Sediment transport plays a major role in the evolution of river beds and estuaries; consequently it exerts a great influence on the topography evolution of earth‘s surface (Yang, 2005). A lot of natural rivers facing sedimentation problems either caused by erosion or human activities, but some are related to river management that arise from the adequate prediction of sediment behaviour. Any persistent changes of sediment in the rivers transport capacity, due to natural activity will promote erosion and/or deposition as the river responds to those conditions. (Bhuiyan et al, 2009). Sedimentation study is more acquaint with the behaviour of sediment load in a river where it is depends on the types of river bed and migration of bed forms such as ripples and dunes (Gui and Jin, 2002; Van Rijn, 2007).
The inconsistent coupling between catchment characteristics and catchment sediment yields can be related to the importance of autogenic processes, i.e. processes taking place within the river channel, such as mobilization and deposition of river bed sediments and river bank erosion. Fluctuations in sediment transport rates occur even under steady boundary conditions as a result of such processes which are initiated once a threshold is reached (Jerolmack and Paola, 2010). Thus, rivers are not simply sediment conduits of hill slope derived sediments but may also act as buffers by storing sediments in alluvial plains for several millennia (Wittmann and Blanckenburg, 2009) and may supply sediment through channel enlargement or incision (Renwick et al., 2005). While it has become increasingly clear that autogenic processes have an important impact on the sensitivity of the river system to catchment changes (Syvitski et al., 2003; Phillips, 2013), little quantitative information is available to allow evaluating their role in explaining river sediment dynamics (Phillips et al., 2005). This is an important gap as the timing and the magnitude of the response of a catch- ment to environmental changes can only be well understood if the impact of autogenic processes is properly accounted for.
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44 computer hard disks, fluorescent and light-emitting-diode (LED) lights, flat screen televisions and 45 electronic displays) and in agricultural activities, yet these elements have only recently been recognized as 46 potentially emergent pollutants in rivers (Hissler et al. 2015; Gwenzi et al. 2018; Censi et al. 2018; Cuss et 47 al. 2018; Blinova et al. 2018; Xu et al. 2018), thereby requiring management decisions targeting 48 contaminated sites (Kulaksiz and Bau 2013; Liang et al. 2014; Ramos et al. 2016; Blinova et al. 2018). 49 Such management can be required since some REEs impact on human health; Gd accumulation, for 50 example, can trigger kidney failure and anaphylactic shock followed by death in extreme cases (Ergun et 51 al. 2006; Idee and Corot 2008; Kay 2008). Even where human health impacts are not reported, fluvial 52 suspended and bed sediment transport govern the transfer of REEs and the environmental conditions of 53 exposure imply a continuous contamination of the world’s estuaries and oceans (Hannigan et al. 2010; 54 Liang et al. 2014; Polyakov et al. 2009; Brito et al. 2018) and represent not only short- but also long-term 55 pollution transfers in rivers (Taylor et al. 2003).
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To conclude our points relevant to sediment transport predictions, we summarize them here. The ﬁrst assumption is that the majority of bleaching of trapped charge occurs during ﬂuvial transport. If this assump- tion were invalidated, there would be a large ﬂux of bleached grains and the bulk bleaching of all grains in the river channel would be faster than expected by the model’s use of and bleaching experiment data. This would cause an overestimation of the drift velocity u, the exchange rate η, and the virtual velocity U, propor- tionate to the relative amounts of surface bleached sediment versus transport bleached sediment. However, as noted above, this may not be a likely scenario. The second assumption, that characteristic transport and storage lengthscales and time scales for a river system have ﬁnite averages and/or variance, would affect the sediment transport predictions in the following ways. If the transport lengthscale has inﬁnite mean, then the scaling relationship between the amount of luminescence bleached and the distance a grain travels breaks down and the luminescence of grains is no longer an effective proxy for the transport lengthscale. Studies of how sensitive river channel luminescence is with respect to different systems may lead to a better understanding on the controls of storage time scale and sediment transport. However, this may not be a pro- blem for most river systems [Bradley and Tucker, 2013]. Inﬁnite variance in the transport lengthscale is poten- tially avoided by measuring large numbers of grains as is done during luminescence measurement of large aliquots. Inﬁnite mean in the storage time scale would mean that some grains in the system would mean that the scaling between the luminescence of the storage center deposits and the storage time breaks down as the grains become saturated with respect to luminescence. This would result in a underestimation of the sto- rage time scale and an overestimation of the virtual velocity. This can be avoided by using a luminescence signal with a saturation limit higher than typical storage time scales such as the pIRIR signals used in the Mojave data set. As with the transport lengthscale, an inﬁnite variance in the storage time scale can be poten- tially avoided by measuring large numbers of grains. The ﬁnal two assumptions, that (a) steady state approx- imations are appropriate over suspended sediment transport time scales and (b) that no signiﬁcant geomorphic disequilibria such as major changes in sediment supply are occurring over the time scales of ﬁne sediment transport, vary in a complex manner. As discussed above, these assumptions may require that the model be modiﬁed for particular ﬁeld sites in order to produce reasonable results. As with the application of analogous methods such as cosmogenic catchment-averaged denudation rates, the context of the state of the landscape will need to be considered with regard to these two assumptions.
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Site1-Rasoolabad Ghat, Site2- Daraganj Ghat, Site3- Ram Ghat, Site4-Sangam and Site 5- Chhatnag Ghat. The river Ganga, the largest of the India Rivers, rises from Gomukh Uttarakhand, enters Uttar Pradesh, Bihar, Jharkhand State and joins the Bay of Bengal near Ganaga Sagar after traversing a distance of 2525 km (Fig. 1). The river provides large quantities of fresh water to 30 crore people and different industry for processing, different river canal and in turn receives enormous quantities of liquid wastes and nutrient transport sediments. Samples were collected in the. River before the entry of wastes at the point of entry of wastes and at proper distance after the entry of wastes into the river. Sewage / waste water were also collected just before their discharge into the river. Samples were analysed for selected variables by following the standard procedures (APHA, 2010). The experimental work has carried out in two phases firstly, the field study and secondly the laboratory work. To collect the water and sediment sample from the river Ganga five sites have selected. Sampling sites were located by using Global Positioning System (GPS) Technology.
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Sediment measurements are complicated and expensive. Total sediment conveyed by streams leads more than fifty percent are during flood event, which creates more challenges in measurement [1, 2]. Flood events from time to time may occur at nights that make it difficult to measure unless there is an automatic measurement system in place. The other real favourable time of measurement are tedious and laborious resulting high cost. There are a lot of sediment sampling techniques available for sampling suspended and bedload sediment in rivers and streams. More than a dozen sediment measurement techniques including bottle sampling, acoustic sampling, surface sampling, optical sampling etc. are available. It is evident that there is no one generally accepted sampling technique considered superior to others in the collection of sediment data. The accessibility of enough reliable high-quality sediment data is vital in the proper and
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The purpose of this research is to develop a 3-D numerical model on the Lower Mississippi River to simulate hydrodynamics and non-cohesive sediment transport. The study reach extends from Bonnet Carré Spillway (RM 127) to Head of Passes (RM 0). Delft3D with sigma coordinates was selected as the river modeling tool. This model River domain is characterized by a complex distributary system that connects the Mississippi River to the Gulf of Mexico. The boundary conditions were: water levels in the Gulf and Head of Passes; and discharges upstream. For the calibration, there are observed data for both types of
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Comparison of the sediment transport rates calculated using TSAND from the measured and the DCSM model currents for the period of the field campaign has shown that, in the longshore direction at the deepest frame F1, modelled currents give values very close to the ones predicted from the measured currents, but as it becomes shallower they tend to give lower transport rates than the measured currents and the difference is highest at the shallowest frame F4. In the cross-shore direction measured currents give onshore-directed total sediment transport at all the locations significantly increasing towards shallow water, while DCSM model currents give small offshore transport at deeper water and onshore transport – at shallower, but much smaller compared to the transport from the measured currents. The sediment transport occurs mainly during storm events and the main difference between the result from the measured and the modelled currents is observed during these events. Comparing the transport rates, calculated using TSAND with the modelled currents for the entire year of 2017, with the ones calculated using measured currents for the period of the KG2 field campaign it was possible to conclude that the mismatch in the currents is very important for the net annual sediment transport on the lower shoreface, especially in the cross-shore direction, as the values for the 20-day period with waves are nearly the same as for the full year. Because of that, it can be concluded that in its present state the offline sediment transport modelling approach cannot be used for the analysis of the net annual sediment transport for the Ameland tidal inlet. This is the case even for the water depth of 20 m, where the yearly cross-shore transport from modelled currents is around zero and about 3 m 3 /m onshore for the field campaign. From a rough estimation of the yearly transport from the transport during field campaign is can be seen that this mismatch can be significant for the determining the required nourishment volume of the Coastal Foundation.
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As water in natural channel flows into a reservoir, so is its energy gradient forced to approach zero, leading to the loss of its transport capacity and eventual deposition of sediments in the reservoir. T he accumulations of these fluvial deposits cause reservoirs to lose their capacities and consequently threaten their performances for various water development purposes such as hydropower generation, water supply and recreation activities. Such havocs woul d continue as long as the reservoir continues to have its storage capacity rapidly depleted unless workable remedial plans and actions are put in place.
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mean SS flux at every monitoring station. Thirty-one mon- itoring stations were chosen for model calibration in this study (Fig. 1). SS concentration and daily flow data were col- lected at each site for the period from 1985 to 2010 by the National Land with Water Information (http://www1.river. go.jp/) monitoring network (Fig. 3). However, some stream- flow gaging stations have short periods of record or miss- ing flow values, but do not over 10 % of the time periods. A streamflow record extension method called the Mainte- nance of Variance-Extension type 3 (MOVE.3) (Vogel and Stedinger, 1985) is employed to estimate missing flow val- ues or to extend the record at a short-record station on the basis of daily streamflow values recorded at nearby, hydro- logically similar index stations. On this basis, the FORTRAN Load Estimator (LOADEST), which uses time-series stream- flow data and constituent concentrations to calibrate a regres- sion model that describes constituent loads in terms of vari- ous functions of streamflow and time, is applied to estimate SS loads. The output regression model equations take the fol- lowing general form (Runkel et al., 2004):
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A river channel can be divided into three parts.Three parts are upstream, middle and downstream. The flow of water in a channel can be a flow in an open channel, and can also be a stream in a pipe (closed channel). In open channels, the water stream has a free surface that is affected by the velocity, viscosity, gradient and channel geometry. This is what usually causes difficulties in obtaining accurate data about the flow on open channels.
Similar bell shape relationship was also found for the multi-year mean annual precipitation and sediment yield ob- served in the United States (Langbein and Schumm, 1958). The data used in the analysis of Langbein and Schumm (1958) were collected in the 1950s from more humid and vegetated catchments with limited human intervention, oppo- site to the YRB where the climate is arid and semi-arid, veg- etation coverage is low, and human activities are intensive. However, a similar bell shape was still observed between sediment yield and precipitation. Given the limited anthro- pogenic activities in these catchments, vegetation growth is probably correlated with annual precipitation due to its adap- tion to climate, as in other US catchments (Fig. S6). Thus it is likely that a bell shape correlation between vegetation and sediment yield would be found at these US catchments as well. This suggests that the bell shape correlation between vegetation and sediment concentration is not only observed in the YRB with intensive human intervention, but could also be valid outside it. More analyses are needed to test this rela- tionship in other catchments outside the YRB for its univer- sality.
Numerical modeling facilitates our understanding of monsoon’s influence on local sediment transport. Sensitivity tests in this study show that, on seasonal to yearly time scales, wind is a most important forcing influencing local sediment transport. The variations in monsoon activities during the Neoglaciation can affect the along-shelf sediment transport and the delta morphodynamics in two ways: (1) during high flow seasons, tropical aridity limits the rainfall, sediment yield, and thus the SSC along the Mekong channel. Less source materials are delivered to the river mouth; and (2) during the low flow season, wave energy and coastal currents are strengthened by an intensified NE winter monsoon, resuspending and transporting more Mekong sediment toward the GOT. Although model results showed that current-induced bed shear stress dominate the shelf, the coastal area is mainly influenced by wave-induced bed shear stress (Figs. 12a and 14c). This is confirmed by the “coarsening- upward succession” in boreholes (Ta et al., 2002a).
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particularly saltating sediment particles further selects against tall taxa (chain forming, filamentous, stalked and upright forms), and pushes communities towards adpressed forms and those that strongly adhere to the substrate (e.g. with mucilage pads), influencing traits associated with both growth form and attachment. More robust cell walls (e.g. thick walls, heavy silicification, costae) enable taxa to withstand physical damage. As such, species with thicker, more rigid cells walls are selected for where the suspended and saltating loads of inorganic sediment are high 223 . However, even these species are
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The role of oxygen. A number of studies have emphasized that aerobic and anaerobic decomposition are comparable for fresh and reactive organic substrates, while aerobic processes proceed much faster for partly degraded and refractory compounds. 34,58,59 In an experiment where carbon mineraliza- tion of 14 C labelled fresh and aged diatoms (Skeletonema costatum) and barley hay (Hordeum vulgare) was followed for about one month, Kristensen and Holmer 59 showed that the initial decay of fresh materials occurs at almost the same rate in both oxic and anoxic sediment (Fig. 16). After ageing, degradation is 5 to 10 times faster under oxic than anoxic conditions. Based on these and similar results it has been argued that introduction of oxygen into anoxic sediment by macrofaunal irrigation or translocation of organic matter from anoxic to oxic environments by particle reworking promotes decomposition of organic matter in sediments. 44,59
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Figure 7(b) shows the sediment discharge during a rainfall event on 12 July. The calculated water discharge agreed very well with the observational results. However, the accumulated sediment discharge from the calculations only roughly expressed the same tendencies as the observational results. In particular, the observed sediment discharge increased gradually and the peak value was much smaller than the simulated one. The simulated sediment discharge increased suddenly with a high peak before decreasing rapidly. One likely reason for this trend is the effect of river morphology, such as step-pool bed formation, on sediment transport processes. It is thought that sediment storage in pools can delay transportation and smooth out time variations in sediment discharge.
The estuaries of the Red River delta are presently silting up, and this is partly due to the water flow regulation of the HBD, which has led to a decrease in sediment transport ca- pacity and an increase in river discharge in the northern delta during the dry season, all of which likely enhance deposition in the Cam–Bach Dang estuary. Moreover, the decrease in the suspended sediment discharge of the Red River induced a decrease in the sedimentation rate along the delta shore- line. Coastal erosion intensifies when sedimentation and ac- cumulation no longer balance sea level rise and tectonic sub- sidence, and this factor needs to be taken into account when considering dam regulation. The increase in the suspended sediment discharge ratio in the northern (Cam, Bach Dang, Lach Tray, Van Uc, Thai Binh) and southern estuaries (Day) and its decrease at the Ba Lat, Tra Ly and Ninh Co mouths in- fluenced not only erosion and accretion zones along the RRD coasts, but also altered the geological, morphological, bio- geochemical and ecological responses in the estuaries, deltas and coastal areas (e.g. Rochelle-Newall et al., 2011; Bui et al., 2012; Navarro et al., 2012).
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The significant difference between results obtained by different formulas emphasizes the necessity of assessing predicted values by different formulas in varying river conditions (Yang, 1996). As the existing formulas predict the maximum sediment transport capacity of a river, so the measured sediment load may be less than the calculated sediment loads by these formulas. A large number of comparison studies have been done to test the predictability of various sediment transport equations covering a wide range of flow conditions and sediment types, but the accuracy of computational sediment transport models has remained a “challenging question” yet (ASCE, 2004). For selecting the most proper equations for estimation of sediment discharge for a river, it is necessary to evaluate them. The literature review show that different studies were performed to predict the suspended sediment load (Wu et al., 2008; Zhang et al., 2012; Heidarnejad, and Gholami, 2012), bed load(Yang and Wan, 1991; Sirdari et al., 2001; Haddadchi et al., 2013) and total load (Wu et al., 2008; Roushangar et al., 2014) of rivers.
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Table 19 summarizes how the models account for different processes. As seen in the table, both N06 and VR07 are quasi-steady models. The two models relate the instantaneous sand transport to the instantaneous flow velocity; the models do not account for the influences of the phase-lag effects. Among the three models, SANTOSS is the only semi-unsteady model. The model accounts for the phase-lag effects in a parameterized way. A phase-lag parameter is calculated as the ratio of stirring height and settling distance during each half cycle. Due to this approach to account for the influences of the phase-lag effects, the SANTOSS model can be applied to rippled-bed conditions and sheet flow conditions with fine sediment, large velocities and short wave periods. However, in contrary to the other two models, the SANTOSS model is not able to calculate time dependent sand transports. Table 19 shows that all three models account for the influences of boundary layer streaming under surface wave conditions. Both N06 and the SANTOSS model use the wave Reynolds stress to do this. The wave Reynolds stress is a constant stress that induces additional onshore directed sand transport. The difference between the wave Reynolds stress of N06 and the SANTOSS model is that N06 uses the friction factor for mobile bed while the SANTOSS model uses the wave friction factor (or the combined wave-current friction factor for the ‘waves combined with current’ conditions). The friction factor used by N06 is larger than the one used by the SANTOSS model, which means that the wave Reynolds stress of N06 induces more additional sand transport. The approach of VR07 to account for the influences of boundary layer streaming is totally different compared to the other two models. Instead of a constant wave Reynolds stress, a steady current velocity is added to the orbital velocity. This current is onshore directed under sheet flow conditions and offshore directed under rippled-bed conditions. Among the streaming components of the three models, only the streaming component of VR07 can be offshore directed.
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After preparing the river bathymetry calibration of the model has been done using the setup of model from 1st of June, 2010 to 31st of August, 2010 and the model was verified for the hydraulic condition of the rest part of the year ranging from 1st of September to 30th of November. Manning’s roughness coefficient has been adjusted after several trial of the model during calibration to an average value of n = 0.025. The value of eddy viscosity has been considered as10.0 m2/s. Then the model has been validated at the Kamarkhali for the period 1st of September to 30th of November that shows a good agreement with the observed data.After completing the calibration and validation of the model, Delft3D Flow and the DELFT 2D-MOR module have been used for morphological simulation and salinity modeling for the year 2010with the original bathymetry (Initial bathymetry of April 2010 has been taken as base for simulation).Mean sediment diameter (D50) has been assumed as 0.150 mm and as the sediment boundary the monthly average sediment data for the year 2010 was given as the input. Morphological scale factor (Morfac) has been taken as the calibration parameter and sensitivity analysis was done using the combination of the Manning’s n and Morfac (1, 5 and 10) which shows that Morfac= 10 produces mediocore results.