topic><topic>Rivers</topic><topic>Science & Technology</topic><topic>Upstream</topic><topic>Water Resources</topic><topic>Yangtze River</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Jiang, Cong</creatorcontrib><creatorcontrib>Xiong, Lihua</creatorcontrib><creatorcontrib>Xu, Chong‐Yu</creatorcontrib><creatorcontrib>Yan, Lei</creatorcontrib><collection>Web of Science Core Collection</collection><collection>Science Citation Index Expanded</collection><collection>Web of Science - Science Citation Index Expanded - 2021</collection><collection>CrossRef</collection><collection>Aqualine</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Water Resources Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Engineering Research Database</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Civil Engineering Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>NORA - Norwegian Open Research Archives</collection><jtitle>Water resources research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Jiang, Cong</au><au>Xiong, Lihua</au><au>Xu, Chong‐Yu</au><au>Yan, Lei</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A River Network‐Based Hierarchical Model for Deriving Flood Frequency Distributions and Its Application to the Upper Yangtze Basin</atitle><jtitle>Water resources research</jtitle><stitle>WATER RESOUR RES</stitle><date>2021-08</date><risdate>2021</risdate><volume>57</volume><issue>8</issue><epage>n/a</epage><artnum>2020</artnum><issn>0043-1397</issn><eissn>1944-7973</eissn><abstract>In flood frequency analysis, expanding the additional information with hydrological reasoning beyond local at‐site flood samples can be very useful for improving the accuracy of flood frequency distribution (FFD) estimation as well as reflecting a better understanding of flood characteristics. In this study, a river network‐based hierarchical model is developed to estimate the FFDs in the Upper Yangtze basin by making full use of the hydrological reasoning information of both the flood dependence within the river network and reservoir regulation. Under this hierarchical model, a covariate analysis based on the generalized additive model for location, scale and shape is performed to obtain the conditional distribution of the interested flood variable given both its upstream flood variables and the reservoir index quantifying reservoir regulation; and then, the FFD of the interested flood variable is derived by combining its conditional distribution with the probability distribution of the upstream flood variables. The application to the Upper Yangtze basin indicates that the proposed hierarchical model suggests a satisfactory performance in FFD estimation. It is also found that the reservoir regulation, especially that of the Three Gorges Reservoir, is of great significance in reducing the flood magnitude in the basin. Compared to the conventional FFD estimation method that directly fits the assumed theoretical probability distributions to the at‐site flood samples, the hierarchical model incorporating the flood dependence within the river network exhibits an advantage in capturing the effect of reservoir regulation on the floods as well as in reducing the uncertainty in flood quantile estimation.
Key Points
A river network‐based hierarchical model is developed to derive flood frequency distributions in the Upper Yangtze basin
The reservoir regulation especially that of the Three Gorges Reservoir remarkably reduces the flood magnitude in the Upper Yangtze basin
The hierarchical model incorporating the flood dependence within river network improves estimation quality of flood frequency distributions</abstract><cop>WASHINGTON</cop><pub>Amer Geophysical Union</pub><doi>10.1029/2020WR029374</doi><tpages>21</tpages><orcidid>https://orcid.org/0000-0002-6390-5646</orcidid><orcidid>https://orcid.org/0000-0003-4826-5350</orcidid><orcidid>https://orcid.org/0000-0001-6990-2414</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Additives Canyons Distribution Environmental Sciences Environmental Sciences & Ecology Flood frequency Flood frequency analysis flood frequency distribution Flood magnitude Floods Frequency analysis Frequency distribution hierarchical model Hydrology Life Sciences & Biomedicine Limnology Marine & Freshwater Biology Modelling Physical Sciences Probability distribution Probability theory Reasoning reservoir effect Reservoirs River discharge River networks Rivers Science & Technology Upstream Water Resources Yangtze River |
title | A River Network‐Based Hierarchical Model for Deriving Flood Frequency Distributions and Its Application to the Upper Yangtze Basin |
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