Extended Einstein's parameters to include vegetation in existing bedload predictors. (June 2021)

• Record Type: Journal Article
• Authors: Bonilla-Porras, José A.; Armanini, Aronne; Crosato, Alessandra
• Is Part Of: Advances in water resources. Volume 152(2021)
• Publication Date: 2021-06
• Subjects: Bedload predictor -- Aquatic vegetation -- Einstein's sediment transport parameters -- Flume experiments; Hydrology -- Periodicals; Hydrodynamics -- Periodicals; Hydraulic engineering -- Periodicals; 551.48
• DOI: 10.1016/j.advwatres.2021.103928
• Languages: English
• ISSNs: 0309-1708

Abstract

Highlights: There is a lack of reliable methods for bedload estimation in vegetated channels. Existing methods based on bed-roughness correction usually yield overestimations. An extension to Einstein's transport parameters is proposed to include plant properties. The resulting approach is valid in settings with emergent and submerged vegetation. Abstract: Observations show that instream vegetation has a strong impact on bedload transport. However, there is a scarcity of sediment transport predictors that directly account for the effects of plants, and existing methods, based on re-calculation of roughness coefficients, may present some inconsistencies. The approach herein proposed extends Einstein's parameters to include the effects of vegetation on sediment transport for implementation in existing bedload predictors of the form Φ = f (Ψ). The new formulations are applicable in presence of submerged and emergent vegetation and reduce to the original Einstein's parameters if vegetation is absent. Calibration is based on laboratory data. For this purpose, an extensive experimental program was carried out on a tilting glass-walled flume with medium-sand bed and plants represented by aluminum cylinders. Validation is based on published bedload measurements from the literature. The results show a much better agreement between measurements and predictions when applying the extended parameters compared to using Einstein's original ones. Predicted bedload rates have, on average, the same order of magnitude of the measured ones, and quantitative agreement is substantially increased. Clear improvements were also observed when comparing the results with the bedload predictions of Baptist's (2005) method, which is based on the re-calculation of bed roughness in the presence of vegetation.