The Influence of Vegetated Alternate Bar on Flow Resistance in an Alluvial Straight Channel

Salleh, M.S.; Ibrahim, Zuhairi; Sa’ari, Radzuan; Shariff, Muhamad Effandi Mohd; Jumain, Mazlin

doi:10.1007/978-981-19-5947-9_14

Cited by 1 publication

(1 citation statement)

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“…Due to the need for local investigation or calibration, either approach would preclude the estimation of n at large scales. In research, it is understood that channel roughness can be governed by multiple environmental factors including the type and size of the river substrate, channel geometries, flow conditions, sediment characteristics, human modifications, aquatic and riparian vegetation, and plant type and density (Addy & Wilkinson, 2019; Brebbia, 2011; Djajadi, 2009; Jarrett, 1985; Nicosia & Ferro, 2023; Salleh et al., 2023; Zhu et al., 2020). In meandering channels, the irregularity of channel geometry and sinuosity (Figure S1 in Supporting Information S1) can also play a role in determining n (James, 1994; Naghavi et al., 2023).…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Variability of Channel Roughness and its Substantial Impacts on Flood Modeling Errors

Al Mehedi,

Saki,

Patel

et al. 2024

Earth's Future

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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.

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