International Journal of Sediment Research最新文献

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.

A parameterized expression for sand wave morphology in rivers is established using a flow resistance law while accounting for sediment incipient velocity. A distinct relation is drawn between the proposed characteristic parameters and the sand wave morphology based on flume data. Support vector machines (SVMs) are then used to separate the boundaries of the sand wave morphology due to the high classification accuracy of SVMs. The boundary line data from each sand wave morphology is extracted and fitted to establish a discriminant standard, which is then successfully validated using experimental and quantifiable data. Also, based on the foregoing methodoly, it is further discovered that the short-term significant fluctuation of sand wave morphology is closely correlated with significant channel changes in rivers with a high width-depth ratio, using Yellow River Estuary as an example.

Aquatic ecosystems, especially freshwater ecosystems, are facing increasing anthropogenic disturbances, highlighting the urgency for accurate ecosystem assessment for informed decision-making and effective management. While ecosystem health and integrity assessments have been widely applied, they often focus on ecosystem elements, overlooking their interconnections and dynamic characteristics. Establishing an integrated ecosystem assessment framework is vital to apply a comprehensive perspective in evaluations. In the current paper, a systematic review of aquatic ecosystem assessment methods is done, encompassing health and integrity assessment, network analysis, and stability and regime shift assessment. These approaches consider not only the individual elements but also the complex interconnections and dynamic characteristics within the ecosystem, which have been rarely studied due to limitations in field data availability. A case study of Poyang Lake is further presented for practical demonstration on the specific operation of the combined assessments. The integration of aquatic ecosystem assessment with ecological modeling is strongly advocated because it not only helps overcome the limitations of field data for assessing historical or current conditions, but it also enables prediction of future developments.

The Shields diagram has been widely used in the description of the threshold condition for incipient sediment motion. Because the current definition of the Shields criterion is generally subjective, the relevant data are subject to high uncertainty. To date, several efforts have been made to define the threshold condition quantitatively, but they are all based on certain low bedload rates that were empirically chosen. The current study aims to provide a theoretical framework to quantify the Shields criterion based on the concept of the pickup probability of sediment particles. The results show that the Shields criterion can be reasonably described with a low pickup probability (p = 1%), which can be further converted to a low dimensionless bedload rate (i.e., $\varphi /{\theta }^{1.5}=0.13$, where $\varphi$ is the Einstein number and $\theta$ is the Shields number). The new approach is finally validated with experimental data of bedload rates published in the literature.