Spatiotemporal Variability of Channel Roughness and its Substantial Impacts on Flood Modeling Errors
Abstract Manning's roughness coefficient, n, is used to describe channel roughness, and is a widely sought‐after key parameter for estimating and predicting flood propagation. Due to its control of flow velocity and shear stress, n is critical for modeling timing of floods and pollutants, aquat...
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Wiley
2024-07-01
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| Series: | Earth's Future |
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| Online Access: | https://doi.org/10.1029/2023EF004257 |
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| author | Md Abdullah Al Mehedi Shah Saki Krutikkumar Patel Chaopeng Shen Sagy Cohen Virginia Smith Adnan Rajib Emmanouil Anagnostou Tadd Bindas Kathryn Lawson |
| author_facet | Md Abdullah Al Mehedi Shah Saki Krutikkumar Patel Chaopeng Shen Sagy Cohen Virginia Smith Adnan Rajib Emmanouil Anagnostou Tadd Bindas Kathryn Lawson |
| author_sort | Md Abdullah Al Mehedi |
| collection | DOAJ |
| description | Abstract Manning's roughness coefficient, n, is used to describe channel roughness, and is a widely sought‐after key parameter for estimating and predicting flood propagation. Due to its control of flow velocity and shear stress, n is critical for modeling timing of floods and pollutants, aquatic ecosystem health, infrastructural safety, and so on. While alternative formulations exist, open‐channel n is typically regarded as temporally constant, determined from lookup tables or calibration, and its spatiotemporal variability was never examined holistically at large scales. Here, we developed and analyzed a continental‐scale n dataset (along with alternative formulations) calculated from observed velocity, slope, and hydraulic radius in 200,000 surveys conducted over 5,000 U.S. sites. These large, diverse observations allowed training of a Random Forest (RF) model capable of predicting n (or alternative parameters) at high accuracy (Nash Sutcliffe model efficiency >0.7) in space and time. We show that predictable time variability explains a large fraction (∼35%) of n variance compared to spatial variability (50%). While exceptions abound, n is generally lower and more stable under higher streamflow conditions. Other factorial influences on n including land cover, sinuosity, and particle sizes largely agree with conventional intuition. Accounting for temporal variability in n could lead to substantially larger (45% at the median site) estimated flow velocities under high‐flow conditions or lower (44%) velocities under low‐flow conditions. Habitual exclusion of n temporal dynamics means flood peaks could arrive days before model‐predicted flood waves, and peak magnitude estimation might also be erroneous. We therefore offer a model of great practical utility. |
| format | Article |
| id | doaj-art-3ff04120942443dcb07a036b61646fa1 |
| institution | Kabale University |
| issn | 2328-4277 |
| language | English |
| publishDate | 2024-07-01 |
| publisher | Wiley |
| record_format | Article |
| series | Earth's Future |
| spelling | doaj-art-3ff04120942443dcb07a036b61646fa12025-01-29T07:58:52ZengWileyEarth's Future2328-42772024-07-01127n/an/a10.1029/2023EF004257Spatiotemporal Variability of Channel Roughness and its Substantial Impacts on Flood Modeling ErrorsMd Abdullah Al Mehedi0Shah Saki1Krutikkumar Patel2Chaopeng Shen3Sagy Cohen4Virginia Smith5Adnan Rajib6Emmanouil Anagnostou7Tadd Bindas8Kathryn Lawson9Department of Civil and Environmental Engineering Villanova University Villanova PA USADepartment of Civil and Environmental Engineering University of Connecticut Storrs CT USADepartment of Civil Engineering University of Texas Arlington TX USADepartment of Civil and Environmental Engineering The Pennsylvania State University University Park PA USADepartment of Geography and the Environment The University of Alabama Tuscaloosa AL USADepartment of Civil and Environmental Engineering Villanova University Villanova PA USADepartment of Civil Engineering University of Texas Arlington TX USADepartment of Civil and Environmental Engineering University of Connecticut Storrs CT USADepartment of Civil and Environmental Engineering The Pennsylvania State University University Park PA USADepartment of Civil and Environmental Engineering The Pennsylvania State University University Park PA USAAbstract Manning's roughness coefficient, n, is used to describe channel roughness, and is a widely sought‐after key parameter for estimating and predicting flood propagation. Due to its control of flow velocity and shear stress, n is critical for modeling timing of floods and pollutants, aquatic ecosystem health, infrastructural safety, and so on. While alternative formulations exist, open‐channel n is typically regarded as temporally constant, determined from lookup tables or calibration, and its spatiotemporal variability was never examined holistically at large scales. Here, we developed and analyzed a continental‐scale n dataset (along with alternative formulations) calculated from observed velocity, slope, and hydraulic radius in 200,000 surveys conducted over 5,000 U.S. sites. These large, diverse observations allowed training of a Random Forest (RF) model capable of predicting n (or alternative parameters) at high accuracy (Nash Sutcliffe model efficiency >0.7) in space and time. We show that predictable time variability explains a large fraction (∼35%) of n variance compared to spatial variability (50%). While exceptions abound, n is generally lower and more stable under higher streamflow conditions. Other factorial influences on n including land cover, sinuosity, and particle sizes largely agree with conventional intuition. Accounting for temporal variability in n could lead to substantially larger (45% at the median site) estimated flow velocities under high‐flow conditions or lower (44%) velocities under low‐flow conditions. Habitual exclusion of n temporal dynamics means flood peaks could arrive days before model‐predicted flood waves, and peak magnitude estimation might also be erroneous. We therefore offer a model of great practical utility.https://doi.org/10.1029/2023EF004257channel roughnessfluvial hydraulicsflood modelingbig data |
| spellingShingle | Md Abdullah Al Mehedi Shah Saki Krutikkumar Patel Chaopeng Shen Sagy Cohen Virginia Smith Adnan Rajib Emmanouil Anagnostou Tadd Bindas Kathryn Lawson Spatiotemporal Variability of Channel Roughness and its Substantial Impacts on Flood Modeling Errors Earth's Future channel roughness fluvial hydraulics flood modeling big data |
| title | Spatiotemporal Variability of Channel Roughness and its Substantial Impacts on Flood Modeling Errors |
| title_full | Spatiotemporal Variability of Channel Roughness and its Substantial Impacts on Flood Modeling Errors |
| title_fullStr | Spatiotemporal Variability of Channel Roughness and its Substantial Impacts on Flood Modeling Errors |
| title_full_unstemmed | Spatiotemporal Variability of Channel Roughness and its Substantial Impacts on Flood Modeling Errors |
| title_short | Spatiotemporal Variability of Channel Roughness and its Substantial Impacts on Flood Modeling Errors |
| title_sort | spatiotemporal variability of channel roughness and its substantial impacts on flood modeling errors |
| topic | channel roughness fluvial hydraulics flood modeling big data |
| url | https://doi.org/10.1029/2023EF004257 |
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