Journal Article

Comment on “Momentum and Energy Predict the Backwater Rise Generated by a Large Wood Jam” by Follett, E., Schalko, I. and Nepf, H

Poppema, Daan W., Wüthrich, Davide

2024

Overview

Follett et al. (2020a, https://doi.org/10.1029/2020gl089346) developed an analytical model to predict backwater rise by log jams, using the size and packing density of logs and the jam length, as well as river slope and bed roughness. We show that the model formulas can be rewritten using the Froude number instead of river slope and roughness, thus improving their applicability in engineering practice. The equation terms and results of Follett et al. (2020a, https://doi.org/10.1029/2020gl089346) are found to be similar to those of the empirically derived formula by Schalko et al. (2018, https://doi.org/10.1061/(asce)hy.1943‐7900.0001501). However, some differences are identified, calling for further study. Most notably, these distinctions pertain to the effect of accumulation porosity, with additional minor differences in the exponent of the Froude number. Lastly, model implications for some broader applications are explored, showing a methodology to calculate the representative log size for log mixtures, and the expected effect of log orientation on backwater rise. Plain Language Summary Accumulations of wood in rivers (log jams) can block the flow and thereby cause water level rise. Follett et al. (2020a, https://doi.org/10.1029/2020gl089346) developed a theoretical model to predict how this water level rise depends on log jam properties and local river conditions. For the local river conditions, they used the river slope and bottom roughness. In this comment, we show that the Froude number can be used instead, with exactly the same result. The Froude number is a dimensionless number that depends directly on the local river conditions, making the adapted formula easier to apply in practice. The resulting formula shows good agreement with an earlier one based on experimental work by Schalko et al. (2018, https://doi.org/10.1061/(asce)hy.1943‐7900.0001501). Still, some differences were found that raise questions. Most notably, the formulas differ for the effect of accumulation porosity. This becomes especially clear when logs are packed closely together. Next, model implications for slightly different settings than those studied by Follett et al. (2020a, https://doi.org/10.1029/2020gl089346) were explored. This showed how to determine the average log size for a mixture of logs with different sizes, and how the expected water level rise changes with log orientation. Key Points Follett et al. (2020a, https://doi.org/10.1029/2020gl089346) predicted backwater rise by log jams using river slope and roughness. We show the Froude number can be used instead By using the Froude number, the link to the local river conditions becomes stronger, improving formula applicability in engineering practice The resulting formula is shown to be similar to earlier empirical work. But differences in jam porosity effects call for further study

• Publisher: John Wiley & Sons, Inc,Wiley
• Subject: Accumulation / backwater rise / Backwaters / Bed roughness / Bottom roughness / Dimensionless numbers / driftwood / Froude number / Froude number effect / jam porosity / large wood accumulation / Log jams / logjam / Mathematical models / Mixtures / Momentum / Orientation effects / Packing density / Porosity / Rivers / Water level fluctuations / Water levels / Wood