Empirical relations for the sediment transport capacity of interrill flow

1991 ◽  
Vol 16 (6) ◽  
pp. 513-532 ◽  
Author(s):  
Wilfried Everaert
Author(s):  
Peng Hu ◽  
Liming Tan ◽  
Jiafeng Xie ◽  
Zhiguo He

Swash sediment transport and beach deformation has received great attention in the past two decades. Quantification of swash-induced sediment transport rate is of vital importance for accurate prediction of beach deformation in the swash zone. Two empirical parameters are involved in this quantification, empirical relations for sediment transport capacity and the bed shear stress that may be used in the former. Since the swash zone is highly unsteady, of short cross-shore distance, sediment transport in this zone may be of high possibility to be lag of the flow variation. Thus we have firstly developed a non-capacity sediment transport model for the swash zone. This model appreciates the fact that the actual sediment transport rate may not be necessarily equal to the sediment transport capacity of the flow. In contrast to traditional capacity models that calculate sediment transport rate using directly empirical relations (Hu et al. 2015), the non-capacity model uses the advection-diffusion equation to calculate depth-averaged sediment concentration firstly, and afterwards compute sediment transport rate as flow depth*velocity*concentration. We have also noted that some empirical relations for sediment transport capacity may predict physically unrealistic high values of sediment concentration in the swash zone. This is attributed to the vanishing water depth in the swash zone, whereas existing empirical relations are developed for relatively large water depths (Hu et al. 2015; Li et al. 2017).


Geoderma ◽  
2019 ◽  
Vol 337 ◽  
pp. 384-393 ◽  
Author(s):  
Hongli Mu ◽  
Xianju Yu ◽  
Suhua Fu ◽  
Bofu Yu ◽  
Yingna Liu ◽  
...  

2012 ◽  
Vol 16 (2) ◽  
pp. 591-601 ◽  
Author(s):  
M. Ali ◽  
G. Sterk ◽  
M. Seeger ◽  
M. Boersema ◽  
P. Peters

Abstract. Sediment transport is an important component of the soil erosion process, which depends on several hydraulic parameters like unit discharge, mean flow velocity, and slope gradient. In most of the previous studies, the impact of these hydraulic parameters on transport capacity was studied for non-erodible bed conditions. Hence, this study aimed to examine the influence of unit discharge, mean flow velocity and slope gradient on sediment transport capacity for erodible beds and also to investigate the relationship between transport capacity and composite force predictors, i.e. shear stress, stream power, unit stream power and effective stream power. In order to accomplish the objectives, experiments were carried out in a 3.0 m long and 0.5 m wide flume using four well sorted sands (0.230, 0.536, 0.719, 1.022 mm). Unit discharges ranging from 0.07 to 2.07 × 10−3 m2 s−1 were simulated inside the flume at four slopes (5.2, 8.7, 13.2 and 17.6%) to analyze their impact on sediment transport rate. The sediment transport rate measured at the bottom end of the flume by taking water and sediment samples was considered equal to sediment transport capacity, because the selected flume length of 3.0 m was found sufficient to reach the transport capacity. The experimental result reveals that the slope gradient has a stronger impact on transport capacity than unit discharge and mean flow velocity due to the fact that the tangential component of gravity force increases with slope gradient. Our results show that unit stream power is an optimal composite force predictor for estimating transport capacity. Stream power and effective stream power can also be successfully related to the transport capacity, however the relations are strongly dependent on grain size. Shear stress showed poor performance, because part of shear stress is dissipated by bed irregularities, bed form evolution and sediment detachment. An empirical transport capacity equation was derived, which illustrates that transport capacity can be predicted from median grain size, total discharge and slope gradient.


1989 ◽  
Vol 32 (5) ◽  
pp. 1545-1550 ◽  
Author(s):  
S. C. Finkner ◽  
M. A. Hearing ◽  
G. R. Foster ◽  
J. E. Gilley

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Hai Xiao ◽  
Gang Liu ◽  
Puling Liu ◽  
Fenli Zheng ◽  
Jiaqiong Zhang ◽  
...  

2020 ◽  
Vol 591 ◽  
pp. 125582
Author(s):  
Shuyuan Wang ◽  
Dennis C. Flanagan ◽  
Bernard A. Engel ◽  
Na Zhou

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