Remote Sensing of River Discharge Based on Critical Flow Theory
Abstract Critical flow theory provides a physical foundation for inferring discharge from measurements of wavelength and channel width made from images. In rivers with hydraulically steep local slopes greater than ∼0.01, flow velocities are high and the Froude number Fr (ratio of inertial to gravita...
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| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2025-05-01
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| Series: | Geophysical Research Letters |
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| Online Access: | https://doi.org/10.1029/2025GL114851 |
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| author | Carl J. Legleiter Gordon Grant Inhyeok Bae Becky Fasth Elowyn Yager Daniel C. White Laura Hempel Merritt E. Harlan Christina Leonard Robert Dudley |
| author_facet | Carl J. Legleiter Gordon Grant Inhyeok Bae Becky Fasth Elowyn Yager Daniel C. White Laura Hempel Merritt E. Harlan Christina Leonard Robert Dudley |
| author_sort | Carl J. Legleiter |
| collection | DOAJ |
| description | Abstract Critical flow theory provides a physical foundation for inferring discharge from measurements of wavelength and channel width made from images. In rivers with hydraulically steep local slopes greater than ∼0.01, flow velocities are high and the Froude number Fr (ratio of inertial to gravitational forces) can approach 1.0 (critical flow) or greater. Under these conditions, undular hydraulic jumps (UHJ's) can form as standing wave trains at slope transitions or constrictions. The presence of UHJ's indicates that mean Fr≈1, implying that the velocity and depth of the flow and the spacing of the waves are uniquely related to one another. Discharges estimated from 82 Google Earth images agreed closely with discharges recorded at gaging stations (R2 = 0.98), with a mean bias of 1% ± 11%. This approach could provide reliable discharge information in many fluvial environments where critical flow occurs, which tend to be underrepresented in gage networks. |
| format | Article |
| id | doaj-art-472d19de791447e8bb70e683be56e691 |
| institution | OA Journals |
| issn | 0094-8276 1944-8007 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Wiley |
| record_format | Article |
| series | Geophysical Research Letters |
| spelling | doaj-art-472d19de791447e8bb70e683be56e6912025-08-20T02:17:01ZengWileyGeophysical Research Letters0094-82761944-80072025-05-01529n/an/a10.1029/2025GL114851Remote Sensing of River Discharge Based on Critical Flow TheoryCarl J. Legleiter0Gordon Grant1Inhyeok Bae2Becky Fasth3Elowyn Yager4Daniel C. White5Laura Hempel6Merritt E. Harlan7Christina Leonard8Robert Dudley9U.S. Geological Survey Observing Systems Division Golden CO USAU.S.D.A. Forest Service Pacific Northwest Research Station Corvallis OR USACenter for Ecohydraulics Research University of Idaho Boise ID USACollege of Earth, Ocean, and Atmospheric Sciences Oregon State University Corvallis OR USACenter for Ecohydraulics Research University of Idaho Boise ID USADepartment of Civil and Environmental Engineering Colorado State University Fort Collins CO USAU.S. Geological Survey Office of Science Quality and Integrity Pueblo CO USAU.S. Geological Survey Water Resources Mission Area Lakewood CO USAWater Resources Division National Park Service Fort Collins CO USAU.S. Geological Survey New England Water Science Center Pembroke NH USAAbstract Critical flow theory provides a physical foundation for inferring discharge from measurements of wavelength and channel width made from images. In rivers with hydraulically steep local slopes greater than ∼0.01, flow velocities are high and the Froude number Fr (ratio of inertial to gravitational forces) can approach 1.0 (critical flow) or greater. Under these conditions, undular hydraulic jumps (UHJ's) can form as standing wave trains at slope transitions or constrictions. The presence of UHJ's indicates that mean Fr≈1, implying that the velocity and depth of the flow and the spacing of the waves are uniquely related to one another. Discharges estimated from 82 Google Earth images agreed closely with discharges recorded at gaging stations (R2 = 0.98), with a mean bias of 1% ± 11%. This approach could provide reliable discharge information in many fluvial environments where critical flow occurs, which tend to be underrepresented in gage networks.https://doi.org/10.1029/2025GL114851critical flowriver dischargeremote sensingundular hydraulic jumpsstanding wavesnon‐contact streamflow measurement |
| spellingShingle | Carl J. Legleiter Gordon Grant Inhyeok Bae Becky Fasth Elowyn Yager Daniel C. White Laura Hempel Merritt E. Harlan Christina Leonard Robert Dudley Remote Sensing of River Discharge Based on Critical Flow Theory Geophysical Research Letters critical flow river discharge remote sensing undular hydraulic jumps standing waves non‐contact streamflow measurement |
| title | Remote Sensing of River Discharge Based on Critical Flow Theory |
| title_full | Remote Sensing of River Discharge Based on Critical Flow Theory |
| title_fullStr | Remote Sensing of River Discharge Based on Critical Flow Theory |
| title_full_unstemmed | Remote Sensing of River Discharge Based on Critical Flow Theory |
| title_short | Remote Sensing of River Discharge Based on Critical Flow Theory |
| title_sort | remote sensing of river discharge based on critical flow theory |
| topic | critical flow river discharge remote sensing undular hydraulic jumps standing waves non‐contact streamflow measurement |
| url | https://doi.org/10.1029/2025GL114851 |
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