Search Results (69)

Stabilizing micron-sized particles in low-viscosity polymer dispersions is challenging when density differences are present. This study demonstrates that graphite particles in aqueous polyurethane dispersions can be efficiently prevented from sedimentation using the capillary suspension concept. Capillary suspensions are solid/liquid/liquid systems and the capillary forces inferred from adding a second immiscible fluid can lead to drastic changes in texture and flow. Here, both spherical and flake-shaped graphite particles were used as fillers, with octanol as the secondary liquid. At low graphite concentrations, octanol increases the low-shear viscosity significantly attributed to the formation of loose particle aggregates immobilizing part of the continuous phase. Above a critical graphite concentration, capillary forces induce a self-assembling, percolating particle network, leading to a sharp yield stress increase (>100 Pa). The corresponding percolating particle network efficiently suppresses sedimentation; for the system including 28 vol% spherical particles, a shelf life of at least six months was achieved. Capillary forces do not affect the high-shear viscosity of suspensions; here, a hydrophobically modified polyether thickener can be used. Transfer of the stabilization concept presented here to other high-density particles like silver or metal oxides suspended in other polymer dispersions is straightforward and is applicable in various fields like flexible printed electronics. Full article

In the 20-year period from 2003 to 2022, water runoff and suspended sediment load in a forested gully system with a total length of 7.5 km was recorded. The branching gully system cuts through an agricultural catchment of 1.24 km² located within the loess plateau of the Nałęczów Plateau (E Poland). A rain gauge was installed close to the watershed and in the gully mouth water runoff was monitored using a limnigraph, installed with a water gauge on a Thomson triangular weir. To determine suspended sediment concentrations during erosion episodes, water was sampled at a frequency dependent on the rate and duration of the runoff. The aim of the monitoring studies was to specify the relationship between flow rate and suspended concentration for different water runoff conditions, which were used to calculate sediment load. The contribution of propluvial and pronivial runoff to gully development was assessed, particularly in the transport of sediment out of the catchment, and unit denudation rates were calculated as less than 1000 Mg km⁻² year⁻¹, average 173 Mg km⁻² year⁻¹, and during a single event as a maximal 900 Mg km⁻². Full article

Forestry operations and climate variability affect hydrologic response and sediment transport. Management of forested catchments under intense forestry activity in areas under climatic stress is critical. This study analyzes the impact of forest operations (thinning and clearcutting) on runoff (Q) and suspended sediment loads (SSL) in three small catchments (named N02, N03 and N04, respectively) in south-central Chile, where rainfall has decreased by 20% since 2010. Using modified double mass curves and piecewise regression, we separated the effects of forest practices and climate. Thinning in N02 initially reduced Q and SSL, with stable Q overtime, while final harvest in N03 and N04 increased Q, although less than expected. SSL surged post-harvest: 3.6 times in N03 and 1.8 times in N04, potentially linked to hillslope instability. Major SSL events contributed over 55% of post-harvest sediment yield in both catchments, with maximum yields reaching 7.2 tons ha⁻¹ yr⁻¹ in N03 and 4.1 tons ha⁻¹ yr⁻¹ in N04. SSL recovered to pre-harvest levels by the third year, except during a rainy year. Management practices likely contributed to lower SSL and faster recovery. These findings improve our understanding of catchment responses to forest practices and climate change, aiding sustainable forest and water resource management. Full article

Sediment deposition significantly impacts soil erosion processes, consequently influencing the geographical morphology and surrounding environments of reservoirs and estuaries. Given the intricate nature of sediment deposition, it is imperative to consolidate and analyze existing research findings. Presently, studies on sediment settling velocity primarily employ theoretical, laboratory, and field experimentation methods. Theoretical approaches, rooted in mechanics, examine the various forces acting on sediment particles in water to derive settling velocity equations. However, they often overlook external factors like temperature, salinity, organic matter, and pH. Although laboratory experiments scrutinize the influence of these external factors on sedimentation velocity, sediment settling is not solely influenced by individual factors but rather by their collective interplay. Field observations offer the most accurate depiction of sediment deposition rates. However, the equipment used in such experiments may disrupt the natural sedimentation process and damage flocs. Moreover, measurements of sediment particle size from different instruments yield varied results. Additionally, this paper synthesizes the impact of suspended sediment concentration, particle size, shape, temperature, salinity, and organic matter on sediment settling velocity. Future research should focus on innovating new laboratory observation methods for sediment settling velocity and utilizing advanced scientific and technological tools for on-site measurements to provide valuable insights for further investigation into sediment settling velocity. Full article

Following the eruption of the Taal Volcano in January 2020 and its continuous signs of unrest in the preceding years, this study delves into the investigation of sediment transport in the Pasig River, Philippines. The historical data of total suspended solids (TSS) and arsenic indicated a notable increase starting from the year 2020. The field measurements were conducted in February and March of 2022, two years after the eruption. Due to the observed homogeneity in the river’s mixing, a refined 1D sediment transport model was developed. In this study, HEC-RAS modeling software was employed. The calibration process using the Laursen transport function yielded an impressive R² value of 0.9989 for the post-eruption model. This predictive accuracy underscores the robustness of the developed model. The study’s scope was further expanded by creating a model for February 2020, incorporating water quality data gathered by the Pasig River Coordinating and Management Office. The model simulation results showed peak TSS values of 120.63 mg/L and 225.15 mg/L in February 2022 and February 2020, respectively. The results of the study highlight the probable impact of geological events on sediment dynamics within the Pasig River, which could help manage and sustain ongoing river improvements. Full article

To minimize the eutrophication pressure along the Gulf of Mexico or reduce the size of the hypoxic zone in the Gulf of Mexico, it is important to understand the underlying temporal and spatial variations and correlations in excess nutrient loads, which are strongly associated with the formation of hypoxia. This study’s objective was to reveal and visualize structures in high-dimensional datasets of nutrient yield distributions throughout the Mississippi/Atchafalaya River Basin (MARB). For this purpose, the annual mean nutrient concentrations were collected from thirty-three US Geological Survey (USGS) water stations scattered in the upper and lower MARB from 1996 to 2020. Eight surface water quality indicators were selected to make comparisons among water stations along the MARB over the past two decades. Principal component analysis (PCA) was used to comprehensively evaluate the nutrient yields across thirty-three USGS monitoring stations and identify the major contributing nutrient loads. The results showed that all samples could be analyzed using two main components, which accounted for 81.6% of the total variance. The PCA results showed that yields of orthophosphate (OP), silica (SI), nitrate–nitrites (NO₃-NO₂), and total suspended sediment (TSS) are major contributors to nutrient yields. It also showed that land-planted crops, density of population, domestic and industrial discharges, and precipitation are fundamental causes of excess nutrient loads in MARB. These factors are of great significance for the excess nutrient load management and pollution control of the Mississippi River. It was found that the average nutrient yields were stable within the sub-MARB area, but the large nitrogen yields in the upper MARB and the large phosphorus yields in the lower MARB were of great concern. t-distributed stochastic neighbor embedding (t-SNE) revealed interesting nonlinear and local structures in nutrient yield distributions. Clustering analysis (CA) showed the detailed development of similarities in the nutrient yield distribution. Moreover, PCA, t-SNE, and CA showed consistent clustering results. This study demonstrated that the integration of dimension reduction techniques, PCA, and t-SNE with CA techniques in machine learning are effective tools for the visualization of the structures of the correlations in high-dimensional datasets of nutrient yields and provide a comprehensive understanding of the correlations in the distributions of nutrient loads across the MARB. Full article

This study was part of a project designed to simulate the long-term landform equilibrium of a rehabilitated mine site. The project utilized event Fine Suspended Sediment (FSS) fluxes in a receiving stream following a rainfall event as an indicator of landform stability. The aim of this study was to use HEC-HMS to determine sediment and discharge quantity upstream to determine how it affects the downstream development of the catchment landform, in terms of sediment changes and geomorphology. Thus, the study focused on hydrology and sediment modelling of the upper catchment with HEC-HMS (Hydrologic Engineering Centre-Hydrologic Modelling System) to determine the daily discharge and sediment output at the catchment outlet. HEC-HMS was used to calibrate the stream discharge and FSS quantities at the catchment outlet to observed continuous discharge and FSS values. The calibration of the HEC-HMS model was carried out for two water years and then the same model parameters were used to validate the model for a third water year. The catchment discharge and FSS were calibrated and validated for continuous rainfall events against observed discharge and FSS data at the catchment outlet. The model was then run for a projected rainfall of 50 years. The denudation rate predicted by the model was 0.0245 mm per year, which falls in the range previously determined for the region. The simulated sediment output was compared to the rainfall trends over the years. As a result, the sediment spikes following a rainfall-runoff event gradually decreased over time. Reducing FSS spikes indicates that the landform gradually attains stability. This modelling study can be used for long-term simulations to determine erosion equilibrium over the years and to quantify sediment yield in catchments for projected time periods. Full article

This paper describes the modelling of suspended sediment yield in a plains region in the European part of Russia (EPR) and its prediction for ungauged catchments. The studied plains area, excluding the Caucasus and Ural Mountains, covers 3.5 × 10⁶ km² of the total area of about 3.8 × 10⁶ km². Multiple regression methods, such as a generalized linear model (GLM) and a generalized additive model (GAM), are used to construct the models. The research methodology is based on a catchment approach. There are 49,516 river basins with an average area of about 75 km² in the plain regions. The suspended sediment yield geodatabase contains data from 385 gauging stations. The linear GLM model of suspended sediment yield explains about 50% and the GAM model about 65% of the data variability (R-squared adjusted). The models include mean slope steepness, percentage of arable land, runoff per unit area, catchment area, soil rank and catchment soil erodibility as significant predictors. They also include a zonal-sectoral gradient (the sum of active temperatures and the standard deviation of air temperature, or directly by geographic coordinates). A GAM model is trained to predict suspended sediment yields for unexplored areas of the area. The paper presents the results of extrapolating suspended sediment yield values to ungauged river basins in a plains region of the EPR. For the first time for such a large area, the models built and the use of the basin approach made it possible to predict runoff values for hydrologically unexplored river basins. Full article

Sediment balance is essential for understanding changes in river morphology and ecosystems and related services depending on them. However, the currently used methods to quantify riverine sediment processes are not adequate enough. We have examined the sediment regime of the Danube River, particularly the suspended sediment yield. This parameter can be calculated based on stage or discharge using a suspended sediment yield rating curve; however, the uncertainty of this method can reach even 150%. The suspended sediment yield of a section does not only depend on processes that take place in the riverbed; thus, it cannot be described by only one easily measurable parameter. An integrated surrogate method based on turbidity registration is tested in order to determine suspended sediment yield on the lower Hungarian (sand-bed alluvial) reach of the Danube River. The near-bank turbidity is converted into suspended sediment concentration and then into suspended sediment yield. The turbidity is measured with a built-in turbidity probe, while the suspended sediment yield is determined with traditional methods (discharge measurement, suspended sediment sampling, laboratory processing, and calculation). The traditional and integrated surrogate methods are compared based on the results of the measurements, and different aspect correlations are established between flow parameters, turbidity, and suspended load. The results achieved with the integrated method are promising, but more measurements are required in order to refine the relationships in a broader interval. Full article

With rapid economic development, extensive human activity has changed landscape patterns (LPs) dramatically, which has further influenced hydrological processes. However, the effects of LPs changes on hydrological processes, especially for the streamflow–sediment relationship in the subtropical monsoon climate zone, have not been reported. In our study, 10 watersheds with different sizes in the subtropical monsoon climate zone of southeastern China were chosen as the study area, and the effect of the 14 most commonly used landscape metrics (LMs) on 4 typical hydrological indices (water yields (WY), the runoff coefficient (RC), the soil erosion modulus (SEM), and the suspended sediment concentration (SSC)) were analyzed based on land use maps and hydrological data from 1990 to 2019. The results reveal that the LP characteristics within the study area have changed significantly. The number of patches and landscape shape indices were significantly positively correlated with watershed size (p < 0.01). For most watersheds, the largest patch index was negatively correlated with WY, RC, and SEM, and the perimeter area fractal dimension was positively correlated with WY, RC, SEM, and SSC. The effects of several LMs on the hydrological indices had scale effects. WY/RC and the interspersion and juxtaposition index were negatively correlated in most larger watersheds but were positively correlated in most smaller watersheds. Similar results were found for Shannon’s diversity/evenness index and SEM. In general, an increase in a small patch of landscape and in landscape diversity would increase WY, the fragmentation of LPs would result in more soil erosion, and LPs would affect the relationship between streamflow and sediment yield. As a result, a proper decrease in landscape fragmentation and physical connectivity in the subtropical monsoon climate zone of southeastern China would benefit soil erosion prevention. These results enhance the knowledge about the relationship between LPs and hydrological processes in the subtropical monsoon climate zone of southeastern China and benefit local water and soil conservation efforts. Full article

Reliable estimations of sediment yields are very important for investigations of river morphology and water resources management. Nowadays, soft computing methods are very helpful and famous regarding the accurate estimation of sediment loads. The present study checked the applicability of the radial M5 tree (RM5Tree) model to accurately estimate sediment yields using daily inputs of the snow cover fraction, air temperature, evapotranspiration and effective rainfall, in addition to the flow, in the Gilgit River, Upper Indus Basin (UIB) tributary, Pakistan. The results of the RM5Tree model were compared with support vector regression (SVR), artificial neural network (ANN), multivariate adaptive regression spline (MARS), M5Tree, sediment rating curve (SRC) and response surface method (RSM) models. The resulting accuracy of the models was assessed using Pearson’s correlation coefficient (R²), the root-mean-square error (RMSE) and the mean absolute percentage error (MAPE). The prediction accuracy of the RM5Tree model during the testing period was superior to the ANN, MARS, SVR, M5Tree, RSM and SRC models with the R², RMSE and MAPE being 0.72, 0.51 tons/day and 11.99%, respectively. The RM5Tree model predicted suspended sediment peaks better, with 84.10% relative accuracy, in comparison to the MARS, ANN, SVR, M5Tree, RSM and SRC models, with 80.62, 77.86, 81.90, 80.20, 74.58 and 62.49% relative accuracies, respectively. Full article

Changes in climate affect the hydrological regime of rivers worldwide and differ with geographic location and basin characteristics. Such changes within a basin are captured in the flux of water and sediment at river mouths, which can impact coastal productivity and development. Here, we model discharge and sediment yield of the Skeena River, a significant river in British Columbia, Canada. We use HydroTrend 3.0, two global climate models (GCMs), and two representative concentration pathways (RCPs) to model changes in fluvial fluxes related to climate change until the end of the century. Contributions of sediment to the river from glaciers decreases throughout the century, while basin-wide overland and instream contributions driven by precipitation increase. Bedload, though increased compared to the period (1981–2010), is on a decreasing trajectory by the end of the century. For overall yield, the model simulations suggest conflicting results, with those GCMs that predict higher increases in precipitation and temperature predicting an increase in total (suspended and bedload) sediment yield by up to 10% in some scenarios, and those predicting more moderate increases predicting a decrease in yield by as much as 20%. The model results highlight the complexity of sediment conveyance in rivers within British Columbia and present the first comprehensive investigation into the sediment fluxes of this understudied river system. Full article

Rivers are dynamic geological agents on the earth which transport the weathered materials of the continent to the sea. Estimation of suspended sediment yield (SSY) is essential for management, planning, and designing in any river basin system. Estimation of SSY is critical due to its complex nonlinear processes, which are not captured by conventional regression methods. Rainfall, temperature, water discharge, SSY, rock type, relief, and catchment area data of 11 gauging stations were utilized to develop robust artificial intelligence (AI), similar to an artificial-neural-network (ANN)-based model for SSY prediction. The developed highly generalized global single ANN model using a large amount of data was applied at individual gauging stations for SSY prediction in the Mahanadi River basin, which is one of India’s largest peninsular rivers. It appeared that the proposed ANN model had the lowest root-mean-squared error (0.0089) and mean absolute error (0.0029) along with the highest coefficient of correlation (0.867) values among all comparative models (sediment rating curve and multiple linear regression). The ANN provided the best accuracy at Tikarapara among all stations. The ANN model was the most suitable substitute over other comparative models for SSY prediction. It was also noticed that the developed ANN model using the combined data of eleven stations performed better at Tikarapara than the other ANN which was developed using data from Tikarapara only. These approaches are suggested for SSY prediction in river basin systems due to their ease of implementation and better performance. Full article

Rivers play a major role within ecosystems and society, including for domestic, industrial, and agricultural uses, and in power generation. Forecasting of suspended sediment yield (SSY) is critical for design, management, planning, and disaster prevention in river basin systems. It is difficult to forecast the SSY using conventional methods because these approaches cannot handle complicated non-stationarity and non-linearity. Artificial intelligence techniques have gained popularity in water resources due to handling complex problems of SSY. In this study, a fully automated generalized single hybrid intelligent artificial neural network (ANN)-based genetic algorithm (GA) forecasting model was developed using water discharge, temperature, rainfall, SSY, rock type, relief, and catchment area data of eleven gauging stations for forecasting the SSY. It is applied at individual gauging stations for SSY forecasting in the Mahanadi River which is one of India’s largest peninsular rivers. All parameters of the ANN are optimized automatically and simultaneously using the GA. The multi-objective algorithm was applied to optimize the two conflicting objective functions (error variance and bias). The mean square error objective function was considered for the single-objective optimization model. Single and multi-objective GA-based ANN, autoregressive and multivariate autoregressive models were compared to each other. It was found that the single-objective GA-based ANN model provided the best accuracy among all comparative models, and it is the most suitable substitute for forecasting SSY. If the measurement of SSY is unavailable, then single-objective GA-based ANN modeling approaches can be recommended for forecasting SSY due to comparatively superior performance and simplicity of implementation. Full article