Quantifying Hydraulic Roughness From Field Data: Can Dune Morphology Tell the Whole Story?
Hydraulic roughness is a fundamental property in river research, as it directly affects water levels, flow strength and the associated sediment transport rates. However, quantification of roughness is challenging, as it is not directly measurable in the field. In lowland rivers, bedforms are a major...
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| description | Hydraulic roughness is a fundamental property in river research, as it directly affects water levels, flow strength and the associated sediment transport rates. However, quantification of roughness is challenging, as it is not directly measurable in the field. In lowland rivers, bedforms are a major source of hydraulic roughness. Decades of research have focused on dunes to allow parameterization of roughness, with relatively little focus on field verification. This study aims to establish the predictive capacity of current roughness predictors, and to identify reasons for the unexplained part of the variance in roughness. We quantified hydraulic roughness based on the Darcy‐Weisbach friction factor (f) calculated from hydraulic field data of a 78 km‐long trajectory of the river Rhine and river Waal in the Netherlands. This is compared to predicted roughness values based on dune geometry, and to the spatial trends in the local topographic leeside angle, both inferred from bathymetric field data. Results from both approaches show the same general trend and magnitude of roughness values (0.019 |
| doi_str_mv | 10.1029/2021WR030329 |
| format | Article |
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Plain Language Summary
Hydraulic roughness is the resistance that a river flow experiences from the bed and banks of the channel. Studying hydraulic roughness aids in the prediction of flooding, as increased resistance causes the water level to rise. Previous research on hydraulic roughness has mainly focused on the effect of the shape of river dunes at the river bed. Dunes cause resistance, which has been captured in many equations predicting roughness. In this study, we tested several of those equations capturing dune roughness, and examined alternative sources of flow resistance. A 78 km‐long segment of the river Rhine and river Waal in the Netherlands was used as a case study. Hydraulic roughness was inferred from longitudinal water surface level profiles measured with a survey vessel, and also from river dune dimensions. Both methodologies resulted in similar values and the same general trend of roughness, however, river dune dimensions only explained about one third of the total observed variation. We found that, contrary to expectations, multiple‐kilometer long fluctuations of the river bed elevation influence roughness as well. As the river deepens, the flow weakens, increasing roughness in that region. This is an important finding, since many rivers feature such multi‐kilometer depth variation.
Key Points
Roughness predictions based on bedform geometry explain about 1/3th of the variance in friction factors inferred from water surface profiles
Metrics capturing dune leeside angle statistics do not outperform classical hydraulic roughness predictors in explaining friction variation
Bed‐level gradients oscillate in counter phase with the friction factor, indicating the importance of multi‐kilometer depth variations</description><identifier>ISSN: 0043-1397</identifier><identifier>EISSN: 1944-7973</identifier><identifier>DOI: 10.1029/2021WR030329</identifier><language>eng</language><publisher>Washington: John Wiley & Sons, Inc</publisher><subject>Angles (geometry) ; Bed forms ; Bedforms ; Depth ; Dimensions ; Divergence ; Dunes ; Elevation ; Energy dissipation ; Energy loss ; field data ; Flood predictions ; Flooding ; Flow control ; Flow resistance ; Flow separation ; Friction factor ; Hydraulic roughness ; Hydraulics ; Mathematical analysis ; Oscillations ; Parameterization ; River banks ; River beds ; river dunes ; River flow ; Riverbeds ; Rivers ; Roughness ; Sediment transport ; Sedimentary structures ; Shape effects ; Statistical methods ; Surveying ; Topography ; Trends ; Variance ; Variation ; Water levels</subject><ispartof>Water resources research, 2021-12, Vol.57 (12), p.n/a</ispartof><rights>2021. The Authors.</rights><rights>2021. This article is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3458-b9ac31c9f629530fe93af0c206bacbaba7a9e01370f0c6d0b7e55e091659fbfa3</citedby><cites>FETCH-LOGICAL-c3458-b9ac31c9f629530fe93af0c206bacbaba7a9e01370f0c6d0b7e55e091659fbfa3</cites><orcidid>0000-0002-3560-941X ; 0000-0003-4996-185X ; 0000-0002-8898-3501</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%2F2021WR030329$$EPDF$$P50$$Gwiley$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1029%2F2021WR030329$$EHTML$$P50$$Gwiley$$Hfree_for_read</linktohtml><link.rule.ids>314,776,780,1411,11493,27901,27902,45550,45551,46443,46867</link.rule.ids></links><search><creatorcontrib>Lange, S. I.</creatorcontrib><creatorcontrib>Naqshband, S.</creatorcontrib><creatorcontrib>Hoitink, A. J. F.</creatorcontrib><title>Quantifying Hydraulic Roughness From Field Data: Can Dune Morphology Tell the Whole Story?</title><title>Water resources research</title><description>Hydraulic roughness is a fundamental property in river research, as it directly affects water levels, flow strength and the associated sediment transport rates. However, quantification of roughness is challenging, as it is not directly measurable in the field. In lowland rivers, bedforms are a major source of hydraulic roughness. Decades of research have focused on dunes to allow parameterization of roughness, with relatively little focus on field verification. This study aims to establish the predictive capacity of current roughness predictors, and to identify reasons for the unexplained part of the variance in roughness. We quantified hydraulic roughness based on the Darcy‐Weisbach friction factor (f) calculated from hydraulic field data of a 78 km‐long trajectory of the river Rhine and river Waal in the Netherlands. This is compared to predicted roughness values based on dune geometry, and to the spatial trends in the local topographic leeside angle, both inferred from bathymetric field data. Results from both approaches show the same general trend and magnitude of roughness values (0.019 < f < 0.069). Roughness inferred from dune geometry explains at best 31% of the variance. Efforts to explain the remaining variance from statistics of the local topographic leeside angles, which supposedly control flow separation, were unsuccessful. Unexpectedly, multi‐kilometer depth oscillations explain 34% of the total roughness variations. We suggest that flow divergence associated with depth increase causes energy loss, which is reflected in an elevated hydraulic roughness. Multi‐kilometer depth variations occur in many rivers worldwide, which implies a cause of flow resistance that needs further study.
Plain Language Summary
Hydraulic roughness is the resistance that a river flow experiences from the bed and banks of the channel. Studying hydraulic roughness aids in the prediction of flooding, as increased resistance causes the water level to rise. Previous research on hydraulic roughness has mainly focused on the effect of the shape of river dunes at the river bed. Dunes cause resistance, which has been captured in many equations predicting roughness. In this study, we tested several of those equations capturing dune roughness, and examined alternative sources of flow resistance. A 78 km‐long segment of the river Rhine and river Waal in the Netherlands was used as a case study. Hydraulic roughness was inferred from longitudinal water surface level profiles measured with a survey vessel, and also from river dune dimensions. Both methodologies resulted in similar values and the same general trend of roughness, however, river dune dimensions only explained about one third of the total observed variation. We found that, contrary to expectations, multiple‐kilometer long fluctuations of the river bed elevation influence roughness as well. As the river deepens, the flow weakens, increasing roughness in that region. This is an important finding, since many rivers feature such multi‐kilometer depth variation.
Key Points
Roughness predictions based on bedform geometry explain about 1/3th of the variance in friction factors inferred from water surface profiles
Metrics capturing dune leeside angle statistics do not outperform classical hydraulic roughness predictors in explaining friction variation
Bed‐level gradients oscillate in counter phase with the friction factor, indicating the importance of multi‐kilometer depth variations</description><subject>Angles (geometry)</subject><subject>Bed forms</subject><subject>Bedforms</subject><subject>Depth</subject><subject>Dimensions</subject><subject>Divergence</subject><subject>Dunes</subject><subject>Elevation</subject><subject>Energy dissipation</subject><subject>Energy loss</subject><subject>field data</subject><subject>Flood predictions</subject><subject>Flooding</subject><subject>Flow control</subject><subject>Flow resistance</subject><subject>Flow separation</subject><subject>Friction factor</subject><subject>Hydraulic roughness</subject><subject>Hydraulics</subject><subject>Mathematical analysis</subject><subject>Oscillations</subject><subject>Parameterization</subject><subject>River banks</subject><subject>River beds</subject><subject>river dunes</subject><subject>River flow</subject><subject>Riverbeds</subject><subject>Rivers</subject><subject>Roughness</subject><subject>Sediment transport</subject><subject>Sedimentary structures</subject><subject>Shape effects</subject><subject>Statistical methods</subject><subject>Surveying</subject><subject>Topography</subject><subject>Trends</subject><subject>Variance</subject><subject>Variation</subject><subject>Water levels</subject><issn>0043-1397</issn><issn>1944-7973</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>24P</sourceid><recordid>eNp90MFLwzAUBvAgCs7pzT8g4NXqS17bLF5EOueEiVgnAy8l7ZKto2u2pEX631uZB0-eHnz8-B58hFwyuGHA5S0HzhYpICCXR2TAZBgGQgo8JgOAEAOGUpySM-83ACyMYjEgn2-tqpvSdGW9otNu6VRblQVNbbta19p7OnF2SyelrpZ0rBp1RxNV03Fba_pi3W5tK7vq6FxXFW3Wmi76QNP3xrru_pycGFV5ffF7h-Rj8jhPpsHs9ek5eZgFBYbRKMilKpAV0sRcRghGS1QGCg5xropc5UooqYGhgD6Nl5ALHUUaJIsjaXKjcEiuDr07Z_et9k22sa2r-5cZj1mIISLHXl0fVOGs906bbOfKrXJdxiD7WS_7u17P8cC_ykp3_9pskSYpjwSM8BsdA3CL</recordid><startdate>202112</startdate><enddate>202112</enddate><creator>Lange, S. I.</creator><creator>Naqshband, S.</creator><creator>Hoitink, A. J. F.</creator><general>John Wiley & Sons, Inc</general><scope>24P</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><orcidid>https://orcid.org/0000-0002-3560-941X</orcidid><orcidid>https://orcid.org/0000-0003-4996-185X</orcidid><orcidid>https://orcid.org/0000-0002-8898-3501</orcidid></search><sort><creationdate>202112</creationdate><title>Quantifying Hydraulic Roughness From Field Data: Can Dune Morphology Tell the Whole Story?</title><author>Lange, S. I. ; Naqshband, S. ; Hoitink, A. J. 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F.</creatorcontrib><collection>Wiley Online Library Open Access</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><jtitle>Water resources research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Lange, S. I.</au><au>Naqshband, S.</au><au>Hoitink, A. J. F.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Quantifying Hydraulic Roughness From Field Data: Can Dune Morphology Tell the Whole Story?</atitle><jtitle>Water resources research</jtitle><date>2021-12</date><risdate>2021</risdate><volume>57</volume><issue>12</issue><epage>n/a</epage><issn>0043-1397</issn><eissn>1944-7973</eissn><abstract>Hydraulic roughness is a fundamental property in river research, as it directly affects water levels, flow strength and the associated sediment transport rates. However, quantification of roughness is challenging, as it is not directly measurable in the field. In lowland rivers, bedforms are a major source of hydraulic roughness. Decades of research have focused on dunes to allow parameterization of roughness, with relatively little focus on field verification. This study aims to establish the predictive capacity of current roughness predictors, and to identify reasons for the unexplained part of the variance in roughness. We quantified hydraulic roughness based on the Darcy‐Weisbach friction factor (f) calculated from hydraulic field data of a 78 km‐long trajectory of the river Rhine and river Waal in the Netherlands. This is compared to predicted roughness values based on dune geometry, and to the spatial trends in the local topographic leeside angle, both inferred from bathymetric field data. Results from both approaches show the same general trend and magnitude of roughness values (0.019 < f < 0.069). Roughness inferred from dune geometry explains at best 31% of the variance. Efforts to explain the remaining variance from statistics of the local topographic leeside angles, which supposedly control flow separation, were unsuccessful. Unexpectedly, multi‐kilometer depth oscillations explain 34% of the total roughness variations. We suggest that flow divergence associated with depth increase causes energy loss, which is reflected in an elevated hydraulic roughness. Multi‐kilometer depth variations occur in many rivers worldwide, which implies a cause of flow resistance that needs further study.
Plain Language Summary
Hydraulic roughness is the resistance that a river flow experiences from the bed and banks of the channel. Studying hydraulic roughness aids in the prediction of flooding, as increased resistance causes the water level to rise. Previous research on hydraulic roughness has mainly focused on the effect of the shape of river dunes at the river bed. Dunes cause resistance, which has been captured in many equations predicting roughness. In this study, we tested several of those equations capturing dune roughness, and examined alternative sources of flow resistance. A 78 km‐long segment of the river Rhine and river Waal in the Netherlands was used as a case study. Hydraulic roughness was inferred from longitudinal water surface level profiles measured with a survey vessel, and also from river dune dimensions. Both methodologies resulted in similar values and the same general trend of roughness, however, river dune dimensions only explained about one third of the total observed variation. We found that, contrary to expectations, multiple‐kilometer long fluctuations of the river bed elevation influence roughness as well. As the river deepens, the flow weakens, increasing roughness in that region. This is an important finding, since many rivers feature such multi‐kilometer depth variation.
Key Points
Roughness predictions based on bedform geometry explain about 1/3th of the variance in friction factors inferred from water surface profiles
Metrics capturing dune leeside angle statistics do not outperform classical hydraulic roughness predictors in explaining friction variation
Bed‐level gradients oscillate in counter phase with the friction factor, indicating the importance of multi‐kilometer depth variations</abstract><cop>Washington</cop><pub>John Wiley & Sons, Inc</pub><doi>10.1029/2021WR030329</doi><tpages>22</tpages><orcidid>https://orcid.org/0000-0002-3560-941X</orcidid><orcidid>https://orcid.org/0000-0003-4996-185X</orcidid><orcidid>https://orcid.org/0000-0002-8898-3501</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Angles (geometry) Bed forms Bedforms Depth Dimensions Divergence Dunes Elevation Energy dissipation Energy loss field data Flood predictions Flooding Flow control Flow resistance Flow separation Friction factor Hydraulic roughness Hydraulics Mathematical analysis Oscillations Parameterization River banks River beds river dunes River flow Riverbeds Rivers Roughness Sediment transport Sedimentary structures Shape effects Statistical methods Surveying Topography Trends Variance Variation Water levels |
| title | Quantifying Hydraulic Roughness From Field Data: Can Dune Morphology Tell the Whole Story? |
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