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New formulas addressing flow resistance of floodplain vegetation from emergent to submerged conditions

Authors :
Box, Walter
Järvelä, Juha
Västilä, Kaisa
Department of Built Environment
Aalto-yliopisto
Aalto University
Publication Year :
2022
Publisher :
International Association of Hydraulic Engineering Research, 2022.

Abstract

Publisher Copyright: © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. Hydraulic modelling of natural floodplain vegetation using leaf area index (LAI) has been applied successfully for non-submerged conditions whereas its suitability for submerged conditions requires further development. This study investigates the vegetative flow resistance at low relative submergences and extends existing LAI-based approaches building upon new flume data and prior experiences from field-scale applications. We provide advanced LAI-based formulas for modelling the flow resistance from emergent to submerged conditions, with water depth up to three times higher than the vegetation height. Such low relative submergences are highly relevant in hydraulic analyses of riverbank and floodplain flows but not adequately represented in existing formulas. The use of the deflected vegetation height as the characteristic height provided the most accurate modelling results, whereas the use of undeflected height resulted in significant errors. As a new development for submerged conditions, we proposed von Kármán scaling factor for improved model predictions. Overall, the results proved that LAI-based modelling is suitable also at low relative submergences for a wide range of vegetation densities (LAI = 1–5) and mean flow velocities (0.05–1.2 m s−1). For both emergent and slightly overtopped vegetation the JAR and VAS approaches outperformed the BAPmod-LAI approach that does not account for reconfiguration. For modellers, we provide a workflow and guidance on the use of the newly developed LAI-based formulas in 1D/2D hydrodynamic models for both emergent and submerged conditions.

Details

Language :
English
Database :
OpenAIRE
Accession number :
edsair.od.......661..14b2a8e603af4d11ccc7ae4eeac97634