Flow Structure and Short-Term Riverbed Evolution in Curved Flumes

River bending is the major effect responsible for bed topography and bank changes.In this study,fluid velocity(measured by a three-dimensional Doppler advanced point current meter)and bed topographical data have been collected in 40 sections of an experimental model.The whole flume was composed of an organic glass bend,upstream and downstream water tanks,two transition straight sections,a circulation pump,and a connection pipeline.Each section has been found to be characterized by a primary circulation and a small reverse circulation,with some sections even presenting three more or more circulation structures.The minimum circulation intensity has been detected in proximity to the top of the curved channel,while a region with small longitudinal velocity has been observed near the concave bank of each bend,corresponding to the flat bed formed after a short period of scouring.The maximum sediment deposition and scour depth in the presence of a uniform distribution of living flexible vegetation within 10 cm of the flume wall have been found to be smaller than those observed in the tests conducted without vegetation.

Responses of river bed evolution to flow-sediment process changes after Three Gorges Project in middle Yangtze River:A case study of Yaojian reach

The Three Gorges Project(TGP)has changed the flow-sediment process in the middle Yangtze River.For navigation purposes,there is an urgent need to study the changes of the river regime over a long-term period and the shoal-channel evolution over different seasons since the completion of the TGP.Based on analysis of the measured data and the results of a two-dimensional mathematical model,the changes of the river regime and river bed evolution in the Yaojian reach downstream of the TGP were studied.Results show that a high sediment transport flux helps to keep the main flow in the North Branch,while a low sediment transport flux helps to keep the main flow in the South Branch.Thus,the main branch will not change in the near future because of the low sediment transport load.In this study,the flow-sediment process adjusted by the TGP was restored to the conditions before the TGP,and the river bed evolution under the adjusted and non-adjusted flow-sediment conditions was calculated.After the completion of the TGP,the reservoir storage accelerated the flood recession process and decreased the erosion by 11.9%under the flow-sediment conditions in 2010,and the deposition in the flood season decreased by 56.4%.

A NEW MODEL FOR PREDICTING BED EVOLUTION IN ESTUARINE AREA AND ITS APPLICATION IN YELLOW RIVER DELTA

This article discusses the process of sediment transport and proposes a morphological model to predict the bed evolution in estuaries. The hydrodynamic module is based on an existent model-Depth Integrated Velocity And Solute Transport （DIVAST） and the wetting and drying method is adopted to deal with the moving boundary. Both cohesive sediment and non-cohesive sediment are taken into consideration in the sediment transport module with the capability of simulating the transport of graded sediments under non-equilibrium conditions. The fall velocity of the suspended sediment is modified in the present model due to the high sediment concentration. A 3-layer approach is adopted to simulate the variations of sediment gradations of bed materials. Furthermore, the model is used to simulate the bed evolution in the Yellow River Delta （YRD） from 1992 to 1995. Field data are used to calibrate the parameters. The numerical results show how the morphology was developed in the Yellow River Estuary with a good agreement with the field data.

Analytical Model for the Palaeoenvironmental Evolution of the Nihewan Beds

In this paper, the evolutional characteristics of palaeoclimate and oxidation-reduction conditions as well asacidity-alkalinity environment are discussed by means of the step-regression, cluster, optimal partitioning andcorrelation analyses of CaCO_3, C / P_2O_5, Fe^(2+) / Fe^(3+), pH and Eh values, taking the Xiaodukou section in theNihewan basin as an example. The CaCO_3, C / P_2O_5 and pH were calculated respectively using the optimalpartitioning method. Thus five cold zones and six warm zones as well as five reduction and six oxidation zoneswere distinguished. Then the inductive method was used to produce four numerical groups: 8.10, 8.3-8.4,8.6-8.7 and 8.9-8,97. The above-mentioned results are respectively based on CaCO_3 content, C/P_2O_5 andpH values. From Fig. 3, Tables 1 and 2 it can be seen that the Nihewan Beds were formed mainly under a re-duction and slightly alkaline environment of cold climate, with pH values of 8.3-8.4. Fig. 3 shows that bed 35is approximately near the boundary between the Brunhes and Matuyama polarity epochs, 0.73 Ma in age; bed26 is roughly near the Jaramillo event (base), 0.97 Ma in age; bed 18 coincides roughly with themagnetostratigraphic boundary of 2.00 Ma (?). Bed 13 may be the Pleistocene-Pliocene boundary, 2.48 Ma inage. Thus geochemical zones Ⅰ, Ⅱ, Ⅲ and Ⅳ include respectively cold zones 1; 2 and 3; 4; and 5.

Simulation of the Fate of Faecal Bacteria in Estuarine and Coastal Waters Based on A Fractionated Sediment Transport Model

A two-dimensional depth-integrated numerical model is refined in this paper to simulate the hydrodynamics, graded sediment transport process and the fate of faecal bacteria in estuarine and coastal waters. The sediment mixture is divided into several fractions according to the grain size. A bed evolution model is adopted to simulate the processes of the bed elevation change and sediment grain size sorting. The faecal bacteria transport equation includes enhanced source and sink terms to represent bacterial kinetic transformation and disappearance or reappearance due to sediment deposition or re-suspension. A novel partition ratio and dynamic decay rates of faecal bacteria are adopted in the numerical model. The model has been applied to the turbid water environment in the Bristol Channel and Severn estuary, UK. The predictions by the present model are compared with field data and those by non-fractionated model.