Abstract
Due to vegetation drag and vegetation-generated turbulence,bedload transport in vegetated channels is more complicated than that in nonvegetated channels.It is challenging to obtain accurate predictions of bedload transport in vegetated channels.Previous studies generally used rigid circular cylinders to simulate vegetation,and the impact of plant morphology on bedload transport was typically ignored,these methods deviate from natural scenarios,resulting in prediction errors in transport rates of more than an order of magnitude.This study measured bedload transport rates inside P.australis,A.calamus and T.latifolia canopies and in arrays of rigid cylinders for comparison.The impact of plant morphology on bedload transport in vegetated channels was examined.Inside the canopies of natural morphology,the primary factor driving bedload transport is the near-bed turbulent kinetic energy(TKE),which consists of both bed-generated and vegetation-generated turbulence.A method was proposed to predict the near-bed TKE inside canopies with natural morphology.For the same solid volume fraction of plants,the transport rate inside canopies with a natural morphology is greater than or equal to that within an array of rigid cylinders,depending on the plant shape.This finding indicates that plant morphology has a significant impact on transport rates in vegetated regions and cannot be ignored,which is typical in practice.Four classic bedload transport equations(the Meyer-Peter-Müller,Einstein,Engelund and Dou equations),which are suitable for bare channels(no vegetation),were modified in terms of the near-bed TKE.The predicted near-bed TKE was inserted into these four equations to predict the transport rate in canopies with natural morphology.A comparison of the predictions indicated that the Meyer-Peter-Müller equation had the highest accuracy in predicting the transport rate in vegetated landscapes.
基金项目
National Key Research and Development Program of China(2022YFE0128200)
National Natural Science Foundation of China(52379072)
National Natural Science Foundation of China(52022063)
Fundamental Research Project of China Yangtze Power Co.,Ltd(2423020045)
NETL
NSTL
万方数据