Seasonality of Roughness - the Indicator of Annual River Flow Resistance Condition in a Lowland Catchment
Accurate estimation of flow resistance restricts the quality of the hydraulic model performance. In this study, we try to investigate the seasonal dynamic of the Manning’s roughness coefficient ( n ) based on the one-dimensional hydraulic model HEC-RAS in a German lowland area. We set up four river...
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| Veröffentlicht in: | Water resources management 2017-09, Vol.31 (11), p.3299-3312 |
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| creator | Song, S. Schmalz, B. Xu, Y. P. Fohrer, N. |
| description | Accurate estimation of flow resistance restricts the quality of the hydraulic model performance. In this study, we try to investigate the seasonal dynamic of the Manning’s roughness coefficient (
n
) based on the one-dimensional hydraulic model HEC-RAS in a German lowland area. We set up four river section models based on the 1 m digital elevation model and field measurements, in which the seasonal roughness factors were calibrated and validated with the gauge record. The results revealed that: 1) the Manning’s
n
varied from 46% to 135% from the base value in autumn; 2) adopting the seasonal roughness factor improved the quality of the model output; 3) the vegetation condition and water elevation dominated the Manning’s
n
in summer (April–September) and winter (October–March) half year respectively. Water temperature increased the flow resistence in winter half year; 4) the peak value of Manning’s
n
appeared in late summer due to the highest biomass, while the minimum roughness occurred in early-spring because of the combined influence of low biomass, high water level and relatively higher temperature. The involvement of seasonal roughness factor improved the model performance and the results are comparable to the previous research of the same area. |
| doi_str_mv | 10.1007/s11269-017-1656-z |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_1923934018</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>1923934018</sourcerecordid><originalsourceid>FETCH-LOGICAL-c316t-b9d8149d7b0d6aec9be22e72aa713dd322a2418c086cbcd954271a39e4e4be443</originalsourceid><addsrcrecordid>eNp1kEFLwzAUx4MoOKcfwFvAczUvzdrmOIrTwUCYeg5p-rZ1dMlMUsf26W2ZBy-eHo_3-__h_Qi5B_YIjOVPAYBnMmGQJ5BNsuR0QUYwydNhY5dkxCRnicgFXJObELaM9SnJRqR5Rx2c1W0Tj9St6NJ1643FEGhC4wbp3NaN0dH54Ti1ttMtXTbf6OmsdQe6xNCEqK1BWroejY2ztLFU04U7tNrWtNTRbHZo4y25Wuk24N3vHJPP2fNH-Zos3l7m5XSRmBSymFSyLkDIOq9YnWk0skLOMeda55DWdcq55gIKw4rMVKaWE8Fz0KlEgaJCIdIxeTj37r376jBEtXWd7z8MCiRPZSoYFD0FZ8p4F4LHldr7Zqf9UQFTg1F1Nqp6o2owqk59hp8zoWftGv2f5n9DP_1gecg</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1923934018</pqid></control><display><type>article</type><title>Seasonality of Roughness - the Indicator of Annual River Flow Resistance Condition in a Lowland Catchment</title><source>SpringerLink Journals - AutoHoldings</source><creator>Song, S. ; Schmalz, B. ; Xu, Y. P. ; Fohrer, N.</creator><creatorcontrib>Song, S. ; Schmalz, B. ; Xu, Y. P. ; Fohrer, N.</creatorcontrib><description>Accurate estimation of flow resistance restricts the quality of the hydraulic model performance. In this study, we try to investigate the seasonal dynamic of the Manning’s roughness coefficient (
n
) based on the one-dimensional hydraulic model HEC-RAS in a German lowland area. We set up four river section models based on the 1 m digital elevation model and field measurements, in which the seasonal roughness factors were calibrated and validated with the gauge record. The results revealed that: 1) the Manning’s
n
varied from 46% to 135% from the base value in autumn; 2) adopting the seasonal roughness factor improved the quality of the model output; 3) the vegetation condition and water elevation dominated the Manning’s
n
in summer (April–September) and winter (October–March) half year respectively. Water temperature increased the flow resistence in winter half year; 4) the peak value of Manning’s
n
appeared in late summer due to the highest biomass, while the minimum roughness occurred in early-spring because of the combined influence of low biomass, high water level and relatively higher temperature. The involvement of seasonal roughness factor improved the model performance and the results are comparable to the previous research of the same area.</description><identifier>ISSN: 0920-4741</identifier><identifier>EISSN: 1573-1650</identifier><identifier>DOI: 10.1007/s11269-017-1656-z</identifier><language>eng</language><publisher>Dordrecht: Springer Netherlands</publisher><subject>Atmospheric Sciences ; Autumn ; Biomass ; Catchment area ; Civil Engineering ; Creeks & streams ; Digital Elevation Models ; Earth and Environmental Science ; Earth Sciences ; Elevation ; Environment ; Flow resistance ; Geometry ; Geotechnical Engineering & Applied Earth Sciences ; High temperature ; Hydraulic models ; Hydraulics ; Hydrogeology ; Hydrology ; Hydrology/Water Resources ; Land use ; River flow ; River networks ; Rivers ; Roughness ; Roughness coefficient ; Seasonal variations ; Seasonality ; Seasons ; Simulation ; Spring ; Spring (season) ; Stream flow ; Summer ; Temperature effects ; Vegetation ; Velocity ; Water levels ; Water temperature ; Winter</subject><ispartof>Water resources management, 2017-09, Vol.31 (11), p.3299-3312</ispartof><rights>Springer Science+Business Media Dordrecht 2017</rights><rights>Water Resources Management is a copyright of Springer, 2017.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c316t-b9d8149d7b0d6aec9be22e72aa713dd322a2418c086cbcd954271a39e4e4be443</citedby><cites>FETCH-LOGICAL-c316t-b9d8149d7b0d6aec9be22e72aa713dd322a2418c086cbcd954271a39e4e4be443</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11269-017-1656-z$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11269-017-1656-z$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids></links><search><creatorcontrib>Song, S.</creatorcontrib><creatorcontrib>Schmalz, B.</creatorcontrib><creatorcontrib>Xu, Y. P.</creatorcontrib><creatorcontrib>Fohrer, N.</creatorcontrib><title>Seasonality of Roughness - the Indicator of Annual River Flow Resistance Condition in a Lowland Catchment</title><title>Water resources management</title><addtitle>Water Resour Manage</addtitle><description>Accurate estimation of flow resistance restricts the quality of the hydraulic model performance. In this study, we try to investigate the seasonal dynamic of the Manning’s roughness coefficient (
n
) based on the one-dimensional hydraulic model HEC-RAS in a German lowland area. We set up four river section models based on the 1 m digital elevation model and field measurements, in which the seasonal roughness factors were calibrated and validated with the gauge record. The results revealed that: 1) the Manning’s
n
varied from 46% to 135% from the base value in autumn; 2) adopting the seasonal roughness factor improved the quality of the model output; 3) the vegetation condition and water elevation dominated the Manning’s
n
in summer (April–September) and winter (October–March) half year respectively. Water temperature increased the flow resistence in winter half year; 4) the peak value of Manning’s
n
appeared in late summer due to the highest biomass, while the minimum roughness occurred in early-spring because of the combined influence of low biomass, high water level and relatively higher temperature. The involvement of seasonal roughness factor improved the model performance and the results are comparable to the previous research of the same area.</description><subject>Atmospheric Sciences</subject><subject>Autumn</subject><subject>Biomass</subject><subject>Catchment area</subject><subject>Civil Engineering</subject><subject>Creeks & streams</subject><subject>Digital Elevation Models</subject><subject>Earth and Environmental Science</subject><subject>Earth Sciences</subject><subject>Elevation</subject><subject>Environment</subject><subject>Flow resistance</subject><subject>Geometry</subject><subject>Geotechnical Engineering & Applied Earth Sciences</subject><subject>High temperature</subject><subject>Hydraulic models</subject><subject>Hydraulics</subject><subject>Hydrogeology</subject><subject>Hydrology</subject><subject>Hydrology/Water Resources</subject><subject>Land use</subject><subject>River flow</subject><subject>River networks</subject><subject>Rivers</subject><subject>Roughness</subject><subject>Roughness coefficient</subject><subject>Seasonal variations</subject><subject>Seasonality</subject><subject>Seasons</subject><subject>Simulation</subject><subject>Spring</subject><subject>Spring (season)</subject><subject>Stream flow</subject><subject>Summer</subject><subject>Temperature effects</subject><subject>Vegetation</subject><subject>Velocity</subject><subject>Water levels</subject><subject>Water temperature</subject><subject>Winter</subject><issn>0920-4741</issn><issn>1573-1650</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp1kEFLwzAUx4MoOKcfwFvAczUvzdrmOIrTwUCYeg5p-rZ1dMlMUsf26W2ZBy-eHo_3-__h_Qi5B_YIjOVPAYBnMmGQJ5BNsuR0QUYwydNhY5dkxCRnicgFXJObELaM9SnJRqR5Rx2c1W0Tj9St6NJ1643FEGhC4wbp3NaN0dH54Ti1ttMtXTbf6OmsdQe6xNCEqK1BWroejY2ztLFU04U7tNrWtNTRbHZo4y25Wuk24N3vHJPP2fNH-Zos3l7m5XSRmBSymFSyLkDIOq9YnWk0skLOMeda55DWdcq55gIKw4rMVKaWE8Fz0KlEgaJCIdIxeTj37r376jBEtXWd7z8MCiRPZSoYFD0FZ8p4F4LHldr7Zqf9UQFTg1F1Nqp6o2owqk59hp8zoWftGv2f5n9DP_1gecg</recordid><startdate>20170901</startdate><enddate>20170901</enddate><creator>Song, S.</creator><creator>Schmalz, B.</creator><creator>Xu, Y. P.</creator><creator>Fohrer, N.</creator><general>Springer Netherlands</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7QH</scope><scope>7ST</scope><scope>7UA</scope><scope>7WY</scope><scope>7WZ</scope><scope>7XB</scope><scope>87Z</scope><scope>88I</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FK</scope><scope>8FL</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>BKSAR</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>F1W</scope><scope>FR3</scope><scope>FRNLG</scope><scope>F~G</scope><scope>GNUQQ</scope><scope>H97</scope><scope>HCIFZ</scope><scope>K60</scope><scope>K6~</scope><scope>KR7</scope><scope>L.-</scope><scope>L.G</scope><scope>L6V</scope><scope>LK8</scope><scope>M0C</scope><scope>M2P</scope><scope>M7P</scope><scope>M7S</scope><scope>PATMY</scope><scope>PCBAR</scope><scope>PQBIZ</scope><scope>PQBZA</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>Q9U</scope><scope>SOI</scope></search><sort><creationdate>20170901</creationdate><title>Seasonality of Roughness - the Indicator of Annual River Flow Resistance Condition in a Lowland Catchment</title><author>Song, S. ; Schmalz, B. ; Xu, Y. P. ; Fohrer, N.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c316t-b9d8149d7b0d6aec9be22e72aa713dd322a2418c086cbcd954271a39e4e4be443</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Atmospheric Sciences</topic><topic>Autumn</topic><topic>Biomass</topic><topic>Catchment area</topic><topic>Civil Engineering</topic><topic>Creeks & streams</topic><topic>Digital Elevation Models</topic><topic>Earth and Environmental Science</topic><topic>Earth Sciences</topic><topic>Elevation</topic><topic>Environment</topic><topic>Flow resistance</topic><topic>Geometry</topic><topic>Geotechnical Engineering & Applied Earth Sciences</topic><topic>High temperature</topic><topic>Hydraulic models</topic><topic>Hydraulics</topic><topic>Hydrogeology</topic><topic>Hydrology</topic><topic>Hydrology/Water Resources</topic><topic>Land use</topic><topic>River flow</topic><topic>River networks</topic><topic>Rivers</topic><topic>Roughness</topic><topic>Roughness coefficient</topic><topic>Seasonal variations</topic><topic>Seasonality</topic><topic>Seasons</topic><topic>Simulation</topic><topic>Spring</topic><topic>Spring (season)</topic><topic>Stream flow</topic><topic>Summer</topic><topic>Temperature effects</topic><topic>Vegetation</topic><topic>Velocity</topic><topic>Water levels</topic><topic>Water temperature</topic><topic>Winter</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Song, S.</creatorcontrib><creatorcontrib>Schmalz, B.</creatorcontrib><creatorcontrib>Xu, Y. P.</creatorcontrib><creatorcontrib>Fohrer, N.</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Aqualine</collection><collection>Environment Abstracts</collection><collection>Water Resources Abstracts</collection><collection>ABI/INFORM Collection</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ABI/INFORM Global (Alumni Edition)</collection><collection>Science Database (Alumni Edition)</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ABI/INFORM Collection (Alumni Edition)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Business Premium Collection</collection><collection>Technology Collection</collection><collection>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 Korea</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Engineering Research Database</collection><collection>Business Premium Collection (Alumni)</collection><collection>ABI/INFORM Global (Corporate)</collection><collection>ProQuest Central Student</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 3: Aquatic Pollution & Environmental Quality</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Business Collection (Alumni Edition)</collection><collection>ProQuest Business Collection</collection><collection>Civil Engineering Abstracts</collection><collection>ABI/INFORM Professional Advanced</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>ProQuest Engineering Collection</collection><collection>ProQuest Biological Science Collection</collection><collection>ABI/INFORM Global</collection><collection>Science Database</collection><collection>Biological Science Database</collection><collection>Engineering Database</collection><collection>Environmental Science Database</collection><collection>Earth, Atmospheric & Aquatic Science Database</collection><collection>One Business (ProQuest)</collection><collection>ProQuest One Business (Alumni)</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>Environmental Science Collection</collection><collection>ProQuest Central Basic</collection><collection>Environment Abstracts</collection><jtitle>Water resources management</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Song, S.</au><au>Schmalz, B.</au><au>Xu, Y. P.</au><au>Fohrer, N.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Seasonality of Roughness - the Indicator of Annual River Flow Resistance Condition in a Lowland Catchment</atitle><jtitle>Water resources management</jtitle><stitle>Water Resour Manage</stitle><date>2017-09-01</date><risdate>2017</risdate><volume>31</volume><issue>11</issue><spage>3299</spage><epage>3312</epage><pages>3299-3312</pages><issn>0920-4741</issn><eissn>1573-1650</eissn><abstract>Accurate estimation of flow resistance restricts the quality of the hydraulic model performance. In this study, we try to investigate the seasonal dynamic of the Manning’s roughness coefficient (
n
) based on the one-dimensional hydraulic model HEC-RAS in a German lowland area. We set up four river section models based on the 1 m digital elevation model and field measurements, in which the seasonal roughness factors were calibrated and validated with the gauge record. The results revealed that: 1) the Manning’s
n
varied from 46% to 135% from the base value in autumn; 2) adopting the seasonal roughness factor improved the quality of the model output; 3) the vegetation condition and water elevation dominated the Manning’s
n
in summer (April–September) and winter (October–March) half year respectively. Water temperature increased the flow resistence in winter half year; 4) the peak value of Manning’s
n
appeared in late summer due to the highest biomass, while the minimum roughness occurred in early-spring because of the combined influence of low biomass, high water level and relatively higher temperature. The involvement of seasonal roughness factor improved the model performance and the results are comparable to the previous research of the same area.</abstract><cop>Dordrecht</cop><pub>Springer Netherlands</pub><doi>10.1007/s11269-017-1656-z</doi><tpages>14</tpages></addata></record> |
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| subjects | Atmospheric Sciences Autumn Biomass Catchment area Civil Engineering Creeks & streams Digital Elevation Models Earth and Environmental Science Earth Sciences Elevation Environment Flow resistance Geometry Geotechnical Engineering & Applied Earth Sciences High temperature Hydraulic models Hydraulics Hydrogeology Hydrology Hydrology/Water Resources Land use River flow River networks Rivers Roughness Roughness coefficient Seasonal variations Seasonality Seasons Simulation Spring Spring (season) Stream flow Summer Temperature effects Vegetation Velocity Water levels Water temperature Winter |
| title | Seasonality of Roughness - the Indicator of Annual River Flow Resistance Condition in a Lowland Catchment |
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