International Journal of Sediment Research最新文献

Due to the complex nature of coastal dynamics, several models were developed to estimate longshore sediment transport (LST) rates. The Coastal Engineering Research Center (CERC), Kamphuis, and Bayram models are among the most used and were applied to three east–west oriented sandy urban beaches from Rio de Janeiro (southeastern Brazil): Macumba, Recreio, and Barra da Tijuca. The wave record from oceanographic buoys and the WaveWatch III (WW3) model were used to identify fairweather periods and increased storminess between 2016 and 2018. Eastwards, median sediment grain-size grades from coarse to medium sand, and is associated with decreasing beach slopes in the same direction. The magnitude of the estimated LST rates varied significantly between the models (Δ ≈ 1,000 m³/day, p < 0.001), although the time-averaged LST rates obtained from the four models indicate eastward transport. The four models identified areas of convergence and divergence of sediment transport with increasing rates to the east. The modeling results may support effective coastal management initiatives when integrated with topographic profiles, numerical modeling, satellite imagery, and historical information.

Reservoir sediment flushing, one of the most effective strategies for alleviating reservoir sedimentation, involves discharging sediment-laden flows downstream through bottom tunnels. However, whether flushing can be accomplished if the intake of a bottom tunnel is initially covered by cohesive sediment remains poorly understood. Here, flume experiments were done to investigate cohesive sediment flushing in a reservoir. It is demonstrated that cohesive sediment in a reservoir is harder to flush than non-cohesive sediment. A higher water level in the reservoir, initially smaller cover layer thickness, and lower dry density of the sediment favor the occurrence of sediment flushing. The flushing process of cohesive sediment is significantly affected by seepage. Under the combined action of gravity erosion and water erosion, the scour hole upstream of the dam is characterized by angular and broken edges. The threshold conditions for flushing of non-cohesive and cohesive sediments are evaluated. Empirical formulas applicable to both non-cohesive and cohesive sediment are proposed to estimate the equilibrium scour depth immediately upstream of the bottom tunnel intake. Also, empirical models are proposed for the time variation of sediment position in the bottom tunnel. The current findings are significant for informing the design and operation of reservoirs on rivers carrying fine-grained cohesive sediment in support of reservoir benefits and capacity preservation.

River sediment is comprised of complex mineral systems composed by different kinds of organic and inorganic matter, and thus, is difficult to characterize. Besides, some standard techniques, such as X-ray diffraction (XRD), energy dispersive X-ray (EDX), optical and scanning electron microscopy, Fourier transmission infrared spectroscopy, inductively couple plasma-mass spectrometry (ICP-MS), and simultaneous Thermogravimetric Analysis – Differential Thermal Analysis (TGA-DTA), Mössbauer spectroscopy and magnetometry can provide substancial information about the compositional, physical, and chemical characteristics. In the current study, the versality of these methods is tested and the information provided by these methods for eight sediment samples, collected from the Moquegua River, Peru is compared. Qualitative analysis indicates that the samples consist of sand grains with different shapes, sizes, and colors coexisting with the presence of some diatoms. The chemical and mineralogical analysis reveal that the samples are composed mainly of silicon (Si), aluminium (Al), sodium (Na), potassium (K), aluminon–silicates, and carbonates, typical for river sediment. More detailed information obtained by these techniques include the discovery of adsorbed oxygen–hydrogen (O–H), carbon–H (C–H) and C, from organic matter, the thermal reactions and decomposition of the components, and the identification of the minor iron–oxides components. Further, other properties such as magnetic interaction are also analyzed in detail.

Annual sediment load transportation in the Indus River varies from 0.725 to 1.0 Mt/d and varies from 260 to 300 Mt/y. Sediment accumulation upstream of the inline structure (Guddu Barrage) has frequently increased. Consequently, the sediment accumulation reduces the intake canal supply, design withdrawals, and flood-carrying capacity of the Guddu Barrage. Furthermore, the Indus River changes its behavior, channel dimensions, pattern, and flooding frequency with respect to temporal and spatial morphology with braided high to low meanders. In the current study, the Hydrologic Engineering Center- River Analysis System (HEC-RAS) model, in combination with ArcGIS, were used to study sediment dynamics, analyze flood profile/water surface elevation, and assess erosion and deposition of sediment. In addition to this, a quasi-unsteady flow analysis method was used to simulate sediment transport from July to September 2010. It was found the invert change due to sediment transport maximum aggradation was 6.40 ft (1.950 m), and the maximum degradation was 30 ft (9.144 m), which further varies with the hydraulic conditions of the model. Cumulative mass bed change, sediment transport aggradation was 10.50 million tons (9.53 million t), and degradation was 3.7 million tons (3.3 million t). Moreover, it was found cumulative longitudinal mass change, sediment transport aggradation was 155 million tons (140.62 million t), and degradation was 10 million tons (9.07 million t). The cumulative mass inflow was 320 million tons (290.3 million t). Whereas the model revealed that the flood level upstream and downstream of the hydraulic structure was 264 ft (80.467 m) and 260.29 ft (79.34 m), respectively. Therefore, the HEC-RAS model accurately represents the sediment transport and water levels observed at a gated weir, which is an inline structure.

Sedimentation traps were used to assess the annual surface renewal dynamics of the low floodplain of the Oka River (a major tributary of the Volga River in central European Russia). Trap-mats and platforms made of crushed bricks were installed in positions near the meandering and relatively straight riverbed, in different sedimentation environments. Stationary research in 2014–2020 covered a section of the bottom of the Oka River valley with a length of more than 400 km along the main channel. The graphical-analytical processing of field data using Ferret's Triangles and sedimentation diagrams showed that transport and deposition of suspended sediment dominated in the accumulation of 87% of alluvium samples. The formation of ripples was not recorded, which was lithologically reflected in the horizontal layering of the new sediment. The determination of the granulometric composition of the removed sediment and their thickness on the traps showed the absence of statistically significant differences according to the Kruskal–Wallis test between the data samples from the trap-mats and trap-platforms. The reference to the daily calendar of synoptic mechanisms according to the classification of Dzerdzeevsky contributed to the identification of meteorological prerequisites for the variation of the hydrograph curve of the Oka River in its middle reaches. Prolonged floods caused by the premature arrival of spring lead to massive deposition of silt and clay particles even on sandbanks. On the other hand, short (15–35 d) March floods and abnormal high water in June, caused by Atlantic cyclone intrusions, can stabilize sand accumulation on the riverine floodplain. The thickest sediment layers on the traps were obtained in 2018 after a very cold March and a powerful April flood, and the overall distribution of alluvium thickness and its particle size distribution also depends on the morphology of the riverine relief. The siltation is caused by the accumulation of silts; the most finely dispersed sediment was deposited in those facies environments for which siltation was also characteristic in historical times.

The current study describes the development of simple, low-cost, and high-throughput digital image colorimetric methods to determine the total iron concentration in river sediment using the spot-test reactions of iron with 1,10-phenanthroline and thiocyanate. The colorimetric assay was done on 96-microzone plates, and a flatbed scanner was applied to acquire the images. The proposed methods offered a linear range from 0.2 to 14.0 mg/L, with a detection limit of 0.11 mg/kg for the 1,10-phenanthroline method, and, for the thiocyanate method, the linear range comprises 2.0–10.0 mg/L, with a detection limit of 0.28 mg/kg. It was observed that both proposed digital image colorimetric methods (1,10-phenanthroline and thiocyanate) yielded statistically similar results to the reference procedures at a 95% confidence level. A standard reference material (NIST 8704) also was utilized for accuracy assessment and the results were statistically equivalent to the certified values within the 95% confidence level. The digital image colorimetric methods can be an alternative method for iron determination in sediment samples, allowing fast sample screening at a low cost.

This study examines patterns of sediment transport in Far Eastern rivers of Russia affected by open-cast placer mining—mostly for gold, rarely for silver, and in a few cases for platinum and diamonds. Long-term monitoring and remote-sensing data are used to determine the location of mining landscapes and to detect sediment concentrations and plumes originating from the mining sites. The current study suggests that catchments of the Amur, Kolyma, and Lena rivers are global mining hot spots accommodating up to 1.1%–3.8% of total mining-related vegetation losses. Here, ∼20,100 km of river valleys (0.48% of the river network length) are currently disturbed by mining, with the maximum density of disturbed river valleys being up to 200–300 m/km² in the basins of the tributaries of the Upper Kolyma and Zeya rivers. To explore the potential mining impact on sediment load, these data were linked with the long-term sediment trends. Concentrations and discharges of mean annual, monthly, and daily suspended sediment decreased from the 1970s and 1980s to the present day at more than 40% of the 40 stream gauge sites assessed across the contiguous Far East. Increasing sediment trends were widespread across 20% of the sites localized in the cluster of greatest mining-related land disturbances. Up to 30% of the sites are characterized by sediment load growth up to the end of the 1980s and a subsequent decline due to the recent abandonment of mining activities. The current study highlights the non-linear relations between mining-related vegetation losses and sediment release into the river network, which is explained by diverse sources of sediment generation within mining areas and other drivers of sediment transport that interact and may attenuate or intensify the signal of mining impact.

Increasing sediment yield is one of the important environmental challenges in river basins resulting from changing land use. The current study develops an adaptive neuro fuzzy inference system (ANFIS) hybridized with evolutionary algorithms to predict annual sediment yield at the catchment scale considering some key factors affecting the alteration of the sediment yield. The key factors consist of the area of the sub-catchments, average slope of the sub-catchments, rainfall, and forest index, and the output of the model is sediment yield. Several indices such as the Nash–Sutcliffe efficiency (NSE), root mean square error and vulnerability index (VI) were applied to evaluate the performance of the models. Moreover, hybrid models were compared in terms of complexities to select the best approach. Based on the results in Talar River basin in Iran, several hybrid models in which particle swarm optimization (PSO), genetic algorithm, invasive weed optimization, biogeography-based optimization, and shuffled complex evolution used to train the neuro fuzzy network are able to generate reliable sediment yield models. The NSE of all previously listed models is more than 0.8 which means they are robust for assessing sediment yield resulting from land use change with a focus on deforestation. The proposed models are fairly similar in terms of computational complexities which implies no priority for selecting the best model. However, PSO-ANFIS performed slightly better than the other models especially in terms of accuracy of the outputs due to a high NSE (0.92) and a low VI (1.9 Mg/ha). Using the proposed models is recommended due to the lower required time and data compared to a physically based models such as the The Soil and Water Assessment Tool. However, some drawbacks restrict the application of the proposed model. For example, the proposed models cannot be used for small temporal scales.

The Pearl River is the second-largest river in China in terms of discharge and has experienced significant changes due to human activities and climate change. The aim of the current study was to detect spatiotemporal variations in runoff and sediment discharge in the Pearl River basin (PRB) over the past 60 years and to reveal the driving factors based on the collection of hydrological and meteorological data and land use data. The results showed that the average sediment load in the PRB was 64.7 Mt/y, with a significant decreasing rate of −7.6 Mt/10 y. The increase in vegetation coverage (by 0.4%/10 y) and the presence of large reservoirs were the main factors leading to the decreasing trend in the sediment load. However, in some subbasins with limited reservoir construction, increased rainfall erosivity during the dry season, along with land use conversion leading to a rapid increase in bare land and construction sites, contributed to an upward trend in the sediment load. The runoff discharge in the PRB remained relatively stable, with a change rate of −2.3 km³/10 y, and its variations were closely related to annual and seasonal rainfall changes. Human water consumption resulted in a lower measured runoff than natural runoff levels. A significant linear relation between the two confirmed the impact of human activities. The current study emphasizes the importance of considering both natural and anthropogenic factors in understanding runoff and sediment dynamics in the PRB and contributes to the knowledge of basin hydrology for guiding the formulation of effective water management strategies for sustainable regional development.

Soil loss management requires reliable data for assessing the conditions prevailing in a watershed. Suspended sediment concentration (SSC) is one of the indicators of soil loss, and its data and associated properties are essential for integrated watershed management. However, until now, practical methods for estimating the temporal variation of SSC at the watershed scale, i.e., a sediment graph (SG), using measured data have been given less attention. Therefore, the current study was planned to simulate the SG through conceptual modeling of the soil erosion process and sediment yield. The Galazchai Watershed in West Azerbaijan Province, Iran, was selected as a case study. In this regard, the isochrone histograms were initially prepared using two methods of the longitudinal channel profile and spatially distributed travel time. Soil erosion was calculated in each isochrone segment using the Revised Universal Soil Loss Equation (RUSLE), applying the lumped and cellular automata approach. The soil erosion between isochrones was subsequently routed using the Hadley, WaTEM/SEDEM, and newly modified U.S. Forest Service methods. The last method was developed based on seven standardized variables for the current research. Synthetic SGs were ultimately derived from 12 different combinations of the study methods. The modeling performance was assessed using 38 storm events collected over several years. The base time, time to peak, peak value, and total sediment load of the simulated and observed SGs were evaluated using relative error. Comparison based on the evaluation indicators indicated better performance of the combination of the spatially distributed travel time method, cellular automata, and modified U.S. Forest Service method with the coefficient of efficiency and the normalized coefficient of efficiency varying from −1.16 to 0.99 and from 0.32 to 0.99 for the calibration and validation stages, respectively. However, none of the models were simulating satisfactorily the entire sediment graphs.