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

• Published in: Earth's future 2024-07, Vol.12 (7), p.n/a
• Main Authors: Al Mehedi, Md Abdullah, Saki, Shah, Patel, Krutikkumar, Shen, Chaopeng, Cohen, Sagy, Smith, Virginia, Rajib, Adnan, Anagnostou, Emmanouil, Bindas, Tadd, Lawson, Kathryn
• Format: Article
• Language: English
• Subjects: Aquatic ecosystems; big data; channel roughness; Creeks & streams; Datasets; flood modeling; Flood predictions; Flood propagation; Flood waves; Floodplains; Floods; Flow control; Flow velocity; fluvial hydraulics; Geology; Geomorphology; High flow; Hydraulic radius; Land cover; Lookup tables; Low flow; Machine learning; Modelling; Parameter estimation; Predictive control; Propagation velocity; Rivers; Roughness; Roughness coefficient; Shear stress; Sinuosity; Spatial variability; Spatiotemporal data; Stream discharge; Stream flow; Stress propagation; Surface water; Temporal variability; Topography; Variability; Vegetation; Velocity
• ISSN: 2328-4277; 2328-4277
• DOI: 10.1029/2023EF004257

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. Plain Language Summary Stream channel roughness is a critical variable for many river‐related applications including modeling of flood inundation extent, pollutant transport, stormwater management, aquatic ecosystem health, infrastructural safety, and so on, and is traditionally assumed as being constant over time. Here we estimate channel roughness using in‐stream measurements from thousands of sites across the United States and show that its temporal dependence can be substantial. Our machine learning model can serve as a valuable and state‐of‐the‐art prediction of roughness, providing great practical value and a holistic view of the spatiotemporal variability of roughness. Moreover, the longstanding exclusion